felixm
5342da2755
The planning view renders a quiet line for the standing-profile state (loading/grounded/absent/unreadable) with a "change" affordance to repoint the file via POST /knowledge/path. The profile text never reaches the browser — only status, path, and size. Also updates the README Status section to describe the M6 knowledge port. Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
65 lines
3.0 KiB
Markdown
65 lines
3.0 KiB
Markdown
# AntiDrift
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A personal focus operating system: treat each work session as an explicit
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commitment (next action, success condition, timebox), and make drift visible.
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This is the Go reimagining. The original Rust implementation is preserved under
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`legacy/` for reference. See `docs/superpowers/specs/` for the design.
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## Run
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```bash
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go run ./cmd/antidriftd
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```
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The daemon serves a local web UI at http://localhost:7777 and opens your
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browser. State is persisted to `~/.antidrift/state.json`.
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## Test
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```bash
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go test ./...
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```
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## Status
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M6 (knowledge port): the planning coach now sharpens intents against who you
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actually are. A single profile file (`~/.antidrift/knowledge.md`, overridable
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via `ANTIDRIFT_KNOWLEDGE_FILE`) holds your standing context — priorities,
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values, what counts as good work — and the daemon loads it asynchronously on
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entering planning, mirroring the tasks fetch. The cached text grounds the AI
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coach prompt only; the drift judge and nudge are untouched, and the profile text
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never crosses the wire. A subtle planning-screen indicator shows whether the
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profile loaded and from where, with a "change" affordance to repoint at another
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file. It degrades gracefully — a missing, blank, or unreadable file just leaves
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the coach ungrounded, and planning still works.
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M3.5 (semantic nudge): the drift interceptor catches the *wrong app*, but not
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the *wrong work inside a right app*. M3.5 adds a third, ambient AI role that —
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only while you are on-task in an allowed app — periodically reads your recent
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window titles and, if the trajectory has wandered from the commitment, shows a
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soft, dismissible "Heads up" line (no interrupt, no buttons to fight). It is
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debounced to roughly one check every five minutes, reuses the same CLI backend
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as the coach and drift judge, and degrades gracefully — without it, everything
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else still works.
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M3 (drift interceptor): while a commitment is Active, the daemon watches the
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focused window. A cheap local match against the session's allowed window classes
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is authoritative for on-task; only unmatched windows are sent to the LLM drift
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judge (debounced and cached per class, run asynchronously). When it judges you
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off-task, the active view shows a dismissible interrupt: "Back to task", "This is
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on task" (which adds the app to the session's allowed list), or "End session".
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The drift judge degrades gracefully — without it, local matching still runs.
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M2 (AI planning coach): in the Planning view, a rough intent is "sharpened"
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into a structured commitment (next action, success condition, timebox) by an
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LLM CLI backend (claude or codex, selectable via `ANTIDRIFT_AI_BACKEND`). The
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coach runs asynchronously and degrades gracefully — manual planning always
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works.
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M1 (evidence & audit): active-window tracking, two-tier evidence store
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(disposable per-session raw log + permanent hash-chained session summaries),
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and live SSE updates. Live drift judgment and the ambient nudge arrive in later
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milestones (see the roadmap in
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`docs/superpowers/specs/2026-05-31-go-focus-os-design.md`).
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