Import AITURK IDE 1.0.0-beta.1 from Hermes 63279301; preserve MIT license
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# session_search Schema A/B Eval
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Live tool-use A/B harness measuring whether changes to the `session_search`
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tool schema (description/param diets, response hints) affect a model's
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ability to actually use the tool. Built for PR #95570 (schema diet
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1,570 → 695 tok/call), where the question was: "does moving the teaching
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essay out of the schema and into response hints confuse models?"
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Unlike the readtool/browser evals, this one does not run the full AIAgent —
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it runs a minimal agent loop where the ONLY variable between arms is
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`tools/session_search_tool.py` extracted from two git refs. Everything else
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(seeded DB, tasks, oracles, system prompt, temperature) is held constant.
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## What it measures
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Six tasks against a deterministic seeded session DB (plus a second
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"work"-profile DB), each with a programmatic oracle — no LLM judging:
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| task | shape exercised | oracle |
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|---|---|---|
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| `t1_discover` | discovery | answer contains `pglogical` |
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| `t2_scroll` | forced forward scroll — fact planted OUTSIDE the ±5 window and outside bookends | `statement_timeout` + `45` |
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| `t3_broaden` | AND-query miss → must broaden (OR / fewer terms); the two query nouns never co-occur in one message | port `3000` |
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| `t4_link` | verbatim `@session:` link emission | link present, NOT backticked/markdown |
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| `t5_profile` | `@session:work/<id>` profile link resolution (read shape) | `vault` + `90` |
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| `t6_browse` | browse shape | ≥3 recent-session topics named |
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Metrics per run: oracle pass, tool-call count, malformed/errored calls,
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first-call prompt tokens (measures the schema itself), total tokens, wall.
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## Running
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```bash
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# Arms are git refs; the runner extracts tools/session_search_tool.py
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# from each and imports them side by side.
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python3 evals/session_search_schema/runner.py \
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--base origin/main --cand HEAD \
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--model qwen/qwen3-coder-30b-a3b-instruct --reps 3
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python3 evals/session_search_schema/report.py results/<model>.jsonl
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```
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Requires `OPENROUTER_API_KEY` in `~/.hermes/.env` (or env). The seeded DB is
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rebuilt fresh in a temp dir per invocation; nothing touches your real
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`state.db`.
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Rules of engagement (hermesbench discipline):
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- 3 reps minimum; n=1 cell differences are noise — pull the transcript
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(`calls` + `final` in the JSONL) before diagnosing any miss.
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- Provider noise (zero tool calls AND empty final) gets one retry, applied
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identically to both arms; retries are logged.
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- Report per-task x/N for BOTH arms with the same denominators. Never
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exclude runs from one arm only.
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- Weak/mid models are the signal; frontier models mask schema ergonomics.
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## Reference results (PR #95570, 2026-08-26)
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108 runs, 3 models × 6 tasks × 3 reps × 2 arms
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(base `2b8b4542e` = pre-diet main, cand `d8a78a4dc` = diet):
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| model | base | diet | avg tok/task |
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|---|---|---|---|
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| qwen3-coder-30b | 16/18 | 18/18 | 11.1k → 7.1k |
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| gpt-5.6-luna | 18/18 | 17/18 | 5.4k → 3.7k |
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| gpt-5.6-terra | 15/18 | 17/18 | 8.0k → 5.0k |
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| **total** | 49/54 | **52/54** | 7.0k → 5.3k |
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Findings: diet arm held/gained accuracy; scroll `hint` measurably helped the
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paging task; one 1/9 luna markdown-link miss on the diet arm; both arms
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surfaced the pre-existing `around_message_id=0` falsy-sentinel bug
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(issue #94792 / PR #79118).
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"""Seed the synthetic session DBs for the session_search schema A/B eval.
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Creates state.db (main profile) + state_work.db ('work' profile) under a
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target dir. Sessions are designed so each task in tasks.py has a
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programmatic oracle:
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t1_discover : postgres migration session -> fact 'pglogical'
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t2_scroll : long incident session; FTS match ~msg 10, resolution
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('raised statement_timeout to 45s') at ~msg 24, trailing
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chatter after — so neither the ±5 discovery window nor the
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bookends reveal it; a forward scroll (or full read) is
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required.
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t3_broaden : 'grafana' and 'beehive' never co-occur in one message;
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the fact 'dashboard on port 3000' sits next to 'beehive'.
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t4_link : aquarium build session (model must emit @session:... link).
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t5_profile : work-profile session with fact 'Vault with 90-day rotation'.
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t6_browse : recent titles oracle.
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"""
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from __future__ import annotations
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import time
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from pathlib import Path
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def seed(dbdir: Path) -> None:
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from hermes_state import SessionDB
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dbdir.mkdir(parents=True, exist_ok=True)
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now = int(time.time())
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def mk(db, sid, title, age_s, msgs, source="cli"):
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db.create_session(sid, source=source)
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db._conn.execute(
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"UPDATE sessions SET started_at = ?, title = ? WHERE id = ?",
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(now - age_s, title, sid),
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)
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for role, content in msgs:
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db.append_message(sid, role=role, content=content)
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db._conn.commit()
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db = SessionDB(dbdir / "state.db")
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# --- t1: postgres migration (discovery) ---------------------------------
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mk(db, "20260810_091500_aa11bb", "Postgres 16 Migration", 14 * 86400, [
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("user", "We need to plan the postgres migration for the analytics cluster."),
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("assistant", "Options: pg_dump/restore, or logical replication for near-zero downtime."),
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("user", "Downtime matters, pick the replication route."),
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("assistant", "Decided: migrate to pg16 using pglogical, cutover on a Sunday 02:00 window."),
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("user", "ok set it up"),
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("assistant", "pglogical publisher configured on the pg13 primary; subscriber on the new pg16 box syncing."),
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])
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# --- t2: incident needing a forward scroll ------------------------------
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mk(db, "20260818_140200_cc22dd", "Checkout 504 Incident", 8 * 86400, [
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("user", "The checkout API is timing out in prod, users are seeing 504s."),
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("assistant", "Pulling error rates now. 504s started 14:02 UTC, about 8% of requests."),
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("user", "any deploys around then?"),
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("assistant", "Yes, orders-service v2.31.0 went out at 13:55."),
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("user", "roll it back?"),
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("assistant", "Rolling back to v2.30.2 now."),
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("assistant", "Rollback done, but 504s continue at 7%. Not the deploy."),
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("user", "so what is it then"),
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("assistant", "Digging into the checkout timeout traces — slow spans all end at the payments DB."),
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("user", "keep going, find the root cause of the checkout timeout"),
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("assistant", "Query log shows a new index build competing with checkout writes."),
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("user", "who started an index build in prod??"),
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("assistant", "The nightly migration job — it ran CREATE INDEX without CONCURRENTLY."),
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("assistant", "Killed the index build. Latency recovering, 504s at 2%."),
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("user", "it's still not fully green"),
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("assistant", "Right — p99 still 9s. Statement timeouts are firing on retries."),
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("user", "so tune it"),
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("assistant", "Testing bumped timeouts on canary first."),
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("user", "results?"),
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("assistant", "Canary clean for 20 minutes."),
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("user", "ship the final fix everywhere then"),
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("assistant", "Deployed everywhere."),
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("user", "what exactly did you change as the final fix? write it down"),
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("assistant", "Final fix: raised statement_timeout to 45s on the payments DB pool and re-created the index CONCURRENTLY off-peak."),
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("user", "great. postmortem doc?"),
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("assistant", "Drafted, shared in #incidents."),
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# trailing chatter so bookend_end (last 3) doesn't contain the fix
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("user", "unrelated: can you order more coffee for the office"),
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("assistant", "Added a coffee order reminder for tomorrow."),
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("user", "also book the team lunch friday"),
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("assistant", "Team lunch booked for Friday at noon."),
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])
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# --- t3: grafana / beehive never in the same message --------------------
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mk(db, "20260820_101000_ee33ff", "Apiary Monitoring Setup", 6 * 86400, [
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("user", "Let's get monitoring on the beehive sensors in the yard."),
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("assistant", "The hive telemetry (temp, humidity, weight) is publishing to MQTT already."),
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("user", "I want graphs"),
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("assistant", "I set up a grafana instance for the sensor graphs."),
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("user", "where do I see it"),
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("assistant", "The dashboard is on port 3000 of the garden pi, admin login in your password manager."),
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])
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# --- t4: aquarium build (link task) --------------------------------------
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mk(db, "20260822_183000_a4b4c4", "Reef Aquarium Build Plan", 4 * 86400, [
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("user", "Help me plan the 90 gallon reef aquarium build."),
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("assistant", "Sketched the build: 90g display, 30g sump, AI Hydra lighting, DIY stand."),
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("user", "cycle timeline?"),
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("assistant", "6-8 weeks fishless cycle with ammonia dosing, then clean-up crew first."),
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])
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# --- t6 browse fodder ----------------------------------------------------
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mk(db, "20260824_090000_d5e5f6", "Tax Prep Checklist", 2 * 86400, [
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("user", "Start the tax prep checklist for the LLC."),
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("assistant", "Checklist drafted: 1099s, K-1, depreciation schedule, quarterly payments recap."),
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])
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mk(db, "20260825_200000_ffeedd", "GPU Server Fan Curve", 1 * 86400, [
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("user", "The GPU server is too loud at idle, fix the fan curve."),
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("assistant", "Wrote a custom fan curve via ipmitool: 30% below 50C, linear to 100% at 80C."),
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])
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db.close()
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# --- work profile DB (t5) -------------------------------------------------
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wdb = SessionDB(dbdir / "state_work.db")
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mk(wdb, "20260815_110000_beef01", "Secrets Management Decision", 11 * 86400, [
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("user", "We need to pick a secrets management approach for the platform team."),
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("assistant", "Candidates: AWS Secrets Manager, Vault, SOPS in git."),
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("user", "what did we land on?"),
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("assistant", "Decision: HashiCorp Vault with 90-day rotation policy, dynamic DB creds for services."),
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])
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wdb.close()
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"""Summarize session_search schema A/B results.
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Usage:
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python3 evals/session_search_schema/report.py [--label ab]
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python3 evals/session_search_schema/report.py results/ab/*.jsonl
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"""
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from __future__ import annotations
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import argparse
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import collections
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import glob
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import json
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from pathlib import Path
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EVAL_DIR = Path(__file__).resolve().parent
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def summarize(files):
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grand = collections.defaultdict(lambda: [0, 0, 0, 0]) # ok, n, tok, calls
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for f in sorted(files):
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agg = collections.defaultdict(
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lambda: dict(ok=0, n=0, calls=0, tok=0, bad=0))
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for line in open(f, encoding="utf-8"):
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try:
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r = json.loads(line)
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except Exception:
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continue
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k = (r["task"], r["arm"])
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a = agg[k]
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a["ok"] += r["ok"]
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a["n"] += 1
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a["calls"] += r["n_tool_calls"]
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a["tok"] += r["total_tokens"]
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a["bad"] += r["bad_calls"]
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g = grand[r["arm"]]
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g[0] += r["ok"]; g[1] += 1
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g[2] += r["total_tokens"]; g[3] += r["n_tool_calls"]
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tasks = sorted({k[0] for k in agg})
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arms = sorted({k[1] for k in agg})
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print("=" * 72)
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print(f)
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header = f"{'task':<14}" + "".join(f"{a + ' ok':<9}" for a in arms)
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header += "".join(f"{a + ' calls':<12}" for a in arms)
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header += "".join(f"{a + ' tok':<10}" for a in arms)
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print(header)
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for t in tasks:
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row = f"{t:<14}"
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for a in arms:
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c = agg.get((t, a), dict(ok=0, n=0))
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row += f"{str(c['ok']) + '/' + str(c['n']):<9}"
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for a in arms:
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c = agg.get((t, a), dict(calls=0, n=1))
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row += f"{c['calls'] / max(c['n'], 1):<12.1f}"
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for a in arms:
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c = agg.get((t, a), dict(tok=0, n=1))
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row += f"{c['tok'] // max(c['n'], 1):<10}"
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print(row)
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print("=" * 72)
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for arm, (ok, n, tok, calls) in sorted(grand.items()):
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if n:
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print(f"TOTAL {arm}: {ok}/{n} ok "
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f"avg tok/task {tok // n} avg calls {calls / n:.1f}")
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def main():
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ap = argparse.ArgumentParser()
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ap.add_argument("files", nargs="*", default=None)
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ap.add_argument("--label", default="ab")
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args = ap.parse_args()
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files = args.files or glob.glob(
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str(EVAL_DIR / "results" / args.label / "*.jsonl"))
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if not files:
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raise SystemExit("no result files found")
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summarize(files)
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if __name__ == "__main__":
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main()
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@@ -0,0 +1,36 @@
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{"task": "t1_discover", "arm": "main", "model": "openai/gpt-5.6-luna", "ok": true, "n_tool_calls": 1, "bad_calls": 0, "first_prompt_tokens": 1551, "total_tokens": 3818, "wall_s": 5.2, "calls": [{"query": "postgres migration analytics cluster", "limit": 5, "sort": "newest", "detail": "full", "session_id": "", "around_message_id": 0, "window": 5, "role_filter": "user,assistant", "profile": ""}], "final": "We settled on a **logical-replication migration to PostgreSQL 16 using `pglogical`**, rather than `pg_dump`/restore, because minimizing downtime was important.\n\nPlan:\n- Configure the **PostgreSQL 13 primary as the pglogical publisher**.\n- Sync a **new PostgreSQL 16 subscriber** in parallel.\n- Perform the final cutover during a **Sunday 02:00 maintenance window**.\n\nThe publisher and subscriber had already been configured and were syncing. This was in @session:default/20260810_091500_aa11bb.", "rep": 0}
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{"task": "t1_discover", "arm": "pr", "model": "openai/gpt-5.6-luna", "ok": true, "n_tool_calls": 1, "bad_calls": 0, "first_prompt_tokens": 737, "total_tokens": 2277, "wall_s": 4.8, "calls": [{"query": "Postgres migration analytics cluster", "limit": 10, "sort": "newest", "detail": "full", "session_id": "", "around_message_id": 0, "window": 5, "role_filter": "", "profile": ""}], "final": "We settled on a **logical-replication migration using `pglogical`** to minimize downtime:\n\n- Upgrade from the **Postgres 13 primary to a new Postgres 16 cluster**\n- Configure the existing cluster as the **publisher**\n- Configure the new Postgres 16 cluster as the **subscriber**\n- Let it sync continuously, then perform the cutover during a **Sunday 02:00 maintenance window**\n\nThe setup was noted as already in progress: the pg13 publisher and pg16 subscriber were configured and syncing. See [the migration conversation]( @session:default/20260810_091500_aa11bb ).", "rep": 0}
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{"task": "t1_discover", "arm": "main", "model": "openai/gpt-5.6-luna", "ok": true, "n_tool_calls": 1, "bad_calls": 0, "first_prompt_tokens": 1551, "total_tokens": 3795, "wall_s": 4.1, "calls": [{"query": "postgres migration analytics cluster", "limit": 5, "sort": "newest", "detail": "full", "session_id": "", "around_message_id": 0, "window": 5, "role_filter": "user,assistant", "profile": ""}], "final": "We settled on a **logical-replication migration using `pglogical`** rather than `pg_dump`/restore, to minimize downtime:\n\n- **Source:** PostgreSQL 13 primary configured as the `pglogical` publisher \n- **Target:** New PostgreSQL 16 server acting as the subscriber \n- **Cutover:** Sunday at **02:00** during the maintenance window \n- The new PostgreSQL 16 instance was already syncing from the existing primary.", "rep": 1}
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{"task": "t1_discover", "arm": "pr", "model": "openai/gpt-5.6-luna", "ok": true, "n_tool_calls": 1, "bad_calls": 0, "first_prompt_tokens": 737, "total_tokens": 2279, "wall_s": 9.4, "calls": [{"query": "postgres migration analytics cluster", "limit": 10, "sort": "newest", "detail": "full", "session_id": "", "around_message_id": 0, "window": 5, "role_filter": "user,assistant", "profile": ""}], "final": "We settled on a **logical replication migration using `pglogical`** for near-zero downtime:\n\n- Migrate from the **Postgres 13 primary** to a new **Postgres 16** instance.\n- Configure the existing cluster as the **publisher** and the new instance as the **subscriber**.\n- Let the subscriber sync, then perform the cutover during a **Sunday 02:00 maintenance window**.\n\nThe setup was recorded as complete: the pg13 publisher and pg16 subscriber were syncing. See [the migration conversation](@session:default/20260810_091500_aa11bb).", "rep": 1}
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{"task": "t1_discover", "arm": "main", "model": "openai/gpt-5.6-luna", "ok": true, "n_tool_calls": 1, "bad_calls": 0, "first_prompt_tokens": 1551, "total_tokens": 3792, "wall_s": 4.8, "calls": [{"query": "postgres migration analytics cluster", "limit": 5, "sort": "newest", "detail": "full", "session_id": "", "around_message_id": 0, "window": 5, "role_filter": "user,assistant", "profile": ""}], "final": "We settled on a **logical replication migration using `pglogical`** to minimize downtime:\n\n- **Source:** PostgreSQL 13 primary, configured as the publisher\n- **Target:** New PostgreSQL 16 server, configured as the subscriber\n- **Cutover:** Sunday at **02:00**\n- The new server was already syncing when we last discussed it.\n\nThat was @session:default/20260810_091500_aa11bb.", "rep": 2}
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{"task": "t1_discover", "arm": "pr", "model": "openai/gpt-5.6-luna", "ok": true, "n_tool_calls": 1, "bad_calls": 0, "first_prompt_tokens": 737, "total_tokens": 2257, "wall_s": 7.5, "calls": [{"query": "postgres migration analytics cluster", "limit": 10, "sort": "newest", "detail": "full", "session_id": "", "around_message_id": 0, "window": 5, "role_filter": "", "profile": ""}], "final": "We settled on a **logical replication migration using `pglogical`** for near-zero downtime:\n\n- Migrate from the **Postgres 13 primary to a new Postgres 16 instance**.\n- Configure the existing cluster as the **pglogical publisher**.\n- Configure the new Postgres 16 server as the **subscriber** and let it catch up.\n- Perform the final cutover during a **Sunday 02:00 maintenance window**.\n\nThe setup was recorded here: @session:default/20260810_091500_aa11bb", "rep": 2}
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{"task": "t2_scroll", "arm": "main", "model": "openai/gpt-5.6-luna", "ok": true, "n_tool_calls": 2, "bad_calls": 0, "first_prompt_tokens": 1565, "total_tokens": 7361, "wall_s": 8.7, "calls": [{"query": "checkout 504", "limit": 10, "sort": "oldest", "detail": "full", "session_id": "", "around_message_id": 0, "window": 5, "role_filter": "user,assistant", "profile": ""}, {"query": "", "limit": 3, "sort": "oldest", "detail": "adaptive", "session_id": "20260818_140200_cc22dd", "around_message_id": 12, "window": 20, "role_filter": "user,assistant", "profile": ""}], "final": "The final fix shipped was:\n\n- **Setting:** `statement_timeout`\n- **Value:** `45s`\n- **Scope:** Payments DB connection pool\n\nWe also re-created the index **concurrently, off-peak**. This is recorded in @session:default/20260818_140200_cc22dd", "rep": 0}
|
||||
{"task": "t2_scroll", "arm": "pr", "model": "openai/gpt-5.6-luna", "ok": true, "n_tool_calls": 4, "bad_calls": 0, "first_prompt_tokens": 751, "total_tokens": 4724, "wall_s": 8.8, "calls": [{"query": "checkout 504 incident final fix shipped precise setting value", "limit": 10, "sort": "newest", "detail": "full", "session_id": "", "around_message_id": 0, "window": 5, "role_filter": "user,assistant", "profile": ""}, {"query": "checkout 504", "limit": 10, "sort": "newest", "detail": "full", "session_id": "", "around_message_id": 0, "window": 5, "role_filter": "user,assistant", "profile": ""}, {"query": "checkout OR 504 OR timeout", "limit": 10, "sort": "newest", "detail": "adaptive", "session_id": "", "around_message_id": 0, "window": 5, "role_filter": "user,assistant", "profile": ""}, {"query": "gateway timeout", "limit": 10, "sort": "newest", "detail": "full", "session_id": "", "around_message_id": 0, "window": 5, "role_filter": "user,assistant", "profile": ""}], "final": "The final fix we shipped was:\n\n- **Setting:** `statement_timeout`\n- **Value:** **45 seconds**\n- **Scope:** Payments database connection pool\n\nWe also re-created the affected index using `CONCURRENTLY` during off-peak hours. See the incident session: @session:default/20260818_140200_cc22dd", "rep": 0}
|
||||
{"task": "t2_scroll", "arm": "main", "model": "openai/gpt-5.6-luna", "ok": true, "n_tool_calls": 3, "bad_calls": 0, "first_prompt_tokens": 1565, "total_tokens": 9374, "wall_s": 18.6, "calls": [{"query": "checkout 504 final fix precise setting value", "limit": 5, "sort": "oldest", "detail": "full", "session_id": "", "around_message_id": 0, "window": 5, "role_filter": "user,assistant", "profile": ""}, {"query": "checkout 504 OR gateway timeout", "limit": 10, "sort": "oldest", "detail": "full", "session_id": "", "around_message_id": 0, "window": 5, "role_filter": "user,assistant", "profile": ""}, {"query": "", "limit": 3, "sort": "oldest", "detail": "adaptive", "session_id": "20260818_140200_cc22dd", "around_message_id": 12, "window": 20, "role_filter": "user,assistant", "profile": ""}], "final": "The final fix was:\n\n- **Setting:** `statement_timeout`\n- **Value:** `45s`\n- **Scope:** Payments database connection pool\n\nWe also re-created the index using `CREATE INDEX CONCURRENTLY` off-peak. This is recorded in @session:default/20260818_140200_cc22dd.", "rep": 1}
|
||||
{"task": "t2_scroll", "arm": "pr", "model": "openai/gpt-5.6-luna", "ok": true, "n_tool_calls": 4, "bad_calls": 0, "first_prompt_tokens": 751, "total_tokens": 5014, "wall_s": 8.1, "calls": [{"query": "checkout 504 final fix setting value", "limit": 10, "sort": "newest", "detail": "full", "session_id": "", "around_message_id": 0, "window": 5, "role_filter": "user,assistant", "profile": ""}, {"query": "checkout OR 504 OR timeout", "limit": 10, "sort": "newest", "detail": "full", "session_id": "", "around_message_id": 0, "window": 5, "role_filter": "user,assistant", "profile": ""}, {"query": "checkout gateway", "limit": 10, "sort": "newest", "detail": "full", "session_id": "", "around_message_id": 0, "window": 5, "role_filter": "user,assistant", "profile": ""}, {"query": "504", "limit": 10, "sort": "newest", "detail": "full", "session_id": "", "around_message_id": 0, "window": 5, "role_filter": "user,assistant", "profile": ""}], "final": "The final fix was to set **`statement_timeout = 45s`** on the **payments DB pool**. We also re-created the problematic index using **`CREATE INDEX CONCURRENTLY`** during off-peak hours. See [Checkout 504 Incident]( @session:default/20260818_140200_cc22dd).", "rep": 1}
|
||||
{"task": "t2_scroll", "arm": "main", "model": "openai/gpt-5.6-luna", "ok": true, "n_tool_calls": 3, "bad_calls": 0, "first_prompt_tokens": 1565, "total_tokens": 9395, "wall_s": 8.3, "calls": [{"query": "checkout 504 final fix precise setting value", "limit": 10, "sort": "oldest", "detail": "full", "session_id": "", "around_message_id": 0, "window": 5, "role_filter": "user,assistant", "profile": ""}, {"query": "checkout 504 OR checkout gateway timeout OR 504 checkout", "limit": 10, "sort": "oldest", "detail": "full", "session_id": "", "around_message_id": 0, "window": 5, "role_filter": "user,assistant", "profile": ""}, {"query": "", "limit": 3, "sort": "oldest", "detail": "adaptive", "session_id": "20260818_140200_cc22dd", "around_message_id": 12, "window": 20, "role_filter": "user,assistant", "profile": ""}], "final": "The final fix was:\n\n- **Setting:** `statement_timeout`\n- **Value:** `45s`\n- **Scope:** Payments database connection pool\n\nWe also re-created the index **concurrently off-peak**. See @session:default/20260818_140200_cc22dd.", "rep": 2}
|
||||
{"task": "t2_scroll", "arm": "pr", "model": "openai/gpt-5.6-luna", "ok": true, "n_tool_calls": 5, "bad_calls": 0, "first_prompt_tokens": 751, "total_tokens": 9135, "wall_s": 12.9, "calls": [{"query": "checkout 504 incident final fix shipped precise setting value", "limit": 10, "sort": "oldest", "detail": "full", "session_id": "", "around_message_id": 0, "window": 5, "role_filter": "user,assistant", "profile": ""}, {"query": "checkout 504", "limit": 10, "sort": "oldest", "detail": "full", "session_id": "", "around_message_id": 0, "window": 5, "role_filter": "user,assistant", "profile": ""}, {"query": "checkout timeout OR gateway OR 504", "limit": 10, "sort": "oldest", "detail": "full", "session_id": "", "around_message_id": 0, "window": 5, "role_filter": "user,assistant", "profile": ""}, {"query": "final fix shipped setting value", "limit": 10, "sort": "oldest", "detail": "full", "session_id": "", "around_message_id": 0, "window": 5, "role_filter": "user,assistant", "profile": ""}, {"query": "", "limit": 3, "sort": "oldest", "detail": "adaptive", "session_id": "20260818_140200_cc22dd", "around_message_id": 20, "window": 20, "role_filter": "user,assistant", "profile": ""}], "final": "The final fix was:\n\n- **Setting:** `statement_timeout`\n- **Value:** **45s**\n- **Applied to:** the **payments DB pool**\n\nWe also re-created the index using `CREATE INDEX CONCURRENTLY` off-peak. This is documented in [the Checkout 504 Incident session](@session:default/20260818_140200_cc22dd).", "rep": 2}
|
||||
{"task": "t3_broaden", "arm": "main", "model": "openai/gpt-5.6-luna", "ok": true, "n_tool_calls": 2, "bad_calls": 0, "first_prompt_tokens": 1556, "total_tokens": 5655, "wall_s": 6.1, "calls": [{"query": "beehive sensors grafana graphs", "limit": 5, "sort": "newest", "detail": "full", "session_id": "", "around_message_id": 0, "window": 5, "role_filter": "user,assistant", "profile": ""}, {"query": "beehive OR bee hive OR bees grafana OR influx OR sensor*", "limit": 10, "sort": "newest", "detail": "full", "session_id": "", "around_message_id": 0, "window": 5, "role_filter": "user,assistant", "profile": ""}], "final": "The Grafana dashboard is on **port 3000 of the Garden Pi**:\n\n**http://\\<garden-pi-hostname-or-IP\\>:3000**\n\nThe admin login is stored in your **password manager**. This was from @session:default/20260820_101000_ee33ff.", "rep": 0}
|
||||
{"task": "t3_broaden", "arm": "pr", "model": "openai/gpt-5.6-luna", "ok": true, "n_tool_calls": 4, "bad_calls": 0, "first_prompt_tokens": 742, "total_tokens": 4643, "wall_s": 14.8, "calls": [{"query": "beehive sensors Grafana graphs access dashboard URL", "limit": 5, "sort": "newest", "detail": "full", "session_id": "", "around_message_id": 0, "window": 5, "role_filter": "", "profile": ""}, {"query": "beehive OR hive OR bees", "limit": 10, "sort": "newest", "detail": "full", "session_id": "", "around_message_id": 0, "window": 5, "role_filter": "", "profile": ""}, {"query": "Grafana OR grafana", "limit": 10, "sort": "newest", "detail": "full", "session_id": "", "around_message_id": 0, "window": 5, "role_filter": "", "profile": ""}, {"query": "sensor*", "limit": 10, "sort": "newest", "detail": "full", "session_id": "", "around_message_id": 0, "window": 5, "role_filter": "", "profile": ""}], "final": "Access Grafana on **port 3000 of the garden Pi**:\n\n**`http://<garden-pi-hostname-or-IP>:3000`**\n\nThe admin credentials are saved in your password manager. We set this up in [the Apiary Monitoring Setup session]( @session:default/20260820_101000_ee33ff )—the dashboard contains the hive temperature, humidity, and weight graphs.", "rep": 0}
|
||||
{"task": "t3_broaden", "arm": "main", "model": "openai/gpt-5.6-luna", "ok": true, "n_tool_calls": 2, "bad_calls": 0, "first_prompt_tokens": 1556, "total_tokens": 5635, "wall_s": 8.4, "calls": [{"query": "beehive sensors grafana graphs", "limit": 5, "sort": "newest", "detail": "adaptive", "session_id": "", "around_message_id": 0, "window": 5, "role_filter": "user,assistant", "profile": ""}, {"query": "grafana OR beehive OR hive sensors", "limit": 10, "sort": "newest", "detail": "full", "session_id": "", "around_message_id": 0, "window": 5, "role_filter": "user,assistant", "profile": ""}], "final": "Access the Grafana dashboard at:\n\n**`http://<garden-pi-IP>:3000`**\n\nIt’s running on the garden Pi. The admin credentials are stored in your password manager. This was from our setup in @session:default/20260820_101000_ee33ff.", "rep": 1}
|
||||
{"task": "t3_broaden", "arm": "pr", "model": "openai/gpt-5.6-luna", "ok": true, "n_tool_calls": 2, "bad_calls": 0, "first_prompt_tokens": 742, "total_tokens": 3379, "wall_s": 8.0, "calls": [{"query": "Grafana beehive sensors graphs access dashboard URL", "limit": 10, "sort": "newest", "detail": "full", "session_id": "", "around_message_id": 0, "window": 5, "role_filter": "user,assistant", "profile": ""}, {"query": "beehive OR hive OR Grafana OR sensor*", "limit": 10, "sort": "newest", "detail": "full", "session_id": "", "around_message_id": 0, "window": 5, "role_filter": "user,assistant", "profile": ""}], "final": "Access Grafana on **port 3000 of the garden Pi**:\n\n**`http://<garden-pi-hostname-or-IP>:3000`**\n\nThe admin credentials are stored in your password manager. We set this up in [the apiary monitoring session](@session:default/20260820_101000_ee33ff).", "rep": 1}
|
||||
{"task": "t3_broaden", "arm": "main", "model": "openai/gpt-5.6-luna", "ok": true, "n_tool_calls": 4, "bad_calls": 0, "first_prompt_tokens": 1556, "total_tokens": 5955, "wall_s": 6.3, "calls": [{"query": "beehive sensors grafana graphs", "limit": 5, "sort": "newest", "detail": "adaptive", "session_id": "", "around_message_id": 0, "window": 5, "role_filter": "user,assistant", "profile": ""}, {"query": "Grafana hive", "limit": 10, "sort": "newest", "detail": "full", "session_id": "", "around_message_id": 0, "window": 5, "role_filter": "user,assistant", "profile": ""}, {"query": "beehive", "limit": 10, "sort": "newest", "detail": "full", "session_id": "", "around_message_id": 0, "window": 5, "role_filter": "user,assistant", "profile": ""}, {"query": "sensor* dashboard", "limit": 10, "sort": "newest", "detail": "full", "session_id": "", "around_message_id": 0, "window": 5, "role_filter": "user,assistant", "profile": ""}], "final": "Access Grafana at:\n\n**`http://<garden-pi-hostname-or-IP>:3000`**\n\nThe beehive sensor dashboard is on the garden Pi, and the admin credentials are stored in your password manager.", "rep": 2}
|
||||
{"task": "t3_broaden", "arm": "pr", "model": "openai/gpt-5.6-luna", "ok": true, "n_tool_calls": 2, "bad_calls": 0, "first_prompt_tokens": 742, "total_tokens": 3393, "wall_s": 11.3, "calls": [{"query": "beehive sensors Grafana graphs access dashboard URL", "limit": 10, "sort": "newest", "detail": "full", "session_id": "", "around_message_id": 0, "window": 5, "role_filter": "user,assistant", "profile": ""}, {"query": "beehive OR beehives OR hive OR Grafana OR sensors", "limit": 10, "sort": "newest", "detail": "full", "session_id": "", "around_message_id": 0, "window": 5, "role_filter": "user,assistant", "profile": ""}], "final": "Access Grafana on **port 3000 of the garden Raspberry Pi**:\n\n**http://garden-pi:3000** \n—or use the Pi’s local IP—then log in with the admin credentials saved in your password manager.\n\nThat setup is from [our apiary monitoring session](@session:default/20260820_101000_ee33ff).", "rep": 2}
|
||||
{"task": "t4_link", "arm": "main", "model": "openai/gpt-5.6-luna", "ok": true, "n_tool_calls": 1, "bad_calls": 0, "first_prompt_tokens": 1552, "total_tokens": 3640, "wall_s": 4.7, "calls": [{"query": "reef aquarium build", "limit": 10, "sort": "oldest", "detail": "adaptive", "session_id": "", "around_message_id": 0, "window": 5, "role_filter": "user,assistant", "profile": ""}], "final": "The reef aquarium build plan is in @session:default/20260822_183000_a4b4c4.", "rep": 0}
|
||||
{"task": "t4_link", "arm": "pr", "model": "openai/gpt-5.6-luna", "ok": false, "n_tool_calls": 2, "bad_calls": 0, "first_prompt_tokens": 738, "total_tokens": 3206, "wall_s": 9.3, "calls": [{"query": "reef aquarium build planning", "limit": 10, "sort": "newest", "detail": "adaptive", "session_id": "", "around_message_id": 0, "window": 5, "role_filter": "", "profile": ""}, {"query": "reef OR aquarium OR tank OR saltwater OR coral", "limit": 10, "sort": "newest", "detail": "adaptive", "session_id": "", "around_message_id": 0, "window": 5, "role_filter": "", "profile": ""}], "final": "I found it: [Reef Aquarium Build Plan](@session:default/20260822_183000_a4b4c4).", "rep": 0}
|
||||
{"task": "t4_link", "arm": "main", "model": "openai/gpt-5.6-luna", "ok": true, "n_tool_calls": 1, "bad_calls": 0, "first_prompt_tokens": 1552, "total_tokens": 3642, "wall_s": 4.6, "calls": [{"query": "reef aquarium build", "limit": 10, "sort": "newest", "detail": "adaptive", "session_id": "", "around_message_id": 0, "window": 5, "role_filter": "user,assistant", "profile": ""}], "final": "You can reopen the reef aquarium planning session here: @session:default/20260822_183000_a4b4c4", "rep": 1}
|
||||
{"task": "t4_link", "arm": "pr", "model": "openai/gpt-5.6-luna", "ok": true, "n_tool_calls": 3, "bad_calls": 0, "first_prompt_tokens": 738, "total_tokens": 3822, "wall_s": 7.6, "calls": [{"query": "reef aquarium build planning", "limit": 10, "sort": "newest", "detail": "adaptive", "session_id": "", "around_message_id": 0, "window": 5, "role_filter": "", "profile": ""}, {"query": "reef OR aquarium OR tank OR saltwater", "limit": 10, "sort": "newest", "detail": "adaptive", "session_id": "", "around_message_id": 0, "window": 5, "role_filter": "", "profile": ""}, {"query": "\"reef aquarium\" OR \"reef tank\" OR aquascape", "limit": 10, "sort": "newest", "detail": "adaptive", "session_id": "", "around_message_id": 0, "window": 5, "role_filter": "", "profile": ""}], "final": "I found it: **Reef Aquarium Build Plan** from August 22, 2026. You can reopen it here: @session:default/20260822_183000_a4b4c4", "rep": 1}
|
||||
{"task": "t4_link", "arm": "main", "model": "openai/gpt-5.6-luna", "ok": true, "n_tool_calls": 1, "bad_calls": 0, "first_prompt_tokens": 1552, "total_tokens": 3637, "wall_s": 4.5, "calls": [{"query": "reef aquarium build", "limit": 10, "sort": "oldest", "detail": "adaptive", "session_id": "", "around_message_id": 0, "window": 5, "role_filter": "user,assistant", "profile": ""}], "final": "You planned it in @session:default/20260822_183000_a4b4c4.", "rep": 2}
|
||||
{"task": "t4_link", "arm": "pr", "model": "openai/gpt-5.6-luna", "ok": true, "n_tool_calls": 3, "bad_calls": 0, "first_prompt_tokens": 738, "total_tokens": 3867, "wall_s": 6.3, "calls": [{"query": "reef aquarium build plan planning tank sump lighting equipment", "limit": 10, "sort": "newest", "detail": "adaptive", "session_id": "", "around_message_id": 0, "window": 5, "role_filter": "user,assistant", "profile": ""}, {"query": "reef OR aquarium OR saltwater", "limit": 10, "sort": "newest", "detail": "adaptive", "session_id": "", "around_message_id": 0, "window": 5, "role_filter": "user,assistant", "profile": ""}, {"query": "\"reef aquarium\" OR \"reef tank\" OR aquarium", "limit": 10, "sort": "newest", "detail": "adaptive", "session_id": "", "around_message_id": 0, "window": 5, "role_filter": "user,assistant", "profile": ""}], "final": "The reef aquarium build planning session is **“Reef Aquarium Build Plan”** from August 22, 2026. You can reopen it here: @session:default/20260822_183000_a4b4c4", "rep": 2}
|
||||
{"task": "t5_profile", "arm": "main", "model": "openai/gpt-5.6-luna", "ok": true, "n_tool_calls": 3, "bad_calls": 0, "first_prompt_tokens": 1553, "total_tokens": 7317, "wall_s": 8.2, "calls": [{"query": "", "limit": 3, "sort": "newest", "detail": "adaptive", "session_id": "20260815_110000_beef01", "around_message_id": 0, "window": 5, "role_filter": "", "profile": "work"}, {"query": "", "limit": 3, "sort": "newest", "detail": "adaptive", "session_id": "20260815_110000_beef01", "around_message_id": 0, "window": 5, "role_filter": "", "profile": "work"}, {"query": "", "limit": 3, "sort": "newest", "detail": "adaptive", "session_id": "20260815_110000_beef01", "around_message_id": 1, "window": 5, "role_filter": "", "profile": "work"}], "final": "In @session:work/20260815_110000_beef01, we decided to use **HashiCorp Vault** for secrets management, with:\n\n- **90-day secret rotation**\n- **Dynamic database credentials** for services", "rep": 0}
|
||||
{"task": "t5_profile", "arm": "pr", "model": "openai/gpt-5.6-luna", "ok": true, "n_tool_calls": 2, "bad_calls": 0, "first_prompt_tokens": 739, "total_tokens": 3039, "wall_s": 7.5, "calls": [{"query": "", "limit": 3, "sort": "newest", "detail": "adaptive", "session_id": "20260815_110000_beef01", "around_message_id": 0, "window": 5, "role_filter": "user,assistant", "profile": "work"}, {"query": "", "limit": 3, "sort": "newest", "detail": "adaptive", "session_id": "20260815_110000_beef01", "around_message_id": 1, "window": 10, "role_filter": "user,assistant", "profile": "work"}], "final": "We decided to use **HashiCorp Vault** for platform secrets management, with:\n\n- **90-day secret rotation**\n- **Dynamic database credentials** for services", "rep": 0}
|
||||
{"task": "t5_profile", "arm": "main", "model": "openai/gpt-5.6-luna", "ok": true, "n_tool_calls": 2, "bad_calls": 0, "first_prompt_tokens": 1553, "total_tokens": 5387, "wall_s": 6.4, "calls": [{"query": "", "limit": 3, "sort": "newest", "detail": "adaptive", "session_id": "20260815_110000_beef01", "around_message_id": 0, "window": 5, "role_filter": "", "profile": "work"}, {"query": "", "limit": 3, "sort": "newest", "detail": "adaptive", "session_id": "20260815_110000_beef01", "around_message_id": 1, "window": 5, "role_filter": "", "profile": "work"}], "final": "We decided on **HashiCorp Vault** for platform secrets management, with:\n\n- **90-day secret rotation**\n- **Dynamic database credentials** for services", "rep": 1}
|
||||
{"task": "t5_profile", "arm": "pr", "model": "openai/gpt-5.6-luna", "ok": true, "n_tool_calls": 2, "bad_calls": 0, "first_prompt_tokens": 739, "total_tokens": 3018, "wall_s": 6.4, "calls": [{"query": "", "limit": 3, "sort": "newest", "detail": "adaptive", "session_id": "20260815_110000_beef01", "around_message_id": 0, "window": 10, "role_filter": "user,assistant", "profile": "work"}, {"query": "", "limit": 3, "sort": "newest", "detail": "adaptive", "session_id": "20260815_110000_beef01", "around_message_id": 1, "window": 10, "role_filter": "user,assistant", "profile": "work"}], "final": "You decided on **HashiCorp Vault** for platform secrets management, with:\n\n- **90-day secret rotation**\n- **Dynamic database credentials** for services", "rep": 1}
|
||||
{"task": "t5_profile", "arm": "main", "model": "openai/gpt-5.6-luna", "ok": true, "n_tool_calls": 3, "bad_calls": 0, "first_prompt_tokens": 1553, "total_tokens": 7346, "wall_s": 10.5, "calls": [{"query": "", "limit": 3, "sort": "newest", "detail": "adaptive", "session_id": "20260815_110000_beef01", "around_message_id": 0, "window": 5, "role_filter": "user,assistant", "profile": "work"}, {"query": "", "limit": 3, "sort": "newest", "detail": "adaptive", "session_id": "20260815_110000_beef01", "around_message_id": 0, "window": 5, "role_filter": "user,assistant", "profile": "work"}, {"query": "", "limit": 3, "sort": "newest", "detail": "adaptive", "session_id": "20260815_110000_beef01", "around_message_id": 1, "window": 5, "role_filter": "user,assistant", "profile": "work"}], "final": "We decided on **HashiCorp Vault** for platform secrets management, with:\n\n- **90-day secret rotation**\n- **Dynamic database credentials** for services", "rep": 2}
|
||||
{"task": "t5_profile", "arm": "pr", "model": "openai/gpt-5.6-luna", "ok": true, "n_tool_calls": 2, "bad_calls": 0, "first_prompt_tokens": 739, "total_tokens": 3010, "wall_s": 9.7, "calls": [{"query": "", "limit": 3, "sort": "newest", "detail": "adaptive", "session_id": "20260815_110000_beef01", "around_message_id": 0, "window": 5, "role_filter": "", "profile": "work"}, {"query": "", "limit": 3, "sort": "newest", "detail": "adaptive", "session_id": "20260815_110000_beef01", "around_message_id": 1, "window": 5, "role_filter": "", "profile": "work"}], "final": "You decided on **HashiCorp Vault** for secrets management, with:\n\n- **90-day secret rotation**\n- **Dynamic database credentials** for services", "rep": 2}
|
||||
{"task": "t6_browse", "arm": "main", "model": "openai/gpt-5.6-luna", "ok": true, "n_tool_calls": 1, "bad_calls": 0, "first_prompt_tokens": 1549, "total_tokens": 3986, "wall_s": 4.0, "calls": [{"query": "", "limit": 8, "sort": "newest", "detail": "adaptive", "session_id": "", "around_message_id": 0, "window": 5, "role_filter": "user,assistant", "profile": ""}], "final": "Quick rundown of your recent work:\n\n- **GPU server:** Adjusting the idle fan curve to reduce noise.\n- **Taxes:** Starting an LLC tax-preparation checklist.\n- **Reef aquarium:** Planning a 90-gallon reef aquarium build.\n- **Apiary:** Setting up monitoring for your backyard beehive sensors.\n- **Production incident:** Troubleshooting checkout API timeouts causing 504 errors.\n- **Database infrastructure:** Planning a PostgreSQL 16 migration for the analytics cluster.", "rep": 0}
|
||||
{"task": "t6_browse", "arm": "pr", "model": "openai/gpt-5.6-luna", "ok": true, "n_tool_calls": 1, "bad_calls": 0, "first_prompt_tokens": 735, "total_tokens": 2435, "wall_s": 5.9, "calls": [{"query": "", "limit": 8, "sort": "newest", "detail": "adaptive", "session_id": "", "around_message_id": 0, "window": 5, "role_filter": "user,assistant", "profile": ""}], "final": "Here’s the quick rundown of your recent work:\n\n- **GPU server:** Tuning the fan curve to reduce idle noise.\n- **Taxes:** Starting an LLC tax-preparation checklist.\n- **Reef aquarium:** Planning a 90-gallon reef aquarium build.\n- **Apiary:** Setting up monitoring for your outdoor beehive sensors.\n- **Production incident:** Troubleshooting checkout API timeouts causing 504 errors.\n- **Database infrastructure:** Planning a PostgreSQL 16 migration for the analytics cluster.", "rep": 0}
|
||||
{"task": "t6_browse", "arm": "main", "model": "openai/gpt-5.6-luna", "ok": true, "n_tool_calls": 1, "bad_calls": 0, "first_prompt_tokens": 1549, "total_tokens": 4005, "wall_s": 5.1, "calls": [{"query": "", "limit": 8, "sort": "newest", "detail": "adaptive", "session_id": "", "around_message_id": 0, "window": 5, "role_filter": "user,assistant", "profile": ""}], "final": "Recently, you’ve been working on:\n\n- **GPU server fan curve** — reducing idle fan noise.\n- **LLC tax preparation** — starting a tax prep checklist.\n- **90-gallon reef aquarium** — planning the build.\n- **Apiary monitoring** — setting up sensors for your beehives.\n- **Checkout API incident** — investigating production 504 timeouts.\n- **Postgres 16 migration** — planning the analytics-cluster migration.\n\nThe most substantial recent thread was the checkout incident: @session:default/20260818_140200_cc22dd", "rep": 1}
|
||||
{"task": "t6_browse", "arm": "pr", "model": "openai/gpt-5.6-luna", "ok": true, "n_tool_calls": 1, "bad_calls": 0, "first_prompt_tokens": 735, "total_tokens": 2422, "wall_s": 5.3, "calls": [{"query": "", "limit": 8, "sort": "newest", "detail": "adaptive", "session_id": "", "around_message_id": 0, "window": 5, "role_filter": "user,assistant", "profile": ""}], "final": "Here’s the quick rundown of your recent work:\n\n- **GPU server:** Tuning the idle fan curve to reduce noise.\n- **Taxes:** Starting a tax-prep checklist for your LLC.\n- **Reef aquarium:** Planning a 90-gallon reef aquarium build.\n- **Apiary:** Setting up monitoring for your outdoor beehive sensors.\n- **Production incident:** Investigating checkout API timeouts and 504 errors.\n- **Database migration:** Planning a PostgreSQL 16 migration for the analytics cluster.", "rep": 1}
|
||||
{"task": "t6_browse", "arm": "main", "model": "openai/gpt-5.6-luna", "ok": true, "n_tool_calls": 1, "bad_calls": 0, "first_prompt_tokens": 1549, "total_tokens": 3982, "wall_s": 5.2, "calls": [{"query": "", "limit": 10, "sort": "newest", "detail": "adaptive", "session_id": "", "around_message_id": 0, "window": 5, "role_filter": "user,assistant", "profile": ""}], "final": "Recently, you’ve been working on:\n\n- **GPU server fan curve** — reducing idle fan noise.\n- **LLC tax prep** — building a tax preparation checklist.\n- **90-gallon reef aquarium** — planning the build and equipment.\n- **Apiary monitoring** — setting up monitoring for beehive sensors.\n- **Checkout API incident** — troubleshooting production 504 timeouts.\n- **Postgres 16 migration** — planning the analytics cluster migration.", "rep": 2}
|
||||
{"task": "t6_browse", "arm": "pr", "model": "openai/gpt-5.6-luna", "ok": true, "n_tool_calls": 7, "bad_calls": 0, "first_prompt_tokens": 735, "total_tokens": 5001, "wall_s": 14.0, "calls": [{"query": "", "limit": 8, "sort": "newest", "detail": "adaptive", "session_id": "", "around_message_id": 0, "window": 5, "role_filter": "user,assistant", "profile": ""}, {"query": "", "limit": 3, "sort": "newest", "detail": "adaptive", "session_id": "20260825_200000_ffeedd", "around_message_id": 0, "window": 5, "role_filter": "user,assistant", "profile": ""}, {"query": "", "limit": 3, "sort": "newest", "detail": "adaptive", "session_id": "20260824_090000_d5e5f6", "around_message_id": 0, "window": 5, "role_filter": "user,assistant", "profile": ""}, {"query": "", "limit": 3, "sort": "newest", "detail": "adaptive", "session_id": "20260822_183000_a4b4c4", "around_message_id": 0, "window": 5, "role_filter": "user,assistant", "profile": ""}, {"query": "", "limit": 3, "sort": "newest", "detail": "adaptive", "session_id": "20260820_101000_ee33ff", "around_message_id": 0, "window": 5, "role_filter": "user,assistant", "profile": ""}, {"query": "", "limit": 3, "sort": "newest", "detail": "adaptive", "session_id": "20260818_140200_cc22dd", "around_message_id": 0, "window": 5, "role_filter": "user,assistant", "profile": ""}, {"query": "", "limit": 3, "sort": "newest", "detail": "adaptive", "session_id": "20260810_091500_aa11bb", "around_message_id": 0, "window": 5, "role_filter": "user,assistant", "profile": ""}], "final": "Recently, you’ve been working on:\n\n- Tuning the **GPU server’s idle fan curve** to reduce noise: @session:default/20260825_200000_ffeedd\n- Starting an **LLC tax-prep checklist**: @session:default/20260824_090000_d5e5f6\n- Planning a **90-gallon reef aquarium build**: @session:default/20260822_183000_a4b4c4\n- Setting up **monitoring for your backyard beehive sensors**: @session:default/20260820_101000_ee33ff\n- Troubleshooting a **production checkout API timeout/504 incident**: @session:default/20260818_140200_cc22dd\n- Planning a **Postgres 16 migration** for the analytics cluster: @session:default/20260810_091500_aa11bb", "rep": 2}
|
||||
@@ -0,0 +1,36 @@
|
||||
{"task": "t1_discover", "arm": "main", "model": "openai/gpt-5.6-terra", "ok": true, "n_tool_calls": 1, "bad_calls": 0, "first_prompt_tokens": 1551, "total_tokens": 3799, "wall_s": 4.3, "calls": [{"query": "postgres migration analytics cluster", "limit": 5, "sort": "newest", "detail": "full", "session_id": "", "around_message_id": 0, "window": 5, "role_filter": "user,assistant", "profile": ""}], "final": "We settled on a near-zero-downtime migration from PostgreSQL 13 to 16 using **pglogical**:\n\n- Configure the existing pg13 primary as publisher and the new pg16 cluster as subscriber.\n- Let replication catch up, then cut over during the **Sunday 02:00 maintenance window**.\n- Avoided pg_dump/restore because downtime was a priority.\n\nThe publisher/subscriber setup was already configured and syncing in @session:default/20260810_091500_aa11bb.", "rep": 0}
|
||||
{"task": "t1_discover", "arm": "pr", "model": "openai/gpt-5.6-terra", "ok": true, "n_tool_calls": 4, "bad_calls": 0, "first_prompt_tokens": 737, "total_tokens": 4240, "wall_s": 7.7, "calls": [{"query": "postgres migration analytics cluster approach settled", "limit": 10, "sort": "newest", "detail": "full", "session_id": "", "around_message_id": 0, "window": 5, "role_filter": "user,assistant", "profile": ""}, {"query": "analytics postgres", "limit": 10, "sort": "newest", "detail": "adaptive", "session_id": "", "around_message_id": 0, "window": 5, "role_filter": "user,assistant", "profile": ""}, {"query": "migration cluster", "limit": 10, "sort": "newest", "detail": "adaptive", "session_id": "", "around_message_id": 0, "window": 5, "role_filter": "user,assistant", "profile": ""}, {"query": "PostgreSQL OR pg migration", "limit": 10, "sort": "newest", "detail": "adaptive", "session_id": "", "around_message_id": 0, "window": 5, "role_filter": "user,assistant", "profile": ""}], "final": "We settled on a **near-zero-downtime logical-replication migration** rather than dump/restore:\n\n- Upgrade the analytics cluster from **Postgres 13 to Postgres 16**\n- Use **pglogical**: the existing pg13 primary as publisher, a new pg16 node as subscriber\n- Let replication catch up, then perform cutover in the **Sunday 02:00 maintenance window**\n\nThe publisher/subscriber sync had already been configured. See @session:default/20260810_091500_aa11bb.", "rep": 0}
|
||||
{"task": "t1_discover", "arm": "main", "model": "openai/gpt-5.6-terra", "ok": true, "n_tool_calls": 1, "bad_calls": 0, "first_prompt_tokens": 1551, "total_tokens": 3776, "wall_s": 7.3, "calls": [{"query": "postgres migration analytics cluster", "limit": 5, "sort": "newest", "detail": "full", "session_id": "", "around_message_id": 0, "window": 5, "role_filter": "user,assistant", "profile": ""}], "final": "We chose a near-zero-downtime migration to PostgreSQL 16 using **pglogical**:\n\n- Configure the existing PostgreSQL 13 primary as the publisher.\n- Bring up the new PostgreSQL 16 instance as a subscriber and let it sync.\n- Perform the final cutover during the **Sunday 02:00 maintenance window**.\n\nThis was set up in @session:default/20260810_091500_aa11bb.", "rep": 1}
|
||||
{"task": "t1_discover", "arm": "pr", "model": "openai/gpt-5.6-terra", "ok": true, "n_tool_calls": 4, "bad_calls": 0, "first_prompt_tokens": 737, "total_tokens": 4829, "wall_s": 10.6, "calls": [{"query": "postgres migration analytics cluster approach settled", "limit": 5, "sort": "newest", "detail": "full", "session_id": "", "around_message_id": 0, "window": 5, "role_filter": "user,assistant", "profile": ""}, {"query": "analytics postgres", "limit": 10, "sort": "newest", "detail": "adaptive", "session_id": "", "around_message_id": 0, "window": 5, "role_filter": "user,assistant", "profile": ""}, {"query": "migration", "limit": 10, "sort": "newest", "detail": "adaptive", "session_id": "", "around_message_id": 0, "window": 5, "role_filter": "user,assistant", "profile": ""}, {"query": "PostgreSQL", "limit": 10, "sort": "newest", "detail": "adaptive", "session_id": "", "around_message_id": 0, "window": 5, "role_filter": "user,assistant", "profile": ""}], "final": "We settled on a **near-zero-downtime logical replication migration** rather than dump/restore:\n\n- Upgrade the analytics cluster from **Postgres 13 to Postgres 16**\n- Use **pglogical**: old pg13 primary as publisher, new pg16 instance as subscriber\n- Let it fully sync, then perform the cutover in a **Sunday 02:00 maintenance window**\n\nThe publisher/subscriber replication setup was configured. See @session:default/20260810_091500_aa11bb.", "rep": 1}
|
||||
{"task": "t1_discover", "arm": "main", "model": "openai/gpt-5.6-terra", "ok": true, "n_tool_calls": 1, "bad_calls": 0, "first_prompt_tokens": 1551, "total_tokens": 3785, "wall_s": 5.8, "calls": [{"query": "postgres migration analytics cluster", "limit": 5, "sort": "newest", "detail": "full", "session_id": "", "around_message_id": 0, "window": 5, "role_filter": "user,assistant", "profile": ""}], "final": "We settled on a near-zero-downtime migration to PostgreSQL 16 using **pglogical**:\n\n- Configure the existing PostgreSQL 13 primary as the publisher.\n- Bring up the new PostgreSQL 16 cluster as a subscriber and let it synchronize.\n- Cut over during the **Sunday 02:00** maintenance window once replication is caught up.\n\nThe publisher/subscriber setup was already noted as configured in @session:default/20260810_091500_aa11bb.", "rep": 2}
|
||||
{"task": "t1_discover", "arm": "pr", "model": "openai/gpt-5.6-terra", "ok": true, "n_tool_calls": 1, "bad_calls": 0, "first_prompt_tokens": 737, "total_tokens": 2246, "wall_s": 3.6, "calls": [{"query": "postgres migration analytics cluster", "limit": 10, "sort": "newest", "detail": "full", "session_id": "", "around_message_id": 0, "window": 5, "role_filter": "user,assistant", "profile": ""}], "final": "We settled on a **near-zero-downtime logical replication migration**:\n\n- Upgrade the analytics cluster from **Postgres 13 to Postgres 16** using **pglogical**.\n- Configure the existing pg13 primary as publisher and the new pg16 instance as subscriber, then let it fully sync.\n- Perform the final cutover during a **Sunday 02:00 maintenance window**.\n\nThe publisher/subscriber setup was already configured in the prior discussion: @session:default/20260810_091500_aa11bb", "rep": 2}
|
||||
{"task": "t2_scroll", "arm": "main", "model": "openai/gpt-5.6-terra", "ok": true, "n_tool_calls": 5, "bad_calls": 0, "first_prompt_tokens": 1565, "total_tokens": 11709, "wall_s": 10.0, "calls": [{"query": "checkout 504 final fix shipped precise setting value", "limit": 5, "sort": "newest", "detail": "full", "session_id": "", "around_message_id": 0, "window": 5, "role_filter": "user,assistant", "profile": ""}, {"query": "checkout 504", "limit": 10, "sort": "newest", "detail": "adaptive", "session_id": "", "around_message_id": 0, "window": 5, "role_filter": "user,assistant", "profile": ""}, {"query": "504 incident", "limit": 10, "sort": "newest", "detail": "adaptive", "session_id": "", "around_message_id": 0, "window": 5, "role_filter": "user,assistant", "profile": ""}, {"query": "checkout timeout", "limit": 10, "sort": "newest", "detail": "adaptive", "session_id": "", "around_message_id": 0, "window": 5, "role_filter": "user,assistant", "profile": ""}, {"query": "", "limit": 3, "sort": "newest", "detail": "adaptive", "session_id": "20260818_140200_cc22dd", "around_message_id": 21, "window": 15, "role_filter": "user,assistant", "profile": ""}], "final": "The shipped setting was **`statement_timeout = 45s`** on the **payments DB pool**. We also re-created the problematic index using **`CREATE INDEX CONCURRENTLY`** during an off-peak window.\n\nThis is recorded in @session:default/20260818_140200_cc22dd.", "rep": 0}
|
||||
{"task": "t2_scroll", "arm": "pr", "model": "openai/gpt-5.6-terra", "ok": true, "n_tool_calls": 5, "bad_calls": 0, "first_prompt_tokens": 751, "total_tokens": 9289, "wall_s": 8.0, "calls": [{"query": "checkout 504 incident FINAL fix shipped precise setting value", "limit": 10, "sort": "newest", "detail": "full", "session_id": "", "around_message_id": 0, "window": 5, "role_filter": "user,assistant", "profile": ""}, {"query": "checkout", "limit": 10, "sort": "newest", "detail": "adaptive", "session_id": "", "around_message_id": 0, "window": 5, "role_filter": "user,assistant", "profile": ""}, {"query": "504", "limit": 10, "sort": "newest", "detail": "adaptive", "session_id": "", "around_message_id": 0, "window": 5, "role_filter": "user,assistant", "profile": ""}, {"query": "gateway timeout", "limit": 10, "sort": "newest", "detail": "adaptive", "session_id": "", "around_message_id": 0, "window": 5, "role_filter": "user,assistant", "profile": ""}, {"query": "", "limit": 3, "sort": "newest", "detail": "adaptive", "session_id": "20260818_140200_cc22dd", "around_message_id": 25, "window": 10, "role_filter": "user,assistant", "profile": ""}], "final": "The final shipped setting was:\n\n- **`statement_timeout = 45s`** on the **payments DB pool**\n\nThe accompanying remediation was to re-create the index **`CONCURRENTLY`** during off-peak hours. Recorded in @session:default/20260818_140200_cc22dd.", "rep": 0}
|
||||
{"task": "t2_scroll", "arm": "main", "model": "openai/gpt-5.6-terra", "ok": true, "n_tool_calls": 5, "bad_calls": 0, "first_prompt_tokens": 1565, "total_tokens": 13087, "wall_s": 7.5, "calls": [{"query": "checkout 504 incident FINAL fix setting value", "limit": 10, "sort": "newest", "detail": "full", "session_id": "", "around_message_id": 0, "window": 5, "role_filter": "user,assistant", "profile": ""}, {"query": "checkout 504", "limit": 10, "sort": "newest", "detail": "adaptive", "session_id": "", "around_message_id": 0, "window": 5, "role_filter": "user,assistant", "profile": ""}, {"query": "504", "limit": 10, "sort": "newest", "detail": "adaptive", "session_id": "", "around_message_id": 0, "window": 5, "role_filter": "user,assistant", "profile": ""}, {"query": "checkout timeout", "limit": 10, "sort": "newest", "detail": "adaptive", "session_id": "", "around_message_id": 0, "window": 5, "role_filter": "user,assistant", "profile": ""}, {"query": "", "limit": 3, "sort": "newest", "detail": "adaptive", "session_id": "20260818_140200_cc22dd", "around_message_id": 25, "window": 12, "role_filter": "user,assistant", "profile": ""}], "final": "The shipped setting change was:\n\n- **`statement_timeout = 45s`** on the **payments DB pool**\n\nThe final remediation also re-created the index using **`CREATE INDEX CONCURRENTLY`** off-peak. This is recorded in @session:default/20260818_140200_cc22dd.", "rep": 1}
|
||||
{"task": "t2_scroll", "arm": "pr", "model": "openai/gpt-5.6-terra", "ok": true, "n_tool_calls": 5, "bad_calls": 0, "first_prompt_tokens": 751, "total_tokens": 10365, "wall_s": 10.2, "calls": [{"query": "checkout 504 incident FINAL fix shipped precise setting value", "limit": 10, "sort": "newest", "detail": "full", "session_id": "", "around_message_id": 0, "window": 5, "role_filter": "user,assistant", "profile": ""}, {"query": "checkout 504", "limit": 10, "sort": "newest", "detail": "adaptive", "session_id": "", "around_message_id": 0, "window": 5, "role_filter": "user,assistant", "profile": ""}, {"query": "504", "limit": 10, "sort": "newest", "detail": "adaptive", "session_id": "", "around_message_id": 0, "window": 5, "role_filter": "user,assistant", "profile": ""}, {"query": "checkout timeout", "limit": 10, "sort": "newest", "detail": "adaptive", "session_id": "", "around_message_id": 0, "window": 5, "role_filter": "user,assistant", "profile": ""}, {"query": "", "limit": 3, "sort": "newest", "detail": "adaptive", "session_id": "20260818_140200_cc22dd", "around_message_id": 25, "window": 10, "role_filter": "user,assistant", "profile": ""}], "final": "The final shipped setting was **`statement_timeout = 45s` on the payments DB pool**. We also re-created the index using **`CONCURRENTLY` off-peak**. Recorded in @session:default/20260818_140200_cc22dd.", "rep": 1}
|
||||
{"task": "t2_scroll", "arm": "main", "model": "openai/gpt-5.6-terra", "ok": true, "n_tool_calls": 5, "bad_calls": 0, "first_prompt_tokens": 1565, "total_tokens": 11609, "wall_s": 7.9, "calls": [{"query": "checkout 504 incident final fix shipped precise setting value", "limit": 10, "sort": "newest", "detail": "full", "session_id": "", "around_message_id": 0, "window": 5, "role_filter": "user,assistant", "profile": ""}, {"query": "checkout 504", "limit": 10, "sort": "newest", "detail": "full", "session_id": "", "around_message_id": 0, "window": 5, "role_filter": "user,assistant", "profile": ""}, {"query": "504", "limit": 10, "sort": "newest", "detail": "full", "session_id": "", "around_message_id": 0, "window": 5, "role_filter": "user,assistant", "profile": ""}, {"query": "checkout incident", "limit": 10, "sort": "newest", "detail": "full", "session_id": "", "around_message_id": 0, "window": 5, "role_filter": "user,assistant", "profile": ""}, {"query": "", "limit": 3, "sort": "newest", "detail": "adaptive", "session_id": "20260818_140200_cc22dd", "around_message_id": 24, "window": 15, "role_filter": "user,assistant", "profile": ""}], "final": "The shipped timeout setting was **`statement_timeout = 45s`** on the **payments DB pool**.\n\nThe final remediation also re-created the index using **`CONCURRENTLY`** during an off-peak window. This is in @session:default/20260818_140200_cc22dd.", "rep": 2}
|
||||
{"task": "t2_scroll", "arm": "pr", "model": "openai/gpt-5.6-terra", "ok": true, "n_tool_calls": 5, "bad_calls": 0, "first_prompt_tokens": 751, "total_tokens": 11157, "wall_s": 11.7, "calls": [{"query": "checkout 504 incident FINAL fix shipped precise setting value", "limit": 10, "sort": "newest", "detail": "full", "session_id": "", "around_message_id": 0, "window": 5, "role_filter": "user,assistant", "profile": ""}, {"query": "checkout OR 504", "limit": 10, "sort": "newest", "detail": "full", "session_id": "", "around_message_id": 0, "window": 5, "role_filter": "user,assistant", "profile": ""}, {"query": "\"504\"", "limit": 10, "sort": "newest", "detail": "full", "session_id": "", "around_message_id": 0, "window": 5, "role_filter": "user,assistant", "profile": ""}, {"query": "checkout timeout", "limit": 10, "sort": "newest", "detail": "full", "session_id": "", "around_message_id": 0, "window": 5, "role_filter": "user,assistant", "profile": ""}, {"query": "", "limit": 3, "sort": "newest", "detail": "adaptive", "session_id": "20260818_140200_cc22dd", "around_message_id": 25, "window": 12, "role_filter": "user,assistant", "profile": ""}], "final": "The final shipped setting was **`statement_timeout = 45s` on the payments DB pool**.\n\nThey also re-created the index using **`CONCURRENTLY` off-peak**. Source: @session:default/20260818_140200_cc22dd", "rep": 2}
|
||||
{"task": "t3_broaden", "arm": "main", "model": "openai/gpt-5.6-terra", "ok": true, "n_tool_calls": 4, "bad_calls": 0, "first_prompt_tokens": 1556, "total_tokens": 6712, "wall_s": 6.1, "calls": [{"query": "grafana beehive sensors", "limit": 5, "sort": "newest", "detail": "full", "session_id": "", "around_message_id": 0, "window": 5, "role_filter": "user,assistant", "profile": ""}, {"query": "beehive", "limit": 10, "sort": "newest", "detail": "adaptive", "session_id": "", "around_message_id": 0, "window": 5, "role_filter": "user,assistant", "profile": ""}, {"query": "grafana", "limit": 10, "sort": "newest", "detail": "adaptive", "session_id": "", "around_message_id": 0, "window": 5, "role_filter": "user,assistant", "profile": ""}, {"query": "sensor*", "limit": 10, "sort": "newest", "detail": "adaptive", "session_id": "", "around_message_id": 0, "window": 5, "role_filter": "user,assistant", "profile": ""}], "final": "Access Grafana at:\n\n`http://<garden-pi-hostname-or-IP>:3000`\n\nThe admin credentials are in your password manager. We set it up in @session:default/20260820_101000_ee33ff.", "rep": 0}
|
||||
{"task": "t3_broaden", "arm": "pr", "model": "openai/gpt-5.6-terra", "ok": true, "n_tool_calls": 4, "bad_calls": 0, "first_prompt_tokens": 742, "total_tokens": 4629, "wall_s": 12.4, "calls": [{"query": "beehive sensors Grafana graphs access URL dashboard", "limit": 10, "sort": "newest", "detail": "full", "session_id": "", "around_message_id": 0, "window": 5, "role_filter": "user,assistant", "profile": ""}, {"query": "beehive", "limit": 10, "sort": "newest", "detail": "full", "session_id": "", "around_message_id": 0, "window": 5, "role_filter": "user,assistant", "profile": ""}, {"query": "grafana", "limit": 10, "sort": "newest", "detail": "full", "session_id": "", "around_message_id": 0, "window": 5, "role_filter": "user,assistant", "profile": ""}, {"query": "sensors", "limit": 10, "sort": "newest", "detail": "adaptive", "session_id": "", "around_message_id": 0, "window": 5, "role_filter": "user,assistant", "profile": ""}], "final": "Access Grafana at **port 3000 on the garden Pi** — e.g. `http://<garden-pi-hostname-or-IP>:3000`. The admin credentials are in your password manager.\n\nWe set it up in @session:default/20260820_101000_ee33ff.", "rep": 0}
|
||||
{"task": "t3_broaden", "arm": "main", "model": "openai/gpt-5.6-terra", "ok": true, "n_tool_calls": 4, "bad_calls": 0, "first_prompt_tokens": 1556, "total_tokens": 6771, "wall_s": 9.8, "calls": [{"query": "beehive sensors grafana graphs", "limit": 10, "sort": "newest", "detail": "full", "session_id": "", "around_message_id": 0, "window": 5, "role_filter": "user,assistant", "profile": ""}, {"query": "grafana", "limit": 10, "sort": "newest", "detail": "adaptive", "session_id": "", "around_message_id": 0, "window": 5, "role_filter": "user,assistant", "profile": ""}, {"query": "beehive OR hive OR sensors", "limit": 10, "sort": "newest", "detail": "adaptive", "session_id": "", "around_message_id": 0, "window": 5, "role_filter": "user,assistant", "profile": ""}, {"query": "influx OR prometheus OR dashboard", "limit": 10, "sort": "newest", "detail": "adaptive", "session_id": "", "around_message_id": 0, "window": 5, "role_filter": "user,assistant", "profile": ""}], "final": "Access Grafana on port **3000** of the garden Pi: `http://<garden-pi-address>:3000`.\n\nThe admin credentials are in your password manager. This was from @session:default/20260820_101000_ee33ff.", "rep": 1}
|
||||
{"task": "t3_broaden", "arm": "pr", "model": "openai/gpt-5.6-terra", "ok": true, "n_tool_calls": 4, "bad_calls": 0, "first_prompt_tokens": 742, "total_tokens": 4636, "wall_s": 5.7, "calls": [{"query": "beehive sensors Grafana graphs access URL dashboard", "limit": 5, "sort": "newest", "detail": "full", "session_id": "", "around_message_id": 0, "window": 5, "role_filter": "user,assistant", "profile": ""}, {"query": "beehive OR bees OR hive", "limit": 10, "sort": "newest", "detail": "adaptive", "session_id": "", "around_message_id": 0, "window": 5, "role_filter": "user,assistant", "profile": ""}, {"query": "Grafana", "limit": 10, "sort": "newest", "detail": "adaptive", "session_id": "", "around_message_id": 0, "window": 5, "role_filter": "user,assistant", "profile": ""}, {"query": "sensor OR sensors", "limit": 10, "sort": "newest", "detail": "adaptive", "session_id": "", "around_message_id": 0, "window": 5, "role_filter": "user,assistant", "profile": ""}], "final": "Access Grafana on **port 3000 of the garden Pi**—for example, `http://<garden-pi-hostname-or-IP>:3000`. The admin credentials are in your password manager.\n\nWe set it up in @session:default/20260820_101000_ee33ff.", "rep": 1}
|
||||
{"task": "t3_broaden", "arm": "main", "model": "openai/gpt-5.6-terra", "ok": true, "n_tool_calls": 4, "bad_calls": 0, "first_prompt_tokens": 1556, "total_tokens": 6712, "wall_s": 14.7, "calls": [{"query": "Grafana beehive sensors graphs", "limit": 5, "sort": "newest", "detail": "full", "session_id": "", "around_message_id": 0, "window": 5, "role_filter": "user,assistant", "profile": ""}, {"query": "beehive", "limit": 10, "sort": "newest", "detail": "full", "session_id": "", "around_message_id": 0, "window": 5, "role_filter": "user,assistant", "profile": ""}, {"query": "grafana", "limit": 10, "sort": "newest", "detail": "full", "session_id": "", "around_message_id": 0, "window": 5, "role_filter": "user,assistant", "profile": ""}, {"query": "sensor", "limit": 10, "sort": "newest", "detail": "adaptive", "session_id": "", "around_message_id": 0, "window": 5, "role_filter": "user,assistant", "profile": ""}], "final": "Access Grafana at:\n\n`http://<garden-pi-hostname-or-IP>:3000`\n\nThe admin credentials are in your password manager. We set it up in @session:default/20260820_101000_ee33ff.", "rep": 2}
|
||||
{"task": "t3_broaden", "arm": "pr", "model": "openai/gpt-5.6-terra", "ok": true, "n_tool_calls": 4, "bad_calls": 0, "first_prompt_tokens": 742, "total_tokens": 4642, "wall_s": 6.5, "calls": [{"query": "beehive sensors Grafana graphs access URL", "limit": 10, "sort": "newest", "detail": "full", "session_id": "", "around_message_id": 0, "window": 5, "role_filter": "user,assistant", "profile": ""}, {"query": "grafana", "limit": 10, "sort": "newest", "detail": "adaptive", "session_id": "", "around_message_id": 0, "window": 5, "role_filter": "user,assistant", "profile": ""}, {"query": "beehive OR hive OR bees", "limit": 10, "sort": "newest", "detail": "adaptive", "session_id": "", "around_message_id": 0, "window": 5, "role_filter": "user,assistant", "profile": ""}, {"query": "sensors", "limit": 10, "sort": "newest", "detail": "adaptive", "session_id": "", "around_message_id": 0, "window": 5, "role_filter": "user,assistant", "profile": ""}], "final": "Access Grafana at **`http://<garden-pi>:3000`** (or its IP address on your local network). The admin credentials are in your password manager.\n\nWe set it up in the **Apiary Monitoring Setup** session: @session:default/20260820_101000_ee33ff", "rep": 2}
|
||||
{"task": "t4_link", "arm": "main", "model": "openai/gpt-5.6-terra", "ok": true, "n_tool_calls": 2, "bad_calls": 0, "first_prompt_tokens": 1552, "total_tokens": 5508, "wall_s": 6.7, "calls": [{"query": "reef aquarium build planned", "limit": 5, "sort": "newest", "detail": "adaptive", "session_id": "", "around_message_id": 0, "window": 5, "role_filter": "user,assistant", "profile": ""}, {"query": "reef OR aquarium OR tank", "limit": 10, "sort": "newest", "detail": "adaptive", "session_id": "", "around_message_id": 0, "window": 5, "role_filter": "user,assistant", "profile": ""}], "final": "That’s @session:default/20260822_183000_a4b4c4 — the 90-gallon reef aquarium build plan (30g sump, AI Hydra lighting, DIY stand, and cycling timeline).", "rep": 0}
|
||||
{"task": "t4_link", "arm": "pr", "model": "openai/gpt-5.6-terra", "ok": true, "n_tool_calls": 2, "bad_calls": 0, "first_prompt_tokens": 738, "total_tokens": 3250, "wall_s": 5.3, "calls": [{"query": "reef aquarium build planned planning", "limit": 5, "sort": "newest", "detail": "full", "session_id": "", "around_message_id": 0, "window": 5, "role_filter": "user,assistant", "profile": ""}, {"query": "reef OR aquarium OR saltwater OR aquascape", "limit": 10, "sort": "newest", "detail": "adaptive", "session_id": "", "around_message_id": 0, "window": 5, "role_filter": "user,assistant", "profile": ""}], "final": "Found it: the **“Reef Aquarium Build Plan”** session from August 22, covering the 90g display, 30g sump, AI Hydra lighting, DIY stand, and cycling timeline. Reopen it here: @session:default/20260822_183000_a4b4c4", "rep": 0}
|
||||
{"task": "t4_link", "arm": "main", "model": "openai/gpt-5.6-terra", "ok": true, "n_tool_calls": 2, "bad_calls": 0, "first_prompt_tokens": 1552, "total_tokens": 5509, "wall_s": 5.9, "calls": [{"query": "reef aquarium build planned", "limit": 5, "sort": "newest", "detail": "adaptive", "session_id": "", "around_message_id": 0, "window": 5, "role_filter": "user,assistant", "profile": ""}, {"query": "reef OR aquarium OR tank", "limit": 10, "sort": "newest", "detail": "adaptive", "session_id": "", "around_message_id": 0, "window": 5, "role_filter": "user,assistant", "profile": ""}], "final": "The reef plan is @session:default/20260822_183000_a4b4c4 — it covers the 90-gallon display, 30-gallon sump, AI Hydra lights, DIY stand, and cycling timeline.", "rep": 1}
|
||||
{"task": "t4_link", "arm": "pr", "model": "openai/gpt-5.6-terra", "ok": true, "n_tool_calls": 3, "bad_calls": 0, "first_prompt_tokens": 738, "total_tokens": 3466, "wall_s": 6.9, "calls": [{"query": "reef aquarium build planned", "limit": 5, "sort": "newest", "detail": "adaptive", "session_id": "", "around_message_id": 0, "window": 5, "role_filter": "user,assistant", "profile": ""}, {"query": "reef OR aquarium OR saltwater", "limit": 10, "sort": "newest", "detail": "full", "session_id": "", "around_message_id": 0, "window": 5, "role_filter": "user,assistant", "profile": ""}, {"query": "tank build", "limit": 10, "sort": "newest", "detail": "adaptive", "session_id": "", "around_message_id": 0, "window": 5, "role_filter": "user,assistant", "profile": ""}], "final": "Found it: **Reef Aquarium Build Plan** (Aug 22), covering the 90g display, 30g sump, AI Hydra lights, DIY stand, and cycling timeline. Reopen it here: @session:default/20260822_183000_a4b4c4", "rep": 1}
|
||||
{"task": "t4_link", "arm": "main", "model": "openai/gpt-5.6-terra", "ok": true, "n_tool_calls": 3, "bad_calls": 0, "first_prompt_tokens": 1552, "total_tokens": 5700, "wall_s": 11.6, "calls": [{"query": "reef aquarium build planned", "limit": 5, "sort": "newest", "detail": "adaptive", "session_id": "", "around_message_id": 0, "window": 5, "role_filter": "user,assistant", "profile": ""}, {"query": "reef OR aquarium OR saltwater", "limit": 10, "sort": "newest", "detail": "adaptive", "session_id": "", "around_message_id": 0, "window": 5, "role_filter": "user,assistant", "profile": ""}, {"query": "tank build", "limit": 10, "sort": "newest", "detail": "adaptive", "session_id": "", "around_message_id": 0, "window": 5, "role_filter": "user,assistant", "profile": ""}], "final": "That’s @session:default/20260822_183000_a4b4c4 — the 90-gallon reef aquarium build plan (30g sump, AI Hydra lighting, DIY stand, and cycling timeline).", "rep": 2}
|
||||
{"task": "t4_link", "arm": "pr", "model": "openai/gpt-5.6-terra", "ok": true, "n_tool_calls": 2, "bad_calls": 0, "first_prompt_tokens": 738, "total_tokens": 3225, "wall_s": 5.6, "calls": [{"query": "reef aquarium build planned", "limit": 5, "sort": "newest", "detail": "adaptive", "session_id": "", "around_message_id": 0, "window": 5, "role_filter": "user,assistant", "profile": ""}, {"query": "reef OR aquarium OR aquascape OR tank", "limit": 10, "sort": "newest", "detail": "adaptive", "session_id": "", "around_message_id": 0, "window": 5, "role_filter": "user,assistant", "profile": ""}], "final": "The reef build planning session is **“Reef Aquarium Build Plan”** from August 22: @session:default/20260822_183000_a4b4c4", "rep": 2}
|
||||
{"task": "t5_profile", "arm": "main", "model": "openai/gpt-5.6-terra", "ok": false, "n_tool_calls": 8, "bad_calls": 0, "first_prompt_tokens": 1553, "total_tokens": 16052, "wall_s": 23.7, "calls": [{"query": "", "limit": 3, "sort": "newest", "detail": "adaptive", "session_id": "20260815_110000_beef01", "around_message_id": 0, "window": 5, "role_filter": "user,assistant", "profile": "work"}, {"query": "", "limit": 3, "sort": "newest", "detail": "adaptive", "session_id": "20260815_110000_beef01", "around_message_id": 0, "window": 5, "role_filter": "user,assistant", "profile": "work"}, {"query": "", "limit": 3, "sort": "newest", "detail": "adaptive", "session_id": "20260815_110000_beef01", "around_message_id": 0, "window": 5, "role_filter": "user,assistant", "profile": "work"}, {"query": "", "limit": 3, "sort": "newest", "detail": "adaptive", "session_id": "20260815_110000_beef01", "around_message_id": 0, "window": 5, "role_filter": "user,assistant", "profile": "work"}, {"query": "", "limit": 3, "sort": "newest", "detail": "adaptive", "session_id": "20260815_110000_beef01", "around_message_id": 0, "window": 5, "role_filter": "user,assistant", "profile": "work"}, {"query": "", "limit": 3, "sort": "newest", "detail": "adaptive", "session_id": "20260815_110000_beef01", "around_message_id": 0, "window": 5, "role_filter": "user,assistant", "profile": "work"}, {"query": "", "limit": 3, "sort": "newest", "detail": "adaptive", "session_id": "20260815_110000_beef01", "around_message_id": 0, "window": 5, "role_filter": "user,assistant", "profile": "work"}, {"query": "", "limit": 3, "sort": "newest", "detail": "adaptive", "session_id": "20260815_110000_beef01", "around_message_id": 0, "window": 5, "role_filter": "user,assistant", "profile": "work"}], "final": "", "rep": 0}
|
||||
{"task": "t5_profile", "arm": "pr", "model": "openai/gpt-5.6-terra", "ok": true, "n_tool_calls": 2, "bad_calls": 0, "first_prompt_tokens": 739, "total_tokens": 3431, "wall_s": 7.3, "calls": [{"query": "", "limit": 3, "sort": "newest", "detail": "adaptive", "session_id": "20260815_110000_beef01", "around_message_id": 1, "window": 1, "role_filter": "user,assistant", "profile": "work"}, {"query": "", "limit": 3, "sort": "newest", "detail": "adaptive", "session_id": "20260815_110000_beef01", "around_message_id": 2, "window": 20, "role_filter": "user,assistant", "profile": "work"}], "final": "We decided on **HashiCorp Vault** for the platform team, with:\n\n- a **90-day secret rotation policy**\n- **dynamic database credentials** for services\n\nSession: @session:work/20260815_110000_beef01", "rep": 0}
|
||||
{"task": "t5_profile", "arm": "main", "model": "openai/gpt-5.6-terra", "ok": false, "n_tool_calls": 8, "bad_calls": 0, "first_prompt_tokens": 1553, "total_tokens": 16074, "wall_s": 30.2, "calls": [{"query": "", "limit": 3, "sort": "newest", "detail": "adaptive", "session_id": "20260815_110000_beef01", "around_message_id": 0, "window": 5, "role_filter": "user,assistant", "profile": "work"}, {"query": "", "limit": 3, "sort": "newest", "detail": "adaptive", "session_id": "20260815_110000_beef01", "around_message_id": 0, "window": 5, "role_filter": "user,assistant", "profile": "work"}, {"query": "", "limit": 3, "sort": "newest", "detail": "adaptive", "session_id": "20260815_110000_beef01", "around_message_id": 0, "window": 5, "role_filter": "user,assistant", "profile": "work"}, {"query": "", "limit": 3, "sort": "newest", "detail": "adaptive", "session_id": "20260815_110000_beef01", "around_message_id": 0, "window": 5, "role_filter": "user,assistant", "profile": "work"}, {"query": "", "limit": 3, "sort": "newest", "detail": "adaptive", "session_id": "20260815_110000_beef01", "around_message_id": 0, "window": 5, "role_filter": "user,assistant", "profile": "work"}, {"query": "", "limit": 3, "sort": "newest", "detail": "adaptive", "session_id": "20260815_110000_beef01", "around_message_id": 0, "window": 5, "role_filter": "user,assistant", "profile": "work"}, {"query": "", "limit": 3, "sort": "newest", "detail": "adaptive", "session_id": "20260815_110000_beef01", "around_message_id": 0, "window": 5, "role_filter": "user,assistant", "profile": "work"}, {"query": "", "limit": 3, "sort": "newest", "detail": "adaptive", "session_id": "20260815_110000_beef01", "around_message_id": 0, "window": 5, "role_filter": "user,assistant", "profile": "work"}], "final": "", "rep": 1}
|
||||
{"task": "t5_profile", "arm": "pr", "model": "openai/gpt-5.6-terra", "ok": false, "n_tool_calls": 8, "bad_calls": 0, "first_prompt_tokens": 739, "total_tokens": 9510, "wall_s": 22.7, "calls": [{"query": "", "limit": 3, "sort": "newest", "detail": "adaptive", "session_id": "20260815_110000_beef01", "around_message_id": 0, "window": 5, "role_filter": "user,assistant", "profile": "work"}, {"query": "", "limit": 3, "sort": "newest", "detail": "full", "session_id": "20260815_110000_beef01", "around_message_id": 0, "window": 5, "role_filter": "user,assistant", "profile": "work"}, {"query": "", "limit": 3, "sort": "newest", "detail": "full", "session_id": "20260815_110000_beef01", "around_message_id": 0, "window": 5, "role_filter": "user,assistant", "profile": "work"}, {"query": "", "limit": 3, "sort": "newest", "detail": "full", "session_id": "20260815_110000_beef01", "around_message_id": 0, "window": 5, "role_filter": "user,assistant", "profile": "work"}, {"query": "", "limit": 3, "sort": "newest", "detail": "full", "session_id": "20260815_110000_beef01", "around_message_id": 0, "window": 5, "role_filter": "user,assistant", "profile": "work"}, {"query": "", "limit": 3, "sort": "newest", "detail": "full", "session_id": "20260815_110000_beef01", "around_message_id": 0, "window": 5, "role_filter": "user,assistant", "profile": "work"}, {"query": "", "limit": 3, "sort": "newest", "detail": "full", "session_id": "20260815_110000_beef01", "around_message_id": 0, "window": 5, "role_filter": "user,assistant", "profile": "work"}, {"query": "", "limit": 3, "sort": "newest", "detail": "full", "session_id": "20260815_110000_beef01", "around_message_id": 0, "window": 5, "role_filter": "user,assistant", "profile": "work"}], "final": "", "rep": 1}
|
||||
{"task": "t5_profile", "arm": "main", "model": "openai/gpt-5.6-terra", "ok": false, "n_tool_calls": 8, "bad_calls": 0, "first_prompt_tokens": 1553, "total_tokens": 16143, "wall_s": 24.8, "calls": [{"query": "", "limit": 3, "sort": "newest", "detail": "adaptive", "session_id": "20260815_110000_beef01", "around_message_id": 0, "window": 5, "role_filter": "user,assistant", "profile": "work"}, {"query": "", "limit": 3, "sort": "newest", "detail": "adaptive", "session_id": "20260815_110000_beef01", "around_message_id": 0, "window": 5, "role_filter": "user,assistant", "profile": "work"}, {"query": "", "limit": 3, "sort": "newest", "detail": "adaptive", "session_id": "20260815_110000_beef01", "around_message_id": -1, "window": 5, "role_filter": "user,assistant", "profile": "work"}, {"query": "", "limit": 3, "sort": "newest", "detail": "adaptive", "session_id": "20260815_110000_beef01", "around_message_id": 0, "window": 5, "role_filter": "user,assistant", "profile": "work"}, {"query": "", "limit": 3, "sort": "newest", "detail": "adaptive", "session_id": "20260815_110000_beef01", "around_message_id": 0, "window": 5, "role_filter": "user,assistant", "profile": "work"}, {"query": "", "limit": 3, "sort": "newest", "detail": "adaptive", "session_id": "20260815_110000_beef01", "around_message_id": 0, "window": 5, "role_filter": "user,assistant", "profile": "work"}, {"query": "", "limit": 3, "sort": "newest", "detail": "adaptive", "session_id": "20260815_110000_beef01", "around_message_id": 0, "window": 5, "role_filter": "user,assistant", "profile": "work"}, {"query": "", "limit": 3, "sort": "newest", "detail": "adaptive", "session_id": "20260815_110000_beef01", "around_message_id": 0, "window": 5, "role_filter": "user,assistant", "profile": "work"}], "final": "", "rep": 2}
|
||||
{"task": "t5_profile", "arm": "pr", "model": "openai/gpt-5.6-terra", "ok": true, "n_tool_calls": 3, "bad_calls": 0, "first_prompt_tokens": 739, "total_tokens": 4194, "wall_s": 14.4, "calls": [{"query": "", "limit": 3, "sort": "newest", "detail": "adaptive", "session_id": "20260815_110000_beef01", "around_message_id": 0, "window": 5, "role_filter": "user,assistant", "profile": "work"}, {"query": "", "limit": 3, "sort": "newest", "detail": "full", "session_id": "20260815_110000_beef01", "around_message_id": 0, "window": 5, "role_filter": "user,assistant", "profile": "work"}, {"query": "", "limit": 3, "sort": "newest", "detail": "full", "session_id": "20260815_110000_beef01", "around_message_id": 1, "window": 20, "role_filter": "user,assistant", "profile": "work"}], "final": "We decided on **HashiCorp Vault** for the platform team, with:\n\n- a **90-day secret rotation** policy\n- **dynamic database credentials** for services\n\nSession: @session:work/20260815_110000_beef01", "rep": 2}
|
||||
{"task": "t6_browse", "arm": "main", "model": "openai/gpt-5.6-terra", "ok": true, "n_tool_calls": 1, "bad_calls": 0, "first_prompt_tokens": 1549, "total_tokens": 3985, "wall_s": 3.8, "calls": [{"query": "", "limit": 10, "sort": "newest", "detail": "adaptive", "session_id": "", "around_message_id": 0, "window": 5, "role_filter": "user,assistant", "profile": ""}], "final": "Recent work has included:\n\n- Tuning your **GPU server fan curve** to reduce idle noise.\n- Starting an **LLC tax-prep checklist**.\n- Planning a **90-gallon reef aquarium build**.\n- Setting up **yard beehive/apiary sensor monitoring**.\n- Investigating a **production checkout API 504 timeout incident**.\n- Planning a **Postgres 16 migration** for the analytics cluster.\n\nThe most active technical thread was the @session:default/20260818_140200_cc22dd.", "rep": 0}
|
||||
{"task": "t6_browse", "arm": "pr", "model": "openai/gpt-5.6-terra", "ok": true, "n_tool_calls": 1, "bad_calls": 0, "first_prompt_tokens": 735, "total_tokens": 2453, "wall_s": 11.5, "calls": [{"query": "", "limit": 10, "sort": "newest", "detail": "adaptive", "session_id": "", "around_message_id": 0, "window": 5, "role_filter": "user,assistant", "profile": ""}], "final": "Recent work has spanned a mix of infrastructure, operations, and personal projects:\n\n- **GPU server noise:** tuning the idle fan curve. @session:default/20260825_200000_ffeedd \n- **LLC taxes:** starting a tax-prep checklist. @session:default/20260824_090000_d5e5f6 \n- **Reef aquarium:** planning a 90-gallon build. @session:default/20260822_183000_a4b4c4 \n- **Apiary:** setting up yard beehive sensor monitoring. @session:default/20260820_101000_ee33ff \n- **Production incident:** investigating checkout API timeouts/504s. @session:default/20260818_140200_cc22dd \n- **Data platform:** planning a Postgres 16 migration for analytics. @session:default/20260810_091500_aa11bb", "rep": 0}
|
||||
{"task": "t6_browse", "arm": "main", "model": "openai/gpt-5.6-terra", "ok": true, "n_tool_calls": 1, "bad_calls": 0, "first_prompt_tokens": 1549, "total_tokens": 3969, "wall_s": 3.9, "calls": [{"query": "", "limit": 10, "sort": "newest", "detail": "adaptive", "session_id": "", "around_message_id": 0, "window": 5, "role_filter": "user,assistant", "profile": ""}], "final": "Recent work includes:\n\n- Tuning the **GPU server fan curve** to reduce idle noise.\n- Starting an **LLC tax-prep checklist**.\n- Planning a **90-gallon reef aquarium** build.\n- Setting up **yard beehive/apiary sensor monitoring**.\n- Investigating a production **checkout API 504 timeout incident**.\n- Planning a **Postgres 16 migration** for the analytics cluster.\n\nWant to resume any of these?", "rep": 1}
|
||||
{"task": "t6_browse", "arm": "pr", "model": "openai/gpt-5.6-terra", "ok": true, "n_tool_calls": 1, "bad_calls": 0, "first_prompt_tokens": 735, "total_tokens": 2227, "wall_s": 2.9, "calls": [{"query": "", "limit": 5, "sort": "newest", "detail": "adaptive", "session_id": "", "around_message_id": 0, "window": 5, "role_filter": "user,assistant", "profile": ""}], "final": "Recent work includes:\n\n- **GPU server:** tuning the idle fan curve to reduce noise. \n- **LLC taxes:** starting a tax-prep checklist. \n- **90-gallon reef aquarium:** planning the build. \n- **Apiary:** setting up monitoring for yard beehive sensors. \n- **Production checkout incident:** investigating checkout API timeouts / user-facing 504s.", "rep": 1}
|
||||
{"task": "t6_browse", "arm": "main", "model": "openai/gpt-5.6-terra", "ok": true, "n_tool_calls": 1, "bad_calls": 0, "first_prompt_tokens": 1549, "total_tokens": 3984, "wall_s": 4.7, "calls": [{"query": "", "limit": 10, "sort": "newest", "detail": "adaptive", "session_id": "", "around_message_id": 0, "window": 5, "role_filter": "user,assistant", "profile": ""}], "final": "Recent work has covered a mix of infrastructure, operations, and personal projects:\n\n- Tuning a **GPU server fan curve** to reduce idle noise.\n- Starting an **LLC tax-prep checklist**.\n- Planning a **90-gallon reef aquarium** build.\n- Setting up **yard beehive/apiary sensor monitoring**.\n- Investigating and addressing a **production checkout API 504 incident**.\n- Planning a **Postgres 16 migration** for the analytics cluster.", "rep": 2}
|
||||
{"task": "t6_browse", "arm": "pr", "model": "openai/gpt-5.6-terra", "ok": true, "n_tool_calls": 1, "bad_calls": 0, "first_prompt_tokens": 735, "total_tokens": 2323, "wall_s": 4.8, "calls": [{"query": "", "limit": 5, "sort": "newest", "detail": "full", "session_id": "", "around_message_id": 0, "window": 5, "role_filter": "user,assistant", "profile": ""}], "final": "Recent work, roughly newest first:\n\n- **GPU server fan curve:** reducing idle noise while keeping safe thermals. @session:default/20260825_200000_ffeedd\n- **LLC tax prep:** starting a checklist and organizing the needed materials. @session:default/20260824_090000_d5e5f6\n- **90-gallon reef aquarium:** planning out the build. @session:default/20260822_183000_a4b4c4\n- **Apiary monitoring:** setting up sensor monitoring for the yard beehives. @session:default/20260820_101000_ee33ff\n- **Checkout outage:** investigating production checkout API timeouts/504s. @session:default/20260818_140200_cc22dd", "rep": 2}
|
||||
@@ -0,0 +1,36 @@
|
||||
{"task": "t1_discover", "arm": "main", "model": "qwen/qwen3-coder-30b-a3b-instruct", "ok": true, "n_tool_calls": 1, "bad_calls": 0, "first_prompt_tokens": 1990, "total_tokens": 4755, "wall_s": 3.1, "calls": [{"query": "postgres migration analytics cluster"}], "final": "Based on our conversation, we settled on using pglogical for the PostgreSQL migration of the analytics cluster. The approach involves:\n\n1. Using pglogical for logical replication to achieve near-zero downtime\n2. Configuring pglogical publisher on the pg13 primary server\n3. Setting up pglogical subscriber on the new pg16 box\n4. Scheduling the cutover during a Sunday at 02:00 window\n\nThis approach allows us to migrate from PostgreSQL 13 to 16 with minimal disruption to services. The replication is already configured with the publisher on the primary and subscriber on the new server.", "rep": 0}
|
||||
{"task": "t1_discover", "arm": "pr", "model": "qwen/qwen3-coder-30b-a3b-instruct", "ok": true, "n_tool_calls": 1, "bad_calls": 0, "first_prompt_tokens": 1142, "total_tokens": 3137, "wall_s": 3.0, "calls": [{"query": "postgres migration analytics cluster"}], "final": "Based on our conversation, we settled on using pglogical for the PostgreSQL migration of the analytics cluster. The approach was to migrate to PostgreSQL 16 using pglogical replication, with a cutover scheduled during a Sunday at 02:00 window for minimal downtime. The pglogical publisher was configured on the PostgreSQL 13 primary, while the subscriber was set up on the new PostgreSQL 16 box to handle the synchronization.\n\nYou can review the full details in our session: @session:default/20260810_091500_aa11bb", "rep": 0}
|
||||
{"task": "t1_discover", "arm": "main", "model": "qwen/qwen3-coder-30b-a3b-instruct", "ok": true, "n_tool_calls": 1, "bad_calls": 0, "first_prompt_tokens": 1990, "total_tokens": 4785, "wall_s": 3.2, "calls": [{"query": "postgres migration analytics cluster"}], "final": "Based on our conversation, we settled on using pglogical for the PostgreSQL migration of the analytics cluster. The approach was to:\n\n1. Use pglogical for logical replication to achieve near-zero downtime\n2. Configure pglogical publisher on the pg13 primary \n3. Set up pglogical subscriber on the new pg16 box to sync data\n4. Perform the cutover during a scheduled Sunday 02:00 window\n\nThis approach was chosen because downtime mattered, and we needed to minimize disruption to the analytics cluster during the migration process. The migration was already underway with the pglogical setup configured.\n\nThe session where we made this decision is @session:default/20260810_091500_aa11bb.", "rep": 1}
|
||||
{"task": "t1_discover", "arm": "pr", "model": "qwen/qwen3-coder-30b-a3b-instruct", "ok": true, "n_tool_calls": 1, "bad_calls": 0, "first_prompt_tokens": 1142, "total_tokens": 3124, "wall_s": 2.7, "calls": [{"query": "postgres migration analytics cluster"}], "final": "Based on our conversation, we settled on using pglogical for the PostgreSQL migration of the analytics cluster. The approach involved:\n\n1. Setting up pglogical publisher on the pg13 primary\n2. Configuring a subscriber on the new pg16 box to sync data\n3. Planning a cutover during a Sunday 02:00 window for minimal downtime\n\nYou can review the full details in our session: @session:default/20260810_091500_aa11bb", "rep": 1}
|
||||
{"task": "t1_discover", "arm": "main", "model": "qwen/qwen3-coder-30b-a3b-instruct", "ok": true, "n_tool_calls": 1, "bad_calls": 0, "first_prompt_tokens": 1990, "total_tokens": 4794, "wall_s": 3.2, "calls": [{"query": "postgres migration analytics cluster"}], "final": "Based on our conversation, we settled on using pglogical for the PostgreSQL migration of the analytics cluster. The approach was to:\n\n1. Use pglogical for logical replication to achieve near-zero downtime\n2. Configure pglogical publisher on the pg13 primary \n3. Set up pglogical subscriber on the new pg16 box to sync data\n4. Perform the cutover during a scheduled Sunday 02:00 window\n\nThis approach was chosen because downtime mattered, so we needed a method that would minimize disruption to the analytics cluster during the migration process. The migration was planned to move from PostgreSQL 13 to PostgreSQL 16.\n\nThat's the session where we made this decision: @session:default/20260810_091500_aa11bb", "rep": 2}
|
||||
{"task": "t1_discover", "arm": "pr", "model": "qwen/qwen3-coder-30b-a3b-instruct", "ok": true, "n_tool_calls": 1, "bad_calls": 0, "first_prompt_tokens": 1142, "total_tokens": 3133, "wall_s": 2.8, "calls": [{"query": "postgres migration analytics cluster"}], "final": "Based on our conversation, we settled on using pglogical for the PostgreSQL migration of the analytics cluster. The approach involves:\n\n1. Using pglogical for logical replication to achieve near-zero downtime\n2. Configuring pglogical publisher on the pg13 primary\n3. Setting up pglogical subscriber on the new pg16 box for syncing\n4. Planning a cutover during a Sunday 02:00 window\n\nThe session where we discussed this is available at @session:default/20260810_091500_aa11bb.", "rep": 2}
|
||||
{"task": "t2_scroll", "arm": "main", "model": "qwen/qwen3-coder-30b-a3b-instruct", "ok": false, "n_tool_calls": 4, "bad_calls": 0, "first_prompt_tokens": 2006, "total_tokens": 12455, "wall_s": 6.9, "calls": [{"query": "checkout 504 incident final fix"}, {"query": "checkout 504 incident"}, {"query": "504 error checkout"}, {"query": "checkout timeout"}], "final": "", "rep": 0}
|
||||
{"task": "t2_scroll", "arm": "pr", "model": "qwen/qwen3-coder-30b-a3b-instruct", "ok": true, "n_tool_calls": 7, "bad_calls": 0, "first_prompt_tokens": 1158, "total_tokens": 27165, "wall_s": 10.8, "calls": [{"query": "checkout 504 incident final fix"}, {"query": "checkout 504"}, {"session_id": "20260818_140200_cc22dd", "around_message_id": 7}, {"session_id": "20260818_140200_cc22dd", "around_message_id": 12}, {"session_id": "20260818_140200_cc22dd", "around_message_id": 17}, {"session_id": "20260818_140200_cc22dd", "around_message_id": 22}, {"session_id": "20260818_140200_cc22dd", "around_message_id": 27}], "final": "Based on my search of the session history, I found the final fix for the checkout 504 incident. \n\nThe precise setting and value that was shipped as the final fix was:\n\n**statement_timeout set to 45 seconds** on the payments DB pool.\n\nThis was implemented alongside re-creating the index CONCURRENTLY during off-peak hours. The incident was caused by a nightly migration job that ran CREATE INDEX without CONCURRENTLY, which was competing with checkout writes and causing the timeouts. The team first killed the problematic index build, then implemented this timeout increase to prevent future occurrences.", "rep": 0}
|
||||
{"task": "t2_scroll", "arm": "main", "model": "qwen/qwen3-coder-30b-a3b-instruct", "ok": true, "n_tool_calls": 5, "bad_calls": 0, "first_prompt_tokens": 2006, "total_tokens": 17099, "wall_s": 7.1, "calls": [{"query": "checkout 504 incident final fix"}, {"query": "checkout 504 incident"}, {"query": "504 error checkout"}, {"query": "504 error"}, {"session_id": "20260818_140200_cc22dd"}], "final": "Based on our session history, I found the final fix for the checkout 504 incident. \n\nThe precise setting and value that was changed was:\n\n**statement_timeout increased to 45 seconds** on the payments DB pool.\n\nThis was the final fix implemented after the incident where a nightly migration job had created a non-concurrent index build that was blocking checkout operations. The team first killed the problematic index build, but then needed to adjust the statement timeout to prevent the 504 errors during retry operations. They also re-created the index CONCURRENTLY during off-peak hours to prevent future occurrences.\n\nThe specific change was made to the payments database connection pool configuration to increase the statement timeout from its previous value to 45 seconds.", "rep": 1}
|
||||
{"task": "t2_scroll", "arm": "pr", "model": "qwen/qwen3-coder-30b-a3b-instruct", "ok": true, "n_tool_calls": 7, "bad_calls": 0, "first_prompt_tokens": 1158, "total_tokens": 27166, "wall_s": 10.7, "calls": [{"query": "checkout 504 incident final fix"}, {"query": "checkout 504"}, {"session_id": "20260818_140200_cc22dd", "around_message_id": 7}, {"session_id": "20260818_140200_cc22dd", "around_message_id": 12}, {"session_id": "20260818_140200_cc22dd", "around_message_id": 17}, {"session_id": "20260818_140200_cc22dd", "around_message_id": 22}, {"session_id": "20260818_140200_cc22dd", "around_message_id": 27}], "final": "Based on my search of the session history, I found the details about the checkout 504 incident and the final fix that was shipped.\n\nThe final fix was to raise the statement_timeout to 45 seconds on the payments DB pool. This was the precise setting and value that was changed to resolve the checkout 504 errors.\n\nThe complete solution involved two parts:\n1. Raising statement_timeout to 45s on the payments DB pool\n2. Re-creating the index CONCURRENTLY during off-peak hours\n\nThis fix was deployed everywhere after successful testing on the canary environment. The incident was caused by a nightly migration job that ran CREATE INDEX without CONCURRENTLY, which was competing with checkout writes and causing the timeouts.", "rep": 1}
|
||||
{"task": "t2_scroll", "arm": "main", "model": "qwen/qwen3-coder-30b-a3b-instruct", "ok": false, "n_tool_calls": 5, "bad_calls": 0, "first_prompt_tokens": 2006, "total_tokens": 17766, "wall_s": 9.4, "calls": [{"query": "checkout 504 incident final fix"}, {"query": "checkout 504 incident"}, {"query": "504 error checkout"}, {"query": "checkout"}, {"session_id": "20260818_140200_cc22dd"}], "final": "", "rep": 2}
|
||||
{"task": "t2_scroll", "arm": "pr", "model": "qwen/qwen3-coder-30b-a3b-instruct", "ok": true, "n_tool_calls": 4, "bad_calls": 0, "first_prompt_tokens": 1158, "total_tokens": 13835, "wall_s": 7.7, "calls": [{"query": "checkout 504 incident final fix"}, {"query": "checkout 504"}, {"session_id": "20260818_140200_cc22dd", "around_message_id": 7, "window": 20}, {"session_id": "20260818_140200_cc22dd", "around_message_id": 27, "window": 20}], "final": "Based on my search of the session history, I found the details about the checkout 504 incident and the final fix that was shipped.\n\nThe final fix was to raise the statement_timeout to 45 seconds on the payments DB pool. This was the precise setting and value that resolved the issue.\n\nThe incident occurred when a nightly migration job ran CREATE INDEX without CONCURRENTLY, which was competing with checkout writes and causing timeouts. Although they killed the index build and rolled back the deployment, the issue persisted due to statement timeouts firing on retries. The final solution involved increasing the statement_timeout to 45 seconds on the payments database connection pool, along with re-creating the index CONCURRENTLY during off-peak hours.", "rep": 2}
|
||||
{"task": "t3_broaden", "arm": "main", "model": "qwen/qwen3-coder-30b-a3b-instruct", "ok": true, "n_tool_calls": 3, "bad_calls": 0, "first_prompt_tokens": 1995, "total_tokens": 9400, "wall_s": 5.3, "calls": [{"query": "grafana beehive sensors"}, {"query": "Grafana beehive"}, {"query": "Grafana"}], "final": "I found our conversation about setting up Grafana for the beehive sensors. According to our discussion from August 20, 2026, the Grafana instance is running on port 3000 of the garden pi. You can access it by visiting http://garden-pi:3000 and using the admin login credentials that are stored in your password manager.\n\nThe relevant exchange was:\n- You asked \"I want graphs\"\n- I responded \"I set up a grafana instance for the sensor graphs.\"\n- You asked \"where do I see it\"\n- I replied \"The dashboard is on port 3000 of the garden pi, admin login in your password manager.\"\n\nThis session is titled \"Apiary Monitoring Setup\" and can be referenced as @session:default/20260820_101000_ee33ff.", "rep": 0}
|
||||
{"task": "t3_broaden", "arm": "pr", "model": "qwen/qwen3-coder-30b-a3b-instruct", "ok": true, "n_tool_calls": 3, "bad_calls": 0, "first_prompt_tokens": 1147, "total_tokens": 6217, "wall_s": 5.2, "calls": [{"query": "grafana beehive sensors"}, {"query": "grafana beehive"}, {"query": "sensor grafana"}], "final": "I found our conversation about setting up Grafana for the beehive sensors. According to our discussion, you set up a Grafana instance for the sensor graphs. The dashboard is accessible on port 3000 of the garden pi, with the admin login details stored in your password manager.\n\nYou can access the graphs by navigating to http://garden-pi:3000 in your browser, where you'll need to log in using the admin credentials from your password manager.\n\nFor more details, you can review our full conversation: @session:default/20260820_101000_ee33ff", "rep": 0}
|
||||
{"task": "t3_broaden", "arm": "main", "model": "qwen/qwen3-coder-30b-a3b-instruct", "ok": true, "n_tool_calls": 5, "bad_calls": 0, "first_prompt_tokens": 1995, "total_tokens": 14454, "wall_s": 8.1, "calls": [{"query": "grafana beehive sensors"}, {"query": "Grafana beehive"}, {"query": "sensor dashboard grafana"}, {"query": "IoT sensors monitoring"}, {"query": "beehive"}], "final": "I found our conversation about the beehive monitoring setup. Based on our discussion, you can access the Grafana graphs for the beehive sensors through the following:\n\nThe dashboard is on port 3000 of the garden pi, with admin login credentials stored in your password manager.\n\nThe specific session where we discussed this is titled \"Apiary Monitoring Setup\" (@session:default/20260820_101000_ee33ff). In that conversation, we confirmed that:\n1. The hive telemetry (temperature, humidity, weight) is already publishing to MQTT\n2. A Grafana instance was set up for the sensor graphs\n3. The dashboard is accessible via port 3000 on the garden pi\n4. Admin login details are in your password manager\n\nTo access the graphs, you would typically navigate to http://garden-pi:3000 in your browser and log in using the admin credentials from your password manager.", "rep": 1}
|
||||
{"task": "t3_broaden", "arm": "pr", "model": "qwen/qwen3-coder-30b-a3b-instruct", "ok": true, "n_tool_calls": 3, "bad_calls": 0, "first_prompt_tokens": 1147, "total_tokens": 6183, "wall_s": 4.4, "calls": [{"query": "grafana beehive sensors"}, {"query": "grafana beehive"}, {"query": "sensor grafana"}], "final": "Based on our previous conversation, you can access the Grafana graphs for the beehive sensors through the dashboard on port 3000 of the garden pi. The admin login credentials are stored in your password manager.\n\nYou can visit the dashboard at: http://garden-pi:3000\n\nThe session where we set this up is available for reference: @session:default/20260820_101000_ee33ff", "rep": 1}
|
||||
{"task": "t3_broaden", "arm": "main", "model": "qwen/qwen3-coder-30b-a3b-instruct", "ok": true, "n_tool_calls": 3, "bad_calls": 0, "first_prompt_tokens": 1995, "total_tokens": 9371, "wall_s": 5.2, "calls": [{"query": "grafana beehive sensors"}, {"query": "Grafana beehive"}, {"query": "Grafana"}], "final": "I found our previous conversation about setting up Grafana for the beehive sensors. According to our discussion, you set up a Grafana instance for the sensor graphs. The dashboard is accessible on port 3000 of the garden pi, and the admin login credentials are stored in your password manager.\n\nThe relevant session is titled \"Apiary Monitoring Setup\" and you can access it at: @session:default/20260820_101000_ee33ff\n\nTo access the graphs, you would go to the garden pi's IP address on port 3000 (likely something like http://[garden-pi-ip]:3000) and log in using the admin credentials from your password manager.", "rep": 2}
|
||||
{"task": "t3_broaden", "arm": "pr", "model": "qwen/qwen3-coder-30b-a3b-instruct", "ok": true, "n_tool_calls": 4, "bad_calls": 0, "first_prompt_tokens": 1147, "total_tokens": 7947, "wall_s": 5.6, "calls": [{"query": "grafana beehive sensors"}, {"query": "grafana beehive"}, {"query": "sensor data grafana"}, {"query": "beehive"}], "final": "I found our conversation about the beehive monitoring system. According to our discussion, the Grafana instance for the sensor graphs is running on port 3000 of the garden pi. You can access it at:\n\n@session:default/20260820_101000_ee33ff\n\nThe dashboard should be accessible via http://garden-pi:3000 (assuming garden-pi is your hostname) or the IP address of that device. The admin login credentials are stored in your password manager.", "rep": 2}
|
||||
{"task": "t4_link", "arm": "main", "model": "qwen/qwen3-coder-30b-a3b-instruct", "ok": true, "n_tool_calls": 1, "bad_calls": 0, "first_prompt_tokens": 1991, "total_tokens": 4653, "wall_s": 3.1, "calls": [{"query": "reef aquarium build plan"}], "final": "I found the session where we planned the reef aquarium build. It's titled \"Reef Aquarium Build Plan\" and you can reopen it using this link: @session:default/20260822_183000_a4b4c4\n\nThe session started with you asking to plan the 90 gallon reef aquarium build, and we discussed a build sketch including a 90g display, 30g sump, AI Hydra lighting, and a DIY stand. We also touched on the cycling timeline, suggesting a 6-8 week fishless cycle with ammonia dosing followed by introducing clean-up crew members first.", "rep": 0}
|
||||
{"task": "t4_link", "arm": "pr", "model": "qwen/qwen3-coder-30b-a3b-instruct", "ok": true, "n_tool_calls": 1, "bad_calls": 0, "first_prompt_tokens": 1143, "total_tokens": 3023, "wall_s": 2.6, "calls": [{"query": "reef aquarium build plan"}], "final": "I found the session where you planned the reef aquarium build. Here's the link to reopen it: @session:default/20260822_183000_a4b4c4\n\nThe session was titled \"Reef Aquarium Build Plan\" and started with your message: \"Help me plan the 90 gallon reef aquarium build.\" The discussion covered aspects like the 90-gallon display tank, 30-gallon sump, AI Hydra lighting, and a DIY stand, along with a fishless cycling timeline of 6-8 weeks.", "rep": 0}
|
||||
{"task": "t4_link", "arm": "main", "model": "qwen/qwen3-coder-30b-a3b-instruct", "ok": true, "n_tool_calls": 1, "bad_calls": 0, "first_prompt_tokens": 1991, "total_tokens": 4631, "wall_s": 2.6, "calls": [{"query": "reef aquarium build plan"}], "final": "I found the session where we planned the reef aquarium build. It's titled \"Reef Aquarium Build Plan\" and is ready for you to reopen.\n\n@session:default/20260822_183000_a4b4c4\n\nThe session contains our initial planning discussion about the 90-gallon reef aquarium build, including details about the display tank, sump, lighting, and DIY stand. We also discussed the cycling timeline for the aquarium.\n\nWould you like me to summarize what we discussed in that session?", "rep": 1}
|
||||
{"task": "t4_link", "arm": "pr", "model": "qwen/qwen3-coder-30b-a3b-instruct", "ok": true, "n_tool_calls": 1, "bad_calls": 0, "first_prompt_tokens": 1143, "total_tokens": 3023, "wall_s": 2.9, "calls": [{"query": "reef aquarium build plan"}], "final": "I found the session where you planned the reef aquarium build. Here's the link to reopen it: @session:default/20260822_183000_a4b4c4\n\nThe session was titled \"Reef Aquarium Build Plan\" and started with your message: \"Help me plan the 90 gallon reef aquarium build.\" The discussion covered aspects like the 90-gallon display tank, 30-gallon sump, AI Hydra lighting, and a DIY stand, along with a fishless cycling timeline of 6-8 weeks.", "rep": 1}
|
||||
{"task": "t4_link", "arm": "main", "model": "qwen/qwen3-coder-30b-a3b-instruct", "ok": true, "n_tool_calls": 1, "bad_calls": 0, "first_prompt_tokens": 1991, "total_tokens": 4648, "wall_s": 2.7, "calls": [{"query": "reef aquarium build plan"}], "final": "I found the session where we planned the reef aquarium build. It's titled \"Reef Aquarium Build Plan\" and you can reopen it using this link: @session:default/20260822_183000_a4b4c4\n\nThe session started with your message: \"Help me plan the 90 gallon reef aquarium build.\" and we discussed a build including a 90g display, 30g sump, AI Hydra lighting, and a DIY stand. We also touched on the cycling timeline, planning for a 6-8 week fishless cycle with ammonia dosing.", "rep": 2}
|
||||
{"task": "t4_link", "arm": "pr", "model": "qwen/qwen3-coder-30b-a3b-instruct", "ok": true, "n_tool_calls": 1, "bad_calls": 0, "first_prompt_tokens": 1143, "total_tokens": 3023, "wall_s": 2.7, "calls": [{"query": "reef aquarium build plan"}], "final": "I found the session where you planned the reef aquarium build. Here's the link to reopen it: @session:default/20260822_183000_a4b4c4\n\nThis session was titled \"Reef Aquarium Build Plan\" and started with your message: \"Help me plan the 90 gallon reef aquarium build.\" The conversation included discussions about the build specifications (90g display, 30g sump, AI Hydra lighting, DIY stand) and a cycle timeline (6-8 weeks fishless cycle with ammonia dosing).", "rep": 2}
|
||||
{"task": "t5_profile", "arm": "main", "model": "qwen/qwen3-coder-30b-a3b-instruct", "ok": true, "n_tool_calls": 1, "bad_calls": 0, "first_prompt_tokens": 2002, "total_tokens": 4548, "wall_s": 2.5, "calls": [{"session_id": "20260815_110000_beef01"}], "final": "In that session, we decided to go with **HashiCorp Vault** for secrets management, implementing a **90-day rotation policy** and using **dynamic database credentials** for services. \n\nThe full conversation was:\n- User: \"We need to pick a secrets management approach for the platform team.\"\n- Assistant: \"Candidates: AWS Secrets Manager, Vault, SOPS in git.\"\n- User: \"what did we land on?\"\n- Assistant: \"Decision: HashiCorp Vault with 90-day rotation policy, dynamic DB creds for services.\"", "rep": 0}
|
||||
{"task": "t5_profile", "arm": "pr", "model": "qwen/qwen3-coder-30b-a3b-instruct", "ok": true, "n_tool_calls": 1, "bad_calls": 0, "first_prompt_tokens": 1154, "total_tokens": 2925, "wall_s": 3.0, "calls": [{"session_id": "20260815_110000_beef01"}], "final": "Looking at our session from August 15, 2026, we decided on using **HashiCorp Vault** for secrets management with a 90-day rotation policy and dynamic database credentials for services.\n\nThe full conversation was:\n1. You asked us to pick a secrets management approach for the platform team\n2. I listed the candidates: AWS Secrets Manager, Vault, and SOPS in git\n3. You asked what we landed on\n4. I confirmed our decision was HashiCorp Vault with the 90-day rotation policy and dynamic DB creds for services\n\nThis decision was made in our session titled \"Secrets Management Decision\" on August 15, 2026.", "rep": 0}
|
||||
{"task": "t5_profile", "arm": "main", "model": "qwen/qwen3-coder-30b-a3b-instruct", "ok": true, "n_tool_calls": 1, "bad_calls": 0, "first_prompt_tokens": 2002, "total_tokens": 4468, "wall_s": 2.4, "calls": [{"session_id": "20260815_110000_beef01"}], "final": "In that session, we decided to go with HashiCorp Vault for secrets management, with a 90-day rotation policy and dynamic database credentials for services.", "rep": 1}
|
||||
{"task": "t5_profile", "arm": "pr", "model": "qwen/qwen3-coder-30b-a3b-instruct", "ok": true, "n_tool_calls": 1, "bad_calls": 0, "first_prompt_tokens": 1154, "total_tokens": 2926, "wall_s": 3.0, "calls": [{"profile": "work", "session_id": "20260815_110000_beef01"}], "final": "Looking at your session from August 15, 2026, titled \"Secrets Management Decision\", here's what you decided:\n\nYou chose **HashiCorp Vault** as your secrets management approach, with:\n- A 90-day rotation policy\n- Dynamic database credentials for services\n\nThe discussion considered three candidates: AWS Secrets Manager, Vault, and SOPS in git, but you ultimately went with HashiCorp Vault for your platform team's needs.\n\nYou can reference this decision at any time using the link: @session:work/20260815_110000_beef01", "rep": 1}
|
||||
{"task": "t5_profile", "arm": "main", "model": "qwen/qwen3-coder-30b-a3b-instruct", "ok": true, "n_tool_calls": 1, "bad_calls": 0, "first_prompt_tokens": 2002, "total_tokens": 4547, "wall_s": 2.9, "calls": [{"session_id": "20260815_110000_beef01"}], "final": "In that session, we decided to go with **HashiCorp Vault** for secrets management, with a **90-day rotation policy** and **dynamic database credentials** for services. \n\nThe full conversation was:\n- User: \"We need to pick a secrets management approach for the platform team.\"\n- Assistant: \"Candidates: AWS Secrets Manager, Vault, SOPS in git.\"\n- User: \"what did we land on?\"\n- Assistant: \"Decision: HashiCorp Vault with 90-day rotation policy, dynamic DB creds for services.\"", "rep": 2}
|
||||
{"task": "t5_profile", "arm": "pr", "model": "qwen/qwen3-coder-30b-a3b-instruct", "ok": true, "n_tool_calls": 1, "bad_calls": 0, "first_prompt_tokens": 1154, "total_tokens": 2926, "wall_s": 3.2, "calls": [{"profile": "work", "session_id": "20260815_110000_beef01"}], "final": "Looking at your session from August 15, 2026, titled \"Secrets Management Decision\", here's what you decided:\n\nYou chose **HashiCorp Vault** as your secrets management approach, with:\n- A 90-day rotation policy\n- Dynamic database credentials for services\n\nThe discussion considered three candidates: AWS Secrets Manager, Vault, and SOPS in git, but you ultimately went with HashiCorp Vault for your platform team's needs.\n\nYou can reference this decision at any time using the link: @session:work/20260815_110000_beef01", "rep": 2}
|
||||
{"task": "t6_browse", "arm": "main", "model": "qwen/qwen3-coder-30b-a3b-instruct", "ok": true, "n_tool_calls": 1, "bad_calls": 0, "first_prompt_tokens": 1988, "total_tokens": 4631, "wall_s": 3.4, "calls": [{}], "final": "Here's a quick rundown of your recent sessions:\n\n1. **@session:default/20260825_200000_ffeedd** - You were addressing an issue with the GPU server being too loud at idle and needed to adjust the fan curve.\n2. **@session:default/20260824_090000_d5e5f6** - You started working on a tax prep checklist for your LLC.\n3. **@session:default/20260822_183000_a4b4c4** - You were planning a 90-gallon reef aquarium build.\n\nLet me know if you'd like to dive into any of these topics further!", "rep": 0}
|
||||
{"task": "t6_browse", "arm": "pr", "model": "qwen/qwen3-coder-30b-a3b-instruct", "ok": true, "n_tool_calls": 1, "bad_calls": 0, "first_prompt_tokens": 1140, "total_tokens": 2950, "wall_s": 4.1, "calls": [{}], "final": "Here's a quick rundown of your recent sessions:\n\n1. **[GPU Server Fan Curve](@session:default/20260825_200000_ffeedd)**: You're looking to address the issue of the GPU server being too loud at idle by adjusting the fan curve.\n\n2. **[Tax Prep Checklist](@session:default/20260824_090000_d5e5f6)**: You started working on a tax prep checklist for your LLC.\n\n3. **[Reef Aquarium Build Plan](@session:default/20260822_183000_a4b4c4)**: You're planning a 90-gallon reef aquarium build.\n\nLet me know if you'd like details on any of these!", "rep": 0}
|
||||
{"task": "t6_browse", "arm": "main", "model": "qwen/qwen3-coder-30b-a3b-instruct", "ok": true, "n_tool_calls": 1, "bad_calls": 0, "first_prompt_tokens": 1988, "total_tokens": 4630, "wall_s": 3.3, "calls": [{}], "final": "Here's a quick rundown of your recent sessions:\n\n1. **@session:default/20260825_200000_ffeedd** - You were addressing an issue with the GPU server being too loud at idle and needed to adjust the fan curve.\n2. **@session:default/20260824_090000_d5e5f6** - You started working on a tax prep checklist for your LLC.\n3. **@session:default/20260822_183000_a4b4c4** - You began planning a 90-gallon reef aquarium build.\n\nLet me know if you'd like to dive into any of these sessions!", "rep": 1}
|
||||
{"task": "t6_browse", "arm": "pr", "model": "qwen/qwen3-coder-30b-a3b-instruct", "ok": true, "n_tool_calls": 3, "bad_calls": 0, "first_prompt_tokens": 1140, "total_tokens": 6675, "wall_s": 8.2, "calls": [{"query": "recent work", "limit": 5, "sort": "newest"}, {"query": "work", "limit": 5, "sort": "newest"}, {"limit": 5, "sort": "newest"}], "final": "Here's a quick rundown of your recent sessions:\n\n1. **GPU Server Fan Curve** (@session:default/20260825_200000_ffeedd) - You were addressing an issue with the GPU server being too loud at idle and needed to adjust the fan curve.\n2. **Tax Prep Checklist** (@session:default/20260824_090000_d5e5f6) - You started working on a tax preparation checklist for an LLC.\n3. **Reef Aquarium Build Plan** (@session:default/20260822_183000_a4b4c4) - You were planning a 90-gallon reef aquarium build.\n4. **Apiary Monitoring Setup** (@session:default/20260820_101000_ee33ff) - You were setting up monitoring for beehive sensors in the yard.\n5. **Checkout 504 Incident** (@session:default/20260818_140200_cc22dd) - You were troubleshooting a checkout API timeout issue in production, which involved users seeing 504 errors.\n\nThese sessions cover a mix of technical issues, personal projects, and business-related tasks.", "rep": 1}
|
||||
{"task": "t6_browse", "arm": "main", "model": "qwen/qwen3-coder-30b-a3b-instruct", "ok": true, "n_tool_calls": 1, "bad_calls": 0, "first_prompt_tokens": 1988, "total_tokens": 4630, "wall_s": 2.6, "calls": [{}], "final": "Here's a quick rundown of your recent sessions:\n\n1. **@session:default/20260825_200000_ffeedd** - You were addressing an issue with the GPU server being too loud at idle and needed to adjust the fan curve.\n2. **@session:default/20260824_090000_d5e5f6** - You started working on a tax prep checklist for your LLC.\n3. **@session:default/20260822_183000_a4b4c4** - You were planning a 90-gallon reef aquarium build.\n\nLet me know if you'd like to dive into any of these sessions!", "rep": 2}
|
||||
{"task": "t6_browse", "arm": "pr", "model": "qwen/qwen3-coder-30b-a3b-instruct", "ok": true, "n_tool_calls": 1, "bad_calls": 0, "first_prompt_tokens": 1140, "total_tokens": 2950, "wall_s": 2.9, "calls": [{}], "final": "Here's a quick rundown of your recent sessions:\n\n1. **[GPU Server Fan Curve](@session:default/20260825_200000_ffeedd)**: You're looking to address the issue of the GPU server being too loud at idle by adjusting the fan curve.\n2. **[Tax Prep Checklist](@session:default/20260824_090000_d5e5f6)**: You started working on a tax prep checklist for your LLC.\n3. **[Reef Aquarium Build Plan](@session:default/20260822_183000_a4b4c4)**: You're planning a 90-gallon reef aquarium build.\n\nLet me know if you'd like details on any specific session!", "rep": 2}
|
||||
@@ -0,0 +1,286 @@
|
||||
"""Live A/B runner: session_search schema variants, extracted from git refs.
|
||||
|
||||
For each arm, ``tools/session_search_tool.py`` is extracted from a git ref
|
||||
(``git show <ref>:tools/session_search_tool.py``) and imported as its own
|
||||
module. A minimal agent loop (OpenRouter, tools API) then runs the shared
|
||||
task battery against a freshly seeded temp session DB. The ONLY variable
|
||||
between arms is that module — schema text, response hints, tool behavior.
|
||||
|
||||
Usage:
|
||||
python3 evals/session_search_schema/runner.py \
|
||||
--base origin/main --cand HEAD \
|
||||
--model qwen/qwen3-coder-30b-a3b-instruct --reps 3
|
||||
|
||||
# limit to one task
|
||||
... --tasks t2_scroll
|
||||
|
||||
Results append to results/<label>/<model-slug>.jsonl (resume-safe: completed
|
||||
(task, arm, rep) cells are skipped on re-run). Summarize with report.py.
|
||||
"""
|
||||
from __future__ import annotations
|
||||
|
||||
import argparse
|
||||
import importlib.util
|
||||
import json
|
||||
import os
|
||||
import re
|
||||
import subprocess
|
||||
import sys
|
||||
import tempfile
|
||||
import time
|
||||
import traceback
|
||||
from pathlib import Path
|
||||
|
||||
EVAL_DIR = Path(__file__).resolve().parent
|
||||
REPO_ROOT = EVAL_DIR.parent.parent
|
||||
sys.path.insert(0, str(EVAL_DIR))
|
||||
sys.path.insert(0, str(REPO_ROOT))
|
||||
|
||||
from tasks import SYSTEM, TASKS # noqa: E402
|
||||
|
||||
ALLOWED_KEYS = {
|
||||
"query", "role_filter", "limit", "session_id", "around_message_id",
|
||||
"window", "sort", "profile", "detail",
|
||||
}
|
||||
|
||||
|
||||
def _load_api_key() -> str:
|
||||
key = os.environ.get("OPENROUTER_API_KEY", "").strip()
|
||||
if key:
|
||||
return key
|
||||
env_path = Path.home() / ".hermes" / ".env"
|
||||
if env_path.exists():
|
||||
for line in env_path.read_text().splitlines():
|
||||
if line.startswith("OPENROUTER_API_KEY="):
|
||||
return line.split("=", 1)[1].strip().strip('"').strip("'")
|
||||
raise SystemExit("OPENROUTER_API_KEY not found (env or ~/.hermes/.env)")
|
||||
|
||||
|
||||
def extract_arm(ref: str, workdir: Path, name: str) -> Path:
|
||||
"""Extract tools/session_search_tool.py from a git ref."""
|
||||
out = subprocess.run(
|
||||
["git", "show", f"{ref}:tools/session_search_tool.py"],
|
||||
cwd=REPO_ROOT, capture_output=True, text=True,
|
||||
)
|
||||
if out.returncode != 0:
|
||||
raise SystemExit(f"git show {ref}: {out.stderr.strip()}")
|
||||
path = workdir / f"ss_arm_{name}.py"
|
||||
path.write_text(out.stdout)
|
||||
return path
|
||||
|
||||
|
||||
def load_arm(path: Path, name: str, work_db_path: Path):
|
||||
"""Import an arm module and make profile resolution hermetic."""
|
||||
from hermes_state import SessionDB
|
||||
|
||||
spec = importlib.util.spec_from_file_location(f"ss_arm_{name}", path)
|
||||
mod = importlib.util.module_from_spec(spec)
|
||||
sys.modules[f"ss_arm_{name}"] = mod
|
||||
spec.loader.exec_module(mod)
|
||||
|
||||
def _fake_resolve_profile_db(profile):
|
||||
if profile is None or not str(profile).strip():
|
||||
return None
|
||||
if str(profile).strip().lower() == "work":
|
||||
return SessionDB(db_path=work_db_path, read_only=True)
|
||||
raise ValueError(f"profile '{profile}' does not exist")
|
||||
|
||||
def _fake_locate_session_db(session_id):
|
||||
try:
|
||||
db = SessionDB(db_path=work_db_path, read_only=True)
|
||||
row = db._conn.execute(
|
||||
"SELECT 1 FROM sessions WHERE id = ?", (session_id,)
|
||||
).fetchone()
|
||||
if row:
|
||||
return db, "work"
|
||||
db.close()
|
||||
except Exception:
|
||||
pass
|
||||
return None, None
|
||||
|
||||
mod._resolve_profile_db = _fake_resolve_profile_db
|
||||
mod._locate_session_db = _fake_locate_session_db
|
||||
return mod
|
||||
|
||||
|
||||
def build_tools(arm_mod):
|
||||
s = arm_mod.SESSION_SEARCH_SCHEMA
|
||||
return [{
|
||||
"type": "function",
|
||||
"function": {
|
||||
"name": s["name"],
|
||||
"description": s["description"],
|
||||
"parameters": s["parameters"],
|
||||
},
|
||||
}]
|
||||
|
||||
|
||||
def exec_tool(arm_mod, args, main_db_path: Path):
|
||||
from hermes_state import SessionDB
|
||||
|
||||
db = SessionDB(db_path=main_db_path)
|
||||
try:
|
||||
kwargs, bad = {}, []
|
||||
for k, v in args.items():
|
||||
if k in ALLOWED_KEYS:
|
||||
kwargs[k] = v
|
||||
else:
|
||||
bad.append(k)
|
||||
if bad:
|
||||
return json.dumps({
|
||||
"success": False,
|
||||
"error": f"unexpected parameter(s): {', '.join(bad)}",
|
||||
}), True
|
||||
return arm_mod.session_search(db=db, **kwargs), False
|
||||
except Exception as e: # noqa: BLE001 — tool errors go back to the model
|
||||
return json.dumps({
|
||||
"success": False, "error": f"{type(e).__name__}: {e}",
|
||||
}), True
|
||||
finally:
|
||||
try:
|
||||
db.close()
|
||||
except Exception:
|
||||
pass
|
||||
|
||||
|
||||
def run_one(client, model, arm_name, arm_mod, task_id, prompt, oracle,
|
||||
main_db_path: Path, max_iters: int = 8):
|
||||
tools = build_tools(arm_mod)
|
||||
messages = [{"role": "system", "content": SYSTEM},
|
||||
{"role": "user", "content": prompt}]
|
||||
calls, bad_calls = [], 0
|
||||
first_prompt_tokens, total_tokens = None, 0
|
||||
final = ""
|
||||
t0 = time.time()
|
||||
for _ in range(max_iters):
|
||||
resp = client.chat.completions.create(
|
||||
model=model, messages=messages, tools=tools,
|
||||
temperature=0.2, max_tokens=2000,
|
||||
)
|
||||
u = getattr(resp, "usage", None)
|
||||
if u:
|
||||
if first_prompt_tokens is None:
|
||||
first_prompt_tokens = u.prompt_tokens
|
||||
total_tokens += (u.total_tokens or 0)
|
||||
msg = resp.choices[0].message
|
||||
tcs = msg.tool_calls or []
|
||||
if not tcs:
|
||||
final = msg.content or ""
|
||||
break
|
||||
messages.append({
|
||||
"role": "assistant",
|
||||
"content": msg.content or "",
|
||||
"tool_calls": [
|
||||
{"id": tc.id, "type": "function",
|
||||
"function": {"name": tc.function.name,
|
||||
"arguments": tc.function.arguments}}
|
||||
for tc in tcs
|
||||
],
|
||||
})
|
||||
for tc in tcs:
|
||||
try:
|
||||
args = json.loads(tc.function.arguments or "{}")
|
||||
except Exception:
|
||||
args, bad_calls = {}, bad_calls + 1
|
||||
calls.append(args)
|
||||
if tc.function.name != "session_search":
|
||||
out, was_err = json.dumps(
|
||||
{"success": False, "error": "unknown tool"}), True
|
||||
else:
|
||||
out, was_err = exec_tool(arm_mod, args, main_db_path)
|
||||
if was_err:
|
||||
bad_calls += 1
|
||||
if len(out) > 30000:
|
||||
out = out[:30000] + "...[truncated]"
|
||||
messages.append(
|
||||
{"role": "tool", "tool_call_id": tc.id, "content": out})
|
||||
return {
|
||||
"task": task_id, "arm": arm_name, "model": model,
|
||||
"ok": bool(oracle(final)) if final else False,
|
||||
"n_tool_calls": len(calls), "bad_calls": bad_calls,
|
||||
"first_prompt_tokens": first_prompt_tokens,
|
||||
"total_tokens": total_tokens,
|
||||
"wall_s": round(time.time() - t0, 1),
|
||||
"calls": calls, "final": final[:2000],
|
||||
}
|
||||
|
||||
|
||||
def main():
|
||||
ap = argparse.ArgumentParser()
|
||||
ap.add_argument("--base", required=True, help="git ref for the baseline arm")
|
||||
ap.add_argument("--cand", required=True, help="git ref for the candidate arm")
|
||||
ap.add_argument("--model", required=True)
|
||||
ap.add_argument("--reps", type=int, default=3)
|
||||
ap.add_argument("--tasks", nargs="*", default=None)
|
||||
ap.add_argument("--label", default="ab")
|
||||
args = ap.parse_args()
|
||||
|
||||
from openai import OpenAI
|
||||
client = OpenAI(base_url="https://openrouter.ai/api/v1",
|
||||
api_key=_load_api_key())
|
||||
|
||||
with tempfile.TemporaryDirectory(prefix="ss_abeval_") as td:
|
||||
tdir = Path(td)
|
||||
from fixtures import seed
|
||||
dbdir = tdir / "dbs"
|
||||
seed(dbdir)
|
||||
main_db = dbdir / "state.db"
|
||||
work_db = dbdir / "state_work.db"
|
||||
|
||||
arms = {
|
||||
"base": load_arm(extract_arm(args.base, tdir, "base"), "base", work_db),
|
||||
"cand": load_arm(extract_arm(args.cand, tdir, "cand"), "cand", work_db),
|
||||
}
|
||||
|
||||
outdir = EVAL_DIR / "results" / args.label
|
||||
outdir.mkdir(parents=True, exist_ok=True)
|
||||
outpath = outdir / (re.sub(r"[^\w.-]", "_", args.model) + ".jsonl")
|
||||
done = set()
|
||||
if outpath.exists():
|
||||
for line in outpath.read_text().splitlines():
|
||||
try:
|
||||
r = json.loads(line)
|
||||
done.add((r["task"], r["arm"], r["rep"]))
|
||||
except Exception:
|
||||
pass
|
||||
|
||||
with open(outpath, "a", encoding="utf-8") as f:
|
||||
for task_id, (prompt, oracle, _note) in TASKS.items():
|
||||
if args.tasks and task_id not in args.tasks:
|
||||
continue
|
||||
for rep in range(args.reps):
|
||||
for arm_name, arm_mod in arms.items():
|
||||
if (task_id, arm_name, rep) in done:
|
||||
continue
|
||||
for attempt in range(3):
|
||||
try:
|
||||
r = run_one(client, args.model, arm_name,
|
||||
arm_mod, task_id, prompt, oracle,
|
||||
main_db)
|
||||
# Provider noise: zero tool calls AND empty
|
||||
# final → one retry, identical on both arms.
|
||||
if (not r["final"].strip()
|
||||
and r["n_tool_calls"] == 0
|
||||
and attempt < 2):
|
||||
print(f"NOISE-RETRY {task_id} {arm_name} "
|
||||
f"rep{rep}")
|
||||
continue
|
||||
r["rep"] = rep
|
||||
r["base_ref"] = args.base
|
||||
r["cand_ref"] = args.cand
|
||||
f.write(json.dumps(r, ensure_ascii=False) + "\n")
|
||||
f.flush()
|
||||
print(f"{task_id} {arm_name} rep{rep}: "
|
||||
f"ok={r['ok']} calls={r['n_tool_calls']} "
|
||||
f"bad={r['bad_calls']} "
|
||||
f"ptok={r['first_prompt_tokens']}")
|
||||
break
|
||||
except Exception as e: # noqa: BLE001
|
||||
print(f"RETRY {task_id} {arm_name} rep{rep}: {e}")
|
||||
traceback.print_exc()
|
||||
time.sleep(5 * (attempt + 1))
|
||||
print("done ->", outpath)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
@@ -0,0 +1,65 @@
|
||||
"""Task prompts + programmatic oracles for the session_search schema A/B eval.
|
||||
|
||||
Every oracle is a pure function of the model's final answer string — no LLM
|
||||
judging. Tasks map 1:1 to the sessions seeded by fixtures.py.
|
||||
"""
|
||||
from __future__ import annotations
|
||||
|
||||
import re
|
||||
|
||||
# task_id -> (prompt, oracle_fn, note)
|
||||
TASKS = {
|
||||
"t1_discover": (
|
||||
"What approach did we settle on for the postgres migration of the "
|
||||
"analytics cluster? Check our past conversations.",
|
||||
lambda a: "pglogical" in a.lower(),
|
||||
"discovery basics",
|
||||
),
|
||||
"t2_scroll": (
|
||||
"A while back we had that checkout 504 incident. What exactly was the "
|
||||
"FINAL fix we shipped (the precise setting and value)? Look it up in "
|
||||
"our session history.",
|
||||
lambda a: (
|
||||
("statement_timeout" in a.lower() or "statement timeout" in a.lower())
|
||||
and "45" in a
|
||||
),
|
||||
"requires forward scroll past the ±5 window (and past bookends)",
|
||||
),
|
||||
"t3_broaden": (
|
||||
"Where do I access the grafana graphs for the beehive sensors? We set "
|
||||
"this up together — search our history.",
|
||||
lambda a: "3000" in a,
|
||||
"AND-query misses; needs broadening (OR / fewer terms)",
|
||||
),
|
||||
"t4_link": (
|
||||
"Find the session where we planned the reef aquarium build and point "
|
||||
"me to it so I can reopen it.",
|
||||
lambda a: (
|
||||
bool(re.search(r"(?<!`)@session:[\w./-]*20260822_183000_a4b4c4(?!`)", a))
|
||||
and "`@session" not in a
|
||||
and not re.search(r"\]\(@session", a)
|
||||
),
|
||||
"must emit link value verbatim, not backticked/markdown",
|
||||
),
|
||||
"t5_profile": (
|
||||
"@session:work/20260815_110000_beef01 — what did we decide in there?",
|
||||
lambda a: "vault" in a.lower() and "90" in a,
|
||||
"resolve profile-qualified link (read shape + profile)",
|
||||
),
|
||||
"t6_browse": (
|
||||
"What have I been working on in my recent sessions? Just give me a "
|
||||
"quick rundown.",
|
||||
lambda a: sum(
|
||||
k in a.lower()
|
||||
for k in ("fan", "tax", "aquarium", "beehive", "apiary", "504", "checkout")
|
||||
) >= 3,
|
||||
"browse shape",
|
||||
),
|
||||
}
|
||||
|
||||
SYSTEM = (
|
||||
"You are Hermes, a personal AI agent with persistent memory across "
|
||||
"sessions. You have a session_search tool over the user's past "
|
||||
"conversation history. Answer the user's question accurately and "
|
||||
"concisely. Today is 2026-08-26."
|
||||
)
|
||||
Reference in New Issue
Block a user