9 Recent decisions + 6 Tried-and-abandoned (dates [2026-06-14]/[2026-06-15]) moved
non-destructively to archival-memory.md, each stamped _Archived 2026-06-19._. Kept
the active [2026-06-14] 'migrate ALL infra access to Claude-specific credentials'
standing directive. Back-ref counts: Recent decisions 79->88, Tried-and-abandoned
70->76. persistent-memory.md 397->363 lines.
Proven 2026-06-16 diagnosing arbo run #5/task 1175: the RUN-level
/runs/{id}/logs 404s, but the per-JOB endpoint
GET /api/v1/repos/{o}/{r}/actions/jobs/{job_id}/logs returns the full
plain-text log (claude-bot basic-auth, internal :3000) — no UI needed.
Also noted gitea's misleading per-step conclusions (every step shows
failure once any fails; trust the log + timestamps).
Build the streaming TTS server MVP per docs/design/chatterbox-fast-plan.md §4.
- scheduler.py: adaptive buffer-ratchet chunker (the meat) — GPU-free pure
logic. First sentence emitted alone for low TTFA, then chunks ratchet ~3x by
packing whole sentences to margin x buffered-audio; drives off measured RTF +
sec/char (EMA). relieve_leader() clause-splits a too-big mid-stream sentence
to avoid starvation (joins land on commas); a long comma-less sentence is the
one honored-but-flagged limitation.
- test_scheduler.py: GPU-free simulation, 13 tests — asserts no-starvation
(incl. overestimated RTF) and the ratchet.
- app.py: FastAPI model holder + POST /tts StreamingResponse (raw PCM s16le
default, wav optional, stream/oneshot) + GET /health.
- bench.py: client — ground-truth TTFB + real 1x-consumer starvation check.
Live test on irv-ml1 (turbo, A6000, GLaDOS voice): streaming TTFB 499ms vs
oneshot 5230ms (~10x), stayed ahead of a 1x player (no starvation), ratchet
1.64->4.08->8.60->8.60s audio, measured RTF self-corrected 3.38->4.01.
Kill the superseded docs/design/chatterbox-fast.md — its §5 windowed-token
streaming was the abandoned native-frame-streaming arc; the adaptive-chunk plan
supersedes it. Repoint persistent-memory + README at the canonical plan.
reference_id=<name> resolves against the DIRECTORY references/<name>/
(audio + same-basename .lab), not a flat references/<name>.wav. Voices
were staged flat with the per-name dirs left empty, so every
reference_id resolved to nothing and Fish fell back to its default
speaker — every dropdown voice produced byte-identical audio (proven:
Abigail == Imogen == no-ref, same text+seed). This was the real "no
accent" root cause, independent of the asset-engine "undefined" select
bug.
Server fix (applied to irv-ml1): populated references/<name>/<name>.wav
+ <name>.lab for all 32 voices; re-test confirms Imogen/Eleanor/
Beatrice/Abigail/no-ref now all distinct.
Durable hardening + record correction:
- playbook: normalize-layout step (flat <name>.wav -> nested dir, cp -u
idempotent, when-gated on count mismatch) + an A/B verify gate that
hard-fails the deploy if two reference_ids yield identical output.
- services.yaml: correct the reference_id resolution doc (dir + .lab,
not flat wav).
- README + persistent-memory: correct the "reference_id-by-name is THE
working path, verified" claim — it was a no-op until this fix; the
prior ECAPA 0.79 result came through the inline base64 path.
Capture this session's durable state: Dia2 live as dia2-2b/dia2-1b (legacy
dia retired), dia2 catalog on /tts with full controls + stable-voice default,
zonos REST adapter (built, stack down), schema regen, all pushed. New
foot-guns: dia2 image-build quirks, predefined-voices-not-in-/tts, voice_mode
clone 'undefined' 404. Archived 6 older entries (4 decisions, 2 foot-guns).
Same lesson family as the /app/web/dist mismatch — encoding
container-internal contract (paths OR env var names) in compose
needs to be verified against the Dockerfile + app, not against
design-doc shorthand. Wrong env var names silently no-op; app
falls back to Dockerfile defaults which orthogonally miss the
bind mount, and state goes to ephemeral layer until next recreate.
Captures the May 18-20 session arc:
- Skaldsong CD shipped on ana-docker:8300 via registry-pull pattern
(differs from althing/asset-engine which build-on-host); three
first-deploy footguns surfaced + canonical-patched.
- Worldtree CD disk-hygiene PR #184 (vh/Worldtree) — watermark gate,
eager post-deploy SHA prune, set -e hardening in SSH blocks.
Approved by worldtree-dev, pending merge.
- mead-hall Bifrost v0.3 wire-layer + data-flow end-to-end smoke
green (task #32 closed) — resolves the "stalled mid-Worldtree"
state in the prior snapshot.
- Volva systemd install — three-stage debug (PATH, ReadWritePaths,
handle pinning); impersonation footgun caught via PID/journal
cross-reference.
- Per-project user keys minted against personal Worldtree (:8081)
for skaldsong, althing, mead-hall + skaldsong-prod. Personal
becomes multi-consumer dev iteration instance; demo stays for
isolation.
File now at 434 lines (over 300 soft cap); next /snapshot run will
surface archival candidates.
Two related changes shipped together. The stack rename is independent
but adding `vllm-reward` to the existing `vllm-qwen3` would have made
that name actively misleading.
**Rename:** `stacks/vllm-qwen3/ → stacks/vllm/`. Updated all in-repo
references (README.md root, servers/ana-ml2/, stacks/llama-swap/,
configs/restic/ana-ml2/, docs/runbooks/disaster-recovery.md). Two
intentional history mentions retained (servers/ana-ml2 + stacks/vllm
README).
**Add `vllm-reward` service:** serves Skywork-Reward-V2-Llama-3.1-8B-AWQ
on port 8003. The AWQ output is a locally-quantized model (not from HF),
so bind-mounts `/tank/aimodels/llm:/local-models:ro` rather than the
shared HF cache. Model config.json declares LlamaForSequenceClassification
which vLLM's pooling runner picks up automatically — produces a single
reward score per input via /classify.
**Flag note:** the user's spec listed `--task classify`, but vLLM 0.19.1
deprecated --task in favor of --runner pooling (model architecture in
config.json drives the classification head). Compose uses --runner
pooling with a comment explaining the substitution.
**GPU memory:** no rebalance needed — production had already tuned
EMBED/RERANK down from 0.40 to 0.20 each (canonical .env.example now
matches reality). Adding REWARD at 0.30 totals 0.70, leaving ~14 GB
headroom on the 48 GB Ada.
**Server-side:** brought existing vllm-qwen3 down, mv'd
/opt/docker/compose/vllm-qwen3 → /opt/docker/compose/vllm, appended
REWARD_* lines to existing .env (preserving API_KEY/HF_TOKEN), deployed
new compose via scripts/deploy-stack.sh, brought all 3 services up.
**Smoke tests:**
- /health on 8001/8002/8003 → 200
- /v1/models on 8003 → lists Skywork/Skywork-Reward-V2-Llama-3.1-8B-AWQ
with max_model_len 16384
- /classify with a sample conversation → returns LABEL_0 with prob 0.9999
(single-output regression-style reward score, expected shape for a
reward model)
vh