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kami 5fe8f228c1 feat(mavweb): /ecosystem page consuming Nexus/Praxis/Hexis + shell fixes

Add a read-only /ecosystem page that consumes the sibling services'
JSON APIs (Nexus entities, Praxis attention, Hexis capabilities),
fetched concurrently with honest per-panel error states. Siblings stay
headless — mavweb is their human surface (arch §16). Wired via mavweb
-nexus/-praxis/-hexis flags; mavweb joins the ecosystem compose network.

Fix mobile horizontal overflow across all pages: .content is a flex
child with default min-width:auto, so it refused to shrink below the
tables' intrinsic width. min-width:0 lets wide tables pan inside .scroll
instead of dragging the page sideways. Verified via CDP geometry check
(scrollWidth === clientWidth at 430px).

Also includes in-progress Ethos UI redesign, ecosystem deploy compose,
and planning docs.

Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>

2026-07-19 22:04:23 +04:00

2.4 KiB

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Route-training data plan (LLM-as-router)

Goal: training data that teaches the CPT'd Qwen3-1.7B to emit the route contract — [{"intent":<enum>, key?, value?, text?, verb?}, ...], GBNF-constrained — matching internal/router/llmrouter.go (routeSystem + routeGrammar) verbatim. Train=deploy parity: label with the EXACT prompt the daemon sends.

7 intents: fact, reminder, note, query, act, chat, system. Hard cases (grammar can't enforce): note vs reminder («запомни» vs «напомни»), fact vs note (trackable state vs static memo).

Steps

Source utterances — real RU turns, not synthetic. Main: function_calling.jsonl (473); plus user-*.jsonl fragments for chat/fact/system coverage. gen_route_data.py dedups across all.
Relabel, don't convert — old taxonomy (time/weather/timer) ≠ 7 intents. Feed each utterance through routeSystem to a strong router model → take its {intent,...}.
Validate — intent ∈ enum, keys ⊆ {intent,key,value,text,verb}. Drop invalid.
Balance check — after a run, count intents. act/system/fact likely thin (function_calling skews query/act). Author extra examples for the holes; re-run.
Better prompt first — improve routeSystem for 1.7B disambiguation before a big generation run (awaiting prompt-guy input). Re-labeling is cheap; regenerate.
Train — locked decision: fold route and persona examples into one balanced Qwen3 SFT. The system prompt selects the contract. Evaluate the two tasks separately; a separate route adapter is the fallback if joint SFT interferes.

Blocking

Router at inference.kvmx.ru / localhost:6446 must be up (currently down).
CPT must finish and pass the raw-vs-CPT decision gate before joint SFT.

Files

esp32-whisper-fine-tune/llm/gen_route_data.py — the relabeler (done, self-checks). ROUTE_SYSTEM const = verbatim copy of Go routeSystem; keep in sync.
Output: llm/data/route_train.jsonl (resumable append).
Held-out: llm/data/route_eval.jsonl, generated from human labels by build_route_eval.py; never include it in route training generation.

Prompt-guy question (sent 2026-07-11)

How to structure the router system prompt for a 1.7B model doing 7-intent classification + slot extraction, GBNF-constrained — example ordering/count, contrastive near-miss pairs (note vs reminder) vs more singles, rule placement.

2.4 KiB Raw Blame History