feat(cog-person-count): release v0.0.1 — signed binaries on GCS, live on cognitum-v0 - #696
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Phase 3 of ADR-103. Cross-compiled aarch64 + x86_64 on ruvultra, signed
with COGNITUM_OWNER_SIGNING_KEY (Ed25519), uploaded to GCS, and live-
installed on the cognitum-v0 Pi 5 alongside cog-pose-estimation.
Real-hardware bench on cognitum-v0:
./cog-person-count-arm health
→ backend=candle-cpu, count=0, confidence=0.49, p95=[0,7]
30 sequential health invocations: 0.276 s → 9.2 ms/invocation cold
Compares to cog-pose-estimation's 8.4 ms — count cog is ~10% slower
because the dual-head (count softmax + confidence sigmoid) does ~2x
the work after the shared encoder.
GCS release artifacts (publicly downloadable, SHA-verified):
arm/cog-person-count-arm 2,168,816 B
sha: 36bc0bb0...0d47b507b3c3
sig: R/00xdzHriyr/2r...JK+a6k71NDg== (Ed25519)
x86_64/cog-person-count-x86_64 2,615,528 B
sha: 76cdd1ec...3923 7392b01db
sig: QB+8cnGSMQmu...ZtTNIQ2rDg== (Ed25519)
arm/cog-person-count-count_v1.safetensors 392,088 B
sha: dacb0551...e6e04ff56d15c3a65a9ff
Live install at /var/lib/cognitum/apps/person-count/ on cognitum-v0
matches the layout of every other installed cog (anomaly-detect,
seizure-detect, pose-estimation): cog-person-count-arm binary,
count_v1.safetensors weights, manifest.json, config.json.
Adds:
* v2/.../cog/artifacts/manifests/{arm,x86_64}/manifest.json — full
ADR-100 schema with all fields filled (sha + sig + size + URL +
build_metadata carrying the v0.0.1 honest training caveats).
* docs/benchmarks/person-count-cog.md — appends "Live appliance
install" and "Signed GCS release artifacts" sections to the
benchmark log.
Honest v0.0.1 caveat still applies (class-1 accuracy 0% on the held-
out tail of the single-session training data) — same data-bound
limit as pose_v1. The shipped artifact is the *vehicle*; production-
quality accuracy follows from multi-room paired data per ADR-103's
v0.2.0 plan + #645.
ruvnet
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May 21, 2026
…al (#697) Phase 4 of ADR-103. Adds the long-running polling loop so the cog's fourth verb (`run`) does real work, completing the ADR-100 runtime contract end-to-end: cog-person-count version → "person-count 0.3.0" cog-person-count manifest → JSON skeleton cog-person-count health → loads weights + 1-shot infer + emit cog-person-count run --config → long-running per-frame emit ← THIS What ships: * src/runtime.rs (new) — `run_loop` polls sensing_url every poll_ms, slides a [56, 20] CSI window, runs InferenceEngine::infer, emits publisher::person_count events. Same shape as cog-pose-estimation::runtime — fetch_frame extracts amplitudes from `snapshot.nodes[0].amplitude[]`, fails open on connect errors with a WARN log rather than crashing. * src/lib.rs — registers the runtime module. * src/main.rs — cmd_run now loads RunConfig from a JSON file, builds the InferenceEngine (with weights if cfg.model_path is set, otherwise auto-discover), emits a run.started event, and hands off to the Tokio multi-thread runtime's block_on(run_loop). Single-node fusion is a no-op for N=1 today; v0.2.0 will append predictions from sibling nodes and call fusion::fuse_confidence_weighted before emit. Verified locally: cargo check -p cog-person-count --no-default-features → clean cargo test -p cog-person-count → 15/15 pass (no regressions) cargo build -p cog-person-count --release → 2.36 MB unchanged ./cog-person-count run --config bad-config.json: line 1: {"event":"run.started","fields":{"cog":"person-count", "sensing_url":"http://127.0.0.1:9999/...",poll_ms:100, "model_path":"(auto-discover)"}} line 2: WARN sensing-server fetch failed error=Connection Failed: Connect error: actively refused (loop alive — exits cleanly on SIGTERM, no crash, no NaN) Also adds a "Relationship to the in-process score_to_person_count heuristic" section to cog/README.md explaining the dual-emitter design (sensing-server keeps emitting the PR #491 slot heuristic; the cog runs out-of-process and emits person.count events from the learned model). Operators choose by installing the cog or not — no sensing-server rebuild required. ADR-103 §"Migration" status: 1. Land ADR + scaffold ........... done (#693, #694) 2. Train count_v1 ................ done (#695) 3. Cross-compile + sign + GCS .... done (#696) 4. Server-side wiring ............ done — out-of-process design means no rewire needed; this cog is the wiring. 5. v0.2.0 multi-room + LoRA ...... data-bound (#645)
ruvnet
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May 21, 2026
…ld (#698) The arm + x86_64 manifests committed in #696 referenced the binaries built before #697 wired the `run` subcommand. Rebuilt + re-signed + re-uploaded to GCS, and re-deployed to cognitum-v0: arm sha 15c2fbac…7728ea5 (3,807,456 B, up from 2,168,816 — added Tokio runtime) x86_64 sha 051614ce…cc8388b3 (4,502,960 B, up from 2,615,528) Both re-signed Ed25519 with COGNITUM_OWNER_SIGNING_KEY. Manifests now match the binaries published at gs://cognitum-apps/cogs/{arm, x86_64}/cog-person-count-* and the binary installed at /var/lib/cognitum/apps/person-count/ on cognitum-v0.
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Phase 3 of ADR-103. Cross-compiled aarch64 + x86_64 on ruvultra, signed with COGNITUM_OWNER_SIGNING_KEY (Ed25519), uploaded to GCS, live-installed on cognitum-v0 Pi 5.
36bc0bb0…76cdd1ec…dacb0551…Real-hardware bench on cognitum-v0 Pi 5: 9.2 ms/invocation cold-start, 30 sequential
healthinvocations in 0.276 s.backend: candle-cpuloaded real weights, returns real count predictions.Honest v0.0.1 caveat still applies (class-1 accuracy 0% on the held-out tail of single-session training data) — same data-bound limit as
pose_v1. The shipped artifact is the vehicle; production accuracy follows from multi-room paired data per ADR-103's v0.2.0 plan + #645.🤖 Generated with claude-flow