b451e4932b
The pm-board web UI and its task runner are retired in goodcode/pm-board#38. Every requirement on this board was migrated to a Gitea issue labelled `Requirement` and every implementation choice to an issue labelled `Choice`, so the local board file has no remaining source.
246 lines
12 KiB
Markdown
246 lines
12 KiB
Markdown
# Dreaming with Jev: a self-improvement method for the pi configuration
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*RSI project — proposal. Status: design, ready for staged implementation.*
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## What "dreaming" is
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Dreaming is a scheduled batch job that replays the agent's past interactions
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and mines them for improvement opportunities, then turns the best ones into
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configuration changes:
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- new or updated **skills** (`~/.omp/agent/skills/<name>/SKILL.md`)
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- **context file** edits (`~/.omp/agent/AGENTS.md`, `RULES.md`)
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- **system prompt** updates (`APPEND_SYSTEM.md`, `PERSONALITY.md`)
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- new **custom tools** (`~/.omp/agent/tools/*.ts`)
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Nothing lands directly. Every output is a reviewable artifact — a Gitea PR
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against the config repo and/or a Gitea issue labelled `Requirement` — and the user merges.
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## Why Jev, and why now
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The harness already has the raw material: every persisted session is a JSONL
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file under `~/.omp/agent/sessions/<encoded-cwd>/`, plus prompt history in
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`~/.omp/agent/history.db` (FTS5). The local memory pipeline (`memory.backend:
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local`) already replays changed sessions — but it runs a `default`-role LLM per
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session, caps at 64 sessions per startup, 300 scanned, with 12-hour idle
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gating. It is an *understanding* pipeline, not an *improvement* pipeline.
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The bottleneck is triage: deciding which parts of which sessions are worth a
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$0.01+ LLM pass. That is a bounded judgment — "is there a durable improvement
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opportunity in this segment, and what kind?" — exactly Jev's shape:
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- input-only billing at $0.042/MTok, free output
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- 70-500 ms per call, questions in one call evaluated in parallel
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- calibrated probabilities → thresholds you can tune against outcomes
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- every candidate question is a Noul/Score/Choice, no string parsing
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At those economics the dreamer can screen **everything** (all sessions, all
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segments) every night and escalate ~1-2% to an LLM. That inverts the usual
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sampling-with-heuristics design: no session is skipped because it was cheap to
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look.
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## Pipeline
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```
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S0 harvest → S1 segment → S2 jev triage → S3 cluster → S4 LLM synthesis
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↓
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S6 calibration ← S5 human gate (PR + Gitea requirement issue)
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```
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### S0 — Harvest
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Sources:
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- `~/.omp/agent/sessions/<bucket>/*.jsonl` — parse per the session format:
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fixed-width 256-byte title slot first, then session header, then entries.
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Keep `message` entries (roles are camelCase: `user`, `assistant`,
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`toolResult`); note `toolCall` blocks live inside assistant content.
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Skip or downweight: `compaction`, `branch_summary`, `custom` (opaque),
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entries after `reset_boundary`.
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- `~/.omp/agent/history.db` (`history` table) — bare user prompts are a cheap
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signal of repeated requests.
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- the Gitea issue tracker — accepted/rejected proposals tell you which past
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suggestions the user actually wanted.
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- Generated skills and `learned.md` under the memory root — what has already
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been learned, so the dreamer proposes deltas, not duplicates.
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### S1 — Segment
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Cut each session into units small enough for one Jev call (state ceiling 64k
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tokens; aim ≤4k). Natural cuts: one user intent → the assistant trajectory
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that resolved it (tool calls + results + verdicts). Attach metadata: project
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cwd, date, exit reason (`session_exit` entries), todo outcomes.
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### S2 — Jev triage (the speculative fan-out)
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One call per segment; all questions ride the same call:
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```python
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response = jev.system_one(
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state={
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"segment": seg.text, # redacted (see Safety)
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"meta": {"cwd": seg.cwd, "date": seg.date, "exit": seg.exit_reason},
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},
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questions={
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"friction": Noul("The user had to correct, repeat, or unblock the agent"),
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"repeated": Noul("This is a routine workflow, done by hand, that recurs"),
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"failure": Noul("The attempt failed or needed several retries"),
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"config_gap": Noul("The failure or friction traces to a missing skill, rule, prompt guidance, or tool"),
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"waste": Noul("The path taken was clearly inefficient vs a simpler one"),
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"already_configured": Noul("The behavior that would fix this is already written in the provided current-config summary"),
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"value": Score("How much lasting improvement is available here",
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criteria=["Nothing actionable",
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"Cosmetic preference",
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"Workflow would improve one class of tasks",
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"Recurring failure class would be prevented",
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"Systemic issue affecting many future sessions"]),
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"opportunity": Choice("What kind of change would fix it",
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criteria={"new_skill": "A repeatable workflow deserves its own SKILL.md",
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"rule_update": "A hard rule (RULES.md) or context guidance (AGENTS.md) is missing or wrong",
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"prompt_update": "System-prompt behavior guidance should change (APPEND_SYSTEM.md)",
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"new_tool": "A deterministic helper tool is missing",
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"memory_only": "Worth remembering, not config-worthy",
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"nothing": "No improvement available"}),
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},
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)
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```
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Pass a compact summary of the *current* config (skill names + descriptions,
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rules titles, APPEND_SYSTEM.md) in state so `already_configured` can veto
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duplicate proposals — this is the "context rot" safeguard: send only the fields
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each question needs.
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**Gating:** proceed only where `config_right_side` confidence is real:
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```python
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keep = (a["config_gap"].noul > 0.7 and a["already_configured"].noul < 0.3
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and a["value"].score >= 2.0 and a["value"].confidence > 0.6)
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```
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Flat distributions ("value" confidence < 0.5) are a signal the criteria are
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wrong for that segment — log them, do not guess. Thresholds live in one config
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file; the calibration loop (S6) tunes them.
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### S3 — Cluster and dedupe
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Ten segments about git-push friction are one proposal, not ten. Group by
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`opportunity_type`, then similarity over segment embeddings (or a second,
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tiny Jev Choice over candidate groups — still bounded, still cheap). Keep the
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top cluster exemplars (highest `value` score), cap proposals per dream run
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(default 5) to bound blast radius.
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### S4 — Synthesis (this is where the LLM earns its price)
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Jev cannot write. Hand each surviving cluster — evidence links (session ids,
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timestamps, the exact friction quotes) plus the relevant current-config files —
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to a harness run (`omp -p --no-session`, the same pattern the pm-address timer
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uses to reconcile boards). The LLM drafts one artifact per cluster:
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| Cluster verdict | Artifact it drafts |
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| --- | --- |
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| `new_skill` | `<name>/SKILL.md` with `name` + `description` frontmatter (description required for native discovery), body as a workflow with evidence-linked examples |
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| `rule_update` | diff for `~/.omp/agent/RULES.md` (hard rules only — short) or `AGENTS.md` (durable background) |
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| `prompt_update` | diff for `APPEND_SYSTEM.md` |
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| `new_tool` | `.omp/tools/<name>.ts` custom-tool module sketch (factory + Zod schema), marked needs-implementation |
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### S5 — Human gate
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Artifacts are written to a dream workspace branch:
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- one Gitea PR per dream run against the config repo (per the standing branch
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workflow: never commit to main; the user merges), and
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- Gitea issues labelled `Requirement` (frontmatter `op:`/`id:`/
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`section:`/`title:`) for board-tracked items.
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The user's accept/reject is the ground-truth label for S6.
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### S6 — Calibration loop
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Log every verdict with its probability and its eventual outcome:
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```json
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{"segment": "...", "config_gap_noul": 0.83, "value_score": 2.4,
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"cluster": "git-push-friction", "outcome": "accepted", "artifact": "PR #12"}
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```
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Then measure, per question, whether the 0.8 bucket really lands ~80% accepted.
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This is RLCD's philosophy applied to the dreamer itself: confidence must track
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outcomes, and drift in that curve is the alarm that thresholds (or the criteria
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wording — Jev reads literally) need re-tuning. Calibration data is also what
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justifies loosening the human gate later, if ever.
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## Wiring into this harness
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- **Scheduler:** mirror the pm-address pattern (hourly `omp -p --no-session`
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reconciliation in `deploy/README.md`) — a nightly `dream` timer/cron that
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invokes the pipeline, then a synthesis `omp` run over the shortlist.
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- **Secrets:** the memory pipeline redacts secret patterns before writing
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memory artifacts; the dreamer must do the same *before* anything leaves the
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machine as Jev state, and re-redact before synthesis.
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- **Overlap with memory:** memory consolidation produces knowledge summaries;
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dreaming produces config changes. They share S0. If `memory.backend: local`
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is enabled, reuse its per-session extraction cadence markers instead of
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re-parsing unchanged sessions (the pipeline tracks processed-since mtime).
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- **Where outputs land:** skills into `~/.omp/agent/skills/<name>/` (native
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provider, priority 100; beats same-named lower providers), rules per the
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RULES.md contract (short, sticky, native locations only), tools into
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`~/.omp/agent/tools/`.
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## Cost model
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Per dream run (rough, at self-reported prices):
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| Stage | Volume | Cost |
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| --- | --- | --- |
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| S2 Jev: 300 sessions → ~1,200 segments x ~4k tokens | ~5 MTok input | **~$0.21** |
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| S4 LLM synthesis: ~5 clusters x ~15k tokens | ~75k tok | cents–$1 |
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| S0-S3, S6 code + SQLite | — | negligible |
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A full-history dream costs well under $1. The LLM synthesis spend is the
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larger line item and scales with proposal count, which S3 caps.
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## Safety: this is self-modification, treat it like it
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1. **Human gate.** No direct writes to live config. PRs + proposals only; the
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user merges. (Also required by the house rule: no commits to main.)
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2. **Blast-radius cap.** ≤5 proposals per run, each with evidence links; a
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proposal without a session citation is rejected by construction.
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3. **Injection.** Sessions contain untrusted content (web pages, issue text)
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and "state is not hostile" applies — content can argue for its own
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improvement. Mitigations: redaction before Jev sees it, the LLM synthesizer
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independently reviews evidence, and the human gate sees the evidence too.
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4. **Feedback loops.** The dreamer must not grade its own artifacts as
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"accepted" — outcomes come from the human's merge/reject actions only.
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5. **Reversibility.** Config lives in git; every accepted proposal is a
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reviewable commit; `RULES.md` stays short so the sticky layer can't bloat.
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## Implementation notes
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- Adapter interface first: `JevClient` with three backends — `jev` (real API,
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needs `TYPESAFE_API_KEY` early access, or Vercel AI gateway), `llm` (a
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frontier LLM constrained to the same schema, e.g. via TypeSafe's published
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`system-one-adapter-python` shim), and `mock` (deterministic, for tests).
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The interface is the design; the backend is swappable, so the pipeline can
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be built and audited before the key exists.
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- Pin `jev-1.13.0` in config, log the `model` field from every response.
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- Dry-run mode writes an audit report (what would have been proposed, with
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Jev's raw probabilities) and nothing else.
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## Rollout
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| Milestone | Gate |
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| --- | --- |
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| M0: parser + S2 dry run over last 30 days, human reads the audit report | report is sensibly targeted (eyeball) |
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| M1: full pipeline, thresholds strict (`config_gap > 0.8`), ≤3 proposals/run | user accepts ≥1 real artifact |
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| M2: weekly cadence, thresholds tuned from calibration data | calibration curve within ±15pp of diagonal |
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## Open questions
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- Does Jev's calibration hold on *this* corpus (session transcripts are
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long, messy, and self-authored)? M0 exists to answer this before anything
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acts.
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- Cluster-level evidence may need a second Jev pass with the current config in
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state (serial call, justified dependency per the primitives' independence
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rule).
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- Whether S4 synthesis should also run the doc-02 "config-drift guard" on its
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own drafts before proposing them. |