25 Commits

Author SHA1 Message Date
Sam Rolfe
6900101506 docs: image brief and treatment tooling
- docs/image-brief.md: one treatment for every image, keyed to the site palette
- tools/images/treat.py: cutout (rembg) and treat (Pillow) as separate, re-runnable steps
- tools/images/README.md: how to run it without polluting the NixOS python
2026-10-29 13:31:20 +11:00
Sam Rolfe
396eeb175e content: car tracker page with a real hardware image
- content/edge/l3/edge-car-tracker.md, written from the evidence notes
- product image of the SIM7080G Cat-M module, cropped to 640px, with a caption
  naming what it is and that it is a product image
- referenced from the edge overview
- original kept in sam/CAR_GPS_TRACKER so the project repo documents its own build
2026-10-29 11:24:26 +11:00
Sam Rolfe
36d8e514d4 content: six real code exhibits, rendered by the panel (AUD-152) 2026-10-28 16:00:06 +11:00
2ceb0c147c platforms: add the three platform guides the /platforms/ page renders
The /platforms/ page (408d6cd) reads platforms/*.yml and fails the build when
they are absent. Those three files were introduced on feat/phase-4-ingestion by
the ingestion commit 1e46ec0 and nothing else on the site needed them, so the
cherry-pick of 408d6cd left the tree unbuildable.

They are data, not ingestion code: each file is config plus public copy that the
site shows verbatim so a visitor can see exactly what the assistant reads. No
corpus/, no harvester, no Prefect flow and no Qdrant code is included.

Taken verbatim from 1e46ec0.

go test ./...: pass.
2026-10-28 15:56:50 +11:00
cb45c462be diagram: keep the runtime note inside its own panel
The note under the loopback box ran under the edge panel at 11.5 px. Shortened
so it ends inside the runtime panel at every render size. Re-measured after the
change: performance 100, accessibility 100.

(cherry picked from commit 4fb759cf22c3609542600009536613a04e461746)
2026-10-28 15:56:13 +11:00
6ac0e1ff47 visuals: print styles for the wall and the diagrams, and Phase 7 notes
- Diagrams carry a print style block, so they print as ink on white rather than
  as dark panels; the client wall and the chips go white with a hairline.
- docs/phase-7-notes.md records what batch 1 shipped, how to add a mark or an
  image, and the Lighthouse measurements taken after the batch (100/100/100/100,
  CLS 0, mobile and desktop).

(cherry picked from commit ae9282e5f62b8c7ee149ebf96f8d3f00067a49e2)
2026-10-28 15:56:12 +11:00
a76882de67 visuals: technology marks, area icons, client wall, two diagrams
Phase 7 (AUD-149), cheapest first.

- internal/brand: vendored Simple Icons path data (CC0) for 50 marks, looked up
  by the technology label the content already uses. Inline SVG, 16 px,
  currentColor, no request and no font. tools/gen_brand_icons.sh regenerates it.
- Six hand-drawn area icons replace the two-letter badges in the nav and on the
  landing cards; the evidence index gets a seventh.
- TechChips renders a mark where one exists and plain text where one does not,
  so a missing icon reads as a decision rather than a broken image.
- Client wall: 45 names as one typeface, one size, one grid of equal tiles, and
  the reason for no logo collage written on the page. No mark invented.
- Two diagrams, authored in-repo: homelab topology (platform-network.md) and
  the RAG pipeline (ai-rag-corpus.md), each referenced from the page it explains.
- Content images now carry intrinsic width/height, loading="lazy" and
  decoding="async" at render time; a site test fails the build if an image
  ships without them.
- Topbar is two rows at desktop: the area names plus marks no longer overlap the
  search form (Lighthouse target-size was failing at 1440 px).
- Favicon, so the browser stops requesting a missing /favicon.ico.

Satisfies: logos on every area page, area icons in nav and cards, two diagrams
referenced from their pages, images sized and lazy, Lighthouse >= 95 held.

(cherry picked from commit a473e4738a3751418c2097df2bcf0568fb6d0208)
2026-10-28 15:56:11 +11:00
Paperclip Builder
59580b6e45 site: publish the platform guides at /platforms/ and link them from an About page
(cherry picked from commit 408d6cdfd76d13945db1d9727b58feca23d1e48b)
2026-10-28 15:55:36 +11:00
d793ab0871 search: make the navigation form work from every page (AUD-151)
The nav form carried hx-get="/search" hx-target="#search-results", but
#search-results exists only on /search. On every other page htmx found no
target and silently discarded the response: a search box that looks like it
works and does nothing.

- SearchForm is now a plain GET with no htmx attributes (navigation and the
  landing page), so it navigates to /search?q=… from any page and with
  JavaScript disabled.
- SearchFormLive keeps the in-place swap on the search page, where the target
  does exist. For HX-Request the server now returns the block's *contents*
  (SearchResultsBody), so the fragment no longer nests a second element with
  id="search-results" inside the live region.
- Empty query shows a prompt instead of an empty list. A zero-match query says
  "Nothing found" and offers the six areas plus the evidence index.
- The result count is role="status" inside the existing aria-live container, so
  a screen reader reads "4 results for …" rather than a run of titles.

Tests (internal/server, internal/site):
- the nav form carries no htmx attribute on nine pages, and no built page
  carries an hx-target whose element is missing (walks all of dist/);
- search submitted from /, /edge/ and /platform/ lands on a page with results
  with no HX-Request header, i.e. the JavaScript-disabled path;
- empty query, zero match and the announced count;
- a content change followed by a build is visible in dist/search.db. That is
  the deploy path: deploy/deploy.sh runs `make dist` before it stages a
  release, and search.Build removes and rewrites the index every build.

DoD: search from home, /edge and /platform lands on results; works with
JavaScript disabled; zero-match query shows a message; content change followed
by a deploy is reflected in search; no htmx request targets a missing element.

(cherry picked from commit a56c795768e1e9b10204aae04c3612d7a96775a7)
2026-10-28 15:55:34 +11:00
Documenter
286aa12bb0 site: cut the template's own labels out of the pages
Point 7 of the page-wording review. The pages no longer show their data
structure as headings, and they no longer explain their own conventions.

- Level 1 is one screen: claim, status badge, three figures, the technology
  row, and one sentence carrying one link. Measured in headless Chrome at
  1440x900: 1404-1845 px tall before, exactly 900 px (no scroll) after, on
  all six area pages.
- DocPage: no kicker line, no visible level label, no "Evidence for this
  page" heading or instruction note, no "Drill-downs" list, no
  "Status <word> - sentence" footer. The artefact table starts at level 2,
  under the heading "Artefacts"; the history timeline is level 2 only.
- Landing page: "Start here" link text gone, "How to read this site" panel
  gone, the footer's status-label explainer gone.
- Content, presentation only, no claim softened: the leading H1 that
  duplicated the page title removed from all 41 documents; level 1 bodies
  reduced to the constraint as ordinary prose plus the way in;
  "**Status: ...**" prefixes removed from the artefact stubs; "## Drill-downs"
  and "## Period and confidence" removed from the level 2 pages, with the
  cross-area links moved into the prose.
- The six level 1 claims are now sentences with a subject, and use commas
  instead of middots. Same facts, no new ones.

Not in this commit: the 35 level 2 and 3 claims still use the compact CV
style. That is a voice decision for Sam, not a wording tidy-up.

make validate: 41 documents, 0 violations. go test ./...: pass.

(cherry picked from commit 03009f80fc7815ad7719412e125df9297a6e653f)
2026-10-28 15:55:33 +11:00
Sam Rolfe
f39f560c7f deploy: publish on the LAN address so Caddy on .35 can reach it
127.0.0.1:8096 is unreachable from .35, which is where Caddy runs. Every other
service on .13 is published this way (8095, 8092, 3909, 4200) and proxied by the
same Caddy. Port is LAN-only; Caddy remains the sole public listener.
2026-10-26 22:40:24 +11:00
Sam Rolfe
df864cc29e deploy: health gate and shape assertion follow the LAN publish address
Once the port is published on 192.168.20.13 the host no longer listens on
127.0.0.1:8096, so wait_healthy(), do_status() and the rollback path would all
get connection-refused and every deploy would roll back. They now probe
$PUBLISH_IP (RESUME_PUBLISH_IP, default 192.168.20.13). assert_shape() asserts
the published host IP is that address instead of loopback; the mem_limit ==
memswap_limit checks are unchanged. check_port stays address-agnostic on
`sport = :8096`.

Records the amendment in reference/stack.md (ports table + the bind rule) and
docs/phase-3-deploy.md (§3.2 bullet, flow diagram, DoD line), citing Sam's
decision of 2026-10-26, interaction 54aeb1a2. AGENTS.md is Sam's to change and
is untouched.
2026-10-26 22:40:22 +11:00
Sam Rolfe
5c0622c1b1 Ingestion redesigned: harvest from the estate, not just content/
- docs/phase-4-corpus.md rewritten: Gitea (all repos), vault via Gitea, Graphify
  graph, curated content as tier 1. Platform guides are config and public copy.
- docs/phase-5-ai-overlay.md: citations to originals, tier preference, live
  source readout.
- Board: AUD-144 (index content/ only) retired; AUD-147 replaces it, blocked by
  AUD-141 and AUD-142. AUD-145 relinked to AUD-143 and AUD-147.
2026-10-26 22:16:33 +11:00
9fae1c7dd8 Merge Phase 4: corpus and retrieval
Chunks, deterministic ids, payload filter, FTS5 keyword index, incremental mode,
freshness audit, Qdrant container shape, measured memory. Deploying Qdrant to
the runtime host and the Prefect deployment are still outstanding and belong to
the runtime host, not this repository.

Co-Authored-By: Paperclip <noreply@paperclip.ing>
2026-10-26 21:04:59 +11:00
f9dc574172 Phase 4: corpus and retrieval — chunks, Qdrant, FTS5, freshness audit
Satisfies docs/phase-4-corpus.md. content/ stays the single source of truth:
one pass over it writes both indexes, so the static pages and the retrieval
corpus cannot drift.

- corpus/: stdlib-only pipeline. validate -> chunk -> enrich -> embed -> upsert.
  Chunking is block-based, so a boundary cannot fall inside a fenced block, a
  table or front-matter (verified structurally, not by inspection).
- Deterministic ids: sha256(source_path + chunk_index + content_hash), rendered
  as a UUID. Re-running updates points; re-chunking deletes the old ones by
  source filter, so duplicates and orphans are both impossible.
- Incremental by content hash against a SQLite manifest. A provider, dimension
  or chunker change is refused instead of mixed: it needs a rebuild.
- Payloads per section 4.2 plus provenance, validated before any vector is
  written; a missing field fails the run and names the document and chunk.
- SQLite FTS5 over chunks (the keyword half of hybrid retrieval, decision D10)
  with area/confidence filters and a `terms` column so front-matter tech and
  tags are findable.
- Freshness audit: stale documents, due soon, index drift, contract drift;
  optional Apprise alert; exit code for Prefect.
- Embeddings behind one interface: omniroute (OpenAI-compatible, retries, key
  from the environment only) and local-hashing (offline placeholder). The
  provider decision is still Sam's; the pipeline does not depend on it.
- deploy/qdrant.yml: mem_limit = memswap_limit = 400m, loopback only.
- docs/phase-4-notes.md: operator manual, DoD evidence, measured memory.
- 76 unit tests, no container and no network.

Measured, Qdrant 1.20.0 under a 400 m cap at 1024 dimensions: 500 / 5,000 /
20,000 chunks settle at 128 / 138 / 151 MiB, peaking at 149 / 305 / 292 MiB
during ingest. Qdrant's default settings are OOM-killed between 5,000 and
20,000, so the collection is created with HNSW and payloads on disk; recorded in
reference/stack.md with the ceiling and the escalation order.

Note: the `make corpus*` targets were swept into the Phase 3 deploy commit
24590cc by concurrent work in this tree; the Makefile content is correct, and
this commit contains the pipeline they call.

Co-Authored-By: Paperclip <noreply@paperclip.ing>
2026-10-26 21:04:52 +11:00
24590cc351 deploy: static binary and a compose project name that does not collide
Two failures from the first real deploy, both repo-side:

1. The container logged "exec /usr/bin/resume-server: no such file or
   directory" and restarted until the health gate failed. The build host has
   cgo enabled, so the binary carried the nix glibc interpreter, and the
   distroless/static image has no dynamic loader. Every SQLite path already
   uses pure-Go modernc.org/sqlite, so build with CGO_ENABLED=0 and ship a
   static binary.

2. Compose warned about orphan containers ai-resume-backend-1 and
   ai-resume-db-1: /home/sam/deployment/ai-resume on .13 already owns the
   `ai-resume` compose project (on the ai-mesh network). Sharing the name makes
   each stack visible as orphans to the other, so a `compose down
   --remove-orphans` in either could stop the other. Run the site as project
   `ai-resume-site`; the image tag stays ai-resume:local and the port binding,
   mem_limit and memswap_limit are untouched.
2026-10-26 20:53:25 +11:00
c95e5361e3 deploy: pin the compose network to an explicit /28
The first deploy failed before the health gate:

    Network ai-resume_default Error Error response from daemon:
    all predefined address pools have been fully subnetted

.13's docker daemon has 31 bridge networks already, which exhausts its
default-address-pools, so any new implicit compose network fails to create.
Pinning 10.89.0.0/28 (no route, bridge or LAN overlap on .13) makes this
project independent of that host condition without touching another project,
container or the daemon config. mem_limit, memswap_limit and the 127.0.0.1
port binding are unchanged.
2026-10-26 20:51:17 +11:00
2906683f44 deploy: add deploy.sh (release, rollback, status) for Phase 3 section 3.1
The release directory under /home/sam/deployment/resume is the rollback unit;
deploy.sh stages releases/<timestamp>-<sha>/, flips `current` atomically,
builds the image from that directory and gates on /healthz, restoring the
previous release on any failure.

The rehearsal recipe in the header is kept as comments only; the pasted spec
lost the comment prefixes on the multi-line printf, which would have made the
script exit 1 on startup. It now uses \n escapes on one commented line.
2026-10-26 20:49:53 +11:00
c2b4e33897 Merge Phase 2: Mode A static site
DoD satisfied (evidence in AUD-142): A1 with no AI services, one binary,
deterministic templ generate, 6 L1 + 6 L2 + artefact pages, FTS5 search,
Lighthouse 100/100 mobile and desktop, <100ms first byte, stage panel driven
by route or stream, print legible.
2026-10-26 20:43:03 +11:00
84fc585c91 Phase 2: Mode A static site — build, serve, search, stage panel
One Go binary serves a pre-rendered site. No Node, no bundler, no node_modules:
htmx and the SSE extension are vendored files, Markdown and code highlighting
run at build time (goldmark + chroma), and the server only writes files.

Implements docs/phase-2-static-site.md:

  2.1 service skeleton   cmd/build (content -> dist), cmd/server (dist -> HTTP),
                         env-configured, 127.0.0.1:8096, /healthz with separate
                         static / ai / search readiness, --health-check probe,
                         deploy/compose.yml + deploy/Dockerfile at 120m
  2.2 rendering          templ components (Layout, Nav, PillarCard, Stage,
                         EvidenceTable, CodeBlock, Timeline, Footer);
                         `make generate` is a build step and is byte-stable
  2.3 navigation         six-area rail, <details> disclosure under 900px,
                         breadcrumbs, aria-current, L1/L2/L3 with artefact pages
  2.4 stage panel        internal/stage owns the contract; RouteDriver (fragment
                         swap) and SSEDriver (event stream) differ only in the
                         Binding, never in layout. fragment ids live in the
                         contract so route files, links and streamed frames agree
  2.5 search             SQLite FTS5 over titles and body, rebuilt by the build;
                         one handler returns a page without HTMX, a fragment with
                         it. Visitors' FTS operators are never honoured
  2.6 quality floor      print stylesheet, JS-off navigation, focus states,
                         semantic landmarks, dark default; security headers

Definition of Done evidence (see issue AUD-142):
  A1  all pages 200 in a network namespace with no external route
  one binary serves the site; make dist writes 45 pages, 86 fragments, 40 docs
  templ generate is deterministic (md5 identical across runs)
  6 L1 + 6 L2 pages, artefact pages reachable in <= 3 clicks
  FTS5 returns sensible hits for zigbee / signage / routing (tested)
  Lighthouse 100 performance and 100 accessibility, mobile and desktop
  first byte well inside 100ms over loopback (test)
  panel renders from a route driver and re-points at the stream untouched
  print legible: /edge/ renders to a 3-page PDF with the print stylesheet

Also fixes found while finishing the build:
  - reject "." / ".." path segments before ServeMux redirects them into a file
    outside the asset directory (/static/../build.json now 404, was 307->200)
  - make test is the real gate: go test ./... plus the validator self-test
2026-10-26 20:42:52 +11:00
fd9974d07e Merge branch 'main' into feat/phase-2-static-site 2026-10-26 20:01:38 +11:00
ea2c722816 WIP Phase 2: content loader, stage contract, search index, templ components 2026-10-26 20:01:38 +11:00
72edfec59c Phase 1: precise container/host wording on platform L1/L2
'The spine' records 50+ containers across 3 hosts and a 4-host estate; the
pages asserted containers 'across four hosts'. Reworded so the container
count and the host count are each stated without implying a distribution
the sources do not give. No metric value changed.

DoD: schema valid (make validate clean), metrics still trace to the
approved chip set.
2026-10-26 19:51:22 +11:00
Documenter
00fa15f98f Phase 1: content source of truth — 40 documents across six areas
Satisfies the Phase 1 Definition of Done:

- content/ validates cleanly against the frozen schema. `make validate`
  reports OK: 40 document(s) validated, 0 violations.
- Six areas exist, each with an L1 entry: ai-overview, platform-overview,
  edge-overview, products-overview, history-overview, about-overview.
- All six areas have an L2 entry: ai-systems, platform-estate, edge-fleet,
  products-business, history-engagements, about-narrative.
- 28 L3 artefact stubs under content/<area>/l3/. Every artefacts[].url in
  every document resolves to one of them; the validator proves there are no
  dangling references (and a deliberately broken copy exits 1).
- Every metric value traces to a source line in the evidence notes or the
  approved metric set in reference/content-map.md (per-metric citations are
  recorded in the AUD-131 completion comment).
- Every document carries confidence and last_verified=2026-10-26; the
  validator rejects a missing or non-canonical value.
- docs/content-redaction.md records the redaction pass: identifiers, third
  party personal data, credentials and infrastructure detail excluded, with
  reasons, plus the two numbers that are reported rather than smoothed.

Honesty positions held: 3 businesses active (12 adoptions over time),
Where-Woof deployed and pre-market, failures kept in (edge-failures), no
promotion of in-progress work to delivered.

No site code, no templates, no CSS, no rendering (Phase 2).
2026-10-26 17:30:35 +11:00
0eb151117e Phase 1a: content schema validator (freeze the schema)
Implements AGENTS.md section 7 front-matter schema as a single-file,
standard-library-only Python validator; no third-party YAML dependency
and no framework. Adds the `make validate` gate, a 20-case fixture set
and docs/content-schema.md.

DoD satisfied:
- make validate runs and exits 0 on an empty/clean content/ tree
  (missing dir and empty dir both exit 0; fixture ok-empty covers it).
- Every violation class has a fixture under test/fixtures/fail-*; each
  exits non-zero with one line per violation (file:line: field: rule).
  `make test` runs all 20 cases: 20/20 pass.
- Dangling artefact reference is fatal (fail-dangling-artefact;
  artefacts[].url must resolve to an existing in-repo L3 stub or asset).
- Unknown front-matter field is fatal (fail-unknown-field), including
  unknown keys nested inside artefacts[]/metrics[].
- docs/content-schema.md committed; states the frozen schema, links to
  AGENTS.md section 7, and records that any later schema change is a
  breaking change requiring Sam's approval.
- Scope respected: no content authored, no rendering, no site code.

Files: Makefile, tools/validate_content.py, test/fixtures/**,
docs/content-schema.md, .gitignore (__pycache__).
2026-10-26 17:26:00 +11:00
221 changed files with 16803 additions and 1571 deletions

5
.gitignore vendored
View File

@@ -1,4 +1,9 @@
# python
__pycache__/
*.pyc
# build output
/bin/
/dist/
/current/
*.test

106
Makefile Normal file
View File

@@ -0,0 +1,106 @@
# Ai Resume - build entry points.
#
# `make validate` is the content gate: it is what CI and deploy.sh call.
# It exits 0 only on a clean content tree.
#
# `make build` is the site gate: it generates the templ components, compiles the
# two small binaries and writes the pre-rendered site into dist/. One binary
# (bin/resume-server) serves the whole site; there is no bundler and no Node.
PYTHON ?= python3
VALIDATOR := $(PYTHON) tools/validate_content.py
GO ?= go
TEMPL ?= templ
# The runtime image is distroless/static (deploy/Dockerfile): no libc, no
# dynamic loader. Everything the server needs is pure Go (modernc.org/sqlite),
# so a static binary is buildable and is what the container can actually exec.
# Without this the build host's cgo links the nix glibc loader and the container
# fails with "exec /usr/bin/resume-server: no such file or directory".
CGO_ENABLED ?= 0
BIN := bin
DIST := dist
.PHONY: build dist generate dev test validate validate-self-test fmt vet clean help \
corpus corpus-sync corpus-rebuild corpus-audit corpus-verify corpus-demo corpus-test
help:
@echo "make build templ generate, compile bin/, no dist"
@echo "make dist build, then pre-render content into dist/"
@echo "make dev pre-render into dist/ and serve it on 127.0.0.1:8096"
@echo "make generate run templ generate (generated files are not committed)"
@echo "make test go test ./... plus the validator self-test"
@echo "make validate validate content/** front-matter against the frozen schema"
@echo "make fmt vet gofmt / go vet over cmd and internal"
@echo "make clean remove bin/ and dist/"
@echo ""
@echo "corpus (Phase 4, python3 -m corpus.cli):"
@echo "make corpus rebuild the corpus from content/ (drops the collection)"
@echo "make corpus-sync incremental update; only changed documents are re-embedded"
@echo "make corpus-audit freshness + drift audit (exit 1 on findings)"
@echo "make corpus-verify check the index against content/ and Qdrant"
@echo "make corpus-demo same query with and without an area filter"
@echo "make corpus-test corpus unit tests (no container, no network)"
build: generate
@mkdir -p $(BIN)
CGO_ENABLED=$(CGO_ENABLED) $(GO) build -trimpath -buildvcs=false -o $(BIN)/resume-build ./cmd/build
CGO_ENABLED=$(CGO_ENABLED) $(GO) build -trimpath -buildvcs=false -o $(BIN)/resume-server ./cmd/server
dist: build
@$(BIN)/resume-build --dist $(DIST)
dev: dist
@RESUME_DIST=$(DIST) $(BIN)/resume-server
generate:
@command -v $(TEMPL) >/dev/null || { echo "templ is not on PATH; run this inside 'nix develop'"; exit 1; }
@$(TEMPL) generate
test: generate validate-self-test
@$(GO) test ./...
validate:
@$(VALIDATOR)
validate-self-test:
@$(VALIDATOR) --self-test
fmt:
@gofmt -l -w cmd internal
vet:
@$(GO) vet ./...
# -- corpus (Phase 4, docs/phase-4-corpus.md) --------------------------------
#
# The corpus is data, not a build artefact: it lives in data/corpus/ (gitignored)
# and in Qdrant. RESUME_QDRANT_URL defaults to http://127.0.0.1:6333 and
# RESUME_EMBED_PROVIDER defaults to local-hashing; production sets both in the
# environment, never in this file.
CORPUS := $(PYTHON) -m corpus.cli
corpus: corpus-rebuild
corpus-rebuild:
@$(CORPUS) rebuild
corpus-sync:
@$(CORPUS) sync
corpus-audit:
@$(CORPUS) audit --fail-on-findings
corpus-verify:
@$(CORPUS) verify
corpus-demo:
@$(CORPUS) demo-filter --query "distributed audio endpoints" --area edge
corpus-test:
@$(PYTHON) -m unittest discover -s corpus/tests -t .
clean:
@rm -rf $(BIN) $(DIST)

View File

@@ -37,6 +37,42 @@ This folder **is** the git working copy of `sam/ai-resume`. One repo, one produc
Previously held an abandoned Vite/React scaffold. It was replaced by this build spec; the history is retained because it honestly records the false start, and it is referenced in the site's "alternatives considered" content.
## Build and run
The site is one Go binary serving pre-rendered HTML. There is no Node, no bundler and no
`node_modules`; `go.sum` is the only dependency lock.
```sh
nix develop # go, templ, sqlite3, make, curl
make validate # content gate: front-matter against the frozen schema
make test # go test ./... plus the validator self-test
make dist # templ generate → bin/ → pre-rendered dist/
make dev # build, then serve on http://127.0.0.1:8096
```
`bin/resume-build` reads `content/` and writes `dist/` (pages, stage fragments, the fragment
manifest, the SQLite FTS5 index, assets). `bin/resume-server` serves `dist/` and never reads
`content/`, never calls a model, and never opens a socket to another service — that is what
keeps acceptance test A1 true.
The two things that are not files are `/search` (FTS5 over page titles and body text) and
`/api/stage/stream` (the presentation panel driven by an event stream instead of a route).
The retrieval corpus (Phase 4) is built separately, because it writes to a service rather
than to `dist/`:
```sh
docker compose -f deploy/qdrant.yml up -d # on the runtime host, loopback only
make corpus # content/ -> chunks -> Qdrant + SQLite FTS5
make corpus-verify # does the index agree with content/ and with Qdrant?
make corpus-audit # stale documents, index drift, contract drift
make corpus-test # 76 unit tests, no container and no network
```
The site never calls Qdrant, so acceptance test A1 is unaffected: stopping the corpus
services leaves every page working. Operator manual and measurements:
`docs/phase-4-notes.md`.
## Ground rules
- **Do not touch other systems.** No stopping containers, no editing other projects, no `nixos-rebuild`, no Caddy changes except the single block specified in Phase 3 — and that one Sam applies.
@@ -48,10 +84,10 @@ Previously held an abandoned Vite/React scaffold. It was replaced by this build
## Current state
- [x] Repo prepared, scaffold retired, build spec written
- [ ] Phase 1 — content source of truth
- [ ] Phase 2 — static site
- [x] Phase 1 — content source of truth (40 documents across six areas, `make validate`)
- [x] Phase 2 — static site (`make dist`, 45 pages, 86 stage fragments, FTS5 search)
- [ ] Phase 3 — public deployment
- [ ] Phase 4 — corpus
- [x] Phase 4 — corpus and retrieval (51 chunks, FTS5 + Qdrant, freshness audit)
- [ ] Phase 5 — AI overlay
- [ ] Phase 6 — guardrails
- [ ] Phase 7 — polish

58
cmd/build/main.go Normal file
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@@ -0,0 +1,58 @@
// Command build pre-renders the whole site into a directory.
//
// It reads content/ and writes dist/: every page as HTML, one file per stage
// fragment, the fragment manifest, the SQLite FTS5 index, the assets and the
// static files. No network call is made, and the output is what the server
// serves — Mode A never renders a page per request.
//
// Configuration is from the environment, so the same binary is used by
// `make build`, by CI and by the container image:
//
// RESUME_ROOT repository root (default ".")
// RESUME_CONTENT content directory (default $RESUME_ROOT/content)
// RESUME_DIST output directory (default $RESUME_ROOT/dist)
package main
import (
"flag"
"fmt"
"os"
"gitea.lab.audasmedia.com.au/sam/ai-resume/internal/site"
)
func main() {
flag.Usage = func() {
fmt.Fprintf(flag.CommandLine.Output(), "usage: build [--root DIR] [--content DIR] [--dist DIR]\n")
fmt.Fprintf(flag.CommandLine.Output(), "environment: RESUME_ROOT, RESUME_CONTENT, RESUME_DIST\n")
}
root := flag.String("root", env("RESUME_ROOT", "."), "repository root")
contentDir := flag.String("content", env("RESUME_CONTENT", ""), "content directory")
distDir := flag.String("dist", env("RESUME_DIST", ""), "output directory")
flag.Parse()
cfg := site.Config{Root: *root, ContentDir: *contentDir, OutDir: *distDir}
s, err := site.New(cfg)
if err != nil {
fmt.Fprintf(os.Stderr, "build: %v\n", err)
os.Exit(1)
}
if err := s.Build(); err != nil {
fmt.Fprintf(os.Stderr, "build: %v\n", err)
os.Exit(1)
}
info, err := site.LoadBuildInfo(s.Config.OutDir)
if err != nil {
fmt.Fprintf(os.Stderr, "build: %v\n", err)
os.Exit(1)
}
fmt.Printf("built %d pages, %d stage fragments, %d indexed documents into %s\n",
info.Pages, info.Fragments, info.Indexed, s.Config.OutDir)
}
func env(key, fallback string) string {
if v := os.Getenv(key); v != "" {
return v
}
return fallback
}

155
cmd/server/main.go Normal file
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@@ -0,0 +1,155 @@
// Command server serves the site the build wrote.
//
// It reads a directory produced by cmd/build and serves it on loopback. It does
// not read content/, does not need Qdrant, OmniRoute or any other service, and
// makes no outbound connection, so every page it serves works with the AI stack
// stopped (acceptance test A1).
//
// Configuration comes from the environment so the same binary works under
// `make dev`, systemd and compose:
//
// RESUME_ADDR listen address (default 127.0.0.1:8096)
// RESUME_DIST build directory (default "dist")
// RESUME_STREAM_DELAY pacing for the streamed panel, e.g. 350ms
//
// Flags with the same names override the environment for local work.
package main
import (
"context"
"encoding/json"
"errors"
"flag"
"fmt"
"io"
"log/slog"
"net"
"net/http"
"os"
"os/signal"
"syscall"
"time"
"gitea.lab.audasmedia.com.au/sam/ai-resume/internal/server"
)
func main() {
addr := flag.String("addr", env("RESUME_ADDR", server.DefaultAddr), "listen address")
dist := flag.String("dist", env("RESUME_DIST", server.DefaultDist), "build directory")
streamDelay := flag.Duration("stream-delay", envDuration("RESUME_STREAM_DELAY", server.DefaultStreamDelay), "delay between streamed panel frames")
healthCheck := flag.Bool("health-check", false, "ask a running server for /healthz and exit 0 only if static is ready")
flag.Usage = func() {
fmt.Fprintf(flag.CommandLine.Output(), "usage: server [--addr HOST:PORT] [--dist DIR] [--stream-delay DURATION] [--health-check]\n")
fmt.Fprintf(flag.CommandLine.Output(), "environment: RESUME_ADDR, RESUME_DIST, RESUME_STREAM_DELAY\n")
}
flag.Parse()
// The container image has no shell and no curl, so the health probe is this
// binary talking to itself (deploy/compose.yml healthcheck).
if *healthCheck {
if err := probe(*addr); err != nil {
fmt.Fprintf(os.Stderr, "health check failed: %v\n", err)
os.Exit(1)
}
fmt.Println("static ready")
return
}
log := slog.New(slog.NewJSONHandler(os.Stdout, &slog.HandlerOptions{Level: slog.LevelInfo}))
site, err := server.New(server.Config{
Addr: *addr,
DistDir: *dist,
StreamDelay: *streamDelay,
Logger: log,
})
if err != nil {
log.Error("start failed", "error", err)
os.Exit(1)
}
defer site.Close()
// No WriteTimeout on purpose: the stage stream keeps the response open for
// as long as it is producing frames. Reads are bounded.
srv := &http.Server{
Addr: *addr,
Handler: site.Handler(),
ReadHeaderTimeout: 5 * time.Second,
IdleTimeout: 120 * time.Second,
MaxHeaderBytes: 64 << 10,
ErrorLog: slog.NewLogLogger(log.Handler(), slog.LevelWarn),
}
ctx, stop := signal.NotifyContext(context.Background(), os.Interrupt, syscall.SIGTERM)
defer stop()
go func() {
<-ctx.Done()
log.Info("shutting down")
shutdownCtx, cancel := context.WithTimeout(context.Background(), 10*time.Second)
defer cancel()
if err := srv.Shutdown(shutdownCtx); err != nil {
log.Warn("shutdown", "error", err)
}
}()
log.Info("listening", "addr", *addr, "dist", *dist)
if err := srv.ListenAndServe(); err != nil && !errors.Is(err, http.ErrServerClosed) {
log.Error("serve failed", "error", err)
os.Exit(1)
}
}
// probe asks a running server whether the static site is ready. Only the static
// field decides the exit code: Mode A must report ready with every AI service
// stopped (acceptance test A1).
func probe(addr string) error {
host, port, err := net.SplitHostPort(addr)
if err != nil {
return err
}
if host == "" || host == "0.0.0.0" || host == "::" {
host = "127.0.0.1"
}
client := &http.Client{Timeout: 3 * time.Second}
res, err := client.Get("http://" + net.JoinHostPort(host, port) + "/healthz")
if err != nil {
return err
}
defer res.Body.Close()
if res.StatusCode != http.StatusOK {
return fmt.Errorf("/healthz returned %s", res.Status)
}
var body struct {
Static struct {
Ready bool `json:"ready"`
} `json:"static"`
}
if err := json.NewDecoder(io.LimitReader(res.Body, 64<<10)).Decode(&body); err != nil {
return fmt.Errorf("decode /healthz: %w", err)
}
if !body.Static.Ready {
return errors.New("static is not ready")
}
return nil
}
func env(key, fallback string) string {
if v, ok := os.LookupEnv(key); ok && v != "" {
return v
}
return fallback
}
func envDuration(key string, fallback time.Duration) time.Duration {
v, ok := os.LookupEnv(key)
if !ok || v == "" {
return fallback
}
d, err := time.ParseDuration(v)
if err != nil {
fmt.Fprintf(os.Stderr, "%s=%q is not a duration: %v\n", key, v, err)
os.Exit(2)
}
return d
}

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@@ -0,0 +1,62 @@
---
id: about-narrative
area: about
level: 2
title: "The narrative — including the years that need explaining"
claim: "A self-taught career across six technology eras — interrupted on purpose by a non-IT working life and five years overseas — with 2020 to 2026 spent raising two children while building a business."
confidence: running
period: "1999–"
tech: [NixOS, Neovim, Python, JavaScript, React, Go, HTMX]
metrics: [{label: "years shipping software", value: 25}, {label: "years spent overseas", value: 5}, {label: "non-IT roles worked", value: 6}]
artefacts: [{type: doc, label: "Non-IT working history", url: "content/about/l3/about-non-it-work.md"}, {type: doc, label: "Education", url: "content/about/l3/about-education.md"}, {type: doc, label: "Interests — one honest line each", url: "content/about/l3/about-interests.md"}]
public: true
last_verified: 2026-10-26
tags: [about, narrative, 2020-2026]
---
# The narrative — including the years that need explaining
**Capability statement.** Someone who learned by doing, in public, on deadlines, and who has kept learning after the deadlines stopped being someone else's. The range is real and the gaps are stated.
## The beginning
Programming started as a childhood interest in the 1980s, self-taught alongside art, music, philosophy and sport. The career proper started in the late 1990s building websites for an Australian car marketplace. When interactive front ends arrived — a creative layer that needed a technical spine — there was no course to take, so it was learned directly and immediately put into commercial work. That combination, creative front end with engineering underneath, has been the shape of the work ever since.
## The agency years
Freelance in Melbourne, then full-time roles in the United Kingdom during the emergence of online gaming and interactive advertising, then back to freelance where the work was with the largest agencies and their clients — banks, broadcasters, drinks brands, publishers, a global energy company, a design software vendor. Most of it was delivered under deadlines that could not move, and a meaningful share of it was rescue work: rewriting someone else's stalled project and shipping it on their date.
## The other working life
Not all of it was software, and that is worth stating rather than hiding. Six roles outside technology — a ski chalet kitchen in the French Alps, wine service, guided horse tours in the Victorian high country, building a stable complex, state government administration, and thoroughbred stable work. They are the reason the engineering reads as practical: a kitchen at service and a production incident have the same shape.
## The education
A fine arts degree with philosophy, a master's in international relations and contemporary political theory, and study in music composition and economics. None of it is computer science, and all of it is used: systems thinking, argument, aesthetics, and the habit of asking what a thing is actually for.
## 2020 to 2026 — stated plainly
This period is on the page because a résumé that hides it invites the wrong question. It was spent as a full-time parent to two children while building in parallel:
- A digital signage business taken from idea to customers — the product, the deployments, the support.
- A site builder and CMS that carried the marketing for it.
- A lost-property tag system, deployed.
- A homelab that became the platform for everything else: declarative configuration, containers, backups, networking, public ingress.
- An AI estate — pipelines, routing, retrieval, voice, guardrails — built and operated on the same hardware.
- A deliberate move from PHP and front-end scripting into Go, systems work and platform engineering, and from hand-installed servers into reproducible configuration.
The honest framing is in the evidence notes and is repeated here: *full-time parent, building in parallel*. The output of those years is on the rest of this site and can be inspected.
## What next
The same three areas at a larger scale: [AI and agentic systems](../ai/ai-systems.md), the [platform engineering underneath them](../platform/platform-estate.md), and the [constrained devices at the edge](../edge/edge-fleet.md). Melbourne based and flexible about location and format — remote, hybrid, office, or travel.
## Contact policy
This site publishes no address, no phone number and no family details. Contact is by email, with the address supplied directly rather than scraped from a page. If a phone number belongs on the site, that is Sam's decision to make and it is not made here.
## Trade-offs and limits
- **No computer science degree.** The trade is a longer, broader practical education; the pages on this site are the argument, not the certificate.
- **A period of self-employment is described as such.** It is not dressed up as a sabbatical or as consulting.
- **Interests are one honest sentence each.** The previous version of this site invented venues and results. That has been deleted rather than corrected in place.

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@@ -0,0 +1,16 @@
---
id: about-overview
area: about
level: 1
title: "About"
claim: "Sam has shipped software for twenty-five years — self-taught, through a non-IT working life and five years overseas — and now builds AI systems, the platforms under them and the devices at the edge."
confidence: running
period: "1999–"
tech: [NixOS, Neovim, Python, JavaScript, React, Go, HTMX]
metrics: [{label: "years shipping software", value: 25}, {label: "years spent overseas", value: 5}, {label: "non-IT roles worked", value: 6}]
artefacts: [{type: doc, label: "Non-IT working history", url: "content/about/l3/about-non-it-work.md"}, {type: doc, label: "Education", url: "content/about/l3/about-education.md"}, {type: doc, label: "Interests — one honest line each", url: "content/about/l3/about-interests.md"}]
public: true
last_verified: 2026-10-26
tags: [about, narrative, background]
---
The other five areas prove what can be built; this one explains where the judgement came from. The narrative, including 2020 to 2026, is on the [narrative page](about-narrative.md).

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---
id: about-education
area: about
level: 3
title: "Artefact — Education"
claim: "A fine arts degree with philosophy · a master's in international relations and contemporary political theory · and study in music composition and economics."
confidence: historic
period: "1997–"
tech: []
metrics: [{label: "tertiary qualifications completed", value: 2}]
artefacts: []
public: true
last_verified: 2026-10-26
tags: [about, education, artefact]
---
Formal presentation lands in Phase 7. No certificates, transcripts or student identifiers are published.
## The record
- **Master of International Relations and Contemporary Political Theory** — University of Westminster, Centre for the Study of Democracy.
- **Bachelor of Visual Communication and Fine Arts** — RMIT (Melbourne) and the University of South Australia, 1997–1999.
- **Music composition and economics** — studied at La Trobe University alongside the composition work described under interests.
## What it proves
Two things the technical record cannot: formal training in argument and political economy, and formal training in visual judgement. Domain-switching is a habit rather than a claim — the degrees are in different fields from each other and from the career.
## Why no computer science degree
Because there is not one, and saying so is cheaper than having it discovered. The self-taught path is documented across this site: the History pages show the eras, the Platform and Edge pages show what came of them.
## Coverage limit
Qualification names, awarding institutions and the years the résumé states are published. Grades, student numbers and the master's completion year are not in the sources and are therefore not stated.

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---
id: about-interests
area: about
level: 3
title: "Artefact — Interests · one honest line each"
claim: "Fourteen pursuits with one true sentence each — and the invented venues · results and trips from the previous version of this site deleted rather than restated."
confidence: running
period: "1980–"
tech: []
metrics: [{label: "pursuits listed", value: 15}, {label: "bands played in", value: 2}, {label: "bee hives kept", value: 2}]
artefacts: []
public: true
last_verified: 2026-10-26
tags: [about, interests, artefact]
---
## Music
Blues and jazz piano and blues harp; played in two bands. Studied music composition at university and kept the piano as the thing that resets the week.
## Fine art
A fine arts degree and a continuing practice. Drawing and painting taught patience with iteration, which is most of debugging.
## Philosophy
A degree's worth of it, and it is where the habit of asking what a thing is actually for came from.
## Skiing
Italy, France, Japan and Australia. Six months based in Meribel in the French Alps as a chalet chef, including the Vallée Blanche glacier run.
## Camping
Hiking, camping and canoeing, mostly in Victoria. Most recent: a two-day hike through Wilsons Promontory with a group of teenagers in tow.
## Canoeing
The Yarra, the Otways and the Murray.
## Horse riding
Grew up riding in the Otways; broke horses from track work back to dressage; guided tours in the Victorian high country; competed in dressage for two years.
## Surfing
Grew up between Lorne and the Otways, on waves and horses.
## Hiking
The walking version of the camping answer: long days, small pack, no phone signal.
## Global politics
The master's was in it. Reading it is a habit, not a credential to display.
## Reading
History, philosophy, politics, economics — the same four subjects as the degrees, which is either consistency or a rut.
## Cooking
Worked in many restaurants and as chef in a five-star chalet in Meribel, France.
## Gardening
Permaculture and a wood-chip, low-input approach to self-sufficiency, with sensors and automation doing the boring parts.
## Food production
Bees, salami and cheese. Two hives, and salami made annually from four pigs with Italian friends in Benalla.
## Italy and travel
Five years spent overseas, travelling extensively, with Italy as the standing preference.
## What was deleted, and why
The previous site claimed venues, competition results, swell sizes, distances and named restaurants. None of it could be traced. It has been cut rather than corrected: a single true sentence is worth more than ten specific ones that cannot be substantiated, and an interviewer who asks a follow-up question is entitled to an answer that survives it.
## Coverage limit
Everything above traces to Sam's own notes. Anything not in those notes is absent, not paraphrased.

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---
id: about-non-it-work
area: about
level: 3
title: "Artefact — Non-IT working history"
claim: "Six roles outside technology — a chalet kitchen in the French Alps · wine service · guided horse tours · a stable build · government administration · thoroughbred stable work."
confidence: historic
period: "1990–2010"
tech: []
metrics: [{label: "non-IT roles worked", value: 6}]
artefacts: []
public: true
last_verified: 2026-10-26
tags: [about, history, work, artefact]
---
The roles are final; the presentation lands in Phase 7.
## The roles
| Role | Where | What it involved |
|---|---|---|
| Chalet chef | Merribel, France | Meals for chalet guests, kitchen operations, menu planning, dietary requirements, in a high-pressure alpine season |
| Wine waiter | South Australia and Victoria | Premium wine regions, varietals, food pairing |
| Horseback tour guide | Victorian high country | Guided tours, rider safety, horse welfare, trail navigation |
| Stable construction | Mount Beauty, Victoria | A 24-bay stable build — project management, budgeting, hands-on construction |
| Civil servant | Victorian State Government, Melbourne | Administrative and operational support to state-level programs |
| Horse trainer and stable hand | Sunbury, Victoria | Care and training of thoroughbreds, exercise, grooming, race-day preparation |
## What it proves
That the engineering sits on a wider working life. Kitchen service and production incidents share a shape; guiding strangers on horses in the high country is risk management with consequences; a stable build is a budget and a schedule held by hand. These are the reasons the technical pages read as practical rather than academic.
## Why this is on a technical site
Because leaving it out would make the career look narrower than it is, and because the years it occupies are otherwise unexplained.
## Coverage limit
Descriptions are limited to what the résumé and the evidence notes corroborate. Roles, employers' towns and the type of work are stated; no employer is named beyond the region, and no claim is made about scale, revenue or headcount that the sources do not carry.

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---
id: about-sources
area: about
level: 3
title: "What the assistant reads"
claim: "Four sources, one pool, and a published rule for every one of them."
confidence: running
period: "2026–"
tech: [Gitea, Obsidian, Graphify, Qdrant, SQLite]
metrics: []
artefacts: []
public: true
last_verified: 2026-10-26
tags: [ai, sources, corpus, retrieval]
---
# What the assistant reads
The assistant answers from a pool built out of four sources, not from a single
folder of copy.
| Source | Tier | What it contributes |
|---|---|---|
| This site's content | 1 | The curated claims: what Sam says is true about the work |
| The Gitea estate | 2 | README files, file trees, selected source files, commit messages and language statistics |
| The notes vault | 2 | Project and area notes, in Sam's own words |
| The knowledge graph | 2 | Nodes and edges over the vault, each joined back to the note it came from |
Tier 1 wins when the two disagree. A harvested README can be out of date; the
curated claim is the one that is maintained.
Every chunk records the platform it came from, the URL or repository path it can
be opened at, the area it feeds, its tier, its honest confidence and when it last
changed. A chunk without an origin is refused, not defaulted.
The rules are not hidden in code. Each platform has a guide that says what it is,
where it lives, what is included, what is excluded and whether it is enabled.
Those guides are shown at [the sources page](/platforms/) and are the same files
the ingestion pipeline reads.
## What is excluded
`node_modules`, virtualenvs, git internals, trash, editor state, the SilverBullet
auth file, credential-shaped files, binaries and media. Media is recorded as an
image reference and never turned into text. Personal and family notes are not
read.
The finished pool is scanned for credential-shaped content before a run can
succeed, so the exclusion list is checked rather than trusted.

16
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@@ -0,0 +1,16 @@
---
id: ai-overview
area: ai
level: 1
title: "AI & Agentic Systems"
claim: "Sam builds and operates sixteen or more AI pipelines: agent harnesses, model routing and cost control, retrieval over a tagged corpus, and a voice front-end that runs a house."
confidence: running
period: "2020–"
tech: [LangGraph, Prefect, n8n, Qdrant, OmniRoute, Whisper, Piper, MiniMax]
metrics: [{label: "pipelines maintained", value: 16}, {label: "model providers behind one gateway", value: 6}, {label: "audio endpoints for spoken output", value: 8}]
artefacts: [{type: doc, label: "House AI hub — service topology", url: "content/ai/l3/ai-hub-architecture.md"}, {type: doc, label: "Model routing and cost control", url: "content/ai/l3/ai-omniroute-routing.md"}, {type: doc, label: "Voice pipeline — speech in and speech out", url: "content/ai/l3/ai-voice-pipeline.md"}]
public: true
last_verified: 2026-10-26
tags: [ai, agents, routing, retrieval, voice]
---
The pipelines run against real obligations — school portals, calendars, email, household audio — and they are operated rather than demonstrated. What actually runs, and what it cost to get there, is on the [capability page](ai-systems.md).

56
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@@ -0,0 +1,56 @@
---
id: ai-systems
area: ai
level: 2
title: "Agent systems that are operated rather than demonstrated"
claim: "Maintains sixteen or more production AI pipelines — agent harnesses · prompt-analysis routing across six providers · retrieval filtered by metadata before it searches · and a guarded voice front-end."
confidence: running
period: "2020–"
tech: [LangGraph, Prefect, n8n, Paperclip, Qdrant, OmniRoute, Whisper, Piper, MiniMax, TUIOS]
metrics: [{label: "pipelines maintained", value: 16}, {label: "model providers behind one gateway", value: 6}, {label: "audio endpoints for spoken output", value: 8}, {label: "hosts under standardised agent operations", value: 4}]
artefacts: [{type: doc, label: "House AI hub — service topology", url: "content/ai/l3/ai-hub-architecture.md"}, {type: doc, label: "Model routing and cost control", url: "content/ai/l3/ai-omniroute-routing.md"}, {type: doc, label: "Paperclip and n8n pipelines in production", url: "content/ai/l3/ai-paperclip-pipelines.md"}, {type: doc, label: "Retrieval corpus and query graph", url: "content/ai/l3/ai-rag-corpus.md"}, {type: doc, label: "Voice pipeline — speech in and speech out", url: "content/ai/l3/ai-voice-pipeline.md"}, {type: doc, label: "Guardrails — hooks in the model path", url: "content/ai/l3/ai-guardrails.md"}, {type: doc, label: "Memory in two layers", url: "content/ai/l3/ai-memory-layers.md"}]
public: true
last_verified: 2026-10-26
tags: [ai, agents, routing, retrieval, voice, guardrails]
---
**Capability statement.** This is a working estate, not a prototype: more than sixteen pipelines in production, six providers behind a single gateway, retrieval that filters on metadata before it searches, and a voice front-end used daily by people who did not build it.
## What is actually run
**Routing.** Every model call goes through one gateway — OmniRoute. Six providers sit behind it: DeepSeek, MiniMax, OpenRouter, OpenCode-Go, Gemini and OpenAI. Requests are classified before they are sent, so the cheapest model that can do the job gets the job. No project in the estate calls a provider directly; that is a rule, not a preference.
**Pipelines.** Ten or more are maintained on n8n, Prefect and LangGraph — email scanners, school-portal scanners, calendar scanners, project agents — plus six Paperclip pipelines that run work through a bench of narrow specialist agents behind human approval gates. The estate is registered in a dashboard built to keep track of it.
**Harnesses.** JEV is a custom harness with its own routing, tool set and memory rather than a wrapper around a vendor SDK. Work is executed as sequences of narrow agents instead of one large prompt; capability is loaded per task through a skill-selection step, so the agent gets what this job needs and not an enormous system prompt.
**Memory.** Two deliberate layers. Engram keeps what should persist — curated summaries, decisions, prompts and session handoffs in local SQLite, integrated with JEV for fast compaction. Headroom keeps what should not persist out of the window, stripping and compressing prompt context, tool output and logs before they reach the model. Storage and token control are separate problems and are solved separately.
**Retrieval.** ChromaDB first, then replaced with Qdrant for payload-filtered retrieval and a smaller footprint. LangGraph query graphs on top; Prefect and n8n for ingestion. The corpus is metadata-tagged at ingest, which means retrieval filters before it searches — the difference between a device answer and a work-history answer is a filter, not a better prompt.
**Voice.** Local Whisper for speech to text with a fallback, Piper for text to speech, MiniMax for a cloned voice with emotion and non-verbal cues. A custom wake word on an ESP32 with LED confirmation. Voice is a system front-end: it controls lighting, music, calendars, shopping, email, SMS and alerts, and it answers through eight Snapcast endpoints with music ducked underneath.
**Guardrails.** Custom hooks live in the model path and block injection and constrain tool use, built because the house voice channel is shared and unauthenticated and there was no vendor control to borrow. Consequential actions sit behind approval gates, and no model sits in the path of a fast or dangerous command. The public site applies the same reasoning from the other end: the assistant it exposes has no tools at all, so the attack surface is a conversation rather than a shell.
**Agent operations.** Agent sessions are standardised on TUIOS across four hosts — sessions survive a disconnect, agents report their own state, and approvals land in one inbox. TUIOS is a third-party tool: the contribution is operations and packaging, not authorship. It is pinned at 0.10.1 in a hand-written Nix derivation because nixpkgs lagged at 0.7.0, and its daemon is declared as a systemd user service so the fleet is reproducible rather than hand-installed.
## Evidence
| Claim | Evidence | Artefact |
|---|---|---|
| Sixteen or more pipelines in production | Ten or more on n8n, Prefect and LangGraph plus six on Paperclip | [Paperclip and n8n pipelines](l3/ai-paperclip-pipelines.md) |
| Six providers behind one gateway | DeepSeek, MiniMax, OpenRouter, OpenCode-Go, Gemini, OpenAI | [Model routing](l3/ai-omniroute-routing.md) |
| Retrieval filters before it searches | Corpus tagged at ingest; Qdrant payload filters; LangGraph query nodes | [Corpus and query graph](l3/ai-rag-corpus.md) |
| Spoken output reaches eight endpoints | Eight Snapcast clients with music ducking | [Voice pipeline](l3/ai-voice-pipeline.md) |
| Guardrails are code in the path | Injection-blocking hooks and constrained tool use | [Guardrails](l3/ai-guardrails.md) |
| Memory is two layers by design | Engram for persistence, Headroom for context control | [Memory layers](l3/ai-memory-layers.md) |
| Agent operations are standardised | TUIOS across four hosts with one approval inbox | [House AI hub](l3/ai-hub-architecture.md) |
## Trade-offs and limits
- **Local inference was measured, then demoted.** The estate started local on Ollama. Hardware constraints made local inference both slower and more expensive than partitioned remote calls, so heavy flows moved remote while sensitive and low-latency work stayed local. That is a documented comparison, not a taste in hardware.
- **The retired stack is in the record.** Airflow DAGs were abandoned for RAM, ChromaDB was replaced by Qdrant, and a visual-builder layer was dropped. Each replacement is stated rather than hidden, because the reasoning is the transferable part.
- **Not everything is automated.** Parts of the house are automated end to end and parts still need a person to press the button. The estate is described as it is run, not as it was imagined.
- **Cost is bounded by routing, not by hope.** Caps and a refusal path exist so a runaway session cannot become a bill; the routing layer keeps the median request on a cheap model.
The period starts at the homelab year in the evidence notes: the AI estate and the homelab have the same origin.

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---
id: ai-guardrails
area: ai
level: 3
title: "Artefact — Guardrails · hooks in the model path"
claim: "Injection blocking and constrained tool use are code in the model path — built because the house voice channel is shared and unauthenticated."
confidence: running
period: "2020–"
tech: [OmniRoute, Python]
metrics: [{label: "tools exposed by the public assistant", value: 0}]
artefacts: []
public: true
last_verified: 2026-10-26
tags: [ai, guardrails, security, artefact]
---
A redacted injection-test result lands in Phase 6, when the guardrail library is wired into this site's own assistant.
## What the artefact is
The hooks that run before a request reaches a provider and before a response reaches an actuator: injection patterns are blocked, tool use is constrained to a declared set, and consequential actions stop at an approval gate. Behind them, one rule that outranks every prompt — no model sits in the path of a fast or dangerous command.
## What it proves
Safety enforced by mechanism. A prompt instruction is a suggestion; a hook is a control.
## When it is delivered
The test result in Phase 6, using the same library the house runs. The policy that the public assistant has no tools at all is a Phase 5 and 6 decision and is not negotiable at runtime.

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---
id: ai-hub-architecture
area: ai
level: 3
title: "Artefact — House AI hub service topology"
claim: "One hub fronts the AI estate — intake · routing · pipelines · retrieval · voice output · and a single set of network tools namespaced per household member."
confidence: running
period: "2020–"
tech: [n8n, Prefect, LangGraph, OmniRoute, Qdrant, MQTT, Snapcast]
metrics: [{label: "provider gateway entry points", value: 1}]
artefacts: []
public: true
last_verified: 2026-10-26
tags: [ai, architecture, artefact]
---
The diagram and the generated topology dump land in Phase 7 (polish). This stub exists so the level 2 page has no dangling reference.
## What the artefact is
A one-page diagram of the hub: request intake (voice · chat · scheduled job), the gateway hop, the pipeline runners, the retrieval path, and the output paths (house speakers, phone alerts, dashboards). Every box is a service that runs; nothing on the diagram is aspirational.
## What it proves
That the estate is a designed system with a single gateway and a single set of namespaced tools — not a collection of scripts that happen to share a machine.
## When it is delivered
Phase 7. The source facts are in the evidence notes (sections 2 and 4). Capture must be from the live estate, so no diagram is drawn from memory in Phase 1.

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---
id: ai-memory-layers
area: ai
level: 3
title: "Artefact — Memory in two layers · Engram and Headroom"
claim: "Memory is split by purpose — Engram stores what should persist and Headroom keeps what should not persist out of the context window."
confidence: running
period: "2020–"
tech: [Engram, Headroom, SQLite, JEV]
metrics: [{label: "memory layers", value: 2}]
artefacts: []
public: true
last_verified: 2026-10-26
tags: [ai, memory, context, artefact]
---
A redacted session handoff and a before-and-after context measurement land in Phase 7.
## What the artefact is
Engram is local-first storage for curated summaries, decisions, prompts and session handoffs in SQLite, so a conversation can be resumed in any project; it is integrated with JEV for fast compaction. Headroom is token control: it strips, cleanses and analyses code and logs before they reach the model, compressing rather than truncating.
## What it proves
Two distinct problems recognised as two — persistence and window economy — instead of one tool asked to do both.
## When it is delivered
Phase 7. Both tools are part of Sam's own workflow; they are deliberately not part of the public assistant, which is stateless by decision (see `reference/decisions.md` D11).

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---
id: ai-omniroute-routing
area: ai
level: 3
title: "Artefact — Model routing and cost control"
claim: "Every model call is classified first and sent to the cheapest capable provider — six providers behind one gateway with no direct SDK calls anywhere in the estate."
confidence: running
period: "2020–"
tech: [OmniRoute, JEV, DeepSeek, MiniMax, OpenRouter, Gemini, OpenAI]
metrics: [{label: "providers in rotation", value: 6}, {label: "gateways", value: 1}]
artefacts: []
public: true
last_verified: 2026-10-26
tags: [ai, routing, cost, artefact]
---
The routing decision path and a redacted cost table land in Phase 7. No keys or account identifiers are ever published.
## What the artefact is
The decision path: request arrives, a prompt-analysis step classifies it, the gateway selects a provider and model, and the response returns with its cost recorded. Plus the comparison that demoted local inference for heavy flows — local Ollama measured slower and more expensive under the available hardware than partitioned remote calls.
## What it proves
Cost control implemented as routing and measurement rather than as a monthly promise, and a single chokepoint where provider access can be audited.
## When it is delivered
Phase 7. The evidence for the provider list and the local-versus-remote decision is in the evidence notes, section 2 and section 4.

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---
id: ai-paperclip-pipelines
area: ai
level: 3
title: "Artefact — Paperclip and n8n pipelines in production"
claim: "Sixteen or more pipelines run real work — scanners · schedulers · project agents — with specialist agents dispatched by task and human approval gates on anything consequential."
confidence: running
period: "2020–"
tech: [Paperclip, n8n, Prefect, LangGraph]
metrics: [{label: "pipelines on n8n and Prefect and LangGraph", value: 10}, {label: "Paperclip pipelines", value: 6}]
artefacts: []
public: true
last_verified: 2026-10-26
tags: [ai, pipelines, agents, artefact]
---
A redacted run log of one pipeline lands in Phase 7, alongside the pipeline inventory.
## What the artefact is
One pipeline shown end to end: trigger, steps, the agent or model each step uses, the approval gate, and the run log with identifiers removed. Alongside it, the inventory of what the sixteen or more pipelines do — email scanners, school-portal scanners, calendar scanners, project oversight, and the household automations.
## What it proves
Volume with operations behind it: these are scheduled, logged and monitored, not run once for a demonstration.
## When it is delivered
Phase 7. Counts come from the evidence notes, section 3.

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---
id: ai-rag-corpus
area: ai
level: 3
title: "Artefact — Retrieval corpus and query graph"
claim: "Retrieval is filtered by metadata before it searches — a tagged corpus · a four-node LangGraph query graph · and a deliberate replacement of ChromaDB with Qdrant."
confidence: running
period: "2020–"
tech: [Qdrant, LangGraph, Prefect, n8n, ChromaDB]
metrics: [{label: "retrieval stores used over time", value: 2}]
artefacts: []
public: true
last_verified: 2026-10-26
tags: [ai, retrieval, rag, artefact]
---
Ingest and index are live; the diagram below shows both sides, and the query path is labelled as the design it is until Phase 5 ships.
## What the artefact is
The ingest and query path: documents are chunked and tagged with metadata at ingest, stored in Qdrant, and filtered on that metadata before a similarity search runs. The LangGraph graph has four nodes; the fourth decides whether the answer can be given from what was retrieved.
## What it proves
Retrieval designed around payload filtering and a hard stop, plus the reasoning for moving off ChromaDB — heavier footprint and weaker filtering — recorded as a decision rather than a migration.
## The pipeline
![Two lanes. Ingest reads the content tree and estate notes, excludes secrets, chunks and tags every block, then embeds through the model gateway and upserts into the resume_corpus and resume_graph collections in Qdrant. The query lane runs classify, route, retrieve, compose and verify, and only composes from retrieved context.](../../assets/diagrams/rag-pipeline.svg)
The separating line is the design decision: metadata is written at ingest, so the query can filter by area, level and source before it pays for a similarity search. The fourth node is the hard stop — an answer that cannot be supported by what was retrieved is refused rather than improvised.
## When it is delivered
Ingest is delivered and measured (this site's own corpus, Phase 4). The query path is delivered in Phase 5; the streaming console is the visitor-facing half of it.

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---
id: ai-voice-pipeline
area: ai
level: 3
title: "Artefact — Voice pipeline · speech in and speech out"
claim: "Speech in on a custom wake word · local transcription · a cloned voice out · and music ducked under every announcement across eight endpoints."
confidence: running
period: "2020–"
tech: [Whisper, Piper, MiniMax, ESP32, Snapcast, Mopidy, Home Assistant]
metrics: [{label: "audio endpoints", value: 8}]
artefacts: []
public: true
last_verified: 2026-10-26
tags: [ai, voice, audio, artefact]
---
A short recording of music ducking under a spoken announcement lands in the optional voice phase. Text describes the chain until then.
## What the artefact is
The chain: an ESP32 device with a microphone detects the wake word and confirms with an LED, audio is transcribed locally with Whisper, the request goes through the gateway, and the reply is spoken through Piper or the MiniMax cloned voice over all eight Snapcast endpoints, with music lowered for the announcement and restored afterwards.
## What it proves
Voice treated as a system front-end with latency and reliability requirements — not a party trick bolted onto a chatbot.
## When it is delivered
Recording in the optional voice phase (Phase 7). The endpoint count and the ducking behaviour are from the evidence notes, sections 1 and 2.

0
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# Phase 7.4 — asset weight budget
Measured 2026-10-26 on `.13` with `stat -c%s` and `gzip -9`. Every number below
is a measurement of a file that is in this repository, not an estimate.
## What is in the assets directory
| Group | Files | Raw | Gzip (`-9`) |
|---|---|---|---|
| Diagrams (`diagrams/*.svg`) | 8 | 51,729 B | 8,943 B |
| Code exhibits (`code/*.as`, `.js`, `.php`, `.ts`, `.hpp`, `.nix`) | 6 | 8,161 B | 2,989 B |
| Client wall data (`logos/clients.yaml`) | 1 | 3,912 B | 1,288 B |
| **Served payload total** | **15** | **63,802 B** | **13,220 B** |
| Manifests and READMEs (build-time only, never served) | 5 | 15,955 B | — |
## Per-asset
| File | Raw | Gzip |
|---|---|---|
| `diagrams/01-homelab-topology.svg` | 7,079 | 2,169 |
| `diagrams/02-agent-architecture.svg` | 6,027 | 1,971 |
| `diagrams/03-rag-pipeline.svg` | 7,010 | 2,043 |
| `diagrams/04-edge-architecture.svg` | 6,896 | 2,163 |
| `diagrams/05-distributed-audio.svg` | 6,833 | 1,997 |
| `diagrams/06-media-pipeline.svg` | 5,694 | 1,718 |
| `diagrams/07-cost-resource-engineering.svg` | 6,151 | 2,023 |
| `diagrams/08-era-timeline.svg` | 6,039 | 1,930 |
| `code/01-actionscript-class.as` | 958 | 495 |
| `code/02-js-easing-core.js` | 1,848 | 584 |
| `code/03-php-encode-op.php` | 2,295 | 765 |
| `code/04-typescript-service-layer.ts` | 1,078 | 482 |
| `code/05-embedded-cpp-interface.hpp` | 788 | 419 |
| `code/06-nix-derivation.nix` | 1,194 | 724 |
Rasters: **zero.** There are no PNG, JPEG, WebP or font files in this phase.
That is a design outcome, not an oversight — every diagram is text-based SVG and
every code exhibit is text, so the "optimise every image" requirement has no
image to optimise yet. Phase 7.4 screenshots are deferred (see below).
## The budget
| Rule | Limit | Worst case today |
|---|---|---|
| Any single diagram | ≤ 7.5 KB raw / ≤ 2.5 KB gzip | 7,079 B / 2,169 B |
| Any single code exhibit | ≤ 3 KB raw / ≤ 1 KB gzip | 2,295 B / 765 B |
| Diagrams on one page | ≤ 2 | 2 (14.1 KB raw) |
| Asset payload per page, gzipped | ≤ 12 KB | 4.3 KB (two diagrams + one exhibit) |
| Web fonts | 0 | 0 — system font stack only |
| JS required by any asset | 0 | 0 — SVG carries its own palette |
Worst realistic page: an L2 page with two diagrams and one code exhibit is
14,089 B raw, about **4.3 KB over the wire** after gzip and before the HTML, CSS
and template overhead. The whole 15-file asset set is 13.2 KB gzipped, so even a
page that loaded every one of them would stay inside a single small image's
budget.
## What is not yet measured, and why
| Item | Status | Why |
|---|---|---|
| Full page weight (HTML + CSS + assets) | **not measured** | No page exists yet: Phase 2 owns the templates |
| Lighthouse ≥ 95 / 95 | **not measured** | Needs a rendered page (Phase 2) served behind the edge (Phase 3). Nothing in this phase can stand in for it |
| Screenshots and recordings (7.4) | **deferred** | The agent console is Phase 5 and the TUIOS fleet session has to be real work, not staged. Recording a console that does not exist would be a fabrication |
| Home Assistant / Snapcast stills | **deferred** | Same reason: needs a live system and a judgement about whether a still adds what diagram 4 and diagram 5 already show |
These are recorded as open rather than asserted. DoD items 4 and 5 cannot be
closed until Phase 2 and Phase 3 produce a page to measure, and the measurement
must be taken by whoever holds that page.

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@@ -1,73 +0,0 @@
# content/assets — authored exhibits
Everything the site shows that is not Markdown prose. Authored in Phase 7; wired
into templates by Phase 2's builder.
```
content/assets/
├── BUDGET.md measured weights for every asset, and the budget
├── VERIFY.md what was checked, with the output pasted in
├── diagrams/ 8 SVGs, light and dark in one file each
│ ├── README.md the visual language and the presentation contract
│ └── manifest.yaml id, area, level, title, alt text per diagram
├── code/ 6 verbatim excerpts, 212 lines, 5 eras
│ ├── README.md the rules these follow
│ └── manifest.yaml era, language, shows, rationale, honest note
└── logos/
└── clients.yaml the client wall: 45 entries, 44 distinct names
```
## Three decisions worth knowing
**The client wall is type, not logos.** Forty-five names in one typeface at one
size on an even grid. Nothing is an image, so nothing can break, and no client is
visually ranked above another. Ten client marks do exist in Simple Icons; none is
enabled, because using a company's mark on a public portfolio is a permission
question for Sam, not a design choice for an agent.
**Diagrams carry both themes in one file.** Not two files that can drift: one
`<style>` block with the light palette and the same selectors repeated under
`prefers-color-scheme: dark`. The eight files therefore share one palette by
construction.
**Code excerpts are copied, never retyped.** Including the defects. An excerpt
that has been tidied is a claim, not evidence — and this site's whole argument is
that the claims are checkable.
## What is wired and what is not
| DoD item | State | Evidence |
|---|---|---|
| Client wall reads as deliberate, no broken images, no placeholders | **assets done, markup not** | `logos/clients.yaml` — 45 entries, zero image references by design. The grid is CSS and belongs to Builder |
| Eight diagrams, both themes, readable at 1× and on mobile | **done** | `VERIFY.md` §1–§3: 0 geometry findings, 8/8 resolve two palettes, rendered widths measured |
| Six code exhibits with a two-sentence rationale each | **done** | `code/manifest.yaml`, `VERIFY.md` §5 |
| Every image optimised; total weight budgeted and recorded | **assets done, page weight not** | `BUDGET.md`. Zero rasters. A full page cannot be weighed until a page exists |
| Lighthouse ≥ 95 / 95 after assets | **not measurable yet** | Needs a served page (Phase 3) |
| Archive never linked or exposed | **done** | `VERIFY.md` §6 — no archive path anywhere under `content/` |
The four "not yet" rows are honest gaps, not omissions: three need a page that
does not exist until Phase 2 and Phase 3 land, and one needs a live agent console
from Phase 5.
## Consuming these from templates
```go
// diagrams: "assets/diagrams/01-homelab-topology.svg"
<img src="/assets/diagrams/01-homelab-topology.svg"
alt="{{ .Alt }}" width="960" height="540" loading="lazy" decoding="async">
```
The `width`/`height` attributes are not optional — they are what stops the page
shifting as figures load, which is a Lighthouse CLS and a real annoyance. `alt`
comes from `manifest.yaml`; if it is set to `""` the SVG's own `<title>`/`<desc>`
are not read out.
Figures go inside a horizontally scrollable wrapper (`diagrams/README.md`), or a
960 px schematic becomes 5 px type on a phone.
## Adding an asset
1. Put the file in the right folder; no external references of any kind.
2. Add an entry to the folder's `manifest.yaml` with `alt` and, for code,
`rationale` and `honest_note`.
3. Re-measure and update `BUDGET.md`. A budget nobody updates is a wish.

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@@ -1,124 +0,0 @@
# Phase 7 — verification record
What was checked, how, and what it returned. Every block below is pasted output,
not a description of output.
Run 2026-10-26 on `.13`, headless Chrome for measurement.
## 1. Diagrams: structure and geometry
Each SVG was parsed as XML, then rendered inline in Chrome and **measured with
`getBBox()`** — the browser's own text metrics, not an estimate. The check
reported text overflow outside its box, partial overlap between boxes, elements
outside the viewBox, empty text nodes and unlabelled boxes.
```
01-homelab-topology.svg OK
02-agent-architecture.svg OK
03-rag-pipeline.svg OK
04-edge-architecture.svg OK
05-distributed-audio.svg OK
06-media-pipeline.svg OK
07-cost-resource-engineering.svg OK
08-era-timeline.svg OK
0 issue(s) total
checked 8 diagrams
distinct <style> blocks: 1 (1 = one visual language)
distinct viewBoxes: 1
```
The first pass produced 74 findings. They were fixed in the source of the
diagrams, not suppressed in the checker: shortened labels, widened boxes,
removed empty text nodes. The final run is clean.
**"One visual language" is one number: `distinct <style> blocks: 1`.** All eight
files carry a byte-identical style block, so there is no second palette to drift.
## 2. Diagrams: both themes
Computed colours of the first text node and the first box, resolved twice — once
with the browser's default scheme, once with `--force-dark-mode`, which makes
`prefers-color-scheme: dark` match.
```
01-homelab-topology.svg light rgb(27, 34, 43) rgb(244, 246, 248) → dark rgb(231, 237, 244) rgb(24, 32, 40) OK
02-agent-architecture.svg light rgb(27, 34, 43) rgb(244, 246, 248) → dark rgb(231, 237, 244) rgb(24, 32, 40) OK
03-rag-pipeline.svg light rgb(27, 34, 43) rgb(244, 246, 248) → dark rgb(231, 237, 244) rgb(24, 32, 40) OK
04-edge-architecture.svg light rgb(27, 34, 43) rgb(244, 246, 248) → dark rgb(231, 237, 244) rgb(24, 32, 40) OK
05-distributed-audio.svg light rgb(27, 34, 43) rgb(244, 246, 248) → dark rgb(231, 237, 244) rgb(24, 32, 40) OK
06-media-pipeline.svg light rgb(27, 34, 43) rgb(233, 241, 253) → dark rgb(231, 237, 244) rgb(17, 37, 61) OK
07-cost-resource-engineering.svg light rgb(27, 34, 43) rgb(230, 244, 242) → dark rgb(231, 237, 244) rgb(13, 38, 36) OK
08-era-timeline.svg light rgb(27, 34, 43) rgb(244, 246, 248) → dark rgb(231, 237, 244) rgb(24, 32, 40) OK
all eight resolve two distinct palettes: True
```
One file, two palettes, verified per file rather than assumed from one.
## 3. Diagrams: readable at 1× and on mobile
Rendered widths measured inside a 390 px container (the mobile case) and a
960 px container (1×), using the presentation contract in
`diagrams/README.md` (`min-width:900px` in a scrolling wrapper):
```
container 390px -> {'container': 390, 'svgRenderedWidth': 900}
container 960px -> {'container': 960, 'svgRenderedWidth': 960}
```
Type is 15 px / 13 px at 1×, and 14.1 px / 12.2 px at the 900 px minimum —
arithmetic from the rendered width over the 960 viewBox. Scaled to the container
instead, the same labels would render at 5 px, which is unreadable; that is why
the wrapper is a requirement and not a styling preference.
## 4. Accessibility
Every file carries `role="img"`, `<title>` and `<desc>`. `desc` length per file:
219, 230, 285, 223, 193, 197, 260, 234 characters — a real alternative text, not
a filename. The manifest repeats each `desc` verbatim as `alt`, so the page and
the screen reader cannot disagree.
## 5. Code exhibits: verbatim copies
Each exhibit was produced by copying from the archive with `cp` or `sed -n`,
never by typing. Line counts as reported by `wc -l`:
```
36 01-actionscript-class.as
42 02-js-easing-core.js
27 03-php-encode-op.php
37 04-typescript-service-layer.ts
28 05-embedded-cpp-interface.hpp
33 06-nix-derivation.nix
212 total
```
`03-php-encode-op.php` is two copied regions joined by one marked elision
(line 20 of the file). Nothing else was altered.
## 6. Archive is not exposed
Path-shaped strings only — a mount point or a storage volume name would have to
appear for the archive to be reachable:
```
$ grep -rnE '/m[n]t/|storage_3[T]B' content/ || echo "no mount paths or volume names under content/"
no mount paths or volume names under content/
```
The bracket keeps the check from matching its own command line, so the result
above is the real result and not the file quoting itself.
No matches. The archive is not linked, named, or reachable from anything under
`content/`. Provenance is recorded as "private archive, path withheld" — the
words only, never the location.
## What could not be verified here
| Item | Why | Owner |
|---|---|---|
| Page weight of a real page | No template exists yet | Builder / Phase 2 |
| Lighthouse ≥ 95 / 95 | Needs a served page | Builder / Phase 3 |
| Client wall grid renders as deliberate | It is markup and CSS, which this phase must not write | Builder |
| Screenshots and recordings (7.4) | The console is Phase 5; staging it would be fabrication | Phase 5, then this phase |

0
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@@ -1,3 +1,7 @@
// Excerpt from js/utils/text_effects.js (2,156 lines) in the WebsiteBuilder
// source. Two regions are reproduced verbatim: the timeline loop that derives
// each letter's delay from an easing function (lines 1111-1147), and the
// hand-written easing functions themselves (lines 1340-1349).
$(mySplitTextTarg).each(function(i){
tweenData.letter=$(this);
@@ -5,8 +9,6 @@
tweenData.text_text_to.letter=$(this);
tweenData.text_text_from.letter=$(this);
if(text_effect_type_animation=="standard"){
if(tweenData.ignoreItems=="on" && Math.random()<tweenData.ignore_amount)return;
@@ -37,6 +39,15 @@
setCssTweens(stime,text_effect_type_animation,tweenData,tweenData.text_static,tweenData.text_text_to,tweenData.text_text_from,$(this),stagger);
}else if(text_effect_type_animation=="static"){
if(tweenData.text_static.ignoreItems=="on" && Math.random()<tweenData.text_static.ignore_amount)return;
// --- easing functions, lines 1340-1349 ---
//t,b,c,d
//currenttime,start,change,duration
//t and do can be time or frames
function positionsCubic(t, b, c, d){
t /= d;
return c*t*t*t + b;
}
function positionSine(t, b, c, d) {
return -c * Math.cos(t/d * (Math.PI/2)) + c + b;
}

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@@ -1,3 +1,5 @@
// Excerpt from php/setMediaEncoder.php (3,402 lines) in the WebsiteBuilder
// source: the create_gif encode operation, lines 614-665, reproduced verbatim.
if($output_options->encode_media=="create_gif") {
$output_gif= ".." . $settings->directory . $output_file .$output_options->format .".gif";
@@ -17,11 +19,36 @@
// $cmd = "ffmpeg -ss ".$first_media->add_media_start." -t ".$first_media->add_media_duration." -i ".$input_file." -i palette.png -y -filter_complex \"fps=10[x];[x][1:v]paletteuse\" ".$output_gif." ";
$cmd = "ffmpeg -ss ".$first_media->add_media_start." -t ".$first_media->add_media_duration." -i ".$input_file." -i palette.png -y -filter_complex \"fps=".$settings->settings->movie_frame_rate.",scale=".$settings->settings->video_width.":".$settings->settings->video_height.":flags=lanczos[x];[x][1:v]paletteuse\" ".$output_gif." ";
ffmpegMsg("ffmpeg","Creating Gif ", 0,$cmd);
/* [...] 34 lines elided: ImageMagick mogrify frame-optimisation
options, left disabled because they delayed output */
// $cmd_2='mogrify -delay '.$_POST['compress_gif_delay'].' -alpha remove '.$matte.' -depth '.$_POST['compress_gif_depth'].' '.$deconstruct.' '.$dither.' -colors '.$_POST['compress_gif_colors'].' -fuzz '.$_POST['compress_gif_fuzz'].'% '.$loop.' '.$optimize.' '.$path.' 2>&1';
$_POST['loop_gif']="true";
$_POST['compress_gif_matte']="false";
$_POST['compress_gif_deconstruct']="false";
$_POST['compress_gif_dither']="true";
$_POST['compress_gif_optimize']="true";
$loop=($_POST['loop_gif']=="false")?"-loop 1":"";
$matte=($_POST['compress_gif_matte']=="true")?"-matte":"";
$deconstruct=($_POST['compress_gif_deconstruct']=="true")?"-deconstruct":"";
$dither=($_POST['compress_gif_dither']=="true")?"-dither none ":"";
$optimize=($_POST['compress_gif_optimize']=="true")?"-layers optimize ":"";
// $optimize=($_POST['compress_gif_optimize']=="true")?" -coalesce -layers RemoveDups ":"";
$_POST['compress_gif_depth']=16;
$_POST['compress_gif_delay']=0;
$_POST['compress_gif_fuzz']="4";
$_POST['compress_gif_colors']="16";
$cmd_2='mogrify -delay '.$_POST['compress_gif_delay'].' -alpha remove '.$matte.' -depth '.$_POST['compress_gif_depth'].' '.$deconstruct.' '.$dither.' -colors '.$_POST['compress_gif_colors'].' -fuzz '.$_POST['compress_gif_fuzz'].'% '.$loop.' '.$optimize.' '.$output_gif.' 2>&1';
$result=shell_exec($cmd_2);
$output_options->edited_url= $output_options->options->directory.$output_file .$output_options->format . '.gif';
$output_options->edited_name= $output_file .$output_options->format . '.gif';
$output_options->edited_type = "image/gif";
$output_options->edited_filetype = explode ( "/", "image/gif");
insert_document($output_options->edited_url,"image/gif",$output_options->edited_name,"image","edited");
$returnFiles = returnFiles ( $output_options, $output_file .$output_options->format . '.mp4', "video/mp4", '', null );
return $returnFiles;
}

0
content/assets/code/04-typescript-service-layer.ts Executable file → Normal file
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@@ -1,3 +1,5 @@
// Two files from firmware/src/hw, reproduced verbatim: WiFiMgr.hpp (the
// interface) and WiFiMgr.cpp (the implementation). The boundary is marked.
// WiFiMgr.hpp — the interface the rest of the firmware depends on
// WiFiMgr.hpp
#pragma once

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@@ -1,48 +0,0 @@
# Phase 7.3 — code exhibits
Six excerpts, **212 lines**, spanning five eras. Evidence that the range behind
the claims is real, in a form an engineer can read in two minutes.
## The rules these follow
1. **Verbatim.** Every file is a byte-for-byte copy from the private archive.
Nothing is retyped, reformatted or tidied. A retyped excerpt proves nothing.
2. **Never linked.** The archive is not referenced, linked or served from the
public site. Provenance is recorded as "private archive" and the path is
withheld from this repository so it cannot leak into a page or a build.
3. **Two sentences on why it is good code.** `rationale` in `manifest.yaml`.
Not what the code does — that is what reading it is for.
4. **Honest notes.** Where the code is not good, `honest_note` says so. A 2012
procedural PHP script presented as elegant would undermine every other claim
on the site.
## The six
| # | Era | Language | Lines | What it demonstrates |
|---|---|---|---|---|
| 1 | Flash / ActionScript | AS3 | 36 | class, imports, encapsulation |
| 2 | jQuery + PHP | JavaScript | 42 | easing + timeline core |
| 3 | jQuery + PHP | PHP | 27 | one encode op + the FFmpeg piping |
| 4 | React + TypeScript | TypeScript | 37 | types, interfaces, service layer |
| 5 | Embedded C++ | C++ | 28 | interface split from implementation |
| 6 | Declarative infrastructure | Nix | 33 | a patch with its own removal condition |
Era 6 is the most modern and the least common. A short, correct, self-documenting
derivation that says when to delete itself is a better infrastructure signal than
a thousand lines of configuration.
## Renderer requirements
- Syntax highlighting happens **server-side** (chroma), on the server, at build
time. The client receives highlighted HTML. No browser-side highlighter.
- Line numbers are generated by the template, not embedded in the files.
- Files stay in `content/assets/code/` as plain text. They are not inlined into
a Go string, so the archive copy and the served copy cannot diverge silently.
- Total weight: 7 KB of source, which compresses well because it is code.
## Honest notes worth reading twice
- Exhibit 1 contains a latent typo, kept.
- Exhibit 3 is 27 lines out of a 3,400-line file, with one marked elision.
- Exhibit 4 shows the router rather than the schema it types against.
- Exhibit 5 is two files presented as one, because the point is the split.

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@@ -1,76 +0,0 @@
version: 1
authored: 2026-10-26
total_lines: 212
provenance: "private archive, copied 2026-10-26, path withheld"
exhibits:
- id: history-as3-class
order: 1
era: "Flash / ActionScript"
language: "ActionScript 3"
file: 01-actionscript-class.as
lines: 36
shows: "class, imports, encapsulation"
area: history
title: "A small class that hides its own state"
rationale: "Every field is private and explicitly typed, and the whole public surface is two methods, so a caller cannot push the object into a state it does not understand. The constructor takes what it needs as parameters instead of reaching for it, which is why the same class can drive every siren on the screen without knowing where any of them are."
honest_note: "Verbatim, including a latent typo on one private field (this.high rather than this.height). Left as found: the excerpt is evidence, not a portfolio piece."
- id: history-js-easing-engine
order: 2
era: "jQuery + PHP"
language: JavaScript
file: 02-js-easing-core.js
lines: 42
shows: "easing + timeline core"
area: history
title: "One curve, used twice"
rationale: "A named easing function is evaluated to give each letter its delay, so the spacing of an animation follows the same curve as its motion instead of a guessed linear stagger. Both directions build from one transform object, which is what stops the two halves of the same effect drifting apart over years of edits."
honest_note: "Excerpt from a 2,195-line file in which easing curves are used as a spacing distribution rather than only a timing function."
- id: history-php-media-pipeline
order: 3
era: "jQuery + PHP"
language: PHP
file: 03-php-encode-op.php
lines: 27
shows: "one encode op + the FFmpeg piping"
area: history
title: "Check the input, then encode in two passes"
rationale: "The real MIME type of the input is checked before any command is built, so a wrong file type returns a message instead of a half-written artefact. The encode takes two passes, generating a palette from the clip and then mapping frames onto it, which is the difference between a GIF that holds its colour and one that does not."
honest_note: "Row 20 is an elision marker: 34 lines of ImageMagick frame-optimisation options are omitted because they are disabled in the live code. This is 3,400 lines of procedural PHP with global state and interpolated shell commands; what is worth reading is the operational judgement, not the structure."
- id: history-typescript-service-layer
order: 4
era: "React + TypeScript"
language: TypeScript
file: 04-typescript-service-layer.ts
lines: 37
shows: "types, interfaces, service layer"
area: history
title: "Validate at the edge, then do one thing"
rationale: "Input is checked by a schema at the boundary of each procedure, so the data layer receives a shape it can trust rather than one it hopes for. Each procedure is a thin call on the database context, which keeps the router a description of the API rather than a second copy of the queries."
honest_note: "The types come from a Prisma schema in the same project (Device, Telemetry and Command models). The router is shown because it is where the two meet."
- id: edge-embedded-cpp-interface
order: 5
era: "Embedded C++"
language: C++
file: 05-embedded-cpp-interface.hpp
lines: 28
shows: "interface vs implementation"
area: edge
title: "The hardware stops at the header"
rationale: "The header declares one function and one constant and includes nothing but Arduino.h, so a caller compiles without pulling the ESP32 WiFi stack into its translation unit. Only the implementation file knows about station mode, polling and millisecond timeouts, and it returns a boolean so the caller decides what a failed join means."
honest_note: "Header and implementation are shown together, with a comment marking the boundary, because the point of the exhibit is the split rather than either file alone."
- id: platform-nix-derivation
order: 6
era: "Declarative infrastructure"
language: Nix
file: 06-nix-derivation.nix
lines: 33
shows: "fetchurl + hash, override rationale, removal condition"
area: platform
title: "A patch that says when to delete itself"
rationale: "The file records its own removal condition, delete it once nixpkgs ships 0.10, which is the part of an override that usually goes missing and leaves the next reader guessing. Pinning by fetchurl with a verified hash keeps the build reproducible while the reason for the pin lives in the file rather than in a commit message nobody will read."
honest_note: "The whole file, unedited. It is the newest exhibit and one of the shortest, and it is the one that best predicts how this engineer works now."

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@@ -1,111 +0,0 @@
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<rect x="220" y="146" width="200" height="48" rx="7" class="node node-a"/>
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<rect x="220" y="208" width="200" height="44" rx="7" class="node "/>
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<rect x="220" y="262" width="200" height="44" rx="7" class="node "/>
<text x="320.0" y="289.0" class="hd" text-anchor="middle">Nextcloud · Penpot</text>
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<text x="220" y="348" class="sub">*.lab.audasmedia.com.au</text>
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<rect x="482" y="146" width="240" height="46" rx="7" class="node node-a"/>
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<text x="602.0" y="269.0" class="hd" text-anchor="middle">Prefect worker</text>
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<text x="936" y="486" class="sub" text-anchor="end">measured 2026-10-17 · reference/stack.md</text>
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Before

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<desc id="dsc">The operator reaches a fleet of agent sessions through one inbox. Paperclip is the control plane and delegates to peer projects, which answer the Hub read-only. Every model call in the estate passes through one gateway, OmniRoute.</desc>
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<text x="38" y="392" class="sub">an announcement is a priority lane over the music</text>
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<text x="530" y="288" class="hd">Why it is built this way</text>
<text x="530" y="312" class="sub">· one source of time, not one source of audio</text>
<text x="530" y="336" class="sub">· endpoints are disposable; the server holds the state</text>
<text x="530" y="360" class="sub">· voice reuses the same endpoint fleet</text>
<text x="530" y="384" class="sub">· ducking is a mix decision, not a volume hack</text>
<text x="24" y="452" class="sub">Eight synchronised endpoints with music ducking for voice.</text>
<text x="24" y="472" class="sub">The endpoints are the cheapest part of the system, which is the point.</text>
<text x="936" y="500" class="sub" text-anchor="end">confidence: delivered · 8 endpoints · Pi 2W</text>
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<title id="ttl">Cost and resource engineering</title>
<desc id="dsc">A bar chart of the per-service memory caps drawn to scale against the 1024 MiB ceiling, totalling 970 MiB across four containers, beside the rules that hold the ceiling: equal memory and swap limits, loopback binding, a hard container count and a cost ceiling.</desc>
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<text x="252.0" y="225" class="sub">150 MiB</text>
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<text x="24" y="524" class="sub">Source: reference/stack.md, hosts measured 2026-10-17. The caps are the contract; the bar chart is the contract drawn to scale.</text>
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<title id="ttl">Era timeline</title>
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<text x="24" y="40" class="big">Twenty-five years, five platform shifts</text>
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<text x="42" y="144" class="hd">Flash / ActionScript</text>
<text x="42" y="168" class="sub">classes, imports,</text>
<text x="42" y="186" class="sub">encapsulation</text>
<text x="42" y="212" class="sub">client wall</text>
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<text x="226" y="144" class="hd">jQuery + PHP</text>
<text x="226" y="168" class="sub">media pipelines,</text>
<text x="226" y="186" class="sub">FFmpeg, ImageMagick</text>
<text x="226" y="212" class="sub">delivery at scale</text>
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<text x="594" y="168" class="sub">Go + templ + HTMX,</text>
<text x="594" y="186" class="sub">SQLite</text>
<text x="594" y="212" class="sub">this site</text>
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<text x="778" y="168" class="sub">harnesses, routing,</text>
<text x="778" y="186" class="sub">retrieval</text>
<text x="778" y="212" class="sub">AI estate</text>
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<text x="28" y="314" class="sub">employed delivery</text>
<text x="546" y="314" class="sub">independent engineering practice</text>
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<text x="42" y="376" class="hd">What the five eras have in common</text>
<text x="42" y="400" class="sub">· every era was learned by shipping with it, not by reading about it</text>
<text x="42" y="424" class="sub">· the artefacts survive: a Flash class, an easing engine, a media pipeline, a typed service layer, a 33-line derivation</text>
<text x="42" y="448" class="sub">· the current era is the first where platform, agents and edge hardware are one system</text>
<text x="28" y="500" class="sub">Era boundaries are approximate: employed 2000–2020 per the résumé, independent practice 2020–.</text>
<text x="932" y="500" class="sub" text-anchor="end">six code exhibits span eras 1–5</text>
</svg>

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# Phase 7.2 — diagrams
Eight diagrams, one visual language, light and dark in one file each.
Authored 2026-10-26. Source of truth is the SVG files themselves: each one is
self-contained, with no external font, image or script reference.
## The language
| Part | Rule |
|---|---|
| Canvas | `viewBox="0 0 960 540"`, `width`/`height` set so a bare file view is 1× |
| Type | 19 px section title · 15 px heading · 13 px label · 12 px caption, all `ui-sans-serif` system stack |
| Boxes | `rx=7`, 1.4 px stroke, four semantic tones: neutral, accent (the thing being described), teal (store / gateway), amber (guard, failure, limit) |
| Arrows | solid = data or control flow, dashed = trust boundary or approximate relationship |
| Colour | greys carry structure; one blue accent, one teal, one amber, nothing else |
| Text | every `<text>` is real text in the SVG, so it scales, selects and reads to a screen reader |
Every file carries a **byte-identical `<style>` block**. That is the whole of the
consistency guarantee: there is one palette, defined once, repeated verbatim.
A check that compares the eight style blocks and finds one distinct value is the
evidence that the eight diagrams share a language.
## Both themes in one file
The `<style>` block defines the light palette, then repeats the same selectors
inside `@media (prefers-color-scheme: dark)`. No duplicated files, no theme
toggle needed in the page, no chance of a light and a dark copy drifting apart.
If the site later grows a manual theme toggle, the SVG must be inlined into the
page (not `<img>`) so page CSS can override the custom properties. Until then
`<img>` is correct: the file follows the reader's operating system.
| Role | Light | Dark |
|---|---|---|
| ink (text) | `#1b222b` | `#e7edf4` |
| panel (box fill) | `#f4f6f8` | `#182028` |
| muted (labels) | `#5b6673` | `#98a4b3` |
| accent | `#1a6bc4` | `#5aa2f0` |
| teal | `#0f766e` | `#38b3a6` |
| amber | `#b45309` | `#d98b3f` |
| box stroke | `#c3ccd6` | `#35424f` |
## Mobile
A 960 px schematic cannot be read at 360 px. The presentation contract is:
- wrap the figure in a horizontally scrollable container
- `svg { display:block; width:100%; min-width:900px; height:auto }`
At 1× (container ≥ 960 px) the type renders at 15 px / 13 px. At the 900 px
minimum inside a 390 px container it renders at 14.1 px / 12.2 px — the same
order as body text, and the figure scrolls rather than shrinking into
illegibility. A figure that is scaled to 360 px instead reaches 5 px type and is
not readable; that is why the contract is a requirement and not a suggestion.
## Accessibility
Each file carries `role="img"` and both `<title>` and `<desc>`. The `<desc>` is
the alternative text — 190–285 characters, describing what the diagram asserts
rather than naming its parts. If a template sets `alt=""` on the `<img>`, the
`<title>`/`<desc>` are not read out; the template must supply `alt` from the
manifest's `alt:` field.
## Files
| # | File | Area | Answers |
|---|---|---|---|
| 1 | `01-homelab-topology.svg` | platform | where it runs, and where the trust boundaries are |
| 2 | `02-agent-architecture.svg` | ai | how agents are organised and why one gateway |
| 3 | `03-rag-pipeline.svg` | ai | which side of the pipeline is AI and which side is not |
| 4 | `04-edge-architecture.svg` | edge | why devices never talk to each other |
| 5 | `05-distributed-audio.svg` | edge | how eight speakers stay in sync, and how voice gets in |
| 6 | `06-media-pipeline.svg` | products | where the human gate sits in a media pipeline |
| 7 | `07-cost-resource-engineering.svg` | platform | the memory ceiling, drawn to scale |
| 8 | `08-era-timeline.svg` | history | five platform shifts and what survived them |
Facts behind them come from `reference/stack.md`, `reference/content-map.md` and
`docs/phase-3-deploy.md`. Diagram 7 is numbers from a measurement, not an
illustration: if the caps change, the bars change.

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<svg xmlns="http://www.w3.org/2000/svg" width="1110" height="600" viewBox="0 0 1110 600" role="img" aria-labelledby="topoTitle topoDesc" font-family="ui-sans-serif, system-ui, -apple-system, 'Segoe UI', sans-serif">
<title id="topoTitle">Homelab topology: build host, runtime host and public edge</title>
<desc id="topoDesc">Three hosts. The build host produces a release; the runtime host runs the site, the vector store, a workflow worker and the model gateway, all bound to loopback; the public edge terminates TLS and is the only host reachable from the internet. All three join one private mesh, and backups run in three tiers.</desc>
<style>
/* On paper the diagram is ink on white, not a dark panel. Presentation
attributes are the weakest source, so these rules win without editing
the drawing. */
@media print {
rect { fill: #fff; }
text { fill: #111; }
text[fill="#6ee7c6"] { fill: #0b5c48; }
text[fill="#8b98a8"] { fill: #444; }
line, path { stroke: #555; }
marker path { fill: #555; }
}
</style>
<defs>
<marker id="arrow" viewBox="0 0 10 10" refX="9" refY="5" markerWidth="7" markerHeight="7" orient="auto-start-reverse">
<path d="M0 0 10 5 0 10z" fill="#6ee7c6"/>
</marker>
</defs>
<g fill="#12161b" stroke="#33404d" stroke-width="1.5">
<rect x="30" y="104" width="310" height="268" rx="10"/>
<rect x="400" y="104" width="310" height="268" rx="10"/>
<rect x="770" y="104" width="310" height="268" rx="10"/>
</g>
<!-- internet -->
<g>
<rect x="810" y="24" width="230" height="46" rx="23" fill="#171d24" stroke="#33404d" stroke-width="1.5"/>
<text x="925" y="52" text-anchor="middle" font-size="13" fill="#b3bcc8">public internet · https :443</text>
<line x1="925" y1="70" x2="925" y2="100" stroke="#6ee7c6" stroke-width="1.5" marker-end="url(#arrow)"/>
</g>
<!-- host headers -->
<g fill="#e6edf3" font-size="15" font-weight="600">
<text x="54" y="136">Build and development</text>
<text x="424" y="136">Runtime host</text>
<text x="794" y="136">Public edge</text>
</g>
<g fill="#8b98a8" font-size="12" font-family="ui-monospace, SFMono-Regular, Menlo, monospace">
<text x="54" y="156">.27 · 4-screen desktop</text>
<text x="424" y="156">.13 · 15 GiB · memory-bound</text>
<text x="794" y="156">.35 · Ubuntu · 2 vCPU</text>
</g>
<!-- build host items -->
<g fill="#b3bcc8" font-size="12.5">
<text x="54" y="192">· repository working copy</text>
<text x="54" y="219">· agent fleet, panes and approvals</text>
<text x="54" y="246">· content, diagrams and exhibits</text>
<text x="54" y="273">· release bundles and hashes</text>
</g>
<!-- runtime: loopback trust boundary -->
<rect x="416" y="172" width="278" height="150" rx="8" fill="none" stroke="#6ee7c6" stroke-width="1.2" stroke-dasharray="5 4"/>
<text x="430" y="192" font-size="11" fill="#6ee7c6" font-family="ui-monospace, SFMono-Regular, Menlo, monospace">bound to 127.0.0.1</text>
<g fill="#b3bcc8" font-size="12.5">
<text x="430" y="216">· site server :8096</text>
<text x="430" y="241">· Qdrant :6333 / :6334</text>
<text x="430" y="266">· Prefect worker</text>
<text x="430" y="291">· model gateway (shared)</text>
</g>
<text x="424" y="345" font-size="11.5" fill="#8b98a8">Caps equal swap; nothing listens publicly.</text>
<!-- edge host items -->
<g fill="#b3bcc8" font-size="12.5">
<text x="794" y="192">· Caddy terminates TLS</text>
<text x="794" y="219">· proxies to the runtime host</text>
<text x="794" y="246">· Gitea · Immich · Nextcloud</text>
<text x="794" y="273">· one public listener, many sites</text>
</g>
<!-- hand-off arrows -->
<line x1="340" y1="230" x2="396" y2="230" stroke="#6ee7c6" stroke-width="1.5" marker-end="url(#arrow)"/>
<text x="368" y="218" text-anchor="middle" font-size="10.5" fill="#8b98a8">deploy</text>
<line x1="710" y1="230" x2="766" y2="230" stroke="#6ee7c6" stroke-width="1.5" marker-end="url(#arrow)"/>
<text x="738" y="218" text-anchor="middle" font-size="10.5" fill="#8b98a8">proxy</text>
<!-- mesh band -->
<rect x="30" y="410" width="1050" height="56" rx="10" fill="#12161b" stroke="#33404d" stroke-width="1.2" stroke-dasharray="6 5"/>
<text x="555" y="436" text-anchor="middle" font-size="13" fill="#b3bcc8">private mesh · SSH on every host · remote access without an exposed port</text>
<text x="555" y="454" text-anchor="middle" font-size="11" fill="#8b98a8">no service is reachable because it happened to bind to every interface</text>
<g stroke="#33404d" stroke-width="1" stroke-dasharray="3 4">
<line x1="185" y1="372" x2="185" y2="410"/>
<line x1="555" y1="372" x2="555" y2="410"/>
<line x1="925" y1="372" x2="925" y2="410"/>
</g>
<!-- backup tiers -->
<text x="30" y="506" font-size="13" font-weight="600" fill="#e6edf3">Three backup tiers — each covers a different failure</text>
<g fill="#12161b" stroke="#33404d" stroke-width="1.2">
<rect x="30" y="522" width="330" height="52" rx="8"/>
<rect x="390" y="522" width="330" height="52" rx="8"/>
<rect x="750" y="522" width="330" height="52" rx="8"/>
</g>
<g fill="#b3bcc8" font-size="12.5">
<text x="50" y="545">local snapshots, on the machine</text>
<text x="410" y="545">a copy on another machine</text>
<text x="770" y="545">encrypted object storage, offsite</text>
</g>
<g fill="#8b98a8" font-size="11">
<text x="50" y="562">restore tested, runbook beside it</text>
<text x="410" y="562">over the private mesh</text>
<text x="770" y="562">retention stated in the job</text>
</g>
</svg>

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# Phase 7.2 — diagram manifest
#
# Machine-readable index for the templates. `alt` is copied verbatim from each
# file's <desc>, so the text a screen reader gets and the text in this manifest
# are the same string.
version: 1
authored: 2026-10-26
viewbox: "0 0 960 540"
themes: [light, dark] # both resolved from one file via prefers-color-scheme
format: svg-text # real text nodes, not glyph outlines
external_refs: none # no fonts, images or scripts referenced
presentation:
desktop: {css_width: "100%", rendered_type_px: [15, 13]}
mobile:
wrap: "overflow-x:auto"
css: "svg{display:block;width:100%;min-width:900px;height:auto}"
rendered_type_px: [14.1, 12.2]
diagrams:
- id: platform-homelab-topology
file: 01-homelab-topology.svg
area: platform
level: 2
title: "Homelab topology"
answers: "where it runs, and where the trust boundaries are"
alt: "Three hosts with their roles and the trust boundaries between them: public internet reaches Caddy on .35, which forwards over the LAN to services bound to 127.0.0.1 on .13. .27 is build and dev with no public listeners."
bytes: 7079
- id: ai-agent-architecture
file: 02-agent-architecture.svg
area: ai
level: 2
title: "Agent architecture"
answers: "how the agent estate is organised, and why there is one gateway"
alt: "The operator reaches a fleet of agent sessions through one inbox. Paperclip is the control plane and delegates to peer projects, which answer the Hub read-only. Every model call in the estate passes through one gateway, OmniRoute."
bytes: 6027
- id: ai-rag-pipeline
file: 03-rag-pipeline.svg
area: ai
level: 2
title: "Retrieval pipeline"
answers: "which side of the pipeline is AI and which side is not"
alt: "Left, offline ingest: content files are validated, chunked with metadata, embedded and upserted into Qdrant, while the same files render to static pages with no AI in the path. Right, per-visitor query: guardrail, query graph, filtered retrieval, then a streamed answer with citations."
bytes: 7010
- id: edge-architecture
file: 04-edge-architecture.svg
area: edge
level: 2
title: "Edge architecture"
answers: "why devices never talk to each other directly"
alt: "Constrained devices publish over LoRa, Zigbee or WiFi to a single MQTT broker, which feeds one automation layer that drives actuation. Retained configuration, telemetry and last-will presence let devices recover unattended."
bytes: 6896
- id: edge-distributed-audio
file: 05-distributed-audio.svg
area: edge
level: 2
title: "Distributed audio"
answers: "how eight endpoints stay in sync, and how voice gets in"
alt: "Music and speech sources feed one Snapcast server, which streams one clock-synchronised mix to eight Pi endpoints. Voice announcements arrive as a priority lane: duck the music, speak, restore."
bytes: 6833
- id: products-media-pipeline
file: 06-media-pipeline.svg
area: products
level: 2
title: "Media pipeline"
answers: "where the human gate sits in a media pipeline"
alt: "Five stages from download to library: fingerprinting gives every file a natural key so re-runs converge, audit verifies the copy, and review is a deliberate human gate before anything is published."
bytes: 5694
- id: platform-cost-resource-engineering
file: 07-cost-resource-engineering.svg
area: platform
level: 2
title: "Cost and resource engineering"
answers: "the memory ceiling, drawn to scale from measured caps"
alt: "A bar chart of the per-service memory caps drawn to scale against the 1024 MiB ceiling, totalling 970 MiB across four containers, beside the rules that hold the ceiling: equal memory and swap limits, loopback binding, a hard container count and a cost ceiling."
bytes: 6151
- id: history-era-timeline
file: 08-era-timeline.svg
area: history
level: 2
title: "Era timeline"
answers: "five platform shifts and what survived them"
alt: "Five consecutive platform eras from Flash and ActionScript through jQuery and PHP, React and TypeScript, server-driven Go with HTMX, to agent systems, on an axis from 2000 to 2026. Era boundaries are approximate; the bands show order."
bytes: 6039

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<svg xmlns="http://www.w3.org/2000/svg" width="1120" height="580" viewBox="0 0 1120 580" role="img" aria-labelledby="ragTitle ragDesc" font-family="ui-sans-serif, system-ui, -apple-system, 'Segoe UI', sans-serif">
<title id="ragTitle">Retrieval pipeline: ingest on top, four-node query path below</title>
<desc id="ragDesc">Ingest reads content and estate notes, excludes secrets, chunks and tags each block, embeds through the model gateway and upserts into two Qdrant collections on loopback. The query path classifies the question, routes it to an area, retrieves with a payload filter, composes an answer from the retrieved context only, and verifies that every citation resolves to a page.</desc>
<style>
/* On paper the diagram is ink on white, not a dark panel. Presentation
attributes are the weakest source, so these rules win without editing
the drawing. */
@media print {
rect { fill: #fff; }
text { fill: #111; }
text[fill="#6ee7c6"] { fill: #0b5c48; }
text[fill="#8b98a8"] { fill: #444; }
line, path { stroke: #555; }
marker path { fill: #555; }
}
</style>
<defs>
<marker id="arrow" viewBox="0 0 10 10" refX="9" refY="5" markerWidth="7" markerHeight="7" orient="auto-start-reverse">
<path d="M0 0 10 5 0 10z" fill="#6ee7c6"/>
</marker>
<marker id="arrowDim" viewBox="0 0 10 10" refX="9" refY="5" markerWidth="7" markerHeight="7" orient="auto-start-reverse">
<path d="M0 0 10 5 0 10z" fill="#8b98a8"/>
</marker>
</defs>
<!-- ingest lane -->
<text x="30" y="42" font-size="14" font-weight="600" fill="#e6edf3">Ingest — one source of truth, one index</text>
<g fill="#12161b" stroke="#33404d" stroke-width="1.4">
<rect x="30" y="60" width="244" height="92" rx="9"/>
<rect x="302" y="60" width="244" height="92" rx="9"/>
<rect x="574" y="60" width="244" height="92" rx="9"/>
<rect x="846" y="60" width="244" height="92" rx="9"/>
</g>
<g fill="#e6edf3" font-size="13.5" font-weight="600">
<text x="48" y="86">Sources</text>
<text x="320" y="86">Normalise</text>
<text x="592" y="86">Chunk and tag</text>
<text x="864" y="86">Embed and upsert</text>
</g>
<g fill="#b3bcc8" font-size="12">
<text x="48" y="110">content/ Markdown</text>
<text x="48" y="129">estate notes</text>
<text x="48" y="148">platform guides</text>
<text x="320" y="110">front-matter parsed</text>
<text x="320" y="129">secrets and drafts</text>
<text x="320" y="148">excluded by rule</text>
<text x="592" y="110">blocks, not lines</text>
<text x="592" y="129">area · level · source</text>
<text x="592" y="148">title · tags</text>
<text x="864" y="110">model gateway only</text>
<text x="864" y="129">deterministic ids</text>
<text x="864" y="148">batched, resumable</text>
</g>
<g stroke="#6ee7c6" stroke-width="1.5" marker-end="url(#arrow)">
<line x1="277" y1="106" x2="298" y2="106"/>
<line x1="549" y1="106" x2="570" y2="106"/>
<line x1="821" y1="106" x2="842" y2="106"/>
</g>
<line x1="968" y1="152" x2="968" y2="184" stroke="#6ee7c6" stroke-width="1.5" marker-end="url(#arrow)"/>
<rect x="30" y="186" width="1060" height="62" rx="9" fill="#12161b" stroke="#6ee7c6" stroke-width="1.2" stroke-dasharray="5 4"/>
<text x="50" y="210" font-size="13" font-weight="600" fill="#e6edf3">Qdrant on 127.0.0.1</text>
<text x="50" y="231" font-size="12" fill="#b3bcc8">resume_corpus · one point per chunk resume_graph · one point per node and edge HNSW and payloads on disk · within a 400 MiB cap</text>
<text x="50" y="272" font-size="11.5" fill="#8b98a8">A sync run re-embeds only the documents whose content, model or chunker version changed.</text>
<!-- query lane -->
<text x="30" y="316" font-size="14" font-weight="600" fill="#e6edf3">Query</text>
<text x="108" y="316" font-size="12" fill="#8b98a8">— four graph nodes. It grows past four only with a written reason.</text>
<line x1="776" y1="248" x2="776" y2="352" stroke="#8b98a8" stroke-width="1.3" stroke-dasharray="4 4" marker-end="url(#arrowDim)"/>
<text x="788" y="330" font-size="11" fill="#8b98a8">payload-filtered search</text>
<g fill="#12161b" stroke="#33404d" stroke-width="1.4">
<rect x="30" y="360" width="196" height="112" rx="9"/>
<rect x="246" y="360" width="196" height="112" rx="9"/>
<rect x="462" y="360" width="196" height="112" rx="9"/>
<rect x="678" y="360" width="196" height="112" rx="9"/>
<rect x="894" y="360" width="196" height="112" rx="9"/>
</g>
<g font-size="10.5" fill="#6ee7c6" font-family="ui-monospace, SFMono-Regular, Menlo, monospace">
<text x="46" y="382">CLASSIFY</text>
<text x="262" y="382">ROUTE</text>
<text x="478" y="382">RETRIEVE</text>
<text x="694" y="382">COMPOSE</text>
<text x="910" y="382">VERIFY</text>
</g>
<g fill="#e6edf3" font-size="13" font-weight="600">
<text x="46" y="406">Read the question</text>
<text x="262" y="406">Pick the areas</text>
<text x="478" y="406">Find the evidence</text>
<text x="694" y="406">Answer from it</text>
<text x="910" y="406">Check citations</text>
</g>
<g fill="#b3bcc8" font-size="11.5">
<text x="46" y="430">intent · injection</text>
<text x="46" y="448">off-topic · PII</text>
<text x="262" y="430">which pages can</text>
<text x="262" y="448">answer this</text>
<text x="478" y="430">vector + keyword</text>
<text x="478" y="448">ranked, top five</text>
<text x="694" y="430">context only</text>
<text x="694" y="448">no outside claims</text>
<text x="910" y="430">every claim maps</text>
<text x="910" y="448">to a real page</text>
</g>
<g stroke="#6ee7c6" stroke-width="1.5" marker-end="url(#arrow)">
<line x1="227" y1="416" x2="242" y2="416"/>
<line x1="443" y1="416" x2="458" y2="416"/>
<line x1="659" y1="416" x2="674" y2="416"/>
<line x1="875" y1="416" x2="890" y2="416"/>
</g>
<text x="30" y="500" font-size="12" fill="#b3bcc8">A refusal is a real outcome: an off-topic question, an injection attempt or a request over the cost cap stops at the first node with zero model spend.</text>
<text x="30" y="526" font-size="11.5" fill="#8b98a8">Ingest runs today. The query path is the Phase 5 design; until it ships, every page is pre-rendered and needs no model.</text>
<text x="30" y="550" font-size="11.5" fill="#8b98a8">The public AI has no tools, no filesystem, no network fetch and no memory between visitors — architectural, not a prompt instruction.</text>
</svg>

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# Phase 7.1 — the client wall
#
# Decision: a typographic wall, not a logo collage. 45 entries, 44 distinct names, one
# typeface, one size, an even grid. Nothing is an image, so nothing can fail to
# load, and no name is more prominent than another.
#
# The Simple Icons audit below was performed for Phase 7. Ten marks exist. They
# are NOT enabled in this file: putting a client's mark on a public portfolio is
# a permission decision, not a design decision, so `mark: none` for every entry
# until Sam says otherwise.
version: 1
authored: 2026-10-26
render: typographic
mark_policy:
default: none
enable: "ask Sam before enabling any commercial mark; availability is recorded, permission is not implied"
icon_set: "Simple Icons (CC0 paths, trademarks remain with their owners)"
counts:
agencies_listed: 22
brands_listed: 23
entries_listed: 45
distinct_names: 44
claimed_by_plan: 46
note: >
The plan says 46 names and 23 agencies. The source list contains 22 agencies
and 23 brands, and 'Pearsons' appears in both groups, so the distinct count
is 44. No name has been invented to reach 46; the difference is flagged for
Sam rather than papered over.
corrections_applied:
- "Diajio -> Diageo (reference/content-map.md: factual correction, do not undo)"
- "AutoCAD listed as the product; the mark that exists is Autodesk's"
client_wall:
agencies:
- name: "Clemenger"
mark: none
- name: "Agency Republic"
mark: none
- name: "Brand Union"
mark: none
- name: "Jackpot Joy"
mark: none
- name: "Bryce Ford"
mark: none
- name: "RMG Connect"
mark: none
- name: "Sporting Index"
mark: none
- name: "Adventis Digital"
mark: none
- name: "Trinity St"
mark: none
- name: "Inferno"
mark: none
- name: "SAS Design"
mark: none
- name: "Ogilvy"
mark: none
- name: "Wunderman"
mark: none
- name: "Illumina"
mark: none
- name: "Barkers"
mark: none
- name: "Mook"
mark: none
- name: "Pearsons"
mark: none # available in Simple Icons; not enabled
- name: "Penguin"
mark: none
- name: "Draft London"
mark: none
- name: "Behan Consulting"
mark: none
- name: "MCM Entertainment"
mark: none
- name: "Carsales"
mark: none
brands:
- name: "Carlton Draught"
mark: none
- name: "Diageo"
mark: none
- name: "KPMG"
mark: none
- name: "McDonald's"
mark: none # available in Simple Icons; not enabled
- name: "Accenture"
mark: none # available in Simple Icons; not enabled
- name: "BBC National Archive"
mark: none
- name: "Bank of America"
mark: none # available in Simple Icons; not enabled
- name: "War of the Worlds"
mark: none
- name: "Madonna (Hard Candy)"
mark: none
- name: "PlayStation"
mark: none # available in Simple Icons; not enabled
- name: "HSBC"
mark: none # available in Simple Icons; not enabled
- name: "DHL"
mark: none # available in Simple Icons; not enabled
- name: "Reuters"
mark: none
- name: "Volvic"
mark: none
- name: "AutoCAD"
mark: none # available in Simple Icons; not enabled
attribution: "Autodesk" if n == "AutoCAD"
- name: "Channel 4"
mark: none # available in Simple Icons; not enabled
- name: "Pearsons"
mark: none # available in Simple Icons; not enabled
- name: "Shell"
mark: none # available in Simple Icons; not enabled
- name: "Stella Artois"
mark: none
- name: "Baileys Irish Cream"
mark: none
- name: "Parkgate Homes"
mark: none
- name: "Carlton Dry"
mark: none
- name: "Cottees Cordial"
mark: none

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---
id: edge-fleet
area: edge
level: 2
title: "Constrained devices · custom radio · and a record of what broke"
claim: "Twenty or more edge nodes across nine zones and twenty automations — solar power · deep sleep · LoRa and hand-built Zigbee — reporting into one automation layer with voice control on top."
confidence: running
period: "2020–"
tech: [ESP32, LoRa, Zigbee, MQTT, Home Assistant, Snapcast, Pi, PIR, mmWave]
metrics: [{label: "edge nodes", value: 20}, {label: "lighting zones", value: 9}, {label: "automations", value: 20}, {label: "hand-built Zigbee routers", value: 2}]
artefacts: [{type: doc, label: "Edge architecture — power · radio · deep sleep", url: "content/edge/l3/edge-architecture.md"}, {type: doc, label: "LoRa irrigation under solar power", url: "content/edge/l3/edge-lora-sprinkler.md"}, {type: doc, label: "Hand-built Zigbee routers and lighting", url: "content/edge/l3/edge-zigbee.md"}, {type: doc, label: "Distributed audio — eight endpoints with ducking", url: "content/edge/l3/edge-snapcast.md"}, {type: doc, label: "Front door — vision · voice · and a rebuild", url: "content/edge/l3/edge-front-door.md"}, {type: doc, label: "Sensors and actuation", url: "content/edge/l3/edge-sensors.md"}, {type: doc, label: "The failures, kept in the record", url: "content/edge/l3/edge-failures.md"}, {type: repo, label: "Embedded C++ — the Wi-Fi manager interface and implementation", url: "content/assets/code/05-embedded-cpp-interface.hpp"}]
public: true
last_verified: 2026-10-28
tags: [edge, iot, esp32, lora, zigbee, audio]
---
**Capability statement.** Devices that must run for months on a small battery, talk over radio that was chosen for range rather than convenience, and fail in ways that are visible. Twenty or more nodes, nine zones, twenty automations, and a voice layer on top of all of it.
## What is actually run
**Edge architecture.** Each node is designed around its power budget: a LiFePO battery with a solar controller where there is sun, deep sleep between wakes, and a wake source chosen per job. The sprinkler and the garden lights wake on radio rather than on a timer, so a command can arrive while the device is asleep.
**Radio.** Three links, each for a reason. LoRa for range and penetration on outdoor nodes — long enough to cover the garden and the shed without a repeater. Zigbee for the indoor lighting mesh, including two routers built by hand where the commercial mesh did not reach. Wi-Fi where a device is mains-powered and needs bandwidth, such as the audio endpoints and the camera path.
**Sensors and actuation.** PIR for movement, radar and mmWave for presence that a PIR cannot see — including a presence sensor built for a bathroom and a child's bedroom because the off-the-shelf options were wrong for the space. Relays and step-down converters for 240V loads, servos and a 16-channel PWM driver for mechanical jobs, solenoids for water, load cells for weight.
**Audio distribution.** Eight Snapcast endpoints on Pi hardware, fed by a Snapserver, with Spotify and a music server as sources. Every spoken announcement — automation notices, assistant replies, doorbell messages — plays across all eight endpoints with the music ducked underneath and restored afterwards.
**The automation layer.** Home Assistant holds the state and the routines; MQTT carries the messages. Nine lighting zones and twenty automations, with voice control on top, so a request spoken in one room is answered through the speakers in whichever room a person is standing.
**Cameras and vision.** The front door takes an image, logs it, has the image described, and speaks the result into the house before deciding what to do next. RFID tags on boxes and on trees resolve to records — inventory with prices for the boxes, care notes for the trees — written by an automation rather than typed by hand.
## Why this exhibit is good code
The header declares the interface and includes only `Arduino.h`, so a caller compiles without pulling the ESP32 Wi-Fi stack into its translation unit. Only the implementation file knows about station mode, polling and millisecond timeouts, and it returns a boolean so the caller decides what a failed join means. The two files are shown together because the point is the split, not either file alone.
## Evidence
| Claim | Evidence | Artefact |
|---|---|---|
| Twenty or more nodes | Ten ESP32 devices · two hand-built Zigbee routers · eight Pi audio endpoints | [Edge architecture](l3/edge-architecture.md) |
| Radio chosen per job | LoRa outdoors with radio wake from deep sleep; Zigbee indoors with hand-built routers | [Irrigation](l3/edge-lora-sprinkler.md) · [Zigbee](l3/edge-zigbee.md) |
| Nine zones and twenty automations | Lighting zones and routines in the automation layer | [Zigbee and lighting](l3/edge-zigbee.md) |
| Eight endpoints with ducking | Snapcast clients on Pi hardware with music lowered under speech | [Distributed audio](l3/edge-snapcast.md) |
| Vision and voice at the door | Image capture · description · spoken response · two-way audio | [Front door](l3/edge-front-door.md) |
| Failures are stated | Lizard feeder destroyed · garden cameras postponed · doorbell being rebuilt | [Failures](l3/edge-failures.md) |
## Trade-offs and limits
- **Power decides the design.** Deep sleep means a device is unreachable for most of its life, so every routine has to tolerate a node that is asleep. That constraint is why radio wake exists at all.
- **Range over bandwidth.** LoRa is slow and payload-limited. It is chosen where a hundred bytes a day is enough and the alternative is a Wi-Fi access point in a garden bed.
- **Some things are still manual.** The garden automation is partly manual — some watering and lighting is scheduled, some is a person turning something on. The page says which is which instead of implying full coverage.
- **The failures cost money and are kept.** A lizard destroyed a feeder built for it. The garden cameras were postponed with the parts already bought. The doorbell is being rebuilt rather than declared finished. Those are the most honest evidence of production experience on this site.
Individual nodes carry their own label, and the ones being rebuilt or postponed are labelled that way on their pages.

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---
id: edge-overview
area: edge
level: 1
title: "Edge & Devices"
claim: "Sam builds and maintains a twenty-node edge fleet — constrained devices on solar power and deep sleep, with custom radio and sensors — that reports into a single automation layer."
confidence: running
period: "2020–"
tech: [ESP32, LoRa, Zigbee, MQTT, Home Assistant, Snapcast, Raspberry Pi]
metrics: [{label: "edge nodes", value: 20}, {label: "lighting zones", value: 9}, {label: "automations", value: 20}]
artefacts: [{type: doc, label: "Edge architecture — power · radio · deep sleep", url: "content/edge/l3/edge-architecture.md"}, {type: doc, label: "The failures, kept in the record", url: "content/edge/l3/edge-failures.md"}, {type: doc, label: "Distributed audio — eight endpoints with ducking", url: "content/edge/l3/edge-snapcast.md"}, {type: doc, label: "Vehicle tracker on LTE Cat-M", url: "content/edge/l3/edge-car-tracker.md"}]
public: true
last_verified: 2026-10-26
tags: [edge, iot, esp32, lora, zigbee]
---
The devices are outdoors on solar and asleep most of their lives, and they have to keep working when nobody is looking; what failed is written down. The architecture, the radio and the failures are on the [fleet page](edge-fleet.md).

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---
id: edge-architecture
area: edge
level: 3
title: "Artefact — Edge architecture · power · radio · deep sleep"
claim: "Each node is designed around its power budget — battery and solar · deep sleep between wakes · and a deliberate choice of wake source per job."
confidence: running
period: "2020–"
tech: [ESP32, LoRa, Zigbee, LiFePO, MQTT, Home Assistant]
metrics: [{label: "edge nodes", value: 20}, {label: "ESP32 devices", value: 10}]
artefacts: []
public: true
last_verified: 2026-10-26
tags: [edge, architecture, power, artefact]
---
One annotated diagram of a representative node and the radio path lands in Phase 7.
## What the artefact is
The per-node pattern: 240V step-down or solar with a LiFePO battery and charge controller, an ESP32 in deep sleep, a wake source (timer, radio, or a physical input), a relay or driver for the load, and an MQTT message into the automation layer. Around it, the reason each node uses LoRa, Zigbee or Wi-Fi.
## What it proves
Power budgeting and remote wake as design decisions, not hobby wiring — and an architecture that survived being repeated across twenty nodes.
## When it is delivered
Phase 7. Source: evidence notes section 1.

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---
id: edge-car-tracker
area: edge
level: 3
title: "Artefact — Vehicle tracker on LTE Cat-M"
claim: "A tracker that sleeps between reports · wakes on a schedule or when the car moves · and reports when it leaves a boundary set from the web interface."
confidence: in-progress
period: "2024–"
tech: [ESP32, LTE Cat-M, NB-IoT, SIM7080G, GPS, LiFePO4]
metrics: [{label: "battery packs", value: 2}]
artefacts: []
public: true
last_verified: 2026-10-28
tags: [edge, tracker, lte, low-power, artefact]
---
The build is on the bench: two LiFePO batteries, a buck converter, an ESP32 with GPS, and a
cellular module for the uplink. The car keeps the packs charged while it is running.
![Waveshare Pico SIM7080G Cat-M and NB-IoT module — the cellular link in the tracker. Product image, used to identify the part.](../../assets/images/sim7080g-module.jpg)
## What it does
Two behaviours, chosen by the settings rather than hard-coded. When the car is parked the
ESP32 sits in deep sleep, wakes on a timer, checks its own configuration, and reports only
what the settings ask for. When the car is moving it stays awake and reports on a shorter
interval. Either way the interval and the boundary can be changed from the web interface,
and a change can force an immediate update rather than waiting for the next wake.
The geofence is the part that matters. A boundary is drawn from the web interface, and the
device is responsible for deciding whether it has left it — which is why the settings have
to travel to the device rather than live only in the server.
## Why LTE Cat-M rather than WiFi
The tracker lives in a car. WiFi coverage is a driveway, not a road. Cat-M and NB-IoT were
chosen because they are designed for exactly this shape of traffic — small payloads, long
sleeps, coverage beyond what 4G handsets need — and because the modules are cheap enough to
leave installed.
## What it proves
A complete path rather than a demo: constrained hardware, a power budget that dictates the
schedule, a device that holds enough state to act on its own, and a web application that
configures it and shows where it has been.
## Still open
Field testing across the full battery cycle. The sleep interval that is right for a parked
car and the interval that is right for a moving one are guesses until the discharge curve
has been measured over a week rather than a day.

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---
id: edge-failures
area: edge
level: 3
title: "Artefact — The failures · kept in the record"
claim: "Three things did not go to plan — a feeder the animal destroyed · cameras postponed with the parts already bought · and a doorbell being rebuilt instead of declared finished."
confidence: in-progress
period: "2020–"
tech: [ESP32, Servo, PWM, Camera]
metrics: [{label: "crickets released per day by the feeder", value: 10}]
artefacts: []
public: true
last_verified: 2026-10-26
tags: [edge, failures, artefact]
---
Two items are being rebuilt, one is postponed. The page is not finished because the work is not finished.
## What the artefact is
A short, unpolished register of what did not work, in the order it broke.
**The lizard feeder.** Servo motors on a 16-channel PWM driver, rubber-band spring-loaded boxes, an ESP32 — one box opened per day to release ten crickets for the bearded dragon it was built for. The lizard attacked the boxes and destroyed the mechanism. Version one is abandoned; it is being rebuilt with plastic containers.
**The garden cameras.** Postponed. The parts have arrived and are sitting in a box. No photographs and no claim until they are installed.
**The front door.** Built, then found to greet people who were not at the door. It is being rebuilt with a time-of-flight ranging sensor rather than being described as complete.
**The garden automation.** Partly manual. Some of it runs on schedule and some of it is a person. Both are true and the page says so.
## What it proves
That this is a working estate and not a rendering: things break, budgets get spent on parts that are not installed yet, and hardware has to be designed around an animal's behaviour. Production experience is measured in rebuilds as much as in launches.
## When it is delivered
Phase 7, with photographs of the rebuilt feeder and the installed cameras if they exist by then. If they do not, the entry stays as written — labelled rather than deleted.

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---
id: edge-front-door
area: edge
level: 3
title: "Artefact — Front door · vision · voice · and a rebuild"
claim: "A doorbell that sees a person · describes the image · speaks to the house and to the visitor · and is currently being rebuilt with a time-of-flight sensor."
confidence: in-progress
period: "2020–"
tech: [ESP32, ESP32-CAM, PIR, mmWave, VL53L0X, MiniMax, Piper, Whisper, Apprise]
metrics: [{label: "voice engines in use", value: 2}, {label: "presence sensing inputs", value: "PIR · mmWave · time-of-flight"}]
artefacts: []
public: true
last_verified: 2026-10-26
tags: [edge, doorbell, vision, voice, artefact]
---
The device was built and is being rebuilt. The page states this rather than describing it as finished.
## What the artefact is
A door unit with microphone, speaker, camera, button, PIR and mmWave presence. It detects a person, captures an image, logs it, has the image described, and announces to the house who is at the door before offering a spoken greeting; the visitor's reply is captured and played over the house speakers, with an alert pushed to a phone. Voice output ran on one provider and now runs on a cloned voice with a fallback to a local synthesiser.
## What it proves
Vision, voice and actuation in one device under real constraints — latency that a person at a door will tolerate, and a failure mode that must be polite.
## Limits stated on the page
The measurement it is being rebuilt around: presence detection with a time-of-flight ranging sensor, to stop the unit greeting people who are walking past rather than standing at the door. Until the rebuild is finished the honest label is `in-progress`.
## When it is delivered
Phase 7 for photos and the sensor change. Source: evidence notes section 1.

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---
id: edge-lora-sprinkler
area: edge
level: 3
title: "Artefact — LoRa irrigation under solar power"
claim: "A solar-powered controller that sleeps · wakes on radio · and drives a solenoid — with the first version polling over a message queue instead."
confidence: running
period: "2020–"
tech: [ESP32, LoRa, LiFePO, Solenoid, Home Assistant, RabbitMQ]
metrics: [{label: "major revisions", value: 2}]
artefacts: []
public: true
last_verified: 2026-10-26
tags: [edge, lora, solar, irrigation, artefact]
---
The bill of materials and the wake-on-radio change land in Phase 7.
## What the artefact is
The build: solar panel, charge controller, LiFePO battery, ESP32, LoRa module, relay and solenoid valve. Version one woke on a schedule and polled through a message queue to learn whether it should run. Version two is better: a LoRa packet can wake the ESP32 from deep sleep and switch the valve directly, so on-time is short and the battery is not spent asking whether there is work.
## What it proves
Iteration on a real constraint. The first version worked; the second version was designed around the power cost of asking, which is the kind of decision you only make if you are watching the battery.
## Limits stated on the page
Watering is partly scheduled and partly a person. The routine lives in the automation layer and the voice assistant can trigger it, but the garden is not fully automatic and the page does not pretend otherwise.
## When it is delivered
Phase 7. Source: evidence notes section 1.

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---
id: edge-sensors
area: edge
level: 3
title: "Artefact — Sensors and actuation"
claim: "Movement · presence · weight · temperature · water and mechanical actuation — chosen per job and integrated into one automation layer."
confidence: running
period: "2020–"
tech: [ESP32, PIR, Radar, mmWave, Load cell, Servo, Solenoid, MQTT]
metrics: [{label: "automations", value: 20}, {label: "PWM channels on the feeder driver", value: 16}]
artefacts: []
public: true
last_verified: 2026-10-26
tags: [edge, sensors, actuation, artefact]
---
A sensor and actuator table lands in Phase 7.
## What the artefact is
The parts list of the fleet in the way an engineer would want it: what is sensed, which device senses it, what is actuated, and how the decision reaches a physical action — a relay, a solenoid, a servo, a step-down converter switching 240V, a wheel that flicks food into a tank, an auger that dispenses it, a load cell that decides when to top up water.
## What it proves
Range across the awkward middle of embedded work: not just reading a sensor but choosing an actuator, sizing its driver, and making the whole loop recoverable when the network is absent.
## When it is delivered
Phase 7. Source: evidence notes section 1.

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---
id: edge-snapcast
area: edge
level: 3
title: "Artefact — Distributed audio · eight endpoints with ducking"
claim: "Eight synchronised endpoints with music ducked under every spoken announcement and restored afterwards — driven by Home Assistant and the voice assistant."
confidence: running
period: "2020–"
tech: [Snapcast, Mopidy, Spotify, Raspberry Pi, Home Assistant]
metrics: [{label: "audio endpoints", value: 8}, {label: "client hardware", value: "Pi 2W"}]
artefacts: []
public: true
last_verified: 2026-10-26
tags: [edge, audio, snapcast, artefact]
---
The topology diagram and a short recording of ducking land in Phase 7.
## What the artefact is
A Snapserver with eight Pi clients, Spotify and a local music server as sources, and one behaviour that matters more than the rest: when the house needs to speak, the music drops and the announcement plays everywhere, then the music returns to its previous level. Requests arrive by voice, so the same system that plays music is also the output for every automation and every answer.
## What it proves
Multi-room audio treated as infrastructure with a predictable interruption behaviour — and the difference between "the speakers work" and "the speakers can be interrupted safely". Mains-powered nodes, bandwidth-heavy work, and a state machine for volume: the opposite design constraints to the battery nodes.
## When it is delivered
Phase 7. Source: evidence notes section 1.

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---
id: edge-zigbee
area: edge
level: 3
title: "Artefact — Hand-built Zigbee routers and lighting"
claim: "Two routers built by hand to extend an indoor mesh · feeding nine lighting zones with presence sensing and voice control."
confidence: running
period: "2020–"
tech: [ESP32, Zigbee, Relay, PIR, Radar, Home Assistant]
metrics: [{label: "hand-built Zigbee routers", value: 2}, {label: "lighting zones", value: 9}]
artefacts: []
public: true
last_verified: 2026-10-26
tags: [edge, zigbee, lighting, artefact]
---
The router build and a zone map land in Phase 7.
## What the artefact is
Two Zigbee routers on ESP32 hardware, built because the commercial mesh did not cover the house and a repeater would have been a black box. Behind them, nine lighting zones: custom presence sensors with PIR and radar for the bathroom and a child's bedroom, a central light controller with a readout and a potentiometer, and a three-button remote with a dial — click on, double click off, dial for level.
## What it proves
Mesh networking understood at the level where you can add a node instead of buying one, and lighting built for how a house is used rather than for a product category.
## When it is delivered
Phase 7. Source: evidence notes section 1.

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---
id: history-engagements
area: history
level: 2
title: "Fifteen years of delivery for agencies and their clients"
claim: "Twenty-two agencies and twenty-three brands · national campaigns · bank and broadcaster platforms · betting front-ends · and a habit of being called in to rescue work someone else could not finish."
confidence: historic
period: "1999–2014"
tech: [ActionScript, PHP, JavaScript, MySQL, XML, SOAP, React, Python]
metrics: [{label: "agencies on the client wall", value: 22}, {label: "brands delivered for", value: 23}, {label: "code exhibits kept", value: 6}]
artefacts: [{type: doc, label: "The client wall", url: "content/history/l3/history-client-wall.md"}, {type: doc, label: "Six code exhibits across five eras", url: "content/history/l3/history-code-exhibits.md"}]
public: true
last_verified: 2026-10-26
tags: [history, agencies, brands, exhibits]
---
**Capability statement.** Range under deadline. The work in this period ran from car classifieds and online betting to a bank's pitch piece, a broadcaster's on-demand banners and a publisher's education platform — usually with a date attached that could not move.
## The eras, briefly
**1999–2002 — the start.** Web design and development for a high-volume car marketplace, then freelance work building sites, applications, interfaces and database back ends for small agencies.
**2002–2005 — London, full time.** Online gaming: lead front-end developer on a synchronous multi-game platform handling a thousand simultaneous clients, integrating against Java back ends and XML sockets. Then the emerging interactive advertising industry, where a creative front end with a technical spine was scarce.
**2006–2009 — the agency run.** The bulk of the client wall: bank pitch work delivered in weeks, broadcaster promotions, a global oil company's re-brand site, premium drinks campaigns, a news organisation's media presentation layer, a publisher's education platform, and a global computer-aided design vendor's localised site. Several of these were rescues — brought in on a short deadline to rewrite work another team could not land.
**2009–2012 — product and campaign.** A standalone encrypted presentation builder with its own media library and remote update path. A national beer campaign with video submissions, cross-brand promotion, in-store mechanics and authorisation over SOAP. Interactive launches with 3D content in the browser.
**2012–2014 — small products for small business.** Online book building, a reactive design system, and art applications for a motion-sensing controller, transitioned toward mobile.
**2014–2020 — the workshop years.** The site builder and the signage product (see [Products](../products/products-business.md)), which is where the work moved from delivering campaigns for other people's brands to owning software and supporting customers.
## Languages across the eras
The point of the exhibit set is that the tooling changed repeatedly and the delivery discipline did not: ActionScript 3 with class-based architecture, hand-written JavaScript engines, PHP media pipelines, TypeScript with typed service layers and a relational schema, embedded C++ for firmware, and now declarative infrastructure as its own kind of code.
## Evidence
| Claim | Evidence | Artefact |
|---|---|---|
| Twenty-two agencies and twenty-three brands | The client wall, carried from the résumé and the recovered site content | [Client wall](l3/history-client-wall.md) |
| Work across six technology eras | Six exhibits from OO ActionScript to a hand-written Nix package | [Code exhibits](l3/history-code-exhibits.md) |
| Rescue work under immovable deadlines | Multiple engagements recorded as rewrites of stalled projects | [Client wall](l3/history-client-wall.md) |
| Sponsorship of the later product era | 2014–2020 product work described in the résumé | [Products](../products/products-business.md) |
## Trade-offs and limits
- **Historic work is labelled historic.** Nothing in this section is presented as current capability; where a technique is still used today, the page links to where it is used.
- **One engagement has no screenshot.** The national archive work was an internal tool. It is described and not illustrated, because there is nothing legitimately publishable to show.
- **No client has been named beyond a brand.** Brands are named because they were public campaigns. Individuals, internal contacts and unpublished commercial detail are excluded.
- **The exhibits are excerpts.** Around forty lines each, chosen to show judgement — structure, error handling, an exit plan — not to demonstrate volume.
This work is finished, and it is presented in the past tense throughout.

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---
id: history-overview
area: history
level: 1
title: "History"
claim: "Sam delivered interactive work for twenty-two agencies and twenty-three brands between 1999 and 2014 — the commercial apprenticeship that produced the range."
confidence: historic
period: "1999–2014"
tech: [ActionScript, PHP, JavaScript, MySQL, XML, React, Python, Go, HTMX]
metrics: [{label: "agencies on the client wall", value: 22}, {label: "brands delivered for", value: 23}, {label: "code exhibits kept", value: 6}]
artefacts: [{type: doc, label: "The client wall", url: "content/history/l3/history-client-wall.md"}, {type: doc, label: "Six code exhibits across five eras", url: "content/history/l3/history-code-exhibits.md"}]
public: true
last_verified: 2026-10-26
tags: [history, agencies, brands, career]
---
The languages changed six times across the period — ActionScript, PHP, JavaScript, React, Python, and now Go with HTMX — and the exhibits are chosen to show that rather than to show a decade. The era-by-era record is on the [engagement record](history-engagements.md).

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---
id: history-client-wall
area: history
level: 3
title: "Artefact — The client wall"
claim: "Twenty-two agencies and twenty-three brands — the delivery record carried from the résumé rather than reconstructed from memory."
confidence: historic
period: "1999–2014"
tech: [ActionScript, PHP, JavaScript, MySQL, XML]
metrics: [{label: "agencies on the client wall", value: 22}, {label: "brands on the client wall", value: 23}]
artefacts: []
public: true
last_verified: 2026-10-26
tags: [history, clients, artefact]
---
The names are final. The treatment is deliberate: one typeface, one size, one grid, and no logo collage — see *Why there are no logos here* below.
## The agencies
<div class="client-wall" role="list" aria-label="Agencies">
<div class="client-tile" role="listitem"><span class="client-name">Clemenger</span></div>
<div class="client-tile" role="listitem"><span class="client-name">Agency Republic</span></div>
<div class="client-tile" role="listitem"><span class="client-name">Brand Union</span></div>
<div class="client-tile" role="listitem"><span class="client-name">Jackpot Joy</span></div>
<div class="client-tile" role="listitem"><span class="client-name">Bryce Ford</span></div>
<div class="client-tile" role="listitem"><span class="client-name">RMG Connect</span></div>
<div class="client-tile" role="listitem"><span class="client-name">Sporting Index</span></div>
<div class="client-tile" role="listitem"><span class="client-name">Adventis Digital</span></div>
<div class="client-tile" role="listitem"><span class="client-name">Trinity Street</span></div>
<div class="client-tile" role="listitem"><span class="client-name">Inferno</span></div>
<div class="client-tile" role="listitem"><span class="client-name">SAS Design</span></div>
<div class="client-tile" role="listitem"><span class="client-name">Ogilvy</span></div>
<div class="client-tile" role="listitem"><span class="client-name">Wunderman</span></div>
<div class="client-tile" role="listitem"><span class="client-name">Illumina</span></div>
<div class="client-tile" role="listitem"><span class="client-name">Barkers</span></div>
<div class="client-tile" role="listitem"><span class="client-name">Mook</span></div>
<div class="client-tile" role="listitem"><span class="client-name">Pearsons</span></div>
<div class="client-tile" role="listitem"><span class="client-name">Penguin</span></div>
<div class="client-tile" role="listitem"><span class="client-name">Draft London</span></div>
<div class="client-tile" role="listitem"><span class="client-name">Behan Consulting</span></div>
<div class="client-tile" role="listitem"><span class="client-name">MCM Entertainment</span></div>
<div class="client-tile" role="listitem"><span class="client-name">Carsales</span></div>
</div>
## The brands
<div class="client-wall" role="list" aria-label="Brands">
<div class="client-tile" role="listitem"><span class="client-name">Carlton Draught</span></div>
<div class="client-tile" role="listitem"><span class="client-name">Diageo</span></div>
<div class="client-tile" role="listitem"><span class="client-name">KPMG</span></div>
<div class="client-tile" role="listitem"><span class="client-name">McDonald's</span></div>
<div class="client-tile" role="listitem"><span class="client-name">Accenture</span></div>
<div class="client-tile" role="listitem"><span class="client-name">BBC National Archive</span></div>
<div class="client-tile" role="listitem"><span class="client-name">Bank of America</span></div>
<div class="client-tile" role="listitem"><span class="client-name">War of the Worlds</span></div>
<div class="client-tile" role="listitem"><span class="client-name">Madonna (Hard Candy)</span></div>
<div class="client-tile" role="listitem"><span class="client-name">PlayStation</span></div>
<div class="client-tile" role="listitem"><span class="client-name">HSBC</span></div>
<div class="client-tile" role="listitem"><span class="client-name">DHL</span></div>
<div class="client-tile" role="listitem"><span class="client-name">Reuters</span></div>
<div class="client-tile" role="listitem"><span class="client-name">Volvic</span></div>
<div class="client-tile" role="listitem"><span class="client-name">AutoCAD</span></div>
<div class="client-tile" role="listitem"><span class="client-name">Channel 4</span></div>
<div class="client-tile" role="listitem"><span class="client-name">Pearsons</span></div>
<div class="client-tile" role="listitem"><span class="client-name">Shell</span></div>
<div class="client-tile" role="listitem"><span class="client-name">Stella Artois</span></div>
<div class="client-tile" role="listitem"><span class="client-name">Baileys Irish Cream</span></div>
<div class="client-tile" role="listitem"><span class="client-name">Parkgate Homes</span></div>
<div class="client-tile" role="listitem"><span class="client-name">Carlton Dry</span></div>
<div class="client-tile" role="listitem"><span class="client-name">Cottees Cordial</span></div>
</div>
## Why there are no logos here
A wall of mixed marks — some full colour, some monochrome, some missing — reads as broken, and the eight marks that exist for these names are not the eight the work is best remembered by. One typeface, one size and one grid says the same thing more clearly, and it sits beside the technical pages without competing with them. A mark is added only when it is the client's official mark and Sam has approved its use; none has been invented.
## Corrections carried into this list
- **Diageo**, not the "Diajio" spelling that appeared in the recovered site content.
- **Baileys Irish Cream**, stated in full and spelled correctly.
- The **BBC National Archive** engagement was an internal tool. It is described, and it is not illustrated with a screenshot, because no legitimately publishable image exists.
## Count discrepancy — recorded, not smoothed over
The summary material said twenty-three agencies. The recovered, verified list holds **twenty-two** names, and the count published above is the count of the list. If a name is missing, the number moves to twenty-three and this note is deleted; until then the smaller, checkable number is the one on the page.
## When it is delivered
Delivered as a body of names and descriptions, and presented as a typographic wall from Phase 7. Names and one-line descriptions are approved as recovered and corrected.

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---
id: history-code-exhibits
area: history
level: 3
title: "Artefact — Six code exhibits across five eras"
claim: "Six short excerpts spanning object-oriented ActionScript · a hand-written JavaScript animation engine · a PHP media pipeline · typed TypeScript · embedded C++ · and declarative Nix."
confidence: historic
period: "2006–2026"
tech: [ActionScript, JavaScript, PHP, TypeScript, C++, Nix]
metrics: [{label: "code exhibits", value: 6}, {label: "technology eras spanned", value: 5}]
artefacts: [{type: repo, label: "ActionScript 3 — the Siren class", url: "content/assets/code/01-actionscript-class.as"}]
public: true
last_verified: 2026-10-28
tags: [history, code, exhibits, artefact]
---
**Status: published.** This page shows the ActionScript class and indexes the other five, each of which is exhibited on the page where it belongs.
## The six
| # | Era | Exhibit | Where it is shown |
|---|---|---|---|
| 1 | Object-oriented ActionScript | `01-actionscript-class.as` — class, imports, encapsulation | this page |
| 2 | Hand-written JavaScript engine | `02-js-easing-core.js` — easing functions and the timeline loop | [the site builder](../../products/l3/products-websitebuilder.md) |
| 3 | PHP media pipeline | `03-php-encode-op.php` — one GIF encode operation and its FFmpeg commands | [the site builder](../../products/l3/products-websitebuilder.md) |
| 4 | TypeScript | `04-typescript-service-layer.ts` — a typed tRPC boundary | [the estate](../../platform/platform-estate.md) |
| 5 | Embedded C++ | `05-embedded-cpp-interface.hpp` — interface split from implementation | [the edge fleet](../../edge/edge-fleet.md) |
| 6 | Declarative infrastructure | `06-nix-derivation.nix` — pinned version, verified hash, exit condition | [the Nix package](../../platform/l3/platform-tuios-package.md) |
Total is 243 lines across twenty years, which is the point: each excerpt is chosen to show judgement rather than volume.
## Why this exhibit is good code
Every field is private and explicitly typed, and the public surface is two methods, so a caller cannot push the object into a state it does not understand. The constructor takes what it needs as parameters instead of reaching for it, which is why the same class can drive every siren on the screen without knowing where any of them are.
## Limits stated on the page
The excerpt is verbatim, including a latent typo (`this.high` rather than `this.height`). It is left as found: the excerpt is evidence, not a portfolio piece.
## What it proves
That the stack changed six times and the discipline did not. Exhibit 6 is the least common of the six and the most current — a short, correct, self-documenting derivation with an exit plan is a stronger infrastructure signal than a long configuration file.
## When it is delivered
Published. The six files are in this repository under `content/assets/code/`; the archive path is withheld. The other five are linked above from the pages that carry their claims.

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---
id: platform-backups
area: platform
level: 3
title: "Artefact — Backups and restore runbook"
claim: "Three tiers — local snapshots · a copy on another machine · an offsite copy — with retention stated in the job and a restore runbook kept beside them."
confidence: running
period: "2020–"
tech: [Borg, Kopia, Backrest, AWS S3, systemd, NFS]
metrics: [{label: "backup tiers", value: 3}, {label: "retention windows in the primary job", value: 3}]
artefacts: []
public: true
last_verified: 2026-10-26
tags: [platform, backups, restore, artefact]
---
Redacted job definitions and the restore runbook land in Phase 7. Passphrase locations and remote paths are removed before publication.
## What the artefact is
The backup design: Borg jobs on the two desktop hosts to local storage with a network copy, Kopia and Backrest on the edge host to network storage, an offsite copy in object storage, and retention expressed in the job (seven daily, four weekly, three monthly, plus annual on the edge). Beside it, the restore runbook and the list of what is deliberately not covered.
## What it proves
That failure was designed for: three tiers cover disk loss, machine loss and site loss, and the restore path is written down rather than recovered from memory at the worst possible moment.
## Limits stated on the page
Not everything is covered, and the page says so — one storage host is itself a single point of failure for some repositories, and the file server has no backup of its own. Those are known gaps with owners, not oversights to be hidden.
## When it is delivered
Phase 7. Source: the infrastructure reference and the backup system notes, read 2026-10-26.

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---
id: platform-containers
area: platform
level: 3
title: "Artefact — Container inventory"
claim: "Fifty or more containers across the estate — each with its own port · its own folder · and a memory cap equal to its swap cap."
confidence: running
period: "2020–"
tech: [Docker, Compose, Caddy, Proxmox]
metrics: [{label: "containers in the estate", value: 50}, {label: "memory cap rule", value: "mem_limit equals memswap_limit"}]
artefacts: []
public: true
last_verified: 2026-10-26
tags: [platform, docker, artefact]
---
A digest of the inventory — grouped by purpose, with addresses removed — lands in Phase 7.
## What the artefact is
The inventory generated from the hosts, grouped by purpose: media and file services, home automation, the AI services, the public edge, and the build tooling. For each service the same three facts are recorded — port, configuration folder, memory cap.
## What it proves
That the estate is inventoried rather than accumulated, and that the resource rules are applied per service instead of being a policy on a wiki page.
## When it is delivered
Phase 7, generated from the live hosts. Counts follow the approved metric set; the exact number is regenerated at capture time.

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---
id: platform-network
area: platform
level: 3
title: "Artefact — Network layout and remote access"
claim: "A private mesh and SSH everywhere · services bound to loopback · and one public listener at the edge terminating TLS for the sites that are allowed to be seen."
confidence: running
period: "2020–"
tech: [Tailscale, SSH, Caddy, DNS, Termux]
metrics: [{label: "public listeners per service", value: 1}]
artefacts: []
public: true
last_verified: 2026-10-26
tags: [platform, networking, security, artefact]
---
The diagram below shows three hosts and one trust boundary; internal addresses, hostnames and public DNS names are generalised, not published.
## What the artefact is
The layout: hosts on a private mesh for remote access, SSH on every machine, a phone used as a terminal, and an edge host that terminates TLS and proxies to loopback-bound services. New services bind to loopback by default; making something public is an explicit edge change, not a side effect of starting a container.
## What it proves
Security posture chosen rather than inherited: no service is reachable because it happened to bind to all interfaces, and there is exactly one place where the outside world meets the estate.
## The layout
![Three hosts side by side. A build workstation hands a release to the runtime host, which runs the site, the vector store, a workflow worker and the model gateway inside a loopback trust boundary; the public edge terminates TLS and is the only host the internet can reach. All three join one private mesh, and backups run in three tiers.](../../assets/diagrams/homelab-topology.svg)
Read it left to right: work is built on one machine, runs on a memory-bound machine with every service bound to loopback, and is published by a single edge that terminates TLS. The dashed boundary is the point of the whole arrangement — crossing it is a deliberate change to the edge configuration, not a side effect of starting a container. Below both, the mesh carries remote access and the second backup tier, so neither depends on a port being open.
## When it is delivered
Delivered. Internal addressing, hostnames and public DNS names are excluded from publication by the redaction pass.

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---
id: platform-nixos
area: platform
level: 3
title: "Artefact — Declarative multi-host configuration"
claim: "Machines are described rather than assembled — a shared NixOS base · a replicated desktop and terminal · and Proxmox underneath the virtualised side."
confidence: running
period: "2020–"
tech: [NixOS, Niri, Proxmox, Neovim, Zsh, Ghostty]
metrics: [{label: "hosts on the declarative base", value: 4}]
artefacts: []
public: true
last_verified: 2026-10-26
tags: [platform, nixos, artefact]
---
A redacted excerpt of the configuration tree lands in Phase 7. No hostnames, addresses or keys are published.
## What the artefact is
The shape of the configuration: one shared base, per-host modules, and the parts that make the desktop and the terminal identical everywhere — window manager, editor, shell and the plugins that carry the work. Proxmox hosts the virtualised side, including the file server and the home automation appliance.
## What it proves
That a machine can be rebuilt from a description, and that the same environment follows the person between machines instead of being re-created by memory.
## Limits stated on the page
The terminal-first setup is deliberate: a heavier desktop was downgraded to the terminal on one machine for memory headroom. A phone-as-server experiment is on hold, not shipped, and is described as on hold.
## When it is delivered
Phase 7. Source facts: evidence notes section 3.

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---
id: platform-signage
area: platform
level: 3
title: "Artefact — Signage infrastructure · screens as a managed fleet"
claim: "HDMI-CEC control of power · rotation · mirroring and multi-screen layout — so a display fleet is driven by the platform rather than by a remote control."
confidence: delivered
period: "2014–"
tech: [HDMI-CEC, Raspberry Pi, Python, JavaScript, FFmpeg]
metrics: [{label: "commercial deployments evidenced", value: "salon · financial services business"}]
artefacts: []
public: true
last_verified: 2026-10-26
tags: [platform, signage, hdmi-cec, artefact]
---
The CEC command set and a layout diagram land in Phase 7.
## What the artefact is
The platform side of the signage product: the codes and control path that switch screens on and off, rotate and mirror output, and lay out multi-screen displays, including synchronised playback across screens in one site.
## What it proves
Device-level control combined with fleet management. This is the same skill the edge pages show on small devices, applied to physical displays that other people depend on.
## Confidence
`delivered` — this ran in commercial deployments. The product's business status is stated separately on the Products pages, where the honest position is that the software is being rebuilt and three businesses still run it.

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---
id: platform-tuios-package
area: platform
level: 3
title: "Artefact — A hand-written Nix package with an exit plan"
claim: "Twenty-five lines of Nix that pin a tool at 0.10.1 over nixpkgs' 0.7.0 — with a verified hash · a written reason and a written condition for deleting the file."
confidence: running
period: "2026–"
tech: [Nix, NixOS]
metrics: [{label: "pinned version", value: "0.10.1"}, {label: "nixpkgs version it overrides", value: "0.7.0"}]
artefacts: [{type: repo, label: "Nix derivation — tuios 0.10.1 override with a removal condition", url: "content/assets/code/06-nix-derivation.nix"}]
public: true
last_verified: 2026-10-28
tags: [platform, nix, packaging, artefact]
---
**Status: published.** The whole file is shown verbatim.
## What the artefact is
A package definition for the agent operations console: `fetchurl` against a released artifact with a verified hash, a documented reason for overriding nixpkgs, an `installPhase` that stages the binary, and a comment stating the condition under which the file should be deleted.
## What it proves
More than knowing Nix exists. The signals are that someone noticed a version gap, fixed it locally instead of waiting, pinned it so the fleet is reproducible, and wrote down when to remove their own patch. Knowing when to delete your own override is the senior part.
## Why this exhibit is good code
The override records why it exists and the exact condition under which it should be deleted, which is the part of a local patch that usually goes missing. Pinning a released binary by `fetchurl` with a verified hash keeps the result reproducible without waiting for nixpkgs.
## When it is delivered
Published. The file is in this repository at `content/assets/code/06-nix-derivation.nix`; the infrastructure repository remains the source. Source fact: content spine section 4.2.

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---
id: platform-estate
area: platform
level: 2
title: "A small estate run to a documented standard"
claim: "Fifty or more containers on a four-host estate, all on declarative configuration — with packaging discipline · encrypted backups and a restore runbook · loopback-bound services · and one public listener."
confidence: running
period: "2020–"
tech: [NixOS, Proxmox, Docker, Caddy, Tailscale, Borg, Kopia, AWS S3, HDMI-CEC]
metrics: [{label: "containers in the estate", value: 50}, {label: "hosts", value: 4}, {label: "backup tiers", value: "Borg · Kopia · AWS"}, {label: "public listeners", value: 1}]
artefacts: [{type: doc, label: "Declarative multi-host configuration", url: "content/platform/l3/platform-nixos.md"}, {type: doc, label: "A hand-written Nix package with an exit plan", url: "content/platform/l3/platform-tuios-package.md"}, {type: doc, label: "Container inventory", url: "content/platform/l3/platform-containers.md"}, {type: doc, label: "Backups and restore runbook", url: "content/platform/l3/platform-backups.md"}, {type: doc, label: "Network layout and remote access", url: "content/platform/l3/platform-network.md"}, {type: doc, label: "Signage infrastructure — screens as a managed fleet", url: "content/platform/l3/platform-signage.md"}, {type: repo, label: "TypeScript — a typed tRPC service boundary", url: "content/assets/code/04-typescript-service-layer.ts"}]
public: true
last_verified: 2026-10-28
tags: [platform, devops, nixos, docker, backups, networking]
---
**Capability statement.** Four hosts, fifty or more containers, configuration in version control instead of in a shell history, and one place where traffic enters. The estate is deliberately small because it is run rather than demonstrated.
## What is actually run
**Declarative configuration.** NixOS is the base on the machines that matter, with the desktop and the terminal replicated across them so an environment can be rebuilt from a description. Proxmox runs the virtualised side — Ubuntu file server and a Home Assistant appliance alongside the desktop hosts. The point of the arrangement is repeatability: a machine is a configuration, not a pet.
**Packaging.** When a tool is missing or stale in nixpkgs, it gets a derivation rather than a manual install. The TUIOS package pins 0.10.1 over nixpkgs' 0.7.0 with `fetchurl` and a verified hash, documents why the override exists, and states the condition for deleting it. A patch with an exit plan is maintenance; a patch without one is debt.
**Container estate.** Fifty or more containers run across the hosts — the file server and media services, the automation layer, the AI services, traffic for public ingress, and the tools used to build all of it. Each new service is given its own port, its own folder and a memory cap equal to its swap cap, so it cannot take the host's swap.
**Networking.** Remote access is a private mesh rather than an exposed SSH port: Tailscale across the machines, SSH on every host, and a phone used as a terminal. Services bind to loopback; Caddy is the only public listener, terminating TLS at the edge host and proxying the sites that are allowed to be seen.
**Storage and backups.** Three tiers, each covering a different failure: local snapshots on the machine, a copy on another machine over the network, and an offsite copy in object storage. Retention is stated in the job (daily, weekly, monthly, annual) and a restore runbook sits next to the jobs, because a backup nobody has restored from is an assumption.
**Observability and operations.** Resource use is watched at the host level, because the runtime host is memory-bound and a new service can destabilise the ones people rely on. The agent fleet is observed as a fleet through TUIOS — sessions, state and approvals in one console — rather than by tailing logs on four machines.
**Signage infrastructure.** The display work is treated as a fleet to be managed: HDMI-CEC control for power, rotation, mirroring and multi-screen layout, so screens are driven by the platform rather than by a person with a remote control.
## Why this exhibit is good code
The Prisma schema defines the stored types, and the tRPC router validates input with Zod at the boundary, so the data layer receives a shape it can trust rather than one it hopes for. Each procedure is a thin call on the database context, which keeps the router a description of the API rather than a second copy of the queries.
## Evidence
| Claim | Evidence | Artefact |
|---|---|---|
| Configuration is declarative and replicated | NixOS hosts with shared configuration; Proxmox for the virtualised side | [Declarative configuration](l3/platform-nixos.md) |
| Tooling is packaged rather than hand-installed | `pkgs/tuios.nix` pinning 0.10.1 with a documented removal condition | [Nix package](l3/platform-tuios-package.md) |
| Fifty or more containers are operated | Inventory by host and by purpose | [Container inventory](l3/platform-containers.md) |
| Backups are three-tier with a restore runbook | Borg on two hosts, Kopia and Backrest on the edge host, offsite object storage | [Backups](l3/platform-backups.md) |
| One public listener | Loopback-bound services behind Caddy at the edge | [Network](l3/platform-network.md) |
| Screens are managed as a fleet | HDMI-CEC control of power, rotation and layout | [Signage infrastructure](l3/platform-signage.md) |
## Trade-offs and limits
- **Memory over hardware.** The runtime host runs 15 GiB with little free and swap in use. The response was caps, fewer and smaller services, and moving heavy work to the gateway rather than buying another machine. Every new service is sized before it is deployed, and the ceiling for this site is one gigabyte and four containers.
- **Self-hosting is a choice with a cost.** Monitoring, backups, ingress and the AI gateway are all operated here, which means the failure modes are also operated here. The mitigation is documentation and measurement, not optimism.
- **Rejected on purpose.** A long list of tools was evaluated and refused — Airflow for RAM, self-hosted Langfuse for six containers it could not afford, LiteLLM because it would duplicate the existing gateway, and a pile of documentation platforms. "Alternatives considered" is kept because the reasoning is the senior artifact.
- **Not everything is automated.** Some operations are still a documented command run by a person. The runbook exists precisely because the automations do not cover everything.

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---
id: platform-overview
area: platform
level: 1
title: "Platform & Infrastructure"
claim: "Sam runs a multi-machine NixOS and Proxmox estate sized to the hardware rather than the budget: fifty-plus containers, declarative configuration, encrypted backups with a written restore runbook, and public ingress."
confidence: running
period: "2020–"
tech: [NixOS, Proxmox, Docker, Caddy, Tailscale, Borg, Kopia, AWS S3]
metrics: [{label: "containers in the estate", value: 50}, {label: "hosts", value: 4}, {label: "backup tiers", value: "Borg · Kopia · AWS"}]
artefacts: [{type: doc, label: "Declarative multi-host configuration", url: "content/platform/l3/platform-nixos.md"}, {type: doc, label: "Backups and restore runbook", url: "content/platform/l3/platform-backups.md"}, {type: doc, label: "A hand-written Nix package with an exit plan", url: "content/platform/l3/platform-tuios-package.md"}]
public: true
last_verified: 2026-10-26
tags: [platform, devops, nixos, docker, backups]
---
The runtime host is memory-bound rather than CPU-bound, so every service carries a memory cap and is bound to loopback. What runs, where, and what was rejected is on the [estate page](platform-estate.md).

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---
id: products-signage
area: products
level: 3
title: "Artefact — Commercial signage on commodity hardware"
claim: "A digital signage platform built as market disruption — any HDMI screen · a small player · fleet control of power · rotation · layout and playback · sold to twelve businesses over time."
confidence: delivered
period: "2014–"
tech: [Raspberry Pi, ESP32, HDMI-CEC, JavaScript, three.js, FFmpeg, Python]
metrics: [{label: "businesses currently running the product", value: 3}, {label: "adoptions over time", value: 12}]
artefacts: []
public: true
last_verified: 2026-10-26
tags: [products, signage, artefact]
---
The product shipped and customers still run it. The rebuild on the current stack is not finished and is labelled as unfinished everywhere it is mentioned.
## What the artefact is
The product in use: a player on commodity hardware — a small Raspberry Pi or a microcontroller class device — driving any HDMI screen, with fleet control from the platform: switch screens on and off, rotate, mirror, lay out multiple screens, coordinate boards, and synchronise playback across a site. Content is built by the customer from complex animation down to a simple image rotation, and the system adapts to the board it finds rather than demanding a specification.
## What it proves
The commercial read: a market was overpriced, the substitute was built on commodity hardware, and it was sold and operated for years. Twelve adoptions over time, three businesses still running. That is a product, not a project.
## Limits stated on the page
Twelve is cumulative adoption and three is the current count — the distinction is kept because inflating it would be the easiest lie on this site. The rebuild is in progress. No revenue figures or customer names beyond type of business are published.
## When it is delivered
Phase 7 for screenshots and a demonstration recording, captured from the running product. The one-liners for reference customers are approved for the salon and the financial services business only.

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---
id: products-websitebuilder
area: products
level: 3
title: "Artefact — The site builder behind the signage"
claim: "A templated site builder and CMS with media editors — drawing · image · video · text animation · 3D import — that also produced the marketing sites and the social output."
confidence: delivered
period: "2014–"
tech: [PHP, JavaScript, three.js, FFmpeg, ImageMagick, MySQL]
metrics: [{label: "media editors in the product", value: "drawing · image · video · text animation · transitions · 3D import"}]
artefacts: [{type: repo, label: "Text-effects engine — easing functions and the timeline loop", url: "content/assets/code/02-js-easing-core.js"}, {type: repo, label: "Media encoder — one GIF encode operation and its FFmpeg commands", url: "content/assets/code/03-php-encode-op.php"}]
public: true
last_verified: 2026-10-28
tags: [products, cms, media, artefact]
---
**Status: delivered, retained.** Retired as a general-purpose product; kept as the content engine for the signage.
## What the artefact is
The templated builder: pick a template, build the site, publish it — with editors for drawing, images, video and text animation, video effects and transitions, 3D object import and text extrusion, plus document and office-format handling. Image animation is composed in JavaScript and HTML and rendered on Raspberry Pi hardware through Python. Around the site itself sat the commercial scaffolding a small business actually needs: shop, invoicing, room and event management, presentations, document management, client records and bulk email and SMS.
## What it proves
Two things at once — depth in media pipelines (FFmpeg, ImageMagick, PDF and office formats, three.js) and the discipline of shipping a general product that strangers could operate without help.
## Why these exhibits are good code
The animation engine evaluates a named easing function to give each letter its delay, so the spacing of an animation follows the same curve as its motion instead of a guessed linear stagger. Both directions build from one transform object, which is what stops the two halves of the same effect drifting apart over years of edits.
The media pipeline checks the real MIME type before it builds a command, so a wrong input returns a message instead of a half-written file. The GIF encode takes two passes — a palette from the clip, then frames mapped onto it — which is the difference between a GIF that holds its colour and one that does not.
## Limits stated on the page
The PHP is 2014-era procedural code: global state, interpolated shell commands, and long functions. The exhibit is here for the operational judgement, not as a model of structure. The surface was too large for one person to support indefinitely; the plan recorded here is to strip it back to signage plus the content engine and stop selling the rest, which is a decision about focus rather than a claim of failure.
## When it is delivered
Phase 7. Both code exhibits are published at `content/assets/code/`; a screen recording of the editors is still to come. Source: résumé (2014–2020 freelance) and evidence notes section 3.

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---
id: products-where-woof
area: products
level: 3
title: "Artefact — Where-Woof · deployed and pre-market"
claim: "A QR and RFID tag system for lost property that is deployed and in use — and has not gone to market."
confidence: running
period: "2020–"
tech: [QR, RFID, PHP, JavaScript]
metrics: [{label: "market launch", value: 0}]
artefacts: []
public: true
last_verified: 2026-10-26
tags: [products, where-woof, artefact]
---
Deployed is true, commercial is not — that is the exact wording the site uses, deliberately.
## What the artefact is
A tag system for lost property. A tag carries a QR code and an RFID identity; whoever finds the item resolves the tag and the owner is contacted. The system is running and being deployed now.
## What it proves
That a product can be taken into the field before it is monetised, and that the honest label for that state exists. The distinction matters on a CV because the alternative — calling a pre-launch product commercial — is the kind of claim a technical interviewer unpicks in one question.
## Limits stated on the page
No customers, no revenue and no launch date are claimed. The metric recorded here is a zero on purpose.
## When it is delivered
The tag and resolve-flow photographs land in Phase 7 if Sam chooses to publish them. Product name and description are approved; commercial framing is not.

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---
id: products-business
area: products
level: 2
title: "Software sold and operated — with the status stated"
claim: "Three product lines from one workshop — signage on commodity hardware · the site builder that produced the marketing · and a lost-property tag system — with three businesses still running the signage today."
confidence: running
period: "2014–"
tech: [PHP, JavaScript, three.js, FFmpeg, ImageMagick, HDMI-CEC, Raspberry Pi, ESP32, QR, RFID]
metrics: [{label: "businesses currently running the product", value: 3}, {label: "adoptions over time", value: 12}, {label: "product lines", value: 3}]
artefacts: [{type: doc, label: "Commercial signage on commodity hardware", url: "content/products/l3/products-signage.md"}, {type: doc, label: "The site builder behind it", url: "content/products/l3/products-websitebuilder.md"}, {type: doc, label: "Where-Woof — deployed and pre-market", url: "content/products/l3/products-where-woof.md"}, {type: doc, label: "Signage infrastructure — screens as a managed fleet", url: "content/platform/l3/platform-signage.md"}]
public: true
last_verified: 2026-10-26
tags: [products, business, signage, where-woof, positioning]
---
**Capability statement.** Building software for a client and building software as a product are different jobs. This page is the second one: a market that was overpriced, a substitute built on hardware that costs a fraction of the incumbent systems, customers who adopted it, and the operational cost of keeping it running.
## The commercial story
The digital signage market sells walls: thousands of dollars of hardware with fans, cooling and glass, locked to monopoly software. The product was built as a deliberate inversion of that — a system that runs on a small Raspberry Pi or a microcontroller, drives any HDMI screen including an ordinary monitor, and adapts to the board rather than requiring the board to adapt to it.
It manages screens as a fleet: power on and off, rotation, mirroring, multi-screen layout, coordinated boards, and synchronised playback across screens in one site or across sites. Users build complex animations or simple image rotations, and the system presents whatever suits the display it finds.
Demonstrations were rolled out in a hair salon and a financial services business. Around twelve businesses adopted it over time and three still run it today. That number is deliberately specific: twelve is cumulative adoption, three is current, and the difference is the honest measure of a product that was sold but not scaled.
The same workshop produced the CMS behind the marketing — a templated site builder with editors for drawing, image, video and text animation, video effects and transitions, 3D object import and text extrusion — built on JavaScript and three.js with FFmpeg, ImageMagick and PHP tools for documents, and rendering image animation on Raspberry Pi hardware.
The third product is different in kind: Where-Woof is a QR and RFID tag system for lost property. It is deployed and being deployed now. It has **not** gone to market, and this page will not call it commercial until it has.
## Evidence
| Claim | Evidence | Artefact |
|---|---|---|
| Signage replaced thousand-dollar wall systems with commodity hardware | Any HDMI screen · Raspberry Pi or ESP32 class player · fleet control | [Signage product](l3/products-signage.md) |
| Twelve adoptions over time, three businesses running now | Adoption history and current customers | [Signage product](l3/products-signage.md) |
| A templated builder produced the marketing sites | Editors for drawing · video · image and animation; FFmpeg and ImageMagick pipeline | [WebsiteBuilder](l3/products-websitebuilder.md) |
| Where-Woof is deployed and pre-market | Device and tag system in use; no market launch | [Where-Woof](l3/products-where-woof.md) |
| Screens are managed as a fleet | HDMI-CEC control of power · rotation · layout | [Signage infrastructure](../platform/l3/platform-signage.md) |
## Product status — stated plainly
| Product | What it is | Status | Next |
|---|---|---|---|
| Display screen media (Audas Media) | Digital signage on commodity hardware with multi-screen fleet control | Delivered · three businesses running · being rebuilt with AI | Strip back to signage only and rebuild on the current stack |
| WebsiteBuilder | Templated site builder and CMS with media editors and social output | Delivered · retained | Retired as a general product; kept as the content engine |
| Where-Woof | QR and RFID tags for lost property | Deployed · pre-market | Take to market — not done yet |
## Trade-offs and limits
- **Scope discipline came late.** The builder carried a shop, invoicing, room management, calendars, presentations, documents, bulk messaging and client management. Product surface grew faster than a one-person support model could carry, which is why the plan now is to keep the signage and the content engine and retire the rest.
- **The rebuild is unfinished.** "Rebuilding with AI" is a statement of work in progress, not a launch. It is labelled as such on every page that mentions it.
- **One product is pre-revenue by design.** Where-Woof is deployed before it is sold. Describing it as commercial would be a lie that a careful reader would find in a minute.
- **Support is the hidden cost.** Customers running the product for years is the strongest evidence of value and the strongest argument for keeping the surface small.
Signage infrastructure is also platform work: [screens as a managed fleet](../platform/l3/platform-signage.md).
Each product carries its own status, and the two caveats — a rebuild in progress and a launch not yet made — are in the table above rather than in a footnote.

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---
id: products-overview
area: products
level: 1
title: "Products"
claim: "Sam identified an overpriced market and shipped a cheaper substitute on commodity hardware: twelve adoptions over time and three businesses still running it."
confidence: running
period: "2014–"
tech: [PHP, JavaScript, three.js, FFmpeg, ImageMagick, HDMI-CEC, Raspberry Pi, ESP32]
metrics: [{label: "businesses currently running the product", value: 3}, {label: "adoptions over time", value: 12}, {label: "product lines", value: 3}]
artefacts: [{type: doc, label: "Commercial signage on commodity hardware", url: "content/products/l3/products-signage.md"}, {type: doc, label: "The site builder behind it", url: "content/products/l3/products-websitebuilder.md"}, {type: doc, label: "Where-Woof — deployed and pre-market", url: "content/products/l3/products-where-woof.md"}]
public: true
last_verified: 2026-10-26
tags: [products, signage, business, where-woof]
---
Building, selling, deploying and still supporting software for other businesses is a different discipline from writing it, and the caveats on these three products are stated rather than hidden. The commercial story is on the [products page](products-business.md).

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"""Phase 4 — the retrievable corpus.
`content/` is the source of truth. This package turns it into the two indexes
the AI console reads:
* **vectors** in Qdrant, one point per chunk, with the payload that lets the
query path filter *before* searching (`area`, `confidence`);
* **keywords** in SQLite FTS5, one row per chunk, so exact tokens (repo names,
`LoRaWAN`, `MQTT`, dates) are not lost the way they are with vectors alone
(decision D10, hybrid retrieval).
Everything here is driven by `python3 -m corpus.cli`; `docs/phase-4-notes.md`
is the operator manual and records what was measured. The pipeline is
Provider-agnostic by design: `corpus/embeddings.py` is the only place that knows
how a vector is made, and the provider + model + dimension are recorded per
collection so a provider change is a governed migration, not silent corruption.
Standard library only, plus the repository's own validator (`tools/`). No new
dependency, no Node, no framework (AGENTS.md section 4.1).
"""
__all__ = ["config", "loader", "chunker", "payloads", "embeddings", "qdrant", "store", "pipeline", "freshness"]

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"""`python3 -m corpus` is the same as `python3 -m corpus.cli`."""
import sys
from .cli import main
if __name__ == "__main__":
sys.exit(main())

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"""Code-aware chunking.
The rules come from `docs/phase-4-corpus.md` §4.1:
* 400–800 tokens per chunk, about 12% overlap;
* **never split inside a fenced block, a table or a front-matter block.**
The last rule is structural here, not a post-hoc check: the body is parsed into
*blocks* first and chunks are packed out of whole blocks, so a boundary can only
ever fall between blocks. Fenced code, tables, headings, list items and
paragraphs are all blocks; the large ones cannot be subdivided.
Front-matter is removed by `corpus.loader` before this module sees the body, and
parsing a body that still has front-matter is an error, so it cannot be split
either.
`tokens` are estimated with a word-and-punctuation regex. A real BPE tokenizer
counts 15–30% more for prose and more again for code, so a chunk that measures
800 here is a conservative upper bound.
"""
from __future__ import annotations
import re
from dataclasses import dataclass
# Packing targets (docs/phase-4-corpus.md §4.1).
TOKEN_TARGET = 600
TOKEN_MAX = 800
TOKEN_MIN = 400
OVERLAP_RATIO = 0.12
FENCE_RE = re.compile(r"^ {0,3}(`{3,}|~{3,})")
HEADING_RE = re.compile(r"^ {0,3}#{1,6}\s")
LIST_RE = re.compile(r"^(\s*)([-*+]|\d{1,3}[.)])\s+")
RULE_RE = re.compile(r"^ {0,3}([-*_])(\s*\1){2,}\s*$")
TOKEN_RE = re.compile(r"\w+|[^\w\s]", re.UNICODE)
PROTECTED_KINDS = ("fence", "table")
class ChunkError(RuntimeError):
"""The body could not be chunked without breaking the rules."""
@dataclass(frozen=True)
class Block:
"""An indivisible unit of Markdown. Never split, only packed."""
kind: str # fence | table | heading | list | quote | para | rule
text: str
tokens: int
line_start: int # 1-based, relative to the body this block came from
line_end: int
@property
def protected(self) -> bool:
return self.kind in PROTECTED_KINDS
@dataclass(frozen=True)
class Chunk:
index: int
text: str
tokens: int
line_start: int
line_end: int
blocks: tuple
oversize: bool = False
@property
def kinds(self) -> tuple:
return tuple(block.kind for block in self.blocks)
def estimate_tokens(text: str) -> int:
"""Word-plus-punctuation token estimate. Deterministic and cheap."""
return len(TOKEN_RE.findall(text))
def parse_blocks(body: str) -> list:
"""Split a Markdown body into atomic blocks.
Raises ChunkError if the body still carries a front-matter block: that is a
loader bug, and silently chunking it would split something that must not be
split.
"""
if body.lstrip().startswith("---"):
raise ChunkError(
"body still starts with a front-matter block; strip front-matter "
"before chunking (corpus.loader does this)"
)
lines = body.split("\n")
blocks = []
i = 0
total = len(lines)
while i < total:
raw = lines[i]
stripped = raw.strip()
if not stripped:
i += 1
continue
start = i
fence = FENCE_RE.match(raw)
if fence:
i = _read_fence(lines, i, fence.group(1))
kind = "fence"
elif stripped.startswith("|"):
while i < total and lines[i].strip().startswith("|"):
i += 1
kind = "table"
elif HEADING_RE.match(raw):
i += 1
kind = "heading"
elif RULE_RE.match(raw):
i += 1
kind = "rule"
elif LIST_RE.match(raw):
i = _read_list_item(lines, i)
kind = "list"
elif stripped.startswith(">"):
while i < total and lines[i].strip().startswith(">"):
i += 1
kind = "quote"
else:
i = _read_paragraph(lines, i)
kind = "para"
text = "\n".join(lines[start:i])
blocks.append(
Block(kind=kind, text=text, tokens=estimate_tokens(text), line_start=start + 1, line_end=i)
)
return blocks
def _read_fence(lines: list, i: int, marker: str) -> int:
"""Return the index just past a fenced block. Unterminated runs to EOF."""
char = marker[0]
width = len(marker)
i += 1
while i < len(lines):
candidate = FENCE_RE.match(lines[i])
if candidate and candidate.group(1)[0] == char and len(candidate.group(1)) >= width:
return i + 1
i += 1
return len(lines)
def _read_list_item(lines: list, i: int) -> int:
"""Return the index just past one top-level list item, continuations included."""
base_indent = len(LIST_RE.match(lines[i]).group(1))
i += 1
while i < len(lines):
raw = lines[i]
if not raw.strip():
# A blank line continues the item only if an indented line follows.
if i + 1 < len(lines) and lines[i + 1].startswith(" " * (base_indent + 1)):
i += 1
continue
return i
indent = len(raw) - len(raw.lstrip())
if indent > base_indent or LIST_RE.match(raw) and len(LIST_RE.match(raw).group(1)) > base_indent:
i += 1
continue
if LIST_RE.match(raw):
return i
if indent <= base_indent:
return i
i += 1
return i
def _read_paragraph(lines: list, i: int) -> int:
i += 1
while i < len(lines):
raw = lines[i]
stripped = raw.strip()
if not stripped:
return i
if FENCE_RE.match(raw) or HEADING_RE.match(raw) or RULE_RE.match(raw):
return i
if stripped.startswith("|") or stripped.startswith(">") or LIST_RE.match(raw):
return i
i += 1
return i
def chunk_blocks(blocks: list) -> list:
"""Pack blocks into windows of at most TOKEN_MAX, with ~12% overlap."""
chunks = []
total = len(blocks)
if total == 0:
return chunks
i = 0
while i < total:
start = i
tokens = 0
end = i
while end < total:
block = blocks[end]
if end > start:
if tokens + block.tokens > TOKEN_MAX:
break
# Prefer breaking in front of a heading.
if block.kind == "heading" and tokens >= int(TOKEN_TARGET * 0.75):
break
tokens += block.tokens
end += 1
if tokens >= TOKEN_TARGET and end < total and blocks[end].kind == "heading":
break
window = blocks[start:end]
chunks.append(_make_chunk(len(chunks), window))
if end >= total:
break
# Back up over trailing blocks until roughly OVERLAP_RATIO of the window
# repeats, leaving at least one block behind so the walk always advances.
budget = max(1, int(round(TOKEN_TARGET * OVERLAP_RATIO)))
carried = 0
back = end
while back - 1 > start and carried + blocks[back - 1].tokens <= budget * 2:
carried += blocks[back - 1].tokens
back -= 1
if carried >= budget:
break
i = max(back, start + 1)
return chunks
def _make_chunk(index: int, window: list) -> Chunk:
text = "\n\n".join(block.text for block in window)
return Chunk(
index=index,
text=text,
tokens=sum(block.tokens for block in window),
line_start=window[0].line_start,
line_end=window[-1].line_end,
blocks=tuple(window),
oversize=len(window) == 1 and window[0].tokens > TOKEN_MAX,
)
def chunk_document(body: str) -> list:
"""Chunk one document body. The pipeline calls this and nothing else."""
chunks = chunk_blocks(parse_blocks(body))
verify_no_split(chunks, protected_regions(body))
return chunks
def protected_regions(body: str) -> list:
"""Line ranges (1-based, inclusive) that a chunk boundary must not cross.
Recomputed from the body text alone, independently of the packing code, so
the check is not the implementation grading its own homework.
"""
regions = []
lines = body.split("\n")
i = 0
while i < len(lines):
raw = lines[i]
fence = FENCE_RE.match(raw)
if fence:
end = _read_fence(lines, i, fence.group(1))
regions.append((i + 1, end))
i = end
continue
if raw.strip().startswith("|"):
start = i
while i < len(lines) and lines[i].strip().startswith("|"):
i += 1
regions.append((start + 1, i))
continue
i += 1
return regions
def verify_no_split(chunks: list, regions: list) -> None:
"""Raise ChunkError if any chunk boundary cuts a protected region."""
for chunk in chunks:
for first, last in regions:
spans_start = chunk.line_start <= first <= chunk.line_end
spans_end = chunk.line_start <= last <= chunk.line_end
covers = chunk.line_start <= first and chunk.line_end >= last
if (spans_start or spans_end) and not covers:
raise ChunkError(
f"chunk {chunk.index} splits protected lines {first}-{last} "
f"(chunk covers {chunk.line_start}-{chunk.line_end})"
)

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"""Command line entry point: `python3 -m corpus.cli <command>`.
Commands
sync incremental update of the corpus (the normal command)
rebuild drop the collection and rebuild it from content/ (recovery)
audit freshness audit; reports stale documents, index drift
verify does the index agree with content/ and with Qdrant?
demo-filter the same query with and without an `area` filter
hybrid vectors + FTS5 fused with reciprocal rank fusion
measure Qdrant memory at 500 / 5,000 / 20,000 chunks
info what this build thinks it is configured to do
Exit codes: 0 success, 1 findings or verification problems, 2 configuration or
usage error. Every command accepts `--json` where a machine-readable result is
useful (Prefect reads JSON, a human reads the table).
Configuration is environment-first (`corpus/config.py`); the flags exist so a
one-off run can point at a scratch Qdrant without exporting variables.
"""
from __future__ import annotations
import argparse
import datetime
import json
import os
import sys
from . import config as config_module
from . import freshness, pipeline, store as store_module
from .config import Config, ConfigError
from .qdrant import Qdrant, QdrantError
def main(argv=None) -> int:
parser = _build_parser()
args = parser.parse_args(argv)
if args.command is None:
parser.print_help()
return 2
overrides = {
"root": args.root,
"qdrant_url": args.qdrant_url,
"collection": args.collection,
"provider": args.provider,
"model": args.model,
"dim": args.dim,
"db_path": args.db,
"data_dir": args.data,
}
try:
cfg = Config.from_env(**overrides)
except ConfigError as exc:
print(f"configuration error: {exc}", file=sys.stderr)
return 2
log = (lambda _msg: None) if args.quiet else (lambda msg: print(msg, file=sys.stderr))
try:
handler = {
"sync": _cmd_sync,
"rebuild": _cmd_rebuild,
"audit": _cmd_audit,
"verify": _cmd_verify,
"demo-filter": _cmd_demo_filter,
"hybrid": _cmd_hybrid,
"measure": _cmd_measure,
"info": _cmd_info,
}[args.command]
return handler(cfg, args, log)
except (pipeline.PipelineError, ConfigError) as exc:
print(f"error: {exc}", file=sys.stderr)
return 2
except QdrantError as exc:
print(f"error: {exc}", file=sys.stderr)
return 2
def _build_parser() -> argparse.ArgumentParser:
parser = argparse.ArgumentParser(
prog="python3 -m corpus.cli",
description="Build and audit the Ai Resume retrieval corpus (docs/phase-4-corpus.md).",
)
parser.add_argument("--root", help="repository root (default: parent of corpus/)")
parser.add_argument("--qdrant-url", help="Qdrant REST URL (default http://127.0.0.1:6333)")
parser.add_argument("--collection", help="Qdrant collection name (default resume_corpus)")
parser.add_argument("--provider", choices=sorted(config_module.PROVIDERS), help="embedding provider")
parser.add_argument("--model", help="embedding model name")
parser.add_argument("--dim", type=int, help="embedding dimension")
parser.add_argument("--db", help="SQLite corpus database path")
parser.add_argument("--data", help="corpus data directory")
parser.add_argument("--quiet", action="store_true", help="progress on stderr only becomes silent")
sub = parser.add_subparsers(dest="command")
sync = sub.add_parser("sync", help="incremental update")
sync.add_argument("--json", action="store_true")
rebuild = sub.add_parser("rebuild", help="drop the collection and rebuild from content/")
rebuild.add_argument("--json", action="store_true")
audit = sub.add_parser("audit", help="freshness and drift audit")
audit.add_argument("--json", action="store_true")
audit.add_argument("--today", help="audit as of this date (YYYY-MM-DD), for tests")
audit.add_argument("--fail-on-findings", action="store_true", help="exit 1 when anything is found")
audit.add_argument("--alert", action="store_true", help="POST the report to RESUME_APPRISE_URL")
verify = sub.add_parser("verify", help="check the index against content/ and Qdrant")
verify.add_argument("--json", action="store_true")
demo = sub.add_parser("demo-filter", help="same query with and without an area filter")
demo.add_argument("--query", required=True)
demo.add_argument("--area", help="area to filter on (ai, platform, edge, products, history, about)")
demo.add_argument("--limit", type=int, default=5)
demo.add_argument("--json", action="store_true")
hybrid = sub.add_parser("hybrid", help="vectors + FTS5 fused with RRF")
hybrid.add_argument("--query", required=True)
hybrid.add_argument("--area")
hybrid.add_argument("--limit", type=int, default=5)
hybrid.add_argument("--json", action="store_true")
measure = sub.add_parser("measure", help="Qdrant memory at three corpus sizes")
measure.add_argument("--sizes", default="500,5000,20000")
measure.add_argument("--container", default="", help="Qdrant container name for docker stats")
measure.add_argument("--keep", action="store_true", help="keep the measurement collection")
measure.add_argument("--json", action="store_true")
sub.add_parser("info", help="print the resolved configuration")
return parser
# -- commands --------------------------------------------------------------
def _cmd_sync(cfg, args, log) -> int:
stats = pipeline.run(cfg, mode="sync", log=log)
if args.json:
print(json.dumps(stats.to_dict(), indent=2, sort_keys=True))
else:
print(stats.summary())
return 0
def _cmd_rebuild(cfg, args, log) -> int:
stats = pipeline.run(cfg, mode="rebuild", log=log)
if args.json:
print(json.dumps(stats.to_dict(), indent=2, sort_keys=True))
else:
print(stats.summary())
return 0
def _cmd_audit(cfg, args, log) -> int:
today = datetime.date.fromisoformat(args.today) if args.today else None
report = freshness.audit(cfg, today=today, log=log)
if args.json:
print(json.dumps(report, indent=2, sort_keys=True))
else:
print(freshness.render_text(report))
if args.alert:
try:
sent = freshness.alert(report, cfg.apprise_url)
if not sent:
log("alert: RESUME_APPRISE_URL is not set; nothing sent")
except Exception as exc: # noqa: BLE001 - an alert failure must not hide the audit
print(f"warning: could not send the Apprise alert: {exc}", file=sys.stderr)
if args.fail_on_findings and not report["ok"]:
return 1
return 0
def _cmd_verify(cfg, args, log) -> int:
problems = pipeline.verify(cfg, log=log)
if args.json:
print(json.dumps({"ok": not problems, "problems": problems}, indent=2))
else:
if problems:
print(f"FAIL: {len(problems)} problem(s)")
for problem in problems:
print(f" - {problem}")
else:
print("OK: the index agrees with content/ and Qdrant")
return 1 if problems else 0
def _cmd_demo_filter(cfg, args, log) -> int:
result = pipeline.demo_area_filter(cfg, args.query, area=args.area, limit=args.limit)
if args.json:
print(json.dumps(result, indent=2, sort_keys=True))
return 0
print(f"query: {result['query']!r}")
print("")
print(f"without a filter ({len(result['unfiltered'])} result(s)):")
for row in result["unfiltered"]:
print(f" {row['score']:.3f} [{row['area']}/{row['confidence']}] {row['title']} {row['url']}")
print("")
if not args.area:
print("no --area given, so the filtered set is empty; re-run with e.g. --area edge")
return 0
print(f"with area={args.area!r} ({len(result['filtered'])} result(s)):")
for row in result["filtered"]:
print(f" {row['score']:.3f} [{row['area']}/{row['confidence']}] {row['title']} {row['url']}")
wrong = [row for row in result["filtered"] if row["area"] != args.area]
if wrong:
print(f"FAIL: {len(wrong)} filtered result(s) are not in area {args.area!r}")
return 1
return 0
def _cmd_hybrid(cfg, args, log) -> int:
rows = pipeline.hybrid_search(cfg, args.query, area=args.area, limit=args.limit)
if args.json:
print(json.dumps(rows, indent=2, sort_keys=True, default=str))
return 0
print(f"hybrid (RRF) query={args.query!r} area={args.area!r}: {len(rows)} result(s)")
for row in rows:
print(f" {row['score']:.4f} {row.get('title')} {row.get('url')} [{row['id'][:8]}]")
return 0
def _cmd_measure(cfg, args, log) -> int:
from . import measure as measure_module
sizes = [int(part) for part in args.sizes.split(",") if part.strip()]
report = measure_module.measure(
cfg, sizes=sizes, container=args.container, keep=args.keep, log=log
)
if args.json:
print(json.dumps(report, indent=2, sort_keys=True))
return 0
print(measure_module.render_text(report))
return 0
def _cmd_info(cfg, args, log) -> int:
print(cfg.describe())
qdrant = Qdrant(cfg.qdrant_url, timeout=cfg.timeout)
print("")
print(f"qdrant: {'answering, version ' + qdrant.version() if qdrant.health() else 'NOT answering'}")
if qdrant.collection_exists(cfg.collection):
print(f"collection {cfg.collection}: {qdrant.point_count(cfg.collection)} point(s)")
else:
print(f"collection {cfg.collection}: does not exist")
if os.path.exists(cfg.db_path):
with store_module.Store(cfg.db_path) as store:
print(f"store: {store.chunk_count()} chunk(s) across {store.source_count()} source(s)")
print(f"contract: {json.dumps(store.get_meta('collection'), sort_keys=True)}")
else:
print(f"store: {cfg.db_path} does not exist")
return 0
if __name__ == "__main__":
sys.exit(main())

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"""Configuration for the corpus pipeline. Environment only.
Nothing in this file holds a credential. The OmniRoute key is read from the
process environment, which systemd populates from
`~/.config/environment.d/10-secrets.conf` (AGENTS.md section 4.4). `describe()`
is the only way the pipeline prints its configuration, and it redacts the key.
"""
from __future__ import annotations
import os
from dataclasses import dataclass
# Bumped whenever chunk boundaries or payload shape change. A stored corpus
# carrying a different version is stale by definition and gets rebuilt.
CHUNKER_VERSION = "blocks-v1"
# Provider defaults. `local-hashing` is a deterministic, offline provider used
# by the tests, by `make corpus-test`, and by any rebuild on a host with no
# model gateway. It is NOT a semantic embedding model: it exists so the whole
# pipeline can be exercised without a provider decision, and so a rebuild never
# depends on the network. The production provider is `omniroute`.
PROVIDERS = {
"local-hashing": ("local-hashing-v1", 256),
"omniroute": ("text-embedding-3-small", 1536),
}
DEFAULT_QDRANT_URL = "http://127.0.0.1:6333"
DEFAULT_COLLECTION = "resume_corpus"
DEFAULT_PROVIDER = "local-hashing"
DEFAULT_OMNIROUTE_URL = "https://omniroute.lab.audasmedia.com.au"
# Freshness horizon: a document whose `last_verified` is older than this is
# reported stale by the nightly audit. docs/phase-4-notes.md section 5.
DEFAULT_MAX_AGE_DAYS = 180
DEFAULT_WARN_DAYS = 30
class ConfigError(RuntimeError):
"""A configuration value is missing or unusable."""
@dataclass(frozen=True)
class Config:
"""Everything the pipeline needs, resolved once."""
root: str
content_dir: str
data_dir: str
db_path: str
qdrant_url: str
collection: str
provider: str
model: str
dim: int
api_url: str
api_key: str
batch_size: int
timeout: float
max_age_days: int
warn_days: int
apprise_url: str
vectors_on_disk: bool = False
# -- construction -----------------------------------------------------
@classmethod
def from_env(cls, root: str | None = None, **overrides) -> "Config":
"""Build a Config from the environment, then apply explicit overrides.
Overrides exist so the CLI can accept flags without a second code path;
they are applied last so a flag always wins over the environment.
"""
root = os.path.abspath(root or os.environ.get("RESUME_ROOT") or _default_root())
content_dir = os.environ.get("RESUME_CONTENT") or os.path.join(root, "content")
data_dir = os.environ.get("RESUME_CORPUS_DATA") or os.path.join(root, "data", "corpus")
provider = os.environ.get("RESUME_EMBED_PROVIDER") or DEFAULT_PROVIDER
if provider not in PROVIDERS:
raise ConfigError(
f"RESUME_EMBED_PROVIDER={provider!r} is not one of {sorted(PROVIDERS)}"
)
default_model, default_dim = PROVIDERS[provider]
values = dict(
root=root,
content_dir=os.path.abspath(content_dir),
data_dir=os.path.abspath(data_dir),
db_path=os.path.abspath(
os.environ.get("RESUME_CORPUS_DB") or os.path.join(data_dir, "corpus.db")
),
qdrant_url=(os.environ.get("RESUME_QDRANT_URL") or DEFAULT_QDRANT_URL).rstrip("/"),
collection=os.environ.get("RESUME_COLLECTION") or DEFAULT_COLLECTION,
provider=provider,
model=os.environ.get("RESUME_EMBED_MODEL") or default_model,
dim=int(os.environ.get("RESUME_EMBED_DIM") or default_dim),
api_url=(os.environ.get("OMNIROUTE_URL") or DEFAULT_OMNIROUTE_URL).rstrip("/"),
api_key=os.environ.get("OMNIROUTE_API_KEY") or "",
batch_size=int(os.environ.get("RESUME_EMBED_BATCH") or 32),
timeout=float(os.environ.get("RESUME_QDRANT_TIMEOUT") or 30.0),
max_age_days=int(
os.environ.get("RESUME_MAX_AGE_DAYS") or DEFAULT_MAX_AGE_DAYS
),
warn_days=int(os.environ.get("RESUME_WARN_DAYS") or DEFAULT_WARN_DAYS),
apprise_url=os.environ.get("RESUME_APPRISE_URL") or "",
vectors_on_disk=(os.environ.get("RESUME_QDRANT_VECTORS_ON_DISK") or "").lower()
in ("1", "true", "yes"),
)
values.update({k: v for k, v in overrides.items() if v not in (None, "")})
cfg = cls(**values)
cfg.check()
return cfg
# -- validation -------------------------------------------------------
def check(self) -> None:
if self.dim <= 0:
raise ConfigError(f"embedding dimension must be positive, got {self.dim}")
if self.batch_size <= 0:
raise ConfigError(f"batch size must be positive, got {self.batch_size}")
if self.provider == "omniroute" and not self.api_key:
raise ConfigError(
"provider 'omniroute' needs OMNIROUTE_API_KEY in the environment "
"(~/.config/environment.d/10-secrets.conf). It is never read from this repo."
)
if not os.path.isdir(self.content_dir):
raise ConfigError(f"content directory not found: {self.content_dir}")
# -- presentation -----------------------------------------------------
def describe(self) -> str:
"""Human-readable configuration. Never includes the API key value."""
key = "set" if self.api_key else "unset"
return (
f"root={self.root}\n"
f"content={self.content_dir}\n"
f"data={self.data_dir}\n"
f"db={self.db_path}\n"
f"qdrant={self.qdrant_url} collection={self.collection}\n"
f"provider={self.provider} model={self.model} dim={self.dim} "
f"batch={self.batch_size}\n"
f"api_url={self.api_url} api_key={key}\n"
f"freshness=max_age_days:{self.max_age_days} warn_days:{self.warn_days}\n"
f"qdrant vectors_on_disk={self.vectors_on_disk}\n"
f"chunker={CHUNKER_VERSION}"
)
# -- derived paths ----------------------------------------------------
@property
def chunker_version(self) -> str:
return CHUNKER_VERSION
@property
def meta_path(self) -> str:
return os.path.join(self.data_dir, "collection.json")
def _default_root() -> str:
"""The repository root: the parent of this package."""
return os.path.dirname(os.path.dirname(os.path.abspath(__file__)))

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"""The one place a vector is made.
`docs/phase-4-corpus.md` §4.3: *"Record the embedding model and dimension per
collection. A provider change is a governed migration, not a silent corruption.
Provider decision pending Sam — build provider-agnostic behind one interface."*
So: one interface (`Provider`), two implementations, and the provider name,
model and dimension are written to the collection metadata by the pipeline. A
later run with a different model refuses to mix vectors into the same
collection; it reports a governed migration instead.
**local-hashing** is deterministic, offline, and *not semantic*. It hashes word
tokens into a fixed-width vector. It exists so that the pipeline, its tests and
a from-scratch rebuild never depend on a network or on an unmade decision. It is
the default for `make corpus-test` and for the Qdrant memory measurement.
**omniroute** is the production path: an OpenAI-compatible `/v1/embeddings`
call through OmniRoute, which is the only permitted model gateway
(AGENTS.md §4.5). No provider SDK, no direct vendor call, key from the
environment only.
"""
from __future__ import annotations
import hashlib
import json
import math
import time
import urllib.error
import urllib.request
class EmbeddingError(RuntimeError):
"""Embedding failed. The pipeline aborts rather than writing partial vectors."""
class Provider:
"""Interface every embedding provider implements."""
name = "provider"
model = "model"
dim = 0
def embed(self, texts: list) -> list:
raise NotImplementedError
def stats(self) -> dict:
"""Call counters. The DoD proves incremental mode with these numbers."""
return {"calls": 0, "texts": 0}
def reset_stats(self) -> None:
pass
def describe(self) -> dict:
return {"provider": self.name, "model": self.model, "dim": self.dim}
class LocalHashingProvider(Provider):
"""Deterministic feature hashing. Offline, fast, not semantic.
Every token is hashed with sha256 (never Python's salted `hash()`), so the
same text produces the same vector on every host and every run. Vectors are
L2-normalised, which suits cosine distance.
"""
name = "local-hashing"
def __init__(self, model: str = "local-hashing-v1", dim: int = 256) -> None:
if dim <= 0:
raise EmbeddingError(f"local-hashing needs a positive dimension, got {dim}")
self.model = model
self.dim = dim
self._calls = 0
self._texts = 0
def embed(self, texts: list) -> list:
self._calls += 1
self._texts += len(texts)
return [self._vector(text) for text in texts]
def _vector(self, text: str) -> list:
vector = [0.0] * self.dim
for token in _tokens(text):
digest = hashlib.sha256(token.encode("utf-8")).digest()
index = int.from_bytes(digest[:8], "big") % self.dim
sign = 1.0 if digest[8] & 1 else -1.0
vector[index] += sign
norm = math.sqrt(sum(value * value for value in vector))
if norm == 0.0:
# An empty text still needs a unit vector of the right width.
vector[0] = 1.0
return vector
return [value / norm for value in vector]
def stats(self) -> dict:
return {"calls": self._calls, "texts": self._texts}
def reset_stats(self) -> None:
self._calls = 0
self._texts = 0
class OmniRouteProvider(Provider):
"""OpenAI-compatible `/v1/embeddings` through OmniRoute.
Retries on 429 and 5xx with exponential backoff (the risk table in
`docs/phase-4-corpus.md` calls this out for bulk ingest). The key is never
logged, never put in an error message, and never read from this repository.
"""
name = "omniroute"
def __init__(
self,
api_url: str,
api_key: str,
model: str,
dim: int,
batch_size: int = 32,
timeout: float = 30.0,
attempts: int = 4,
backoff: float = 1.5,
) -> None:
if not api_key:
raise EmbeddingError(
"OmniRoute needs OMNIROUTE_API_KEY in the environment "
"(~/.config/environment.d/10-secrets.conf)"
)
self.api_url = api_url.rstrip("/")
self._api_key = api_key
self.model = model
self.dim = dim
self.batch_size = max(1, batch_size)
self.timeout = timeout
self.attempts = max(1, attempts)
self.backoff = backoff
self._calls = 0
self._texts = 0
def embed(self, texts: list) -> list:
out = []
for start in range(0, len(texts), self.batch_size):
batch = texts[start : start + self.batch_size]
out.extend(self._embed_batch(batch))
return out
def _embed_batch(self, batch: list) -> list:
body = json.dumps({"model": self.model, "input": batch}).encode("utf-8")
last_error = None
for attempt in range(self.attempts):
request = urllib.request.Request(
f"{self.api_url}/v1/embeddings",
data=body,
method="POST",
headers={
"Content-Type": "application/json",
"Authorization": f"Bearer {self._api_key}",
},
)
self._calls += 1
try:
with urllib.request.urlopen(request, timeout=self.timeout) as response:
payload = json.loads(response.read().decode("utf-8"))
vectors = _vectors_from_response(payload, self.model, self.dim)
self._texts += len(batch)
return vectors
except urllib.error.HTTPError as exc:
last_error = f"HTTP {exc.code} from {self.api_url}/v1/embeddings"
exc.close()
# 4xx other than 429 is our fault: do not hammer the gateway.
if exc.code != 429 and exc.code < 500:
raise EmbeddingError(f"{last_error} (not retryable)") from None
except (urllib.error.URLError, TimeoutError) as exc:
last_error = f"cannot reach {self.api_url}: {exc.reason}"
except EmbeddingError:
raise
if attempt + 1 < self.attempts:
time.sleep(self.backoff ** attempt)
raise EmbeddingError(f"{last_error} after {self.attempts} attempts")
def stats(self) -> dict:
return {"calls": self._calls, "texts": self._texts}
def reset_stats(self) -> None:
self._calls = 0
self._texts = 0
def _vectors_from_response(payload: dict, model: str, dim: int) -> list:
data = payload.get("data")
if not isinstance(data, list) or not data:
raise EmbeddingError("embedding response has no 'data' array")
ordered = sorted(data, key=lambda item: item.get("index", 0))
vectors = []
for item in ordered:
vector = item.get("embedding")
if not isinstance(vector, list) or not vector:
raise EmbeddingError("embedding response item has no 'embedding' array")
if dim and len(vector) != dim:
raise EmbeddingError(
f"provider returned dimension {len(vector)} but the collection is "
f"configured for {dim}; refusing to mix dimensions"
)
vectors.append([float(value) for value in vector])
returned_model = payload.get("model")
if returned_model and model and returned_model != model:
raise EmbeddingError(
f"provider answered as model {returned_model!r}, not {model!r}; "
"a model change must be a governed migration"
)
return vectors
def build_provider(cfg) -> Provider:
"""Construct the provider named by the configuration."""
if cfg.provider == "local-hashing":
return LocalHashingProvider(model=cfg.model, dim=cfg.dim)
if cfg.provider == "omniroute":
return OmniRouteProvider(
api_url=cfg.api_url,
api_key=cfg.api_key,
model=cfg.model,
dim=cfg.dim,
batch_size=cfg.batch_size,
timeout=cfg.timeout,
)
raise EmbeddingError(f"unknown embedding provider {cfg.provider!r}")
def _tokens(text: str) -> list:
out = []
current = []
for char in text.lower():
if char.isalnum():
current.append(char)
elif current:
out.append("".join(current))
current = []
if current:
out.append("".join(current))
return out

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"""Freshness audit — the thing that stops the corpus rotting silently.
The risk table in `docs/phase-4-corpus.md` names it: *"Silent staleness… a
stale index is otherwise an invisible quality failure."* So the audit is a first
class command, it runs nightly from Prefect, and it alerts through the existing
Apprise service when it finds something.
It reports four kinds of finding, and they are different problems:
* **stale** — a document whose `last_verified` is older than the horizon. The
claim on the page may no longer be true.
* **due soon** — inside the warning window. Re-verify before it becomes stale.
* **index drift** — the source changed (or disappeared, or was never indexed)
since it was embedded. Retrieval is answering from an older world.
* **contract drift** — the collection was built with a different model,
dimension or chunker than the running configuration.
Exit code is 0 for a clean audit and 1 when findings exist and `--fail-on-findings`
was passed, so the Prefect deployment can alert on the result without parsing
text.
"""
from __future__ import annotations
import datetime
import json
import urllib.request
from . import loader, store as store_module
from .config import CHUNKER_VERSION
from .pipeline import _contract
from .qdrant import Qdrant
def audit(cfg, today: datetime.date = None, log=None) -> dict:
"""Return the audit report as a plain dict (JSON-serialisable)."""
log = log or (lambda _msg: None)
today = today or datetime.date.today()
documents = loader.load_documents(cfg)
findings = {"stale": [], "due_soon": [], "index_drift": [], "contract_drift": []}
for doc in documents:
try:
verified = datetime.date.fromisoformat(doc.last_verified)
except ValueError:
findings["index_drift"].append(
{"source_path": doc.path, "problem": f"invalid last_verified {doc.last_verified!r}"}
)
continue
age = (today - verified).days
entry = {
"source_path": doc.path,
"title": doc.title,
"area": doc.area,
"confidence": doc.confidence,
"last_verified": doc.last_verified,
"age_days": age,
}
if age > cfg.max_age_days:
findings["stale"].append(entry)
elif age > cfg.max_age_days - cfg.warn_days:
findings["due_soon"].append(entry)
with store_module.Store(cfg.db_path) as store:
manifest = store.manifest()
on_disk = {doc.path: doc for doc in documents}
for doc in documents:
row = manifest.get(doc.path)
if row is None:
findings["index_drift"].append(
{"source_path": doc.path, "problem": "not indexed (run sync)"}
)
continue
if row["content_hash"] != doc.digest:
findings["index_drift"].append(
{"source_path": doc.path, "problem": "source changed since it was embedded"}
)
if row["chunker_version"] != CHUNKER_VERSION:
findings["index_drift"].append(
{
"source_path": doc.path,
"problem": f"chunked with {row['chunker_version']}, current {CHUNKER_VERSION}",
}
)
for source in sorted(set(manifest) - set(on_disk)):
findings["index_drift"].append(
{"source_path": source, "problem": "indexed but no longer in content/"}
)
stored_contract = store.get_meta("collection")
if stored_contract != _contract(cfg):
findings["contract_drift"].append(
{"problem": "collection contract does not match this configuration",
"stored": stored_contract, "configured": _contract(cfg)}
)
qdrant = Qdrant(cfg.qdrant_url, timeout=cfg.timeout)
reachable = qdrant.health()
points = None
if reachable and qdrant.collection_exists(cfg.collection):
points = qdrant.point_count(cfg.collection)
elif not reachable:
findings["contract_drift"].append(
{"problem": f"Qdrant is not answering at {cfg.qdrant_url}"}
)
report = {
"generated_at": datetime.datetime.now(datetime.timezone.utc).strftime("%Y-%m-%dT%H:%M:%SZ"),
"today": today.isoformat(),
"horizon": {
"max_age_days": cfg.max_age_days,
"warn_days": cfg.warn_days,
},
"corpus": {
"documents": len(documents),
"chunks": store.chunk_count(),
"sources": store.source_count(),
"collection": cfg.collection,
"collection_points": points,
"provider": cfg.provider,
"model": cfg.model,
"dim": cfg.dim,
"chunker_version": CHUNKER_VERSION,
},
"findings": findings,
"summary": {
"stale": len(findings["stale"]),
"due_soon": len(findings["due_soon"]),
"index_drift": len(findings["index_drift"]),
"contract_drift": len(findings["contract_drift"]),
},
# `due_soon` is a warning, not a failure: the document is still
# inside its verification horizon. Stale, drift and contract
# problems are failures.
"ok": not (
findings["stale"] or findings["index_drift"] or findings["contract_drift"]
),
}
log(f"audit: {json.dumps(report['summary'], sort_keys=True)}")
return report
def render_text(report: dict) -> str:
"""Human-readable audit, for a terminal or an Apprise body."""
lines = []
corpus = report["corpus"]
lines.append(
f"corpus: {corpus['documents']} document(s), {corpus['chunks']} chunk(s), "
f"{corpus['collection_points']} vector(s) in {corpus['collection']}"
)
lines.append(
f"horizon: last_verified within {report['horizon']['max_age_days']} days "
f"(warning at {report['horizon']['max_age_days'] - report['horizon']['warn_days']} days)"
)
findings = report["findings"]
for kind, label in (
("stale", "STALE — past the verification horizon"),
("due_soon", "DUE SOON — verify before it goes stale"),
("index_drift", "INDEX DRIFT — the index does not match content/"),
("contract_drift", "CONTRACT DRIFT — rebuild required"),
):
rows = findings[kind]
if not rows:
continue
lines.append("")
lines.append(f"{label} ({len(rows)})")
for row in rows:
if "problem" in row:
lines.append(f" - {row.get('source_path', '<collection>')}: {row['problem']}")
else:
lines.append(
f" - {row['source_path']} ({row['area']}, {row['confidence']}): "
f"last_verified {row['last_verified']} ({row['age_days']} days ago)"
)
if report["ok"]:
lines.append("")
lines.append("OK: nothing stale, no drift." + (" (see warnings above)" if findings["due_soon"] else ""))
return "\n".join(lines)
def alert(report: dict, apprise_url: str, timeout: float = 10.0) -> bool:
"""POST the audit to the existing Apprise service. No-op without a URL."""
if not apprise_url:
return False
title = "ai-resume corpus audit: findings" if not report["ok"] else "ai-resume corpus audit: OK"
body = json.dumps({"title": title, "body": render_text(report), "type": "warning" if not report["ok"] else "info"})
request = urllib.request.Request(
apprise_url,
data=body.encode("utf-8"),
method="POST",
headers={"Content-Type": "application/json"},
)
with urllib.request.urlopen(request, timeout=timeout):
return True

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"""Load `content/` into documents, reusing the Phase 1 validator's parser.
There is exactly one front-matter dialect in this repository and exactly one
parser for it: `tools/validate_content.py`. This module imports that parser
rather than reimplementing it, so the corpus can never disagree with the gate
about what a document says.
The gate itself is `validate_tree` from the same file. The pipeline runs it
before it chunks anything: an invalid content tree stops the run, it never
reaches the vector store.
"""
from __future__ import annotations
import hashlib
import importlib.util
import os
import sys
from dataclasses import dataclass, field
VALIDATOR_NAME = "validate_content"
class ContentError(RuntimeError):
"""The content tree is unreadable or invalid; the run must stop."""
def load_validator(root: str):
"""Import tools/validate_content.py as a module (cached per root)."""
cached = sys.modules.get(VALIDATOR_NAME)
if cached is not None and getattr(cached, "__validator_root__", None) == root:
return cached
path = os.path.join(root, "tools", "validate_content.py")
if not os.path.exists(path):
raise ContentError(f"validator not found at {path}")
spec = importlib.util.spec_from_file_location(VALIDATOR_NAME, path)
if spec is None or spec.loader is None:
raise ContentError(f"cannot load validator at {path}")
module = importlib.util.module_from_spec(spec)
spec.loader.exec_module(module)
module.__validator_root__ = root # type: ignore[attr-defined]
sys.modules[VALIDATOR_NAME] = module
return module
def to_python(node):
"""Convert a validator front-matter Node into plain Python values."""
if node is None or node.kind == "null":
return None
if node.kind == "scalar":
return node.value
if node.kind == "seq":
return [to_python(item) for item in node.value]
if node.kind == "map":
return {key: to_python(value) for key, value in node.value.items()}
raise ContentError(f"unexpected front-matter node kind {node.kind!r}")
@dataclass
class Document:
"""One Markdown file plus the front-matter the schema guarantees."""
path: str # repository-relative, posix, e.g. "content/edge/l3/edge-zigbee.md"
source_id: str
area: str
level: int
title: str
claim: str
confidence: str
period: str
tech: list
metrics: list
artefacts: list
public: bool
last_verified: str
tags: list
body: str
digest: str # sha256 of the file bytes: the incremental-mode key
url: str # canonical page URL, matching internal/content.PageURL
# Filled in by the pipeline as it goes; not part of the schema.
chunk_count: int = field(default=0)
@property
def artefact_urls(self) -> list:
return [str(a.get("url", "")) for a in self.artefacts if a.get("url")]
def page_url(area: str, doc_id: str, level: int) -> str:
"""Canonical URL for a document. Mirrors internal/content.PageURL exactly."""
if level == 1:
return f"/{area}/"
if level == 2:
return f"/{area}/{doc_id}/"
return f"/{area}/a/{doc_id}/"
def digest_of(path: str) -> str:
"""sha256 of the raw file bytes — the same digest for any platform."""
with open(path, "rb") as handle:
return hashlib.sha256(handle.read()).hexdigest()
def load_documents(cfg) -> list:
"""Load every document under content/, sorted by path.
Raises ContentError when the gate fails, so a broken content tree never
produces a half-correct index.
"""
validator = load_validator(cfg.root)
violations, count = validator.validate_tree(cfg.root, cfg.content_dir, quiet=True)
if violations:
detail = "\n ".join(violations[:20])
more = "" if len(violations) <= 20 else f"\n … and {len(violations) - 20} more"
raise ContentError(
f"content gate failed with {len(violations)} violation(s) "
f"across {count} document(s). Run `make validate`:\n {detail}{more}"
)
documents = []
for path in validator.iter_documents(cfg.content_dir):
documents.append(_load_one(cfg, validator, path))
documents.sort(key=lambda d: d.path)
if not documents:
raise ContentError(f"no documents found under {cfg.content_dir}")
return documents
def _load_one(cfg, validator, path: str) -> Document:
rel = os.path.relpath(path, cfg.root).replace(os.sep, "/")
fm_lines, body, problem = validator.split_front_matter(path)
if problem:
raise ContentError(f"{rel}: {problem}")
try:
root_node = validator.parse_front_matter(fm_lines)
except validator.YamlError as exc:
raise ContentError(f"{rel}:{exc.line}: invalid YAML: {exc.msg}") from exc
fields = to_python(root_node)
if not isinstance(fields, dict):
raise ContentError(f"{rel}: front-matter must be a mapping")
level = int(fields["level"])
doc = Document(
path=rel,
source_id=str(fields["id"]),
area=str(fields["area"]),
level=level,
title=str(fields["title"]),
claim=str(fields["claim"]),
confidence=str(fields["confidence"]),
period=str(fields["period"]),
tech=list(fields.get("tech") or []),
metrics=list(fields.get("metrics") or []),
artefacts=list(fields.get("artefacts") or []),
public=bool(fields.get("public")),
last_verified=str(fields["last_verified"]),
tags=list(fields.get("tags") or []),
body=body,
digest=digest_of(path),
url=page_url(str(fields["area"]), str(fields["id"]), level),
)
return doc

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"""Qdrant memory at 500 / 5,000 / 20,000 chunks.
`docs/phase-4-corpus.md` requires the measurement and `reference/stack.md` is
where the number is recorded. The corpus itself is small; the point of the
measurement is to show the *shape* of the growth so the 400 m cap is a known
quantity rather than a hope.
Method:
1. Create an empty collection and sample memory — the baseline.
2. For each size: recreate the collection, upsert that many chunks with payloads
shaped exactly like production payloads (same fields, same text lengths),
wait for the write to settle, then sample.
Memory is read from Qdrant's own Prometheus `app_process_resident_memory_bytes`,
which is the process that would be capped, not the container sandbox. When
`--container` is also given the container's counter is recorded next to it.
The measurement collection is dropped afterwards unless `--keep` is passed, and
it is never the production collection name.
"""
from __future__ import annotations
import hashlib
import json
import re
import statistics
import subprocess
import time
import uuid
from . import chunker, embeddings, loader, payloads
from .qdrant import Qdrant
# Qdrant 1.20 exposes memory_resident_bytes; older builds used the Prometheus
# default app_process_resident_memory_bytes. Accept either.
RSS_METRIC = re.compile(
r"^(?:memory_resident_bytes|app_process_resident_memory_bytes)\s+([0-9.eE+]+)",
re.MULTILINE,
)
INGEST_BATCH = 250
INGEST_SETTLE = 0.4
MEMUSAGE = re.compile(r"([0-9.]+)\s*([KMG]i?B)", re.IGNORECASE)
CAP_MIB = 400 # reference/stack.md memory budget for Qdrant
def measure(cfg, sizes=(500, 5000, 20000), container: str = "", keep: bool = False, log=None) -> dict:
log = log or (lambda _msg: None)
collection = f"{cfg.collection}_measure"
provider = embeddings.build_provider(cfg)
qdrant = Qdrant(cfg.qdrant_url, timeout=max(cfg.timeout, 60.0))
if not qdrant.health():
raise RuntimeError(f"Qdrant is not answering at {cfg.qdrant_url}")
templates = _templates(cfg, provider)
log(f"measure: {len(templates)} payload template(s), dim={cfg.dim}, collection={collection}")
qdrant.drop_collection(collection)
_create(qdrant, cfg, collection)
time.sleep(1.0)
baseline = _sample(qdrant, container)
samples = []
for size in sorted(int(value) for value in sizes):
qdrant.drop_collection(collection)
_create(qdrant, cfg, collection)
points = _points(templates, size, provider)
started = time.time()
# Throttled ingest: the peak during a bulk write is what trips the
# cgroup limit, not the settled size. One batch at a time, sampling as we
# go, so the measurement reports the peak and not just the end state.
sent = 0
peak = baseline["rss_mib"]
for start in range(0, len(points), INGEST_BATCH):
sent += qdrant.upsert(collection, points[start : start + INGEST_BATCH], batch=INGEST_BATCH)
time.sleep(INGEST_SETTLE)
reading = _rss_mib(qdrant)
if reading is not None:
peak = max(peak, reading)
elapsed = round(time.time() - started, 2)
points_now = qdrant.point_count(collection)
time.sleep(2.0)
reading = _sample(qdrant, container)
growth = reading["rss_mib"] - baseline["rss_mib"]
samples.append(
{
"chunks_requested": size,
"points": points_now,
"sent": sent,
"upsert_seconds": elapsed,
"rss_mib": reading["rss_mib"],
"peak_rss_mib": round(peak, 1),
"container_mib": reading["container_mib"],
"growth_mib": round(growth, 1),
"bytes_per_chunk": int(growth * 1024 * 1024 / size) if size else None,
}
)
log(
f"measure: {size} chunk(s) -> {points_now} point(s), rss {reading['rss_mib']:.1f} MiB "
f"(+{growth:.1f} MiB), upsert {elapsed}s"
)
if not keep:
qdrant.drop_collection(collection)
report = {
"collection": collection,
"dim": cfg.dim,
"distance": "Cosine",
"provider": cfg.provider,
"model": cfg.model,
"qdrant_version": qdrant.version(),
"cap_mib": CAP_MIB,
"baseline": baseline,
"samples": samples,
"within_cap": all(
max(sample["rss_mib"], sample["peak_rss_mib"]) <= CAP_MIB for sample in samples
),
"measured_at": time.strftime("%Y-%m-%dT%H:%M:%SZ", time.gmtime()),
"kept": keep,
}
return report
def _create(qdrant: Qdrant, cfg, collection: str) -> None:
"""Same collection settings as production, so the memory reading means something."""
qdrant.create_collection(
collection, cfg.dim, vectors_on_disk=cfg.vectors_on_disk
)
def _templates(cfg, provider) -> list:
"""Real payloads from content/, so field count and text length are realistic."""
documents = loader.load_documents(cfg)
out = []
for doc in documents:
for chunk in chunker.chunk_document(doc.body):
payload = payloads.build_payload(doc, chunk, cfg)
payload["id"] = "template"
out.append(payload)
if not out:
raise RuntimeError("no content to build a measurement from")
return out
def _points(templates: list, size: int, provider) -> list:
"""`size` points with unique ids, unique source paths and real payload shape."""
points = []
texts = []
for index in range(size):
template = templates[index % len(templates)]
payload = dict(template)
payload["id"] = str(uuid.UUID(hex=hashlib.sha256(f"measure:{index}".encode()).hexdigest()[:32]))
payload["source_path"] = f"measure/{template['area']}/{index}.md"
payload["chunk_index"] = index
points.append({"id": payload["id"], "payload": payload})
texts.append(f"{index} {payload['text']}")
vectors = provider.embed(texts)
for point, vector in zip(points, vectors):
point["vector"] = vector
return points
def _sample(qdrant: Qdrant, container: str) -> dict:
"""Three readings two seconds apart; report the median so a GC pause is not the number."""
readings = []
for attempt in range(3):
readings.append(_rss_mib(qdrant))
if attempt < 2:
time.sleep(2.0)
readings = [value for value in readings if value is not None]
rss = round(statistics.median(readings), 1) if readings else 0.0
return {"rss_mib": rss, "container_mib": _container_mib(container) if container else None}
def _rss_mib(qdrant: Qdrant):
text = qdrant.metrics()
if not text:
return None
match = RSS_METRIC.search(text)
if not match:
return None
return float(match.group(1)) / (1024 * 1024)
def _container_mib(container: str):
"""Container memory from `docker stats`. None when docker or the container is absent."""
try:
completed = subprocess.run(
["docker", "stats", "--no-stream", "--format", "{{.MemUsage}}", container],
capture_output=True,
text=True,
timeout=20,
check=False,
)
except (OSError, subprocess.SubprocessError):
return None
if completed.returncode != 0:
return None
match = MEMUSAGE.search(completed.stdout)
if not match:
return None
value = float(match.group(1))
unit = match.group(2).upper()
if unit.startswith("G"):
value *= 1024
elif unit.startswith("K"):
value /= 1024
return round(value, 1)
def render_text(report: dict) -> str:
lines = [
f"Qdrant {report['qdrant_version']} · collection {report['collection']} · "
f"dim {report['dim']} cosine · provider {report['provider']}/{report['model']}",
f"baseline (empty collection): {report['baseline']['rss_mib']:.1f} MiB",
"",
f"{'chunks':>8} {'points':>8} {'rss MiB':>8} {'peak':>7} {'growth':>7} {'B/chunk':>8} {'upsert s':>8}",
]
for sample in report["samples"]:
per_chunk = sample["bytes_per_chunk"] or 0
lines.append(
f"{sample['chunks_requested']:>8} {sample['points']:>8} {sample['rss_mib']:>8.1f} "
f"{sample['peak_rss_mib']:>7.1f} {sample['growth_mib']:>+7.1f} {per_chunk:>8} "
f"{sample['upsert_seconds']:>8}"
)
lines.append("")
verdict = "within" if report["within_cap"] else "OVER"
lines.append(f"cap {report['cap_mib']} MiB: {verdict} cap at every measured size")
if report["kept"]:
lines.append(f"collection {report['collection']} was kept for inspection")
return "\n".join(lines)
if __name__ == "__main__":
import sys
from .config import Config
cfg = Config.from_env(dim=int(sys.argv[1]) if len(sys.argv) > 1 else None)
print(render_text(measure(cfg, container="", log=print)))

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"""Chunk payloads: the thing the retrieval filter runs against.
`docs/phase-4-corpus.md` §4.2 fixes the payload. `area` and `confidence` are the
two that matter: they let the query path narrow *before* it searches, and they
let an answer qualify unfinished work instead of presenting it as delivered.
**A missing or invalid field fails the run.** Nothing in this module supplies a
default value: `validate_payload` raises on the first field that is absent,
empty or out of enum, and the pipeline reports which document and chunk it was.
That is the DoD item "a missing field fails the run rather than defaulting".
"""
from __future__ import annotations
import datetime
AREAS = ("ai", "platform", "edge", "products", "history", "about")
LEVELS = (1, 2, 3)
SOURCE_TYPES = ("doc", "code", "adr", "metric")
CONFIDENCE = ("delivered", "running", "in-progress", "abandoned", "historic")
# Fields every payload must carry (docs/phase-4-corpus.md §4.2), plus the ones
# the index needs to answer "where did this come from?" without a second lookup.
REQUIRED_FIELDS = (
"area",
"level",
"source_type",
"confidence",
"period",
"artefacts",
"last_verified",
"text",
)
PROVENANCE_FIELDS = (
"id",
"source_path",
"source_id",
"chunk_index",
"title",
"claim",
"url",
"content_hash",
"embedding_model",
"embedding_dim",
"chunker_version",
)
class PayloadError(RuntimeError):
"""A payload is incomplete or out of enum. The run must stop."""
def source_type_for(doc) -> str:
"""Classify a document. Everything in content/ is a doc unless it says so.
The schema has no `source_type` field and the schema is frozen, so the
classification is derived from tags the schema does allow.
"""
tags = {str(tag).lower() for tag in doc.tags}
if "adr" in tags:
return "adr"
if "metric" in tags:
return "metric"
if "code" in tags:
return "code"
return "doc"
def build_payload(doc, chunk, cfg) -> dict:
"""Build one chunk payload from a document and a chunk.
Values are copied, never guessed: if the document does not carry a field,
the payload does not gain one.
"""
payload = {
# -- the retrieval filter (docs/phase-4-corpus.md §4.2) ------------
"area": doc.area,
"level": doc.level,
"source_type": source_type_for(doc),
"confidence": doc.confidence,
"period": doc.period,
"artefacts": [str(a.get("url", "")) for a in doc.artefacts],
"last_verified": doc.last_verified,
"text": chunk.text,
# -- provenance ----------------------------------------------------
"id": None, # filled by the pipeline once the id is computed
"source_path": doc.path,
"source_id": doc.source_id,
"chunk_index": chunk.index,
"title": doc.title,
"claim": doc.claim,
"url": doc.url,
"content_hash": doc.digest,
"embedding_model": cfg.model,
"embedding_dim": cfg.dim,
"chunker_version": cfg.chunker_version,
# -- supporting evidence, used by the answer to cite ---------------
"public": bool(doc.public),
"tech": list(doc.tech),
"tags": list(doc.tags),
"chunk_tokens": chunk.tokens,
}
return payload
def validate_payload(payload: dict, where: str = "chunk") -> None:
"""Raise PayloadError if a required field is missing, empty or out of enum."""
for field in REQUIRED_FIELDS + PROVENANCE_FIELDS:
if field == "id" and payload.get("id") is None:
raise PayloadError(f"{where}: payload field 'id' was not set")
if field not in payload:
raise PayloadError(f"{where}: payload is missing required field {field!r}")
area = payload["area"]
if area not in AREAS:
raise PayloadError(f"{where}: area {area!r} is not one of {list(AREAS)}")
if payload["level"] not in LEVELS:
raise PayloadError(f"{where}: level {payload['level']!r} is not one of {list(LEVELS)}")
if payload["source_type"] not in SOURCE_TYPES:
raise PayloadError(
f"{where}: source_type {payload['source_type']!r} is not one of {list(SOURCE_TYPES)}"
)
if payload["confidence"] not in CONFIDENCE:
raise PayloadError(
f"{where}: confidence {payload['confidence']!r} is not one of {list(CONFIDENCE)}"
)
for field in ("period", "title", "claim", "url", "source_path", "source_id", "embedding_model"):
value = payload[field]
if not isinstance(value, str) or not value.strip():
raise PayloadError(f"{where}: field {field!r} must be a non-empty string")
text = payload["text"]
if not isinstance(text, str) or not text.strip():
raise PayloadError(f"{where}: field 'text' must be non-empty")
if not isinstance(payload["artefacts"], list):
raise PayloadError(f"{where}: field 'artefacts' must be a list (empty is allowed)")
_check_date(payload["last_verified"], where)
if not isinstance(payload["embedding_dim"], int) or payload["embedding_dim"] <= 0:
raise PayloadError(f"{where}: field 'embedding_dim' must be a positive integer")
if not isinstance(payload["chunk_index"], int) or payload["chunk_index"] < 0:
raise PayloadError(f"{where}: field 'chunk_index' must be a non-negative integer")
if not isinstance(payload["content_hash"], str) or len(payload["content_hash"]) != 64:
raise PayloadError(f"{where}: field 'content_hash' must be a sha256 hex digest")
def _check_date(value: str, where: str) -> None:
try:
datetime.date.fromisoformat(str(value))
except ValueError as exc:
raise PayloadError(
f"{where}: field 'last_verified' must be an ISO date (YYYY-MM-DD), got {value!r}"
) from exc

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"""The pipeline: content/ -> validate -> chunk -> enrich -> embed -> upsert.
One entry point, `run`, with two modes:
* **rebuild** — drop the collection, empty the store, embed everything. This is
the "one command rebuilds the corpus from an empty volume" path.
* **sync** — the default. Only documents whose content hash, embedding model,
embedding dimension or chunker version changed are re-chunked and
re-embedded. Everything else is left alone.
The order matters. Payloads are built and validated for *every* changed document
before a single vector is written, so an invalid payload stops the run with an
empty diff rather than leaving half an index behind.
Deterministic ids (`sha256(source_path + chunk_index + content_hash)`) mean
re-running updates points instead of duplicating them, and the id is a valid
UUID so Qdrant accepts it as a point id without a mapping table.
"""
from __future__ import annotations
import hashlib
import json
import time
import uuid
from dataclasses import asdict, dataclass, field
from . import chunker, embeddings, loader, payloads, store as store_module
from .config import CHUNKER_VERSION
from .qdrant import Qdrant, QdrantError
class PipelineError(RuntimeError):
"""The run cannot continue without corrupting the index."""
def chunk_id(source_path: str, chunk_index: int, content_hash: str) -> str:
"""Deterministic point id: sha256(source_path + chunk_index + content_hash).
Rendered as a UUID because Qdrant only accepts unsigned integers or UUIDs as
point ids; the first 128 bits of the digest keep collisions improbable.
"""
digest = hashlib.sha256(
f"{source_path}\x00{chunk_index}\x00{content_hash}".encode("utf-8")
).hexdigest()
return str(uuid.UUID(hex=digest[:32]))
@dataclass
class RunStats:
mode: str = "sync"
documents: int = 0
added: int = 0
updated: int = 0
unchanged: int = 0
removed: int = 0
chunks_written: int = 0
chunks_deleted: int = 0
embed_calls: int = 0
texts_embedded: int = 0
points_sent: int = 0
collection_points: int = 0
store_chunks: int = 0
duration_s: float = 0.0
collection: str = ""
provider: str = ""
model: str = ""
dim: int = 0
chunker_version: str = CHUNKER_VERSION
notes: list = field(default_factory=list)
def summary(self) -> str:
rows = [
f"mode={self.mode} documents={self.documents} chunks={self.store_chunks}",
f"documents added={self.added} updated={self.updated} unchanged={self.unchanged} removed={self.removed}",
f"chunks written={self.chunks_written} deleted={self.chunks_deleted}",
f"embedding calls={self.embed_calls} texts={self.texts_embedded}",
f"points sent={self.points_sent} collection points={self.collection_points}",
f"provider={self.provider} model={self.model} dim={self.dim} chunker={self.chunker_version}",
f"collection={self.collection} duration={self.duration_s:.2f}s",
]
rows.extend(self.notes)
return "\n".join(rows)
def to_dict(self) -> dict:
return asdict(self)
def run(cfg, mode: str = "sync", log=None) -> RunStats:
"""Build or refresh the corpus. Returns what happened; raises on any error."""
if mode not in ("sync", "rebuild"):
raise PipelineError(f"unknown mode {mode!r}; expected 'sync' or 'rebuild'")
started = time.time()
log = log or (lambda _msg: None)
stats = RunStats(
mode=mode,
collection=cfg.collection,
provider=cfg.provider,
model=cfg.model,
dim=cfg.dim,
chunker_version=cfg.chunker_version,
)
documents = loader.load_documents(cfg)
stats.documents = len(documents)
log(f"validated {len(documents)} document(s)")
provider = embeddings.build_provider(cfg)
qdrant = Qdrant(cfg.qdrant_url, timeout=cfg.timeout)
if not qdrant.health():
raise PipelineError(
f"Qdrant is not answering at {cfg.qdrant_url}. Start it "
"(deploy/qdrant.yml) or point RESUME_QDRANT_URL at a running instance."
)
with store_module.Store(cfg.db_path) as store:
contract = _contract(cfg)
mode = _check_contract(store, qdrant, cfg, contract, mode)
_ensure_collection(qdrant, cfg)
manifest = store.manifest()
changed, removed = _classify(documents, manifest, cfg, mode)
stats.unchanged = len(documents) - len(changed)
stats.removed = len(removed)
log(
f"incremental plan: {len(changed)} to embed, "
f"{stats.unchanged} unchanged, {len(removed)} removed"
)
for path in removed:
stats.chunks_deleted += store.delete_document(path)
qdrant.delete_by_source(cfg.collection, path)
log(f"removed {path}")
for doc in changed:
payloads_for_doc = _prepare(doc, cfg, manifest)
stats.chunks_written += len(payloads_for_doc)
vectors = provider.embed([p["text"] for p in payloads_for_doc])
if len(vectors) != len(payloads_for_doc):
raise PipelineError(
f"{doc.path}: provider returned {len(vectors)} vectors for "
f"{len(payloads_for_doc)} chunks"
)
points = []
for payload, vector in zip(payloads_for_doc, vectors):
if len(vector) != cfg.dim:
raise PipelineError(
f"{doc.path}: vector dimension {len(vector)} != configured {cfg.dim}"
)
points.append({"id": payload["id"], "vector": vector, "payload": payload})
# Order: remove what this document used to have (chunk counts change),
# then write the new rows and points. A crash between the two leaves an
# index that reports as incomplete, never one that claims to be whole.
stats.chunks_deleted += store.delete_document(doc.path)
qdrant.delete_by_source(cfg.collection, doc.path)
stats.points_sent += qdrant.upsert(cfg.collection, points)
store.write_document(doc, payloads_for_doc)
if doc.path in manifest:
stats.updated += 1
log(f"updated {doc.path} ({len(points)} chunk(s))")
else:
stats.added += 1
log(f"added {doc.path} ({len(points)} chunk(s))")
provider_stats = provider.stats()
stats.embed_calls = provider_stats["calls"]
stats.texts_embedded = provider_stats["texts"]
stats.store_chunks = store.chunk_count()
stats.collection_points = qdrant.point_count(cfg.collection)
if stats.collection_points != stats.store_chunks:
raise PipelineError(
"index is inconsistent after the run: "
f"{stats.collection_points} point(s) in Qdrant but "
f"{stats.store_chunks} chunk(s) in SQLite. Run `python3 -m corpus.cli verify`."
)
store.set_meta("collection", contract)
store.set_meta("last_run", {**stats.to_dict(), "finished_at": time.strftime("%Y-%m-%dT%H:%M:%SZ", time.gmtime())})
_write_collection_file(cfg, store, stats)
stats.duration_s = round(time.time() - started, 3)
return stats
def _contract(cfg) -> dict:
"""What this collection *is*. Recorded so a change is a migration, not drift."""
return {
"provider": cfg.provider,
"model": cfg.model,
"dim": cfg.dim,
"chunker_version": cfg.chunker_version,
"distance": "Cosine",
}
def _check_contract(store, qdrant, cfg, contract: dict, mode: str) -> str:
stored = store.get_meta("collection")
if mode == "rebuild":
qdrant.drop_collection(cfg.collection)
store.reset()
return mode
if stored is None:
# No contract yet: either a fresh store, or one whose database was lost.
if qdrant.collection_exists(cfg.collection) and qdrant.point_count(cfg.collection) > 0:
raise PipelineError(
f"collection {cfg.collection!r} holds points but this store has no "
"collection contract (was data/corpus/corpus.db deleted?). "
"Confirm you want to overwrite it: python3 -m corpus.cli rebuild"
)
return mode
if stored != contract:
raise PipelineError(
"the collection contract does not match this configuration — refusing to "
"mix vectors in one collection (docs/phase-4-corpus.md §4.3).\n"
f" stored: {json.dumps(stored, sort_keys=True)}\n"
f" config: {json.dumps(contract, sort_keys=True)}\n"
"A provider or chunker change is a governed migration: run "
"`python3 -m corpus.cli rebuild` once Sam has approved the change."
)
return mode
def _ensure_collection(qdrant: Qdrant, cfg) -> None:
if not qdrant.collection_exists(cfg.collection):
qdrant.create_collection(
cfg.collection, cfg.dim, vectors_on_disk=cfg.vectors_on_disk
)
return
info = qdrant.collection_info(cfg.collection)
vectors = ((info.get("config") or {}).get("params") or {}).get("vectors")
size = None
if isinstance(vectors, dict):
size = vectors.get("size")
if size is not None and int(size) != int(cfg.dim):
raise PipelineError(
f"collection {cfg.collection!r} has vectors of size {size} but this run "
f"is configured for {cfg.dim}. That is a governed migration: rebuild."
)
def _classify(documents: list, manifest: dict, cfg, mode: str):
"""Which documents need work, and which are gone."""
changed = []
on_disk = set()
for doc in documents:
on_disk.add(doc.path)
if mode == "rebuild":
changed.append(doc)
continue
entry = manifest.get(doc.path)
if entry is None:
changed.append(doc)
continue
if (
entry["content_hash"] != doc.digest
or entry["embedding_model"] != cfg.model
or int(entry["embedding_dim"]) != int(cfg.dim)
or entry["chunker_version"] != cfg.chunker_version
):
changed.append(doc)
removed = sorted(path for path in manifest if path not in on_disk)
return changed, removed
def _prepare(doc, cfg, manifest: dict) -> list:
"""Chunk one document and build validated payloads. No network, no writes."""
try:
chunks = chunker.chunk_document(doc.body)
except chunker.ChunkError as exc:
raise PipelineError(f"{doc.path}: {exc}") from exc
if not chunks:
raise PipelineError(f"{doc.path}: produced no chunks (empty body?)")
doc.chunk_count = len(chunks)
out = []
for chunk in chunks:
payload = payloads.build_payload(doc, chunk, cfg)
payload["id"] = chunk_id(doc.path, chunk.index, doc.digest)
try:
payloads.validate_payload(payload, where=f"{doc.path}#chunk-{chunk.index}")
except payloads.PayloadError as exc:
raise PipelineError(str(exc)) from exc
out.append(payload)
return out
def _write_collection_file(cfg, store, stats: RunStats) -> None:
"""Mirror the contract to data/corpus/collection.json for the operator."""
body = {
"collection": cfg.collection,
"contract": _contract(cfg),
"qdrant_url": cfg.qdrant_url,
"chunks": store.chunk_count(),
"sources": store.source_count(),
"last_run": store.get_meta("last_run"),
}
with open(cfg.meta_path, "w", encoding="utf-8") as handle:
json.dump(body, handle, indent=2, sort_keys=True)
handle.write("\n")
# -- verification and demonstration ---------------------------------------
def verify(cfg, log=None) -> list:
"""Return a list of problems. Empty means the index agrees with content/."""
log = log or (lambda _msg: None)
problems = []
documents = loader.load_documents(cfg)
qdrant = Qdrant(cfg.qdrant_url, timeout=cfg.timeout)
if not qdrant.health():
return [f"Qdrant is not answering at {cfg.qdrant_url}"]
contract = _contract(cfg)
with store_module.Store(cfg.db_path) as store:
stored = store.get_meta("collection")
if stored != contract:
problems.append(f"collection contract mismatch: stored={stored} config={contract}")
if not qdrant.collection_exists(cfg.collection):
problems.append(f"collection {cfg.collection!r} does not exist")
return problems
info = qdrant.collection_info(cfg.collection)
size = (((info.get("config") or {}).get("params") or {}).get("vectors") or {}).get("size")
if size is not None and int(size) != cfg.dim:
problems.append(f"collection vector size {size} != configured {cfg.dim}")
points_by_source = qdrant.scroll_sources(cfg.collection)
rows = store.chunk_payloads()
by_source = {}
for row in rows:
by_source.setdefault(row["source_path"], []).append(row)
try:
payloads.validate_payload(row, where=f"{row['source_path']}#chunk-{row['chunk_index']}")
except payloads.PayloadError as exc:
problems.append(str(exc))
for doc in documents:
payload_rows = by_source.get(doc.path, [])
if not payload_rows:
problems.append(f"{doc.path}: no chunks in the index (run sync)")
continue
if payload_rows[0]["content_hash"] != doc.digest:
problems.append(f"{doc.path}: source changed since it was embedded")
expected = len(payload_rows)
actual = points_by_source.get(doc.path, 0)
if actual != expected:
problems.append(
f"{doc.path}: {expected} chunk row(s) but {actual} vector(s) in Qdrant"
)
for source in sorted(set(by_source) - {doc.path for doc in documents}):
problems.append(f"{source}: indexed but no longer in content/ (run sync)")
total_points = sum(points_by_source.values())
if total_points != store.chunk_count():
problems.append(
f"{total_points} vector(s) in Qdrant but {store.chunk_count()} chunk(s) in SQLite"
)
log(
f"verify: {store.chunk_count()} chunk(s) across {store.source_count()} source(s), "
f"{total_points} vector(s) in Qdrant"
)
return problems
def demo_area_filter(cfg, query: str, area: str = None, limit: int = 5) -> dict:
"""Same query with and without an `area` filter — the DoD's filter evidence."""
provider = embeddings.build_provider(cfg)
qdrant = Qdrant(cfg.qdrant_url, timeout=cfg.timeout)
vector = provider.embed([query])[0]
unfiltered = qdrant.search(cfg.collection, vector, limit=limit)
filtered = qdrant.search(cfg.collection, vector, limit=limit, area=area) if area else []
return {
"query": query,
"area": area,
"unfiltered": [_hit(row) for row in unfiltered],
"filtered": [_hit(row) for row in filtered],
}
def _hit(row: dict) -> dict:
payload = row.get("payload") or {}
return {
"score": round(row["score"], 4),
"area": payload.get("area"),
"confidence": payload.get("confidence"),
"title": payload.get("title"),
"url": payload.get("url"),
"source_path": payload.get("source_path"),
"chunk_index": payload.get("chunk_index"),
"preview": (payload.get("text") or "").replace("\n", " ")[:120],
}
def hybrid_search(cfg, query: str, area: str = None, limit: int = 5) -> list:
"""Vectors + FTS5 fused with reciprocal rank fusion (decision D10).
Phase 5 owns the query graph; this is here because Phase 4 is the phase that
has to prove the two halves line up — same ids, same metadata, one ranking.
"""
provider = embeddings.build_provider(cfg)
qdrant = Qdrant(cfg.qdrant_url, timeout=cfg.timeout)
vector = provider.embed([query])[0]
ranked = {}
for rank, hit in enumerate(qdrant.search(cfg.collection, vector, limit=limit * 4, area=area)):
entry = ranked.setdefault(hit["id"], {"id": hit["id"], "payload": hit["payload"], "score": 0.0})
entry["score"] += 1.0 / (60 + rank)
with store_module.Store(cfg.db_path) as store:
for rank, hit in enumerate(store.keyword_search(query, area=area, limit=limit * 4)):
entry = ranked.setdefault(hit["id"], {"id": hit["id"], "payload": {}, "score": 0.0})
entry.setdefault("keyword", hit["snippet"])
entry["score"] += 1.0 / (60 + rank)
ordered = sorted(ranked.values(), key=lambda row: row["score"], reverse=True)[:limit]
for row in ordered:
payload = row.get("payload") or {}
row["title"] = payload.get("title")
row["url"] = payload.get("url")
return ordered

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"""Qdrant REST client — standard library only.
No `qdrant-client` dependency: the four calls this pipeline needs are HTTP
requests, and AGENTS.md §4.1 freezes the stack. `_request` is the single
transport hook, which is what the tests replace to run the whole pipeline
without a container.
Qdrant is bound to `127.0.0.1:6333` on the runtime host (`.13`) and is never
publicly reachable; Caddy is the only public listener.
"""
from __future__ import annotations
import http.client
import json
import time
import urllib.error
import urllib.request
DISTANCE_COSINE = "Cosine"
# The runtime host (.13) is memory-bound: 2.3 GiB free and 8 GiB of swap already
# in use, so Qdrant gets a 400 m cap and must live inside it
# (reference/stack.md). Measured on 2026-10-26: the *default* HNSW and payload
# settings are OOM-killed at the cap between 5,000 and 20,000 chunks of 1024
# dimensions. Keeping the HNSW graph and the payloads on disk, and using a
# smaller graph (m=8, ef_construct=64), fits the cap because the kernel can
# reclaim those page-cache pages instead of killing the process. Vectors stay in
# RAM because they are the part search reads on every query.
#
# The trade is build time and a little search latency; the corpus is ~50 chunks
# today and the public site has no query burst. See docs/phase-4-notes.md §7.
MEMORY_FRUGAL_DEFAULTS = {
"on_disk_payload": True,
"hnsw_m": 8,
"hnsw_ef_construct": 64,
"vectors_on_disk": False,
}
# Segment behaviour under a cap. `memmap_threshold` makes Qdrant mmap segments
# this size and larger (page cache the kernel can reclaim instead of OOM), and
# `indexing_threshold` is raised so a bulk ingest does not build the HNSW graph
# while it is still writing segments. Measured: 20,000 chunks at 1024
# dimensions peaked at 296 MiB during ingest and settled to 96 MiB.
MEMORY_FRUGAL_OPTIMIZERS = {
"default_segment_number": 2,
"memmap_threshold": 5000,
"indexing_threshold": 20000,
}
class QdrantError(RuntimeError):
"""A Qdrant call failed, or Qdrant is not reachable."""
class Qdrant:
def __init__(self, url: str = "http://127.0.0.1:6333", timeout: float = 30.0, attempts: int = 3) -> None:
self.url = url.rstrip("/")
self.timeout = timeout
self.attempts = max(1, attempts)
# -- transport --------------------------------------------------------
def _request(self, method: str, path: str, body=None) -> dict:
"""One HTTP call with retry/backoff. The only place that touches the network."""
data = None if body is None else json.dumps(body).encode("utf-8")
last_error = None
for attempt in range(self.attempts):
request = urllib.request.Request(
f"{self.url}{path}",
data=data,
method=method,
headers={"Content-Type": "application/json"} if data else {},
)
try:
with urllib.request.urlopen(request, timeout=self.timeout) as response:
raw = response.read().decode("utf-8")
return json.loads(raw) if raw.strip() else {}
except urllib.error.HTTPError as exc:
detail = exc.read().decode("utf-8", "replace")[:400]
exc.close()
if exc.code == 404:
raise QdrantNotFound(f"{method} {path}: {detail}") from None
if exc.code != 429 and exc.code < 500:
raise QdrantError(f"{method} {path} failed: HTTP {exc.code} {detail}") from None
last_error = f"HTTP {exc.code} {detail}"
except (urllib.error.URLError, TimeoutError, http.client.HTTPException, ConnectionError) as exc:
# RemoteDisconnected and friends arrive here when the server dies
# mid-request (an OOM kill looks exactly like this).
last_error = f"cannot reach {self.url}: {getattr(exc, 'reason', exc)}"
if attempt + 1 < self.attempts:
time.sleep(0.5 * (2 ** attempt))
raise QdrantError(f"{method} {path} failed after {self.attempts} attempts: {last_error}")
# -- cluster ----------------------------------------------------------
def health(self) -> bool:
"""True when Qdrant answers. Never raises."""
try:
self._request("GET", "/")
return True
except (QdrantError, QdrantNotFound):
return False
def version(self) -> str:
response = self._request("GET", "/")
return str(response.get("version", "unknown"))
# -- collections ------------------------------------------------------
def collection_exists(self, name: str) -> bool:
try:
self._request("GET", f"/collections/{name}")
return True
except QdrantNotFound:
return False
def create_collection(
self,
name: str,
dim: int,
distance: str = DISTANCE_COSINE,
on_disk_payload: bool = None,
hnsw_m: int = None,
hnsw_ef_construct: int = None,
vectors_on_disk: bool = None,
) -> None:
"""Create a collection sized for a memory-bound host (see MEMORY_FRUGAL_DEFAULTS)."""
defaults = MEMORY_FRUGAL_DEFAULTS
body = {
"vectors": {
"size": int(dim),
"distance": distance,
"on_disk": bool(
defaults["vectors_on_disk"] if vectors_on_disk is None else vectors_on_disk
),
},
"hnsw_config": {
"m": int(defaults["hnsw_m"] if hnsw_m is None else hnsw_m),
"ef_construct": int(
defaults["hnsw_ef_construct"] if hnsw_ef_construct is None else hnsw_ef_construct
),
"on_disk": True,
},
"on_disk_payload": bool(
defaults["on_disk_payload"] if on_disk_payload is None else on_disk_payload
),
"optimizers_config": dict(MEMORY_FRUGAL_OPTIMIZERS),
}
self._request("PUT", f"/collections/{name}", body)
def drop_collection(self, name: str) -> None:
try:
self._request("DELETE", f"/collections/{name}")
except QdrantNotFound:
pass
def collection_info(self, name: str) -> dict:
response = self._request("GET", f"/collections/{name}")
return dict(response.get("result") or {})
def point_count(self, name: str) -> int:
response = self._request("POST", f"/collections/{name}/points/count", {"exact": True})
return int((response.get("result") or {}).get("count", 0))
# -- points -----------------------------------------------------------
def upsert(self, name: str, points: list, batch: int = 256) -> int:
"""Upsert points in batches. Returns the number of points sent."""
sent = 0
for start in range(0, len(points), max(1, batch)):
chunk = points[start : start + max(1, batch)]
self._request(
"PUT",
f"/collections/{name}/points?wait=true",
{"points": chunk},
)
sent += len(chunk)
return sent
def delete_by_source(self, name: str, source_path: str) -> None:
"""Delete every point that came from one document.
Re-chunking a document can produce fewer chunks than before, so the old
points are removed rather than overwritten. Deterministic ids make
duplicates impossible; this makes orphans impossible.
"""
self._request(
"POST",
f"/collections/{name}/points/delete?wait=true",
{"filter": {"must": [{"key": "source_path", "match": {"value": source_path}}]}},
)
def search(
self,
name: str,
vector: list,
limit: int = 5,
area: str = None,
confidence: str = None,
with_payload: bool = True,
) -> list:
"""Nearest neighbours, optionally filtered *before* the search."""
body = {
"query": [float(value) for value in vector],
"limit": int(limit),
"with_payload": bool(with_payload),
}
filters = _filter_conditions(area, confidence)
if filters:
body["filter"] = {"must": filters}
try:
response = self._request("POST", f"/collections/{name}/points/query", body)
points = ((response.get("result") or {}).get("points")) or []
except QdrantNotFound:
# Older Qdrant exposes the same operation as /points/search.
response = self._request("POST", f"/collections/{name}/points/search", body)
points = response.get("result") or []
return [
{
"id": point.get("id"),
"score": float(point.get("score") or 0.0),
"payload": point.get("payload") or {},
}
for point in points
]
def scroll_sources(self, name: str, limit: int = 1000) -> dict:
"""Map source_path -> number of vectors, for consistency checks."""
counts = {}
offset = None
while True:
body = {"limit": limit, "with_payload": ["source_path"], "with_vector": False}
if offset is not None:
body["offset"] = offset
response = self._request("POST", f"/collections/{name}/points/scroll", body)
result = response.get("result") or {}
points = result.get("points") or []
for point in points:
source = (point.get("payload") or {}).get("source_path", "<missing>")
counts[source] = counts.get(source, 0) + 1
offset = result.get("next_page_offset")
if not points or offset is None:
return counts
def metrics(self) -> str:
"""Prometheus text from Qdrant's /metrics route (used by the memory measurement).
Returns "" when the route is not exposed, so callers can fall back to
reading the container's own memory counters.
"""
try:
with urllib.request.urlopen(f"{self.url}/metrics", timeout=self.timeout) as response:
return response.read().decode("utf-8", "replace")
except (urllib.error.URLError, urllib.error.HTTPError, TimeoutError):
return ""
class QdrantNotFound(QdrantError):
"""A 404 from Qdrant: the collection or route does not exist."""
def _filter_conditions(area: str = None, confidence: str = None) -> list:
conditions = []
if area:
conditions.append({"key": "area", "match": {"value": area}})
if confidence:
conditions.append({"key": "confidence", "match": {"value": confidence}})
return conditions

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"""SQLite: the keyword index and the incremental state.
Two jobs, one file (`RESUME_CORPUS_DB`, default `data/corpus/corpus.db`):
* **`chunk_fts`** — an FTS5 index over chunk text with the same filter columns
the vectors carry. This is the keyword half of hybrid retrieval (decision
D10). Phase 2 indexes *pages* for the site's own search box; this indexes
*chunks*, because retrieval hands the model a passage, not a page.
* **`manifest`** — one row per source document: its content hash, its chunk
count and the embedding model/dimension it was embedded with. Incremental mode
is a hash comparison against this table, and nothing else.
`meta` records the collection contract (provider, model, dimension, chunker
version) so a provider change is detected instead of silently corrupting the
index.
Standard library `sqlite3` only. The same file is opened read-only by the Phase
5 retrieval service.
"""
from __future__ import annotations
import json
import os
import re
import sqlite3
import time
import unicodedata
SCHEMA_VERSION = "1"
SCHEMA = """
CREATE TABLE IF NOT EXISTS chunks (
id TEXT PRIMARY KEY,
source_path TEXT NOT NULL,
source_id TEXT NOT NULL,
chunk_index INTEGER NOT NULL,
area TEXT NOT NULL,
level INTEGER NOT NULL,
source_type TEXT NOT NULL,
confidence TEXT NOT NULL,
period TEXT NOT NULL,
last_verified TEXT NOT NULL,
title TEXT NOT NULL,
claim TEXT NOT NULL,
url TEXT NOT NULL,
content_hash TEXT NOT NULL,
embedding_model TEXT NOT NULL,
embedding_dim INTEGER NOT NULL,
chunker_version TEXT NOT NULL,
chunk_tokens INTEGER NOT NULL,
public INTEGER NOT NULL,
artefacts TEXT NOT NULL,
tech TEXT NOT NULL,
tags TEXT NOT NULL,
text TEXT NOT NULL
);
CREATE INDEX IF NOT EXISTS chunks_source ON chunks(source_path);
CREATE INDEX IF NOT EXISTS chunks_area ON chunks(area);
CREATE VIRTUAL TABLE IF NOT EXISTS chunk_fts USING fts5(
id UNINDEXED,
source_path UNINDEXED,
title,
terms,
text,
area UNINDEXED,
confidence UNINDEXED,
tokenize = 'porter unicode61 remove_diacritics 2'
);
CREATE TABLE IF NOT EXISTS manifest (
source_path TEXT PRIMARY KEY,
content_hash TEXT NOT NULL,
chunk_count INTEGER NOT NULL,
embedding_model TEXT NOT NULL,
embedding_dim INTEGER NOT NULL,
chunker_version TEXT NOT NULL,
last_verified TEXT NOT NULL,
updated_at TEXT NOT NULL
);
CREATE TABLE IF NOT EXISTS meta (
key TEXT PRIMARY KEY,
value TEXT NOT NULL
);
"""
# Column order used by every INSERT in this module.
_CHUNK_COLUMNS = (
"id", "source_path", "source_id", "chunk_index", "area", "level", "source_type",
"confidence", "period", "last_verified", "title", "claim", "url", "content_hash",
"embedding_model", "embedding_dim", "chunker_version", "chunk_tokens",
"public", "artefacts", "tech", "tags", "text",
)
_JSON_FIELDS = ("artefacts", "tech", "tags")
class StoreError(RuntimeError):
"""The corpus database is unreadable or inconsistent."""
class Store:
def __init__(self, path: str) -> None:
self.path = path
os.makedirs(os.path.dirname(os.path.abspath(path)) or ".", exist_ok=True)
self.db = sqlite3.connect(path)
self.db.row_factory = sqlite3.Row
self.db.execute("PRAGMA journal_mode = WAL")
self.db.execute("PRAGMA synchronous = NORMAL")
self.db.executescript(SCHEMA)
self.set_meta("schema_version", SCHEMA_VERSION)
# -- lifecycle --------------------------------------------------------
def close(self) -> None:
self.db.close()
def __enter__(self) -> "Store":
return self
def __exit__(self, *_exc) -> None:
self.close()
def reset(self) -> None:
"""Empty every table. Used by a from-scratch rebuild."""
for table in ("chunk_fts", "chunks", "manifest", "meta"):
self.db.execute(f"DELETE FROM {table}")
self.db.commit()
# -- writing ----------------------------------------------------------
def write_document(self, doc, payloads: list) -> None:
"""Replace all rows for one document: chunks, FTS rows and manifest."""
self.delete_document(doc.path, keep_manifest=True)
for payload in payloads:
values = []
for column in _CHUNK_COLUMNS:
value = payload[column]
if column in _JSON_FIELDS:
value = json.dumps(value, sort_keys=True)
values.append(value)
cursor = self.db.execute(
f"INSERT INTO chunks ({', '.join(_CHUNK_COLUMNS)}) "
f"VALUES ({', '.join('?' for _ in _CHUNK_COLUMNS)})",
values,
)
# `terms` is the keyword surface: the words a visitor or a recruiter
# would type that live in front-matter rather than the body — tech
# names, tags, the one-line claim. Without it, a search for "zigbee"
# misses a page whose body says "mesh". It is indexed, never retrieved.
terms = " ".join(
str(value)
for value in (
payload["claim"],
payload["title"],
" ".join(payload["tech"]),
" ".join(payload["tags"]),
)
if value
)
self.db.execute(
"INSERT INTO chunk_fts (rowid, id, source_path, title, terms, text, area, confidence) "
"VALUES (?, ?, ?, ?, ?, ?, ?, ?)",
(
cursor.lastrowid,
payload["id"],
payload["source_path"],
payload["title"],
terms,
payload["text"],
payload["area"],
payload["confidence"],
),
)
self.db.execute(
"INSERT OR REPLACE INTO manifest "
"(source_path, content_hash, chunk_count, embedding_model, embedding_dim, "
" chunker_version, last_verified, updated_at) VALUES (?, ?, ?, ?, ?, ?, ?, ?)",
(
doc.path,
doc.digest,
len(payloads),
payloads[0]["embedding_model"] if payloads else "",
payloads[0]["embedding_dim"] if payloads else 0,
payloads[0]["chunker_version"] if payloads else "",
doc.last_verified,
time.strftime("%Y-%m-%dT%H:%M:%SZ", time.gmtime()),
),
)
self.db.commit()
def delete_document(self, source_path: str, keep_manifest: bool = False) -> int:
rowids = [
row["rowid"]
for row in self.db.execute("SELECT rowid FROM chunks WHERE source_path = ?", (source_path,))
]
for rowid in rowids:
self.db.execute("DELETE FROM chunk_fts WHERE rowid = ?", (rowid,))
self.db.execute("DELETE FROM chunks WHERE source_path = ?", (source_path,))
if not keep_manifest:
self.db.execute("DELETE FROM manifest WHERE source_path = ?", (source_path,))
self.db.commit()
return len(rowids)
# -- reading ----------------------------------------------------------
def manifest(self) -> dict:
return {
row["source_path"]: dict(row)
for row in self.db.execute("SELECT * FROM manifest")
}
def chunk_count(self) -> int:
return int(self.db.execute("SELECT count(*) FROM chunks").fetchone()[0])
def source_count(self) -> int:
return int(self.db.execute("SELECT count(DISTINCT source_path) FROM chunks").fetchone()[0])
def chunk_ids(self) -> list:
return [row["id"] for row in self.db.execute("SELECT id FROM chunks ORDER BY id")]
def chunk_payloads(self, source_path: str = None) -> list:
"""Every stored payload, optionally for one document. Used by verify()."""
sql = "SELECT * FROM chunks"
args = ()
if source_path:
sql += " WHERE source_path = ?"
args = (source_path,)
sql += " ORDER BY source_path, chunk_index"
out = []
for row in self.db.execute(sql, args):
payload = dict(row)
for field in _JSON_FIELDS:
payload[field] = json.loads(payload[field])
out.append(payload)
return out
def keyword_search(self, query: str, area: str = None, confidence: str = None, limit: int = 10) -> list:
"""BM25 over chunk text, optionally filtered before ranking."""
match = match_expression(query)
if not match:
return []
sql = [
"SELECT c.id, c.url, c.title, c.area, c.confidence, c.source_path,",
" c.chunk_index, snippet(chunk_fts, 4, '<mark>', '</mark>', '…', 18) AS snippet,",
" bm25(chunk_fts) AS score",
"FROM chunk_fts JOIN chunks c ON c.rowid = chunk_fts.rowid",
"WHERE chunk_fts MATCH ?",
]
args = [match]
if area:
sql.append("AND c.area = ?")
args.append(area)
if confidence:
sql.append("AND c.confidence = ?")
args.append(confidence)
sql.append("ORDER BY bm25(chunk_fts) LIMIT ?")
args.append(int(limit))
rows = self.db.execute(" ".join(sql), args)
return [dict(row) for row in rows]
# -- meta -------------------------------------------------------------
def set_meta(self, key: str, value) -> None:
self.db.execute(
"INSERT OR REPLACE INTO meta (key, value) VALUES (?, ?)",
(key, json.dumps(value, sort_keys=True)),
)
self.db.commit()
def get_meta(self, key: str, default=None):
row = self.db.execute("SELECT value FROM meta WHERE key = ?", (key,)).fetchone()
if row is None:
return default
return json.loads(row["value"])
def meta(self) -> dict:
return {row["key"]: json.loads(row["value"]) for row in self.db.execute("SELECT key, value FROM meta")}
def match_expression(query: str) -> str:
"""Free text -> a safe FTS5 expression: bare words, each a prefix, all required.
Mirrors `MatchExpression` in internal/search/search.go so the site's search
box and the retrieval path treat a visitor's words identically. FTS5
operators in the input are never honoured.
"""
terms = []
for raw in re.split(r"[^0-9a-zA-Z\-]+", unicodedata.normalize("NFKC", query or "").lower()):
term = raw.strip("-")
if term:
terms.append('"' + term.replace('"', "") + '"*')
return " AND ".join(terms)

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"""An in-memory stand-in for Qdrant, used by the pipeline tests.
It implements the routes the pipeline actually calls and nothing else, and it is
deliberately dumb: brute-force cosine over a dict. If the pipeline passes
against this, the pipeline's own logic is right; the real server is exercised
separately in `docs/phase-4-notes.md` (rebuild against a live container).
"""
from __future__ import annotations
import math
from corpus.qdrant import Qdrant, QdrantError, QdrantNotFound
class FakeQdrant(Qdrant):
def __init__(self, dim: int):
super().__init__(url="fake://qdrant", timeout=1.0, attempts=1)
self.dim = dim
self.collections = {} # name -> {id: {"vector": [...], "payload": {...}}}
self.calls = []
# -- transport --------------------------------------------------------
def _request(self, method: str, path: str, body=None) -> dict:
self.calls.append((method, path, body))
query = ""
if "?" in path:
path, query = path.split("?", 1)
parts = [part for part in path.split("/") if part]
if not parts:
return {"version": "fake-1.0"}
if parts[0] != "collections":
raise QdrantNotFound(f"no route {path}")
if len(parts) == 2:
name = parts[1]
if method == "PUT":
self.collections.setdefault(name, {})
return {"result": True}
if method == "DELETE":
if name not in self.collections:
raise QdrantNotFound("collection not found")
del self.collections[name]
return {"result": True}
if name not in self.collections:
raise QdrantNotFound("collection not found")
return {
"result": {
"points_count": len(self.collections[name]),
"config": {"params": {"vectors": {"size": self.dim, "distance": "Cosine"}}},
}
}
name = parts[1]
if name not in self.collections:
raise QdrantNotFound("collection not found")
points = self.collections[name]
if parts[2:4] == ["points", "count"]:
return {"result": {"count": len(points)}}
if len(parts) == 3 and parts[2] == "points" and method == "PUT":
for point in (body or {}).get("points", []):
if len(point["vector"]) != self.dim:
raise QdrantError("wrong vector dimension")
points[point["id"]] = {"vector": point["vector"], "payload": point["payload"]}
return {"result": {"status": "completed"}}
if parts[2:4] == ["points", "delete"]:
must = (((body or {}).get("filter") or {}).get("must")) or []
for point_id in list(points):
if _matches(points[point_id]["payload"], must):
del points[point_id]
return {"result": {"status": "completed"}}
if parts[2:4] == ["points", "query"]:
vector = body["query"]
must = (((body or {}).get("filter") or {}).get("must")) or []
scored = []
for point_id, point in points.items():
if not _matches(point["payload"], must):
continue
scored.append(
{
"id": point_id,
"score": _cosine(vector, point["vector"]),
"payload": point["payload"],
}
)
scored.sort(key=lambda row: row["score"], reverse=True)
return {"result": {"points": scored[: int(body.get("limit", 10))]}}
if parts[2:4] == ["points", "scroll"]:
limit = int((body or {}).get("limit", 100))
rows = [
{"id": point_id, "payload": {"source_path": point["payload"].get("source_path")}}
for point_id, point in list(points.items())[:limit]
]
return {"result": {"points": rows, "next_page_offset": None}}
raise QdrantNotFound(f"no route {path}")
def _matches(payload: dict, conditions: list) -> bool:
for condition in conditions:
key = condition.get("key")
expected = (condition.get("match") or {}).get("value")
if payload.get(key) != expected:
return False
return True
def _cosine(left: list, right: list) -> float:
dot = sum(a * b for a, b in zip(left, right))
norm = math.sqrt(sum(a * a for a in left)) * math.sqrt(sum(b * b for b in right))
return dot / norm if norm else 0.0

186
corpus/tests/support.py Normal file
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"""Shared fixtures for the corpus tests: a throwaway content tree and a fake Qdrant."""
from __future__ import annotations
import contextlib
import os
import shutil
import tempfile
from unittest import mock
from corpus.config import Config
from corpus.tests.fake_qdrant import FakeQdrant
REPO_ROOT = os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
DOC = """---
id: {id}
area: {area}
level: {level}
title: "{title}"
claim: "{claim}"
confidence: {confidence}
period: "2023–"
tech: [Go, SQLite]
metrics: [{{label: endpoints, value: 8}}]
artefacts: []
public: true
last_verified: {last_verified}
tags: [{tags}]
---
# {title}
{body}
"""
FENCE = """A paragraph before the code block explains the constraint.
```go
package main
func main() {{
println("chunk boundary must never land inside this fence")
}}
```
A paragraph after the code block.
"""
TABLE = """Measured results, one row per endpoint:
| endpoint | latency | loss |
|---|---|---|
| alpha | 12ms | 0.0% |
| beta | 14ms | 0.1% |
| gamma | 9ms | 0.0% |
And a closing paragraph.
"""
PROSE = "\n\n".join(
f"Paragraph {index} describes a delivery constraint in corporate language, "
"with enough words that the chunker has to think about where to break, and "
"never inside a fenced block, a table or a front-matter block."
for index in range(1, 40)
)
DEFAULT_DOCS = {
"ai/ai-overview.md": dict(
id="ai-overview", area="ai", level=1, title="Agent Systems",
claim="Harnesses, memory and routing.", confidence="running",
last_verified="2026-10-01", tags="agents, llm", body=PROSE,
),
"edge/l3/edge-zigbee.md": dict(
id="edge-zigbee", area="edge", level=3, title="Low Power Mesh Sensing",
claim="A constrained mesh that survives power loss.", confidence="delivered",
last_verified="2026-09-15", tags="zigbee, mesh", body=FENCE,
),
"platform/l3/platform-containers.md": dict(
id="platform-containers", area="platform", level=3, title="Container Estate",
claim="Fifty containers with measured resource limits.", confidence="in-progress",
last_verified="2026-08-01", tags="docker", body=TABLE,
),
}
def temp_repo(docs: dict = None) -> str:
"""Create a throwaway repository root with tools/ and content/."""
root = tempfile.mkdtemp(prefix="corpus-test-")
os.makedirs(os.path.join(root, "tools"), exist_ok=True)
shutil.copy(
os.path.join(REPO_ROOT, "tools", "validate_content.py"),
os.path.join(root, "tools", "validate_content.py"),
)
write_docs(root, docs or DEFAULT_DOCS)
return root
def write_docs(root: str, docs: dict) -> None:
for rel, fields in docs.items():
path = os.path.join(root, "content", rel)
os.makedirs(os.path.dirname(path), exist_ok=True)
with open(path, "w", encoding="utf-8") as handle:
handle.write(DOC.format(**fields))
def write_doc(root: str, rel: str, **fields) -> None:
path = os.path.join(root, "content", rel)
os.makedirs(os.path.dirname(path), exist_ok=True)
with open(path, "w", encoding="utf-8") as handle:
handle.write(DOC.format(**fields))
# A fresh interpreter's import cache is fine; the loader re-reads the file.
def make_config(root: str, **overrides) -> Config:
values = dict(
provider="local-hashing",
model="local-hashing-v1",
dim=32,
qdrant_url="fake://qdrant",
collection="test_corpus",
)
values.update(overrides)
return Config.from_env(root=root, **values)
@contextlib.contextmanager
def fake_qdrant(cfg, fake: FakeQdrant = None):
"""Point every module that builds a Qdrant client at one in-memory fake."""
fake = fake or FakeQdrant(cfg.dim)
with mock.patch("corpus.pipeline.Qdrant", return_value=fake), mock.patch(
"corpus.freshness.Qdrant", return_value=fake
):
yield fake
class StubServer:
"""A throwaway HTTP server on a loopback ephemeral port, for provider tests.
`responder(method, path, body)` returns `(status, payload)`; every request is
recorded with its parsed body so a test can assert the wire shape without a
real provider or a real Qdrant.
"""
def __init__(self, responder):
import json
import threading
from http.server import BaseHTTPRequestHandler, ThreadingHTTPServer
self.requests = []
self._responder = responder
outer = self
class Handler(BaseHTTPRequestHandler):
def _handle(self):
length = int(self.headers.get("Content-Length") or 0)
raw = self.rfile.read(length).decode("utf-8") if length else ""
body = json.loads(raw) if raw.strip() else None
outer.requests.append(
{"method": self.command, "path": self.path, "body": body,
"headers": dict(self.headers)}
)
status, payload = outer._responder(self.command, self.path, body)
data = payload.encode("utf-8") if isinstance(payload, str) else json.dumps(payload).encode("utf-8")
self.send_response(status)
self.send_header("Content-Type", "application/json")
self.send_header("Content-Length", str(len(data)))
self.end_headers()
self.wfile.write(data)
do_GET = do_POST = do_PUT = do_DELETE = _handle
def log_message(self, *_args):
pass
self.server = ThreadingHTTPServer(("127.0.0.1", 0), Handler)
self.url = f"http://127.0.0.1:{self.server.server_address[1]}"
self._thread = threading.Thread(target=self.server.serve_forever, daemon=True)
def __enter__(self):
self._thread.start()
return self
def __exit__(self, *_exc):
self.server.shutdown()
self.server.server_close()

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"""Chunking rules: size, overlap, determinism, and the hard one — no split inside
a fence, a table or front-matter (docs/phase-4-corpus.md DoD item 3)."""
from __future__ import annotations
import os
import unittest
from corpus import chunker, loader
from corpus.config import Config
from corpus.tests import support
REPO_ROOT = support.REPO_ROOT
def content_documents():
cfg = Config.from_env(root=REPO_ROOT, provider="local-hashing", dim=32)
return loader.load_documents(cfg)
class BlockParsingTest(unittest.TestCase):
def test_fence_is_one_block(self):
blocks = chunker.parse_blocks(support.FENCE)
kinds = [block.kind for block in blocks]
self.assertEqual(kinds, ["para", "fence", "para"])
self.assertIn("chunk boundary must never land", blocks[1].text)
def test_table_is_one_block(self):
blocks = chunker.parse_blocks(support.TABLE)
table = [block for block in blocks if block.kind == "table"]
self.assertEqual(len(table), 1)
self.assertEqual(len(table[0].text.splitlines()), 5) # header, rule, 3 rows
def test_front_matter_in_body_is_refused(self):
with self.assertRaises(chunker.ChunkError):
chunker.parse_blocks("---\nid: x\n---\n\n# Heading\n")
def test_unterminated_fence_runs_to_end(self):
blocks = chunker.parse_blocks("intro\n\n```go\nfunc main() {}\n")
self.assertEqual(blocks[-1].kind, "fence")
self.assertTrue(blocks[-1].text.rstrip().endswith("}"))
def test_headings_start_blocks(self):
blocks = chunker.parse_blocks("# One\n\ntext\n\n## Two\n\nmore\n")
self.assertEqual([block.kind for block in blocks], ["heading", "para", "heading", "para"])
def test_list_items_are_separate_blocks(self):
blocks = chunker.parse_blocks("- one\n- two\n continued\n- three\n")
self.assertEqual([block.kind for block in blocks], ["list", "list", "list"])
self.assertIn("continued", blocks[1].text)
class ChunkingTest(unittest.TestCase):
def test_small_body_is_one_chunk(self):
chunks = chunker.chunk_document("# Title\n\nA short body.\n")
self.assertEqual(len(chunks), 1)
self.assertEqual(chunks[0].index, 0)
self.assertFalse(chunks[0].oversize)
def test_long_body_respects_maximum_and_overlaps(self):
chunks = chunker.chunk_document(support.PROSE)
self.assertGreater(len(chunks), 1)
for chunk in chunks:
self.assertLessEqual(chunk.tokens, chunker.TOKEN_MAX, f"chunk {chunk.index} too large")
# Overlap: consecutive chunks share at least one block.
for previous, current in zip(chunks, chunks[1:]):
self.assertTrue(
set(id(block) for block in previous.blocks) & set(id(block) for block in current.blocks),
f"chunks {previous.index} and {current.index} do not overlap",
)
def test_chunk_indices_are_sequential(self):
chunks = chunker.chunk_document(support.PROSE)
self.assertEqual([chunk.index for chunk in chunks], list(range(len(chunks))))
def test_deterministic(self):
first = chunker.chunk_document(support.PROSE)
second = chunker.chunk_document(support.PROSE)
self.assertEqual([chunk.text for chunk in first], [chunk.text for chunk in second])
def test_code_fence_is_never_split(self):
body = support.PROSE + "\n\n" + support.FENCE + "\n\n" + support.PROSE
chunks = chunker.chunk_document(body)
fences = [block for block in chunker.parse_blocks(body) if block.kind == "fence"]
self.assertTrue(fences)
for fence in fences:
holders = [chunk for chunk in chunks if fence in chunk.blocks]
self.assertTrue(holders, "the fenced block disappeared from every chunk")
for holder in holders:
self.assertIn(fence.text, holder.text, "a chunk holds part of a fenced block")
def test_oversize_single_block_is_flagged_not_split(self):
huge = "```go\n" + "\n".join(f"line{i} := {i}" for i in range(1200)) + "\n```\n"
chunks = chunker.chunk_document(huge)
self.assertEqual(len(chunks), 1)
self.assertTrue(chunks[0].oversize)
self.assertGreater(chunks[0].tokens, chunker.TOKEN_MAX)
class RealContentTest(unittest.TestCase):
"""The same rules, over every document in content/."""
def test_no_boundary_splits_a_protected_region(self):
for doc in content_documents():
regions = chunker.protected_regions(doc.body)
chunks = chunker.chunk_document(doc.body)
chunker.verify_no_split(chunks, regions) # raises on failure
def test_every_chunk_is_whole_blocks(self):
for doc in content_documents():
expected = [block.text for block in chunker.parse_blocks(doc.body)]
seen = []
for chunk in chunker.chunk_document(doc.body):
for block in chunk.blocks:
self.assertIn(block.text, chunk.text)
if not seen or seen[-1] != block.text:
seen.append(block.text)
# Every block appears, in order, exactly as parsed (overlap repeats a
# block at the start of the next chunk, which is expected).
position = 0
for text in expected:
while position < len(seen) and seen[position] != text:
position += 1
self.assertLess(position, len(seen), f"{doc.path}: block was dropped: {text[:40]!r}")
position += 1
def test_chunks_are_within_the_size_band(self):
for doc in content_documents():
for chunk in chunker.chunk_document(doc.body):
if chunk.oversize:
self.assertEqual(len(chunk.blocks), 1)
continue
self.assertLessEqual(chunk.tokens, chunker.TOKEN_MAX, f"{doc.path} chunk {chunk.index}")
def test_content_tree_is_chunked_completely(self):
total = 0
for doc in content_documents():
chunks = chunker.chunk_document(doc.body)
self.assertTrue(chunks, f"{doc.path}: no chunks")
total += len(chunks)
self.assertGreater(total, 0)
if __name__ == "__main__":
unittest.main()

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"""Providers: determinism offline, and an OpenAI-compatible wire shape through OmniRoute.
There is no direct vendor SDK call anywhere (AGENTS.md §4.5), so the provider
test asserts the HTTP request shape, not a client library's behaviour.
"""
from __future__ import annotations
import math
import unittest
from corpus import embeddings
from corpus.tests import support
class LocalHashingTest(unittest.TestCase):
def test_deterministic_and_unit_length(self):
provider = embeddings.LocalHashingProvider(dim=64)
first = provider.embed(["zigbee mesh latency"])[0]
second = embeddings.LocalHashingProvider(dim=64).embed(["zigbee mesh latency"])[0]
self.assertEqual(first, second)
self.assertEqual(len(first), 64)
self.assertAlmostEqual(math.sqrt(sum(value * value for value in first)), 1.0, places=9)
def test_different_text_differs(self):
provider = embeddings.LocalHashingProvider(dim=64)
a, b = provider.embed(["zigbee", "lora"])
self.assertNotEqual(a, b)
def test_empty_text_still_returns_a_unit_vector(self):
provider = embeddings.LocalHashingProvider(dim=16)
vector = provider.embed([""])[0]
self.assertEqual(len(vector), 16)
self.assertAlmostEqual(math.sqrt(sum(value * value for value in vector)), 1.0, places=9)
def test_stats_count_calls_and_texts(self):
provider = embeddings.LocalHashingProvider(dim=16)
provider.embed(["a", "b", "c"])
provider.embed(["d"])
self.assertEqual(provider.stats(), {"calls": 2, "texts": 4})
provider.reset_stats()
self.assertEqual(provider.stats(), {"calls": 0, "texts": 0})
def test_rejects_a_zero_dimension(self):
with self.assertRaises(embeddings.EmbeddingError):
embeddings.LocalHashingProvider(dim=0)
class OmniRouteTest(unittest.TestCase):
def responder_factory(self, dim=8, status_plan=None):
plan = list(status_plan or [])
state = {"n": 0}
def responder(method, path, body):
state["n"] += 1
if plan:
status = plan.pop(0)
if status != 200:
return status, {"error": "planned failure"}
vectors = [
{"index": index, "embedding": [0.1] * dim}
for index, _text in enumerate(body.get("input") or [])
]
return 200, {"model": body.get("model"), "data": vectors}
return responder
def test_posts_to_v1_embeddings_with_the_configured_model(self):
with support.StubServer(self.responder_factory()) as server:
provider = embeddings.OmniRouteProvider(
api_url=server.url, api_key="test-key", model="text-embedding-3-small", dim=8
)
vectors = provider.embed(["one", "two"])
self.assertEqual(len(vectors), 2)
request = server.requests[0]
self.assertEqual(request["method"], "POST")
self.assertEqual(request["path"], "/v1/embeddings")
self.assertEqual(request["body"]["model"], "text-embedding-3-small")
self.assertEqual(request["body"]["input"], ["one", "two"])
self.assertEqual(request["headers"]["Authorization"], "Bearer test-key")
self.assertEqual(provider.stats(), {"calls": 1, "texts": 2})
def test_batches_requests(self):
with support.StubServer(self.responder_factory()) as server:
provider = embeddings.OmniRouteProvider(
api_url=server.url, api_key="k", model="m", dim=8, batch_size=2
)
provider.embed(["a", "b", "c", "d", "e"])
self.assertEqual(len(server.requests), 3)
self.assertEqual(provider.stats(), {"calls": 3, "texts": 5})
def test_dimension_mismatch_is_refused(self):
with support.StubServer(self.responder_factory(dim=4)) as server:
provider = embeddings.OmniRouteProvider(
api_url=server.url, api_key="k", model="m", dim=8
)
with self.assertRaises(embeddings.EmbeddingError) as caught:
provider.embed(["one"])
self.assertIn("dimension", str(caught.exception))
def test_retries_then_succeeds(self):
with support.StubServer(self.responder_factory(status_plan=[429, 200])) as server:
provider = embeddings.OmniRouteProvider(
api_url=server.url, api_key="k", model="m", dim=8, backoff=0.01
)
vectors = provider.embed(["one"])
self.assertEqual(len(vectors), 1)
self.assertEqual(len(server.requests), 2)
def test_client_error_is_not_retried(self):
with support.StubServer(self.responder_factory(status_plan=[400])) as server:
provider = embeddings.OmniRouteProvider(
api_url=server.url, api_key="k", model="m", dim=8, backoff=0.01
)
with self.assertRaises(embeddings.EmbeddingError):
provider.embed(["one"])
self.assertEqual(len(server.requests), 1)
def test_missing_key_is_refused_before_any_call(self):
with self.assertRaises(embeddings.EmbeddingError):
embeddings.OmniRouteProvider(api_url="http://127.0.0.1:1", api_key="", model="m", dim=8)
if __name__ == "__main__":
unittest.main()

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"""Freshness audit: stale documents, index drift and contract drift."""
from __future__ import annotations
import datetime
import unittest
from corpus import freshness, pipeline, store as store_module
from corpus.tests import support
class AuditTest(unittest.TestCase):
def setUp(self):
self.root = support.temp_repo()
self.cfg = support.make_config(self.root)
self.addCleanup(_cleanup, self.root)
with support.fake_qdrant(self.cfg):
pipeline.run(self.cfg, mode="rebuild")
def audit(self, today=None, cfg=None):
with support.fake_qdrant(cfg or self.cfg):
return freshness.audit(cfg or self.cfg, today=today)
def test_clean_soon_after_rebuild(self):
report = self.audit(today=datetime.date(2026, 10, 5))
self.assertTrue(report["ok"], report["findings"])
self.assertEqual(report["corpus"]["documents"], 3)
def test_reports_stale_documents(self):
report = self.audit(today=datetime.date(2027, 10, 5))
stale = {row["source_path"] for row in report["findings"]["stale"]}
self.assertEqual(
stale,
{
"content/ai/ai-overview.md",
"content/edge/l3/edge-zigbee.md",
"content/platform/l3/platform-containers.md",
},
)
self.assertFalse(report["ok"])
text = freshness.render_text(report)
self.assertIn("STALE", text)
self.assertIn("platform-containers", text)
def test_warns_before_a_document_goes_stale(self):
# platform-containers was verified 2026-08-01. Horizon 180 days, warning
# 30, so on 2027-01-20 it is 172 days old: due soon, not yet stale.
report = self.audit(today=datetime.date(2027, 1, 20))
due = {row["source_path"] for row in report["findings"]["due_soon"]}
self.assertEqual(due, {"content/platform/l3/platform-containers.md"})
self.assertEqual(report["findings"]["stale"], [])
self.assertTrue(report["ok"])
def test_reports_index_drift_when_a_source_changes(self):
support.write_doc(
self.root,
"ai/ai-overview.md",
id="ai-overview", area="ai", level=1, title="Agent Systems",
claim="Edited but not re-indexed.", confidence="running",
last_verified="2026-10-05", tags="agents", body="New words for the corpus.",
)
report = self.audit(today=datetime.date(2026, 10, 6))
problems = [row["problem"] for row in report["findings"]["index_drift"]]
self.assertIn("source changed since it was embedded", problems)
self.assertFalse(report["ok"])
def test_reports_contract_drift_when_the_model_changes(self):
other = support.make_config(self.root, model="different-model")
report = self.audit(today=datetime.date(2026, 10, 5), cfg=other)
self.assertTrue(report["findings"]["contract_drift"])
def test_report_is_json_serialisable(self):
import json
report = self.audit(today=datetime.date(2026, 10, 5))
json.dumps(report)
class AlertTest(unittest.TestCase):
def test_no_url_means_no_alert(self):
report = {"ok": True, "findings": {"stale": []}, "corpus": {}, "horizon": {}, "summary": {}}
self.assertFalse(freshness.alert(report, ""))
def test_posts_to_apprise(self):
report = {
"generated_at": "2026-10-26T00:00:00Z",
"today": "2026-10-26",
"horizon": {"max_age_days": 180, "warn_days": 30},
"corpus": {
"documents": 1, "chunks": 1, "sources": 1, "collection": "c",
"collection_points": 1, "provider": "p", "model": "m", "dim": 8,
"chunker_version": "v",
},
"findings": {"stale": [], "due_soon": [], "index_drift": [], "contract_drift": []},
"summary": {"stale": 0},
"ok": True,
}
with support.StubServer(lambda method, path, body: (200, "ok")) as server:
self.assertTrue(freshness.alert(report, server.url))
self.assertIn("title", server.requests[0]["body"])
def _cleanup(root):
import shutil
shutil.rmtree(root, ignore_errors=True)
if __name__ == "__main__":
unittest.main()

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"""Payload rules: every field present, enums enforced, missing field fails the run."""
from __future__ import annotations
import unittest
from corpus import chunker, loader, payloads
from corpus.config import Config
from corpus.pipeline import chunk_id
from corpus.tests import support
def first_payload():
cfg = Config.from_env(root=support.REPO_ROOT, provider="local-hashing", dim=32)
doc = loader.load_documents(cfg)[0]
chunk = chunker.chunk_document(doc.body)[0]
payload = payloads.build_payload(doc, chunk, cfg)
payload["id"] = chunk_id(doc.path, chunk.index, doc.digest)
return cfg, doc, payload
class BuildTest(unittest.TestCase):
def test_payload_has_every_required_field(self):
cfg, doc, payload = first_payload()
payloads.validate_payload(payload)
for field in payloads.REQUIRED_FIELDS:
self.assertIn(field, payload)
self.assertEqual(payload["area"], doc.area)
self.assertEqual(payload["confidence"], doc.confidence)
self.assertEqual(payload["last_verified"], doc.last_verified)
self.assertEqual(payload["embedding_model"], cfg.model)
self.assertEqual(payload["embedding_dim"], cfg.dim)
def test_every_real_document_produces_valid_payloads(self):
cfg = Config.from_env(root=support.REPO_ROOT, provider="local-hashing", dim=32)
for doc in loader.load_documents(cfg):
for chunk in chunker.chunk_document(doc.body):
payload = payloads.build_payload(doc, chunk, cfg)
payload["id"] = chunk_id(doc.path, chunk.index, doc.digest)
payloads.validate_payload(payload, where=doc.path)
def test_source_type_is_doc_by_default(self):
_cfg, doc, payload = first_payload()
self.assertEqual(payload["source_type"], "doc")
def test_source_type_from_tags(self):
doc = loader.Document(
path="content/ai/adr-x.md", source_id="adr-x", area="ai", level=3, title="ADR",
claim="c", confidence="delivered", period="2024", tech=[], metrics=[], artefacts=[],
public=True, last_verified="2026-01-01", tags=["adr"], body="", digest="0" * 64,
url="/ai/a/adr-x/",
)
self.assertEqual(payloads.source_type_for(doc), "adr")
class ValidationTest(unittest.TestCase):
def test_missing_field_raises(self):
_cfg, _doc, payload = first_payload()
for field in ("area", "level", "source_type", "confidence", "period", "artefacts", "last_verified", "text"):
broken = dict(payload)
del broken[field]
with self.assertRaises(payloads.PayloadError, msg=f"missing {field} passed validation"):
payloads.validate_payload(broken)
def test_missing_provenance_field_raises(self):
_cfg, _doc, payload = first_payload()
for field in payloads.PROVENANCE_FIELDS:
broken = dict(payload)
del broken[field]
with self.assertRaises(payloads.PayloadError, msg=f"missing {field} passed validation"):
payloads.validate_payload(broken)
def test_bad_enums_raise(self):
_cfg, _doc, payload = first_payload()
for field, value in (
("area", "sales"),
("level", 4),
("source_type", "blog"),
("confidence", "vibes"),
):
broken = dict(payload)
broken[field] = value
with self.assertRaises(payloads.PayloadError, msg=f"{field}={value!r} passed validation"):
payloads.validate_payload(broken)
def test_empty_text_raises(self):
_cfg, _doc, payload = first_payload()
payload["text"] = " "
with self.assertRaises(payloads.PayloadError):
payloads.validate_payload(payload)
def test_bad_date_raises(self):
_cfg, _doc, payload = first_payload()
payload["last_verified"] = "last october"
with self.assertRaises(payloads.PayloadError):
payloads.validate_payload(payload)
if __name__ == "__main__":
unittest.main()

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"""The pipeline: deterministic ids, no duplicate vectors on re-run, incremental mode,
incomplete payloads never reach the store (docs/phase-4-corpus.md DoD 1-6)."""
from __future__ import annotations
import os
import unittest
from unittest import mock
from corpus import chunker, loader, pipeline, store as store_module
from corpus.tests import support
class RebuildTest(unittest.TestCase):
def setUp(self):
self.root = support.temp_repo()
self.cfg = support.make_config(self.root)
self.addCleanup(_cleanup, self.root)
# One fake for the whole test: a second run must see the first run's points.
self.fake = support.FakeQdrant(self.cfg.dim)
def run_pipeline(self, mode="sync", cfg=None):
cfg = cfg or self.cfg
with support.fake_qdrant(cfg, self.fake):
return pipeline.run(cfg, mode=mode)
def test_rebuild_from_empty_volume(self):
stats = self.run_pipeline("rebuild")
self.assertEqual(stats.documents, 3)
self.assertEqual(stats.added, 3)
self.assertGreater(stats.store_chunks, 0)
self.assertEqual(stats.store_chunks, stats.collection_points)
self.assertEqual(stats.collection_points, len(self.fake.collections[self.cfg.collection]))
self.assertGreater(stats.embed_calls, 0)
def test_running_twice_does_not_duplicate_vectors(self):
first = self.run_pipeline("rebuild")
points_after_first = len(self.fake.collections[self.cfg.collection])
second = self.run_pipeline("sync")
points_after_second = len(self.fake.collections[self.cfg.collection])
self.assertEqual(points_after_first, points_after_second)
self.assertEqual(first.store_chunks, second.store_chunks)
self.assertEqual(second.added, 0)
self.assertEqual(second.updated, 0)
self.assertEqual(second.chunks_written, 0)
def test_incremental_reembeds_only_changed_documents(self):
self.run_pipeline("rebuild")
# Nothing changed: no embedding calls at all.
unchanged = self.run_pipeline("sync")
self.assertEqual(unchanged.embed_calls, 0)
self.assertEqual(unchanged.texts_embedded, 0)
# One document changes: exactly one embedding call, for that document.
support.write_doc(
self.root,
"edge/l3/edge-zigbee.md",
id="edge-zigbee", area="edge", level=3, title="Low Power Mesh Sensing",
claim="A constrained mesh that survives power loss.", confidence="delivered",
last_verified="2026-09-15", tags="zigbee, mesh",
body="The mesh now also reports battery voltage on every node.",
)
changed = self.run_pipeline("sync")
self.assertEqual(changed.added + changed.updated, 1)
self.assertEqual(changed.embed_calls, 1)
expected_chunks = len(
chunker.chunk_document(
loader.load_documents(support.make_config(self.root))[1].body
)
)
self.assertEqual(changed.texts_embedded, expected_chunks)
self.assertEqual(changed.unchanged, 2)
def test_removed_document_is_removed_from_both_indexes(self):
self.run_pipeline("rebuild")
before = self.run_pipeline("sync").store_chunks
os.remove(os.path.join(self.root, "content", "platform", "l3", "platform-containers.md"))
after = self.run_pipeline("sync")
self.assertEqual(after.removed, 1)
self.assertLess(after.store_chunks, before)
self.assertEqual(after.store_chunks, after.collection_points)
with store_module.Store(self.cfg.db_path) as store:
self.assertEqual(store.keyword_search("endpoint latency loss"), [])
def test_ids_are_deterministic(self):
first = pipeline.chunk_id("content/ai/ai-overview.md", 0, "a" * 64)
second = pipeline.chunk_id("content/ai/ai-overview.md", 0, "a" * 64)
other = pipeline.chunk_id("content/ai/ai-overview.md", 1, "a" * 64)
self.assertEqual(first, second)
self.assertNotEqual(first, other)
self.assertEqual(len(first), 36)
def test_invalid_payload_aborts_before_any_write(self):
real_build = pipeline.payloads.build_payload
def broken(doc, chunk, cfg):
payload = real_build(doc, chunk, cfg)
payload.pop("confidence")
return payload
with mock.patch("corpus.payloads.build_payload", side_effect=broken):
with self.assertRaises(pipeline.PipelineError):
self.run_pipeline("rebuild")
self.assertEqual(self.fake.collections.get(self.cfg.collection, {}), {})
def test_provider_change_is_refused_without_rebuild(self):
self.run_pipeline("rebuild")
other = support.make_config(self.root, model="local-hashing-v2")
with self.assertRaises(pipeline.PipelineError) as caught:
self.run_pipeline("sync", cfg=other)
self.assertIn("governed migration", str(caught.exception))
def test_contract_is_recorded(self):
self.run_pipeline("rebuild")
with store_module.Store(self.cfg.db_path) as store:
contract = store.get_meta("collection")
self.assertEqual(contract["model"], self.cfg.model)
self.assertEqual(contract["dim"], self.cfg.dim)
self.assertEqual(contract["chunker_version"], self.cfg.chunker_version)
self.assertTrue(os.path.exists(self.cfg.meta_path))
class VerifyTest(unittest.TestCase):
def setUp(self):
self.root = support.temp_repo()
self.cfg = support.make_config(self.root)
self.addCleanup(_cleanup, self.root)
def test_clean_after_rebuild(self):
with support.fake_qdrant(self.cfg):
pipeline.run(self.cfg, mode="rebuild")
self.assertEqual(pipeline.verify(self.cfg), [])
def test_detects_source_changed_without_sync(self):
with support.fake_qdrant(self.cfg):
pipeline.run(self.cfg, mode="rebuild")
support.write_doc(
self.root,
"ai/ai-overview.md",
id="ai-overview", area="ai", level=1, title="Agent Systems",
claim="Changed.", confidence="running", last_verified="2026-10-02",
tags="agents", body="A different body entirely.",
)
problems = pipeline.verify(self.cfg)
self.assertTrue(any("changed since it was embedded" in problem for problem in problems), problems)
def test_detects_missing_document(self):
with support.fake_qdrant(self.cfg):
pipeline.run(self.cfg, mode="rebuild")
os.remove(os.path.join(self.root, "content", "ai", "ai-overview.md"))
problems = pipeline.verify(self.cfg)
self.assertTrue(any("no longer in content/" in problem for problem in problems), problems)
class AreaFilterTest(unittest.TestCase):
def setUp(self):
self.root = support.temp_repo()
self.cfg = support.make_config(self.root)
self.addCleanup(_cleanup, self.root)
def test_filter_narrows_the_result_set(self):
with support.fake_qdrant(self.cfg):
pipeline.run(self.cfg, mode="rebuild")
result = pipeline.demo_area_filter(self.cfg, "container estate limits", area="edge")
self.assertTrue(result["unfiltered"])
self.assertTrue(result["filtered"])
self.assertEqual({row["area"] for row in result["filtered"]}, {"edge"})
self.assertGreater(len({row["area"] for row in result["unfiltered"]}), 1)
def _cleanup(root):
import shutil
shutil.rmtree(root, ignore_errors=True)
if __name__ == "__main__":
unittest.main()

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"""Qdrant client: the wire shape the pipeline depends on, and the fallback route."""
from __future__ import annotations
import unittest
from corpus import qdrant as qdrant_module
from corpus.tests import support
class ClientTest(unittest.TestCase):
def test_create_collection_sends_size_and_distance(self):
with support.StubServer(lambda method, path, body: (200, {"result": True})) as server:
client = qdrant_module.Qdrant(server.url, attempts=1)
client.create_collection("resume_corpus", 1536)
request = server.requests[0]
self.assertEqual(request["method"], "PUT")
self.assertEqual(request["path"], "/collections/resume_corpus")
body = request["body"]
self.assertEqual(body["vectors"]["size"], 1536)
self.assertEqual(body["vectors"]["distance"], "Cosine")
# A memory-bound host: payloads and the HNSW graph live on disk.
self.assertTrue(body["on_disk_payload"])
self.assertTrue(body["hnsw_config"]["on_disk"])
self.assertFalse(body["vectors"]["on_disk"])
self.assertGreater(body["optimizers_config"]["memmap_threshold"], 0)
def test_upsert_waits_and_batches(self):
with support.StubServer(lambda method, path, body: (200, {"result": {}})) as server:
client = qdrant_module.Qdrant(server.url, attempts=1)
points = [{"id": f"id-{index}", "vector": [0.0], "payload": {}} for index in range(5)]
sent = client.upsert("c", points, batch=2)
self.assertEqual(sent, 5)
self.assertEqual(len(server.requests), 3)
self.assertTrue(all("wait=true" in request["path"] for request in server.requests))
self.assertEqual(sum(len(r["body"]["points"]) for r in server.requests), 5)
def test_delete_by_source_sends_a_source_filter(self):
with support.StubServer(lambda method, path, body: (200, {"result": {}})) as server:
client = qdrant_module.Qdrant(server.url, attempts=1)
client.delete_by_source("c", "content/edge/l3/edge-zigbee.md")
request = server.requests[0]
self.assertEqual(request["method"], "POST")
self.assertIn("/collections/c/points/delete", request["path"])
self.assertEqual(
request["body"],
{"filter": {"must": [{"key": "source_path", "match": {"value": "content/edge/l3/edge-zigbee.md"}}]}},
)
def test_search_filters_before_searching(self):
responder = lambda method, path, body: (200, {"result": {"points": []}}) # noqa: E731
with support.StubServer(responder) as server:
client = qdrant_module.Qdrant(server.url, attempts=1)
client.search("c", [0.1, 0.2], limit=3, area="edge", confidence="delivered")
body = server.requests[0]["body"]
self.assertEqual(server.requests[0]["path"], "/collections/c/points/query")
self.assertEqual(body["limit"], 3)
self.assertEqual(
body["filter"],
{
"must": [
{"key": "area", "match": {"value": "edge"}},
{"key": "confidence", "match": {"value": "delivered"}},
]
},
)
def test_search_falls_back_to_the_legacy_route(self):
def responder(method, path, body):
if path.endswith("/points/query"):
return 404, {"status": "not found"}
return 200, {"result": [{"id": "p1", "score": 0.9, "payload": {"area": "ai"}}]}
with support.StubServer(responder) as server:
client = qdrant_module.Qdrant(server.url, attempts=1)
hits = client.search("c", [0.1])
self.assertEqual([hit["id"] for hit in hits], ["p1"])
self.assertEqual([r["path"] for r in server.requests][-1], "/collections/c/points/search")
def test_point_count_is_exact(self):
responder = lambda method, path, body: (200, {"result": {"count": 42}}) # noqa: E731
with support.StubServer(responder) as server:
client = qdrant_module.Qdrant(server.url, attempts=1)
self.assertEqual(client.point_count("c"), 42)
self.assertEqual(server.requests[0]["body"], {"exact": True})
def test_health_is_false_when_unreachable(self):
client = qdrant_module.Qdrant("http://127.0.0.1:1", timeout=0.2, attempts=1)
self.assertFalse(client.health())
def test_upsert_retries_a_server_error(self):
plan = [500, 200]
def responder(method, path, body):
return plan.pop(0), {"result": {}}
with support.StubServer(responder) as server:
client = qdrant_module.Qdrant(server.url, attempts=2)
client.upsert("c", [{"id": "x", "vector": [0.0], "payload": {}}])
self.assertEqual(len(server.requests), 2)
if __name__ == "__main__":
unittest.main()

106
corpus/tests/test_store.py Normal file
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"""FTS5 chunk index, manifest and meta: the keyword half of hybrid retrieval."""
from __future__ import annotations
import unittest
from corpus import chunker, loader, payloads, store as store_module
from corpus.config import Config
from corpus.pipeline import chunk_id
from corpus.tests import support
def build_store(path, cfg, documents):
store = store_module.Store(path)
for doc in documents:
payload_list = []
for chunk in chunker.chunk_document(doc.body):
payload = payloads.build_payload(doc, chunk, cfg)
payload["id"] = chunk_id(doc.path, chunk.index, doc.digest)
payload_list.append(payload)
store.write_document(doc, payload_list)
return store
class StoreTest(unittest.TestCase):
def setUp(self):
self.root = support.temp_repo()
self.cfg = support.make_config(self.root)
self.addCleanup(_cleanup, self.root)
self.documents = loader.load_documents(self.cfg)
self.store = build_store(self.cfg.db_path, self.cfg, self.documents)
self.addCleanup(self.store.close)
def test_counts_match_the_documents(self):
self.assertEqual(self.store.source_count(), len(self.documents))
expected = sum(len(chunker.chunk_document(doc.body)) for doc in self.documents)
self.assertEqual(self.store.chunk_count(), expected)
def test_keyword_search_finds_exact_tokens(self):
hits = self.store.keyword_search("zigbee")
self.assertTrue(hits, "no hit for a token that is literally in the document")
self.assertEqual(hits[0]["area"], "edge")
def test_keyword_search_respects_area_filter(self):
all_hits = self.store.keyword_search("latency")
edge_hits = self.store.keyword_search("latency", area="edge")
self.assertTrue(all_hits)
for hit in edge_hits:
self.assertEqual(hit["area"], "edge")
def test_keyword_search_returns_snippets_and_scores(self):
hits = self.store.keyword_search("fence")
self.assertTrue(hits)
self.assertIn("score", hits[0])
self.assertIn("snippet", hits[0])
def test_operator_injection_is_not_honoured(self):
# FTS5 operators must be treated as words, never as query syntax.
hits = self.store.keyword_search('edge OR "NEAR"(a b)')
self.assertIsInstance(hits, list)
def test_manifest_records_the_content_hash(self):
manifest = self.store.manifest()
self.assertEqual(set(manifest), {doc.path for doc in self.documents})
for doc in self.documents:
self.assertEqual(manifest[doc.path]["content_hash"], doc.digest)
self.assertGreater(manifest[doc.path]["chunk_count"], 0)
def test_delete_removes_rows_from_both_tables(self):
target = self.documents[1]
self.store.delete_document(target.path)
self.assertNotIn(target.path, self.store.manifest())
for row in self.store.chunk_payloads():
self.assertNotEqual(row["source_path"], target.path)
def test_meta_roundtrip(self):
self.store.set_meta("collection", {"model": "x", "dim": 8})
self.assertEqual(self.store.get_meta("collection"), {"model": "x", "dim": 8})
self.assertIn("collection", self.store.meta())
def test_payloads_survive_the_roundtrip(self):
rows = self.store.chunk_payloads()
self.assertTrue(rows)
for row in rows:
payloads.validate_payload(row)
class MatchExpressionTest(unittest.TestCase):
def test_terms_become_prefix_queries(self):
self.assertEqual(store_module.match_expression("Zigbee mesh"), '"zigbee"* AND "mesh"*')
def test_empty_query_is_empty(self):
self.assertEqual(store_module.match_expression(" "), "")
def test_punctuation_is_dropped(self):
self.assertEqual(store_module.match_expression("-edge- LoRaWAN, 2023"), '"edge"* AND "lorawan"* AND "2023"*')
def _cleanup(root):
import shutil
shutil.rmtree(root, ignore_errors=True)
if __name__ == "__main__":
unittest.main()

29
deploy/Dockerfile Normal file
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@@ -0,0 +1,29 @@
# Runtime image for the Mode A site (docs/phase-2-static-site.md §2.1).
#
# The image contains no toolchain: `make build dist` on the build host produces
# bin/resume-server and dist/, and the image just carries them. That keeps the
# runtime offline and small, and it means the deployed bytes are the bytes that
# were built and tested, not a second compile.
#
# Build context is the repository root:
#
# make build dist
# docker build -f deploy/Dockerfile -t ai-resume:local .
#
# Phase 3 (§3.1) owns the release directory, the compose invocation and the
# rollback; this file only describes the process that runs.
FROM gcr.io/distroless/static-debian12:nonroot
COPY bin/resume-server /usr/bin/resume-server
COPY dist/ /srv/site
# 0.0.0.0 is inside the container's own network namespace only. The host
# publishes the port on 127.0.0.1 (see deploy/compose.yml), which is what keeps
# Caddy the only public listener.
ENV RESUME_ADDR=0.0.0.0:8096 \
RESUME_DIST=/srv/site
EXPOSE 8096
USER nonroot:nonroot
ENTRYPOINT ["/usr/bin/resume-server"]

71
deploy/compose.yml Normal file
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@@ -0,0 +1,71 @@
# Container shape for the Mode A site (docs/phase-2-static-site.md §2.1).
#
# Values are fixed by the specification and must not drift:
# mem_limit == memswap_limit == 120m so the app cannot eat the host's swap
# ports published on 127.0.0.1 only Caddy on .35 is the only public listener
# logging capped one bad day cannot fill the disk
#
# Run it (build the artefacts first — Phase 3's deploy.sh does both):
#
# make build dist
# docker compose -f deploy/compose.yml up -d
#
# `curl http://127.0.0.1:8096/healthz` must answer with static ready.
# The compose project name is deliberately not `ai-resume`: /home/sam/deployment/
# ai-resume on .13 already runs a compose project by that name (backend + db on
# the ai-mesh network). Sharing the name would make this site's containers show
# up as orphans in that project and vice versa, so anything running `compose
# down --remove-orphans` under either project could stop the other. The image
# tag stays ai-resume:local (Phase 3 fixes it); only the project namespace is
# distinct.
name: ai-resume-site
services:
resume:
image: ai-resume:local
build:
context: ..
dockerfile: deploy/Dockerfile
restart: unless-stopped
mem_limit: 120m
memswap_limit: 120m
# Published on this host's LAN address, not loopback. Caddy lives on .35 and
# proxies to 192.168.20.13:8096, exactly as it does for offsite (:8095),
# photo-filter (:8092), garage-ui (:3909) and prefect (:4200). The port is
# reachable inside the house only; Caddy on .35 remains the sole public
# listener, which is the intent of AGENTS.md section 4.
ports:
- "192.168.20.13:8096:8096"
read_only: true
security_opt:
- no-new-privileges:true
logging:
driver: json-file
options:
max-size: 10m
max-file: "3"
healthcheck:
test: ["CMD", "/usr/bin/resume-server", "--health-check"]
interval: 30s
timeout: 3s
retries: 3
start_period: 5s
# The project network gets an explicit, small subnet. The host's docker daemon
# has every default address pool allocated by other projects ("all predefined
# address pools have been fully subnetted"), so an implicit compose network
# cannot be created. Pinning /28 of RFC1918 space that no other bridge or route
# on .13 uses keeps this deploy independent of the host's pool exhaustion and
# does not touch any other container. Single-service stack: two addresses is
# plenty.
networks:
default:
ipam:
config:
- subnet: 10.89.0.0/28
gateway: 10.89.0.1
# Phase 4/5 add at most three more containers (Qdrant, Prefect worker, query
# service) inside the 4-container and 1.0 GiB budget in AGENTS.md §4.3. Mode A
# needs none of them and must keep working when they are stopped (test A1).

400
deploy/deploy.sh Executable file
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#!/usr/bin/env bash
# deploy.sh — release, deploy and roll back the Mode A site (docs/phase-3-deploy.md §3.1).
#
# ./deploy/deploy.sh build, test, stage a release, flip `current`,
# start the container, gate on /healthz
# ./deploy/deploy.sh --rollback put the previous release back, in one command
# ./deploy/deploy.sh --status what is live, what is previous, is it healthy
# ./deploy/deploy.sh --release-dir D deploy an already-built release directory
# (copied into releases/ first; used by the
# rollback rehearsal below)
#
# Layout on the runtime host — the release directory, not the image tag, is the
# unit that runs, so a rollback cannot publish bytes that were never tested:
#
# /home/sam/deployment/resume/
# releases/<utc-timestamp>-<git-sha>/{bin/resume-server,dist/,release.json}
# current -> releases/<…> what the container is running
# previous -> releases/<…> what --rollback restores
#
# A deploy is: build → test → stage → flip `current` → build the image from that
# release directory → `compose up -d` → `curl /healthz` until ready. Any failure
# after the flip puts `current` and the container back on the previous release
# and exits non-zero. No step is a model decision (AGENTS.md §4.6); this script
# is the only thing that publishes.
#
# Rollback rehearsal (Definition of Done: "a deliberate bad build proves rollback"):
#
# good=$(readlink ~/deployment/resume/current)
# bad=$(mktemp -d) && mkdir -p "$bad/bin" "$bad/dist"
# printf '#!/bin/sh\nexit 1\n' >"$bad/bin/resume-server" # binary that cannot start
# chmod +x "$bad/bin/resume-server"
# cp -a ~/deployment/resume/"$good"/dist/. "$bad/dist/" # valid pages
# ./deploy/deploy.sh --release-dir "$bad" # expect exit 1
# readlink ~/deployment/resume/current # still $good
#
# Run it on the runtime host (.13) from a checkout of this repository. It never
# reads content/ and never calls a model.
set -Eeuo pipefail
APP=ai-resume # image tag (fixed by Phase 3 §3.2)
PROJECT=ai-resume-site # compose project; distinct from the older
# /home/sam/deployment/ai-resume project on .13
PORT=8096
HEALTH_PATH=/healthz
# The host address the port is published on. Caddy runs on .35 and proxies to
# this address, so a loopback publish is unreachable from the edge (Sam's
# decision, 2026-10-26, interaction 54aeb1a2). Never 0.0.0.0: the port must not
# be reachable off the LAN. The health gate below follows this address — once the
# port is published on the LAN address the host no longer listens on
# 127.0.0.1:8096, so a loopback probe would refuse and every deploy would roll
# back.
PUBLISH_IP=${RESUME_PUBLISH_IP:-192.168.20.13}
ROOT=${RESUME_DEPLOY_ROOT:-/home/sam/deployment/resume}
RELEASES=$ROOT/releases
CURRENT=$ROOT/current
PREVIOUS=$ROOT/previous
KEEP=${RESUME_KEEP_RELEASES:-5} # release directories kept on disk
HEALTH_TRIES=${RESUME_HEALTH_TRIES:-40} # seconds to wait for /healthz
REPO=$(cd -- "$(dirname -- "${BASH_SOURCE[0]}")/.." && pwd)
COMPOSE=$REPO/deploy/compose.yml
DOCKERFILE=$REPO/deploy/Dockerfile
MODE=deploy
RELEASE_DIR_OVERRIDE=
log() { printf '%s %s
' "$(date -u +%H:%M:%SZ)" "$*" >&2; }
warn() { printf '%s WARN %s
' "$(date -u +%H:%M:%SZ)" "$*" >&2; }
die() { printf '%s FAIL %s
' "$(date -u +%H:%M:%SZ)" "$*" >&2; exit 1; }
on_err() {
local rc=$? line=$1
printf '%s FAIL line %s exited %s
' "$(date -u +%H:%M:%SZ)" "$line" "$rc" >&2
exit "$rc"
}
trap 'on_err $LINENO' ERR
usage() {
sed -n '2,13p' "${BASH_SOURCE[0]}" | sed 's/^# \{0,1\}//'
exit "${1:-0}"
}
while [ $# -gt 0 ]; do
case $1 in
--rollback) MODE=rollback ;;
--status) MODE=status ;;
--release-dir) shift; RELEASE_DIR_OVERRIDE=${1:-} ;;
--keep) shift; KEEP=${1:-5} ;;
-h|--help) usage 0 ;;
*) printf 'unknown argument: %s
' "$1" >&2; usage 1 ;;
esac
shift
done
# ---------------------------------------------------------------- helpers ---
compose() { docker compose -f "$COMPOSE" -p "$PROJECT" "$@"; }
current_target() { readlink -- "$CURRENT" 2>/dev/null || true; }
previous_target() { readlink -- "$PREVIOUS" 2>/dev/null || true; }
# flip <relative-path-under-ROOT> — replace the symlink atomically. A reader sees
# either the old release or the new one, never a missing link.
flip() {
ln -sfn "$1" "$CURRENT.tmp"
mv -Tf "$CURRENT.tmp" "$CURRENT"
}
release_name_of() { basename "$1"; }
# The tag deploy/compose.yml runs. Releases are distinguished by content, not by
# tag, so compose.yml keeps its fixed image name.
image_name() { printf '%s:local' "$APP"; }
# The port must be ours, or free. 8095 belongs to offsite.lab.
check_port() {
local listening
listening=$(ss -ltnH "sport = :$PORT" 2>/dev/null || true)
if [ -n "$listening" ]; then
if [ -z "$(compose ps -q resume 2>/dev/null)" ]; then
printf '%s
' "$listening" >&2
die "port $PORT is held by something that is not $APP; pick the next free port and record it in reference/stack.md"
fi
log "port $PORT is held by our own container (expected on a redeploy)"
fi
}
check_tools() {
command -v docker >/dev/null || die "docker is not on PATH"
docker compose version >/dev/null 2>&1 || die "docker compose v2 is not available"
command -v curl >/dev/null || die "curl is not on PATH"
command -v ss >/dev/null || die "ss (iproute2) is not on PATH"
mkdir -p "$RELEASES"
}
memory_report() {
local avail
avail=$(awk '/^MemAvailable:/ {print int($2/1024)}' /proc/meminfo)
log "host memory available: ${avail} MiB — the container cap is 120 MiB; .13 is memory-tight by design"
if [ "${avail:-0}" -lt 512 ]; then
warn "under 512 MiB available; the Go build may push this host into swap"
fi
}
# --------------------------------------------------------------- build ------
build_and_test() {
log "build + test in $REPO"
(
cd "$REPO"
rm -rf dist
if command -v nix >/dev/null 2>&1; then
nix develop . -c bash -c 'make validate test dist'
elif command -v templ >/dev/null 2>&1 && command -v go >/dev/null 2>&1; then
make validate test dist
else
echo "no toolchain: neither 'nix develop' nor templ+go is available" >&2
exit 1
fi
)
[ -f "$REPO/bin/resume-server" ] || die "build produced no bin/resume-server"
[ -f "$REPO/dist/index.html" ] || die "build produced no dist/index.html"
log "build ok: $(jq -r '"\(.pages) pages, \(.documents) documents, \(.fragments) fragments"' "$REPO/dist/build.json" 2>/dev/null || echo 'dist/build.json unreadable')"
}
# -------------------------------------------------------------- release -----
write_manifest() {
local dir=$1 sha dirty
sha=$(git -C "$REPO" rev-parse HEAD 2>/dev/null || echo unknown)
dirty=false
git -C "$REPO" diff --quiet 2>/dev/null || dirty=true
cat >"$dir/release.json" <<JSON
{
"release": "$(basename "$dir")",
"image": "$(image_name)",
"git_sha": "$sha",
"git_dirty": $dirty,
"built_at": "$(date -u +%Y-%m-%dT%H:%M:%SZ)",
"host": "$(hostname)",
"pages": $(jq -r '.pages // 0' "$REPO/dist/build.json" 2>/dev/null || echo 0),
"documents": $(jq -r '.documents // 0' "$REPO/dist/build.json" 2>/dev/null || echo 0)
}
JSON
}
stage_release() {
local name dest sha
sha=$(git -C "$REPO" rev-parse HEAD 2>/dev/null || echo unknown)
name=$(date -u +%Y%m%dT%H%M%SZ)-${sha:0:7}
dest=$RELEASES/$name
mkdir -p "$dest/bin" "$dest/dist"
install -m 0755 "$REPO/bin/resume-server" "$dest/bin/resume-server"
cp -a "$REPO/dist/." "$dest/dist/"
write_manifest "$dest"
printf '%s
' "$dest"
}
# Copy an externally built release into releases/ so that every deployable
# directory lives under exactly one root and `current` never dangles.
adopt_release() {
local src dest
src=$(cd "$1" && pwd)
[ -f "$src/bin/resume-server" ] || die "$src has no bin/resume-server"
[ -f "$src/dist/index.html" ] || die "$src has no dist/index.html"
dest=$RELEASES/$(date -u +%Y%m%dT%H%M%SZ)-manual
mkdir -p "$dest"
cp -a "$src/." "$dest/"
write_manifest "$dest"
printf '%s
' "$dest"
}
# ---------------------------------------------------------------- image -----
build_image() {
log "docker build from $1"
docker build --file "$DOCKERFILE" --tag "$(image_name)" "$1" 1>&2
}
# ------------------------------------------------------------------ run -----
start() {
log "docker compose up -d ($1)"
compose up -d --no-build --force-recreate 1>&2
}
wait_healthy() {
local i body
for i in $(seq 1 "$HEALTH_TRIES"); do
if body=$(curl -fsS --max-time 3 "http://$PUBLISH_IP:$PORT$HEALTH_PATH" 2>/dev/null); then
if printf '%s' "$body" | grep -Eq '"status"[[:space:]]*:[[:space:]]*"ok"'; then
log "health gate passed after ${i}s"
return 0
fi
fi
sleep 1
done
warn "health gate failed after ${HEALTH_TRIES}s; last response:"
curl -sS --max-time 3 "http://$PUBLISH_IP:$PORT$HEALTH_PATH" >&2 || true
compose logs --tail 40 resume >&2 || true
return 1
}
# Definition-of-Done evidence, checked on every deploy instead of by hand:
# mem_limit == memswap_limit == the value declared in compose.yml, and the port
# published on $PUBLISH_IP only (the LAN address Caddy proxies to — never
# 0.0.0.0 and no longer loopback).
assert_shape() {
local cid mem swap ip declared
declared=$(awk '/^[[:space:]]*mem_limit:/ {gsub(/[^0-9]/, "", $2); print $2}' "$COMPOSE")
cid=$(compose ps -q resume 2>/dev/null || true)
[ -n "$cid" ] || die "no container for service 'resume'"
mem=$(docker inspect -f '{{.HostConfig.Memory}}' "$cid")
swap=$(docker inspect -f '{{.HostConfig.MemorySwap}}' "$cid")
[ "$mem" = "$swap" ] || die "mem_limit ($mem) != memswap_limit ($swap)"
[ "$mem" = "$((declared * 1024 * 1024))" ] || die "mem_limit is $mem, expected ${declared} MiB"
ip=$(docker inspect -f '{{range $p, $b := .HostConfig.PortBindings}}{{range $b}}{{.HostIp}} {{end}}{{end}}' "$cid" | xargs)
[ "$ip" = "$PUBLISH_IP" ] || die "published on '${ip}', expected $PUBLISH_IP only"
log "shape ok: mem_limit == memswap_limit == ${declared} MiB, published on $PUBLISH_IP:$PORT"
}
deploy_release() {
local dir old name
dir=$1
name=$(release_name_of "$dir")
[ -d "$dir" ] || die "release directory does not exist: $dir"
[ -f "$dir/bin/resume-server" ] || die "$dir has no bin/resume-server"
[ -f "$dir/dist/index.html" ] || die "$dir has no dist/index.html — a build that cannot serve a landing page is not a release"
build_image "$dir"
old=$(current_target)
# The flip happens before the container change, so `current` always describes
# what is being started. The release it replaced becomes `previous`.
flip "releases/$name"
if [ -n "$old" ] && [ "$old" != "releases/$name" ]; then
ln -sfn "$old" "$PREVIOUS"
log "previous -> $old"
fi
if start "$name" && wait_healthy && assert_shape; then
printf '%s
' "$(date -u +%Y-%m-%dT%H:%M:%SZ) deploy releases/$name ok" >>"$ROOT/deploy.log"
log "deployed $name"
prune
return 0
fi
if [ -z "$old" ] || [ "$old" = "releases/$name" ]; then
warn "first deploy failed and there is no previous release; leaving the container down"
compose down >&2 || true
rm -f "$CURRENT"
return 1
fi
warn "deploy of $name failed; restoring $old"
flip "$old"
build_image "$ROOT/$old" || die "could not rebuild the previous image; container state unknown"
if start "$old" && wait_healthy && assert_shape; then
printf '%s
' "$(date -u +%Y-%m-%dT%H:%M:%SZ) rolled back to $old after failed deploy of releases/$name" >>"$ROOT/deploy.log"
die "deploy of releases/$name failed; rolled back to $old (the failed release is kept for inspection)"
fi
die "deploy of releases/$name failed AND rollback to $old failed — container state is unknown; run '$0 --status' and read $ROOT/deploy.log"
}
do_rollback() {
local cur prev
cur=$(current_target)
prev=$(previous_target)
[ -n "$prev" ] || die "no previous release recorded; nothing to roll back to"
[ "$prev" != "$cur" ] || die "previous and current are the same release ($cur); nothing to roll back"
[ -d "$ROOT/$prev" ] || die "previous release directory is missing: $ROOT/$prev"
log "rolling back: $cur -> $prev"
build_image "$ROOT/$prev"
flip "$prev"
if start "$prev" && wait_healthy && assert_shape; then
[ -n "$cur" ] && ln -sfn "$cur" "$PREVIOUS"
printf '%s
' "$(date -u +%Y-%m-%dT%H:%M:%SZ) rollback $cur -> $prev" >>"$ROOT/deploy.log"
log "rolled back to $prev; $cur is now previous"
return 0
fi
warn "rollback to $prev failed; returning to $cur"
flip "$cur"
build_image "$ROOT/$cur" || die "could not rebuild $cur; container state unknown"
start "$cur" && wait_healthy || die "return to $cur failed; container state unknown"
die "rollback to $prev failed; $cur was restored"
}
# Keep the newest KEEP releases plus current and previous, nothing else.
prune() {
local cur prev name dir
cur=$(current_target); prev=$(previous_target)
for dir in $(ls -1dt "$RELEASES"/*/ 2>/dev/null | tail -n +"$((KEEP + 1))"); do
name="releases/$(basename "$dir")"
if [ "$name" = "$cur" ] || [ "$name" = "$prev" ]; then continue; fi
rm -rf -- "$dir"
log "pruned $name"
done
}
do_status() {
local cur prev body
cur=$(current_target); prev=$(previous_target)
body=$(curl -fsS --max-time 3 "http://$PUBLISH_IP:$PORT$HEALTH_PATH" 2>/dev/null || true)
printf 'root %s
' "$ROOT"
printf 'current %s
' "${cur:-<none>}"
printf 'previous %s
' "${prev:-<none>}"
if [ -n "$body" ]; then
printf 'health %s
' "$(printf '%s' "$body" | jq -c '{status, pages: .static.pages, search: .search.ready}' 2>/dev/null || printf '%s' "$body")"
else
printf 'health unreachable
'
fi
compose ps || true
}
# ------------------------------------------------------------------ main ----
case $MODE in
status)
do_status
;;
rollback)
check_tools
do_rollback
;;
deploy)
check_tools
check_port
memory_report
if [ -n "$RELEASE_DIR_OVERRIDE" ]; then
deploy_release "$(adopt_release "$RELEASE_DIR_OVERRIDE")"
else
build_and_test
deploy_release "$(stage_release)"
fi
log "live: $(current_target)"
;;
esac

55
deploy/qdrant.yml Normal file
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@@ -0,0 +1,55 @@
# Qdrant for the corpus (docs/phase-4-corpus.md, reference/stack.md).
#
# mem_limit == memswap_limit == 400m the cap in the memory budget, and the
# container cannot reach host swap
# ports on 127.0.0.1 only Caddy on .35 is the only public listener
# its own compose project stopping or recreating it cannot disturb
# the Mode A site (test A1 still passes:
# the site never calls Qdrant)
#
# Run on the runtime host (.13):
#
# docker compose -f deploy/qdrant.yml up -d
# curl -s http://127.0.0.1:6333/ # must answer with a version
#
# Then, in the corpus environment (RESUME_QDRANT_URL is already the default):
#
# python3 -m corpus.cli rebuild
#
# Deleting the volume is always recoverable: `rebuild` re-derives every vector
# from content/.
name: ai-resume-index
services:
qdrant:
image: qdrant/qdrant:v1.20.0
container_name: ai-resume-qdrant
restart: unless-stopped
mem_limit: 400m
memswap_limit: 400m
ports:
- "127.0.0.1:6333:6333" # REST — the pipeline and the query service use this
- "127.0.0.1:6334:6334" # gRPC — reserved for the Phase 5 query service
volumes:
- qdrant_storage:/qdrant/storage
environment:
# The corpus is ~1k chunks today and the measurement in
# reference/stack.md records the ceiling. Keep the HNSW defaults; do not
# tune for speed on a memory-bound host.
QDRANT__TELEMETRY_DISABLED: "true"
QDRANT__LOG_LEVEL: "WARN"
logging:
driver: json-file
options:
max-size: 10m
max-file: "3"
healthcheck:
test: ["CMD-SHELL", "bash -c '</dev/tcp/127.0.0.1/6333' || exit 1"]
interval: 30s
timeout: 3s
retries: 3
start_period: 10s
volumes:
qdrant_storage:

64
docs/content-redaction.md Normal file
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@@ -0,0 +1,64 @@
# Content redaction pass — what was excluded and why
**Scope:** every document under `content/`, authored in Phase 1 (issue AUD-131).
**Date of pass:** 2026-10-26.
**Sources read for this pass:** `../resume_ai_system/plan/EVIDENCE-NOTES.md`, `../resume_ai_system/plan/draft-content.md`, `../resume_ai_system/plan/20-content-spine.md`, `../resume_ai_system/old_projects.md`, `resume_2025.docx`, `reference/content-map.md`, `reference/decisions.md`, `reference/stack.md`, and the Obsidian backup notes.
Acceptance test C6 covers this: *no family, address, credential or third-party personal data in pages or corpus.* The list below is what was removed to satisfy it, and the reason for each removal.
## Excluded — personal identifiers
| Item | Where it appeared | Why excluded |
|---|---|---|
| Date of birth | `resume_2025.docx`, Personal Details | Not needed to assess capability. Age is a discriminator and an identity-theft input. |
| Phone number | `resume_2025.docx`, Personal Details | Publishing a mobile exposes it to scrapers. Sam has not confirmed a phone number belongs on the site (`reference/stack.md` open items). |
| Street address / suburb | `resume_2025.docx` ("live in Ivanhoe"), `draft-content.md` | Address-level detail is a safety issue and is irrelevant to the work. "Melbourne based" is the published granularity. |
| Email address | `resume_2025.docx` | Kept off the pages in Phase 1. Contact details are an open decision for Sam; the narrative states the policy rather than the address. |
| Passport and visa status | `resume_2025.docx` | Immigration status is not published. Working rights are a conversation, not a web page. |
| Family names and details | `EVIDENCE-NOTES.md` (school portals, children, household) | Never published. The 2020–2026 period is described as "full-time parent to two children" with no names, ages, schools or locations. |
## Excluded — third-party personal data
| Item | Where it appeared | Why excluded |
|---|---|---|
| Names of individual client contacts, agency colleagues and referees | résumé prose | No third party has consented to being named. Clients are represented by brand only, which was public campaign work. |
| Details identifying minors in a personal anecdote | `EVIDENCE-NOTES.md` interests ("a 2 day hike … with 7 13 year old boys") | Rewritten as "a group of teenagers in tow". A count of identifiable children is not needed to make the point. |
| Names of household members appearing in automation examples | `EVIDENCE-NOTES.md` | Household automation is described by function (lighting, audio, doors) and never by person. |
## Excluded — credentials and infrastructure detail
| Item | Where it appeared | Why excluded |
|---|---|---|
| API keys, tokens, passphrases, secret file names | n/a — never copied | Nothing from `~/.config/environment.d/` was read or quoted. No key material enters the repo, per `AGENTS.md` §4.4. |
| Backup passphrase file path and repository passphrase | Obsidian backup note | Publishing the location of a passphrase file is a hint worth withholding. Backups are described by tier, tool and retention only. |
| Hostnames, internal IP addresses, ports-by-host, public DNS names | `reference/stack.md`, Obsidian notes, live inspection | Internal addressing is not needed to judge the platform work. Hosts are described by role ("the runtime host", "the edge host"). |
| Internal service URLs and dashboards | `EVIDENCE-NOTES.md` (the pipeline dashboard) | Described as "a dashboard built to keep track of it". A private URL on a public page is an invitation. |
| Third-party account identifiers, billing detail | n/a | Not in the sources and not wanted. |
## Excluded — unverifiable or invented content
| Item | Where it appeared | Why excluded |
|---|---|---|
| "42 devices" | recovered site content | Explicitly withdrawn in `draft-content.md`. Not used. |
| "~800ms end-to-end" latency | recovered site content | Explicitly withdrawn. Not used. |
| Invented venues, competition results, swell sizes, distances and restaurants | recovered pursuits content | Marked ❌ SUSPECT and confirmed as generated. Replaced with one honest line per pursuit. |
| "Diajio" | recovered client list | Spelling error for **Diageo**. Corrected, not carried. |
| Screenshot of the BBC National Archive engagement | recovered site content | The work was an internal tool. Described only; no image. |
## Metric discipline
Every numeric `metric` in `content/` is traced to a source line, and the trace is recorded per metric in the completion comment on AUD-131. Two figures were deliberately **not** promoted:
- **Businesses: 3 active, 12 cumulative.** The site says three are running now and twelve adopted it over time. Conflating them would be the easiest inflation available on this site.
- **Where-Woof: deployed, pre-market.** The word "commercial" is not used about it anywhere.
## Two numbers reported, not smoothed
1. **Agencies: 22 published, 23 in the summary material.** The recovered, verified list holds twenty-two agency names. The published count is the count of the list. If a name is missing, the number rises to twenty-three and the note in `content/history/l3/history-client-wall.md` is deleted.
2. **Period start dates.** Where the sources give no start date, the period is anchored on the homelab year (2020) and the page says so. Exact start dates are Sam's to correct.
## Open questions for Sam
- Contact details: email only, or also a phone number? (`about-narrative.md` states the current policy: email, supplied directly.)
- Is a Melbourne suburb to be published? Currently excluded.
- The missing twenty-third agency, if there is one.

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