0d97ccccfa65764150e7976ee697bf47a9bd578e
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Commits
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  mayatnikovandClaude Opus 4.7
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0d97ccccfa |
feat(v0.3.0): paradigm shift — TimeWeb-only LLM + persistent advisor trail + improvement queue
This is the rc.4 batch the user requested:
1. Emergency triggers (low HP + close hostile, lava-under-foot)
bypass the long cooldown so the LLM is consulted BEFORE the bot
dies, not after.
2. Active manifesto need is now included in the advisor user prompt
— the LLM picks suggestions that satisfy the bot's current
concrete need (L2 tools_wood → "gather logs nearby" not
"explore further").
3. Every advisor recommendation is persisted to SQLite
(advisor_recommendations table) with full token usage. The
reflex marks 'applied=1' when it dispatches and updates
outcome_ok/code when the dispatch completes. Ground truth for
"is the LLM actually helping" lives in the DB, not in logs.
4. Pi CLI is OUT of every background loop. coach/postmortem and
coach/reflect now go through the same TimeWeb endpoint
fast-advisor uses, via the shared coach/llm-call.js helper.
Pi is reserved for manual operator commands.
5. The LLM (postmortem, reflect, advisor) can flag "structural
gaps" — missing skills/features the operator should implement.
These land in the new improvement_requests table. Dedup by
title bumps `votes` instead of inserting duplicates so the
queue doesn't bloat. Operator views via
`node scripts/list-improvements.js`.
6. A deterministic trigger-tuner runs hourly: reads 24h of
recommendation stats, flags triggers whose success rate is
below 25% (sample ≥ 5) or whose prompts are expensive (>1000
input tokens) with mediocre payoff. Improvements get
source="tuner", category="tuning". No LLM call.
New files:
runtime/coach/llm-call.js — askAnalytical() helper
runtime/coach/trigger-tuner.js — stats → improvements
runtime/coach/trigger-tuner.test.js
scripts/list-improvements.js — operator CLI
Schema additions:
advisor_recommendations: id, ts, trigger_reason, planned_skill,
recommended_skill, action, rationale, active_need, tokens_in,
tokens_out, latency_ms, applied, outcome_ok, outcome_code, outcome_at
improvement_requests: id, ts, source, category, title, description,
context, priority, status, duplicate_of, votes, implemented_at, notes
Renamed env-var consumers:
Pi-coach drainOnce({ askPi }) → drainOnce({ askAnalyticalFn? })
Pi-reflect runOnce({ askPi }) → runOnce({ askAnalyticalFn? })
bot.js attachCoach/attachReflect no longer pass askPi
attachTuner() added to bot.js spawn handler
lessons.source 'pi-coach' → 'timeweb-coach'
lessons.source 'pi-reflect' → 'timeweb-reflect'
Token cost measured live:
~705 input + 45 output = ~750 total per advisor call
worst case @ 6 calls/hour rate cap = ~108K tokens/day
OpenAI gpt-5-mini reference price: ~$0.60/month
Operator usage:
node scripts/list-improvements.js # open queue
node scripts/list-improvements.js --stats # advisor performance
node scripts/list-improvements.js --done 17 "shipped in 0.3.1"
node scripts/list-improvements.js --reject 18 "duplicate"
Tests: 360 green (was 332, +28 new).
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
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e84148d189 |
v0.2.0-rc.2: P0 hardening — Pi headless, test state isolation, advice fixes (#21)
P0 (correctness):
1. PEPA_HEADLESS=1 guard in extensions/mineflayer-bridge.ts. When `pi -p`
spawns a subprocess (banter, coach, planner, reflect, auto-patch), the
bridge no longer attempts a second MC connect — the hybrid runtime
already owns the nickname. runtime/pi-bridge.js sets the env var on
every spawn. Root cause of the "two pepa_bot's racing for the slot"
bug seen in reply-pi stderr.
2. Test state isolation in runtime/config.js. When running under the node
test runner (detected via execArgv/argv) — or when PEPA_STATE_DIR is
set — stateDir redirects to /tmp/pepa-test-state-<pid>/. log.js,
scenario-memory, world-journal, and knowledge.db all follow.
`npm test` no longer pollutes live scenarios.jsonl, world-journal.jsonl,
or daily log files. Verified empirically: post-fix run added 0 test
rows to the live scenarios file. Cleaned ~550 historical test rows
from live state in the same change.
3. defendReflex outcome reporting (runtime/reflex.js). Previously a
creeper-rule override marked the lesson succeeded=false BEFORE the
flee skill returned. Now dispatchDefendFlee accepts {lessonId} and
the onComplete fires reportAdviceOutcome with the actual flee result.
4. Mode-name → skill-id translation in runtime/coach/advice.js. Pi-coach
occasionally returns prefer_skill values that are mode names
("night_shelter", "self_preservation", "hunger"). normalisePreferSkill
maps these to SAFE_OVERRIDES entries before dispatch. Also handles
"tunnel-out", "survive_flee", "survive flee" shapes.
New behavior:
5. Self-reflection loop (runtime/coach/reflect.js). Every 30 min, the
bot asks Pi: "Are you making progress, or stuck in a loop? What
should you do differently?" Pi answers with a verdict
(progress/loop/recovering/idle/emergency), summary, next-action, and
0-N new lessons. The reflection is written to
state/<host>/reflections/<ts>.md and lessons land in the DB with
source="pi-reflect". Rate-limited to 2 calls/hour. Wired through
bot.js with the existing askPi + lastSnapshot accessor.
Tests: 246/246 green (+9 new: 6 advice mode-name + 4 reflect).
Co-authored-by: Yuriy Mayatnikov <mayatnikov@me.com>
Co-authored-by: Claude Opus 4.7 <noreply@anthropic.com>
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