Thin wrapper over kon_storage::list_recent_completions, parameterised
by day count. Serialises to camelCase JSON (day, count).
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
Closes Phase 3 of the 2026-04-23 feature-complete roadmap. Incorporates
the Codex plan-review fixes from this session: profile-free index, tri-
state update command, and de-prioritise-not-hide semantics.
Storage (kon-storage):
- Migration v11 adds `energy TEXT` to `tasks` with a CHECK constraint on
`high | medium | brain_dead | NULL`. Index `(energy, created_at DESC)`
— deliberately not per-profile because the tasks table carries no
profile_id column yet (tracked as a separate gap in HANDOVER).
- `TaskRow.energy: Option<String>` plus `task_row_from` read.
- `insert_task` signature grows by one optional arg (`energy`). Allowed
`too_many_arguments` with a rationale comment — the positional shape
matches the column order and flipping to a params struct would have
rippled through every caller for cosmetic benefit only.
- New `set_task_energy(pool, id, Option<&str>) -> TaskRow`. Lives as its
own function because `update_task` uses COALESCE to let `None` mean
"preserve" — which would make clearing the tag impossible.
- Two new tests: round-trip including explicit NULL clear, and CHECK
constraint rejection of unknown values.
- Tests updated for the v10 → v11 version bump.
Tauri (src-tauri):
- `TaskDto.energy`. `CreateTaskRequest.energy` (optional). Inline
validation against the allowed set before hitting the DB, so frontend
bugs surface as friendly errors instead of CHECK-constraint failures.
- New `set_task_energy_cmd` command mirroring the storage tri-state API.
Frontend (svelte):
- `EnergyLevel` type added to `types/app.ts`. `TaskDto`, `TaskEntry`, and
`TaskDraft` grow an `energy` field.
- `SettingsState.currentEnergy` (persisted) + `matchMyEnergy` (persisted
toggle). Defaults: null + false — no surface change until user opts in.
- `setTaskEnergy(id, EnergyLevel | null)` action on the task store.
Calls the dedicated Tauri command, updates local state, broadcasts to
sibling windows.
- `EnergyChip.svelte` — new component. Cycles unset → High → Medium →
Brain-Dead → unset on click. Colour tokens: accent / warning /
text-tertiary (deliberately not danger-red for Brain-Dead — the brief
is explicit that this state must not feel pathologised).
- Chip rendered on every task row in TasksPage and every row in
WipTaskList. Hidden-until-hover when energy is unset so untagged rows
stay calm; always visible once tagged because the colour is the signal.
- Tasks page header gains a "I feel" segmented control and a
"Match my energy" toggle. When both are active, matching tasks sort
to the top — unset tasks are treated as Medium-equivalent. Nothing is
ever hidden; this is a de-prioritisation, not a filter.
Deferred / out of scope:
- LLM-driven surfacing (brief says "The AI surfaces...") — deterministic
client-side sort is v1; LLM layer is a later phase.
- tasks.profile_id + per-profile energy sort — separate migration.
All green: cargo build + 251 tests + clippy -D warnings (0 warnings)
+ fmt + svelte-check (0/0) + npm run build.
Independent review surfaced three majors and one nit. All actioned.
MAJOR 1 — profile scoping:
`decompose_and_store` and `extract_tasks_from_transcript_cmd` now
accept an optional `profile_id` (wired from `profilesStore.activeProfileId`
in MicroSteps.svelte and DictationPage.svelte), and thread it into the
feedback-retrieval query so per-profile decomposition styles do not
leak into each other. `record_feedback` gets the same treatment.
MAJOR 2 — prompt-budget regression on long inputs:
New `trim_to_budget` helper + `FEW_SHOT_CHAR_BUDGET = 2000` char cap
in `src-tauri/src/commands/tasks.rs`. Retrieval still pulls up to 5
rows but they are char-counted and truncated against the budget
before being sent to the LLM. Char cost matches the `Input: ...\n
Good output: ...` render path so the budget maps cleanly to ~570
Qwen3 tokens, well inside the 8192-context reserve after the 512-
or 768-token response allocation. Oldest-first drop order (iteration
stops at cost exceeded) preserves the most recent correction which
is the one carrying the user's live preference.
MAJOR 3 — inline edit stale-rollback race:
`saveEdit` in MicroSteps.svelte now stamps a monotonic per-step
`saveToken`. Each edit bumps the token; on failure the rollback
only fires if `saveToken[step.id] === myToken`, so a slow-failing
first save can no longer overwrite a faster successful second save.
NIT — retrieval ordering stability:
`list_feedback_examples` ORDER BY now `created_at DESC, id DESC`.
SQLite timestamp precision is one second; without the secondary
key, bursty feedback within the same second would select
non-deterministically.
Also: malformed `context_json` now warns via eprintln! rather than
disappearing silently — Codex minor.
All green: cargo build + 249 tests + clippy -D warnings + fmt
+ svelte-check (0/0) + npm run build.
Closes the human-in-the-loop gap from docs/brief/feature-set.md and
Phase 2 of the 2026-04-23 feature-complete roadmap.
Storage (kon-storage):
- Migration v10 adds the `feedback` table: (target_type, target_id,
rating, original_text, corrected_text, context_json, profile_id,
created_at) with CHECK constraints on target_type and rating, plus
indexes on (target_type, rating, created_at DESC) for prompt-time
retrieval and (profile_id, target_type, created_at DESC) for
per-profile scoping.
- New public API: `FeedbackTargetType`, `RecordFeedbackParams`,
`FeedbackRow`, `record_feedback`, `list_feedback_examples`.
- Tests updated — the RB-02 rollback regression now discovers the
real max version at runtime instead of hard-coding v10 for its
poison migration.
LLM (kon-llm):
- `prompts::FeedbackExample` — local shape for few-shot exemplars so
kon-llm stays independent of kon-storage.
- `prompts::build_conditioned_system_prompt` — appends a "here is
the style this user prefers" block to the base system prompt
when examples are available; returns the base prompt unchanged
when empty, so new users and early sessions see generic output.
- `LlmEngine::decompose_task_with_feedback` and
`LlmEngine::extract_tasks_with_feedback` thread examples through
to the builder. The old one-arg variants are preserved and now
call through with an empty slice.
- 4 unit tests covering empty, empty-input-skip, correction-wins,
and thumbs-up-only fallback.
Tauri (src-tauri):
- New commands::feedback module: `record_feedback`,
`list_feedback_examples_cmd`.
- `decompose_and_store` and `extract_tasks_from_transcript_cmd`
now fetch the last 5 positive/neutral feedback rows for their
target type and pass them through to the LLM, wiring the
learning loop end-to-end.
- Shared `to_llm_examples` helper parses the `context_json.input`
field (where the recorder stashes the parent task text / transcript
chunk) back into the exemplar shape.
Frontend (MicroSteps.svelte):
- Thumbs-up and thumbs-down buttons on every micro-step row.
Hover-revealed; the vote recolours the icon; clicking again
clears the local highlight (the row itself stays in the audit
trail).
- Pencil icon + double-click to edit step text. Save flows through
update_task_cmd for persistence and records a correction feedback
row with (original_text, corrected_text) — the highest-value
training signal.
- Parent task text is captured in context_json.input at record time
so the prompt builder can reconstruct the (input, preferred-output)
pair on subsequent decompositions.
- Feedback capture is best-effort — a record_feedback failure never
interrupts the primary action.
What's deferred to a later phase:
- Thumbs + corrections on extracted tasks (same pipeline, different
surface — probably TasksPage after the AI-extraction path)
- Thumbs on transcript cleanup output
- Semantic retrieval over the feedback corpus (once there is enough
data to justify embedding infrastructure; the storage shape is
already ready for it)
kon-llm now owns a real LlamaBackend + LlamaModel, with three Qwen3 tiers
(1.7B Q4, 4B-Instruct-2507 Q4, 14B Q5) selectable per hardware. Downloads
are resumable with SHA-256 verification and stored under ~/.kon/models/llm.
Engine exposes three high-level surfaces — all greedy/temp-0, GBNF-constrained
where output shape matters:
- cleanup_text (prompt-injection-hardened system prompt; profile terms
appended as "preserve these spellings" suffix)
- decompose_task (3–7 micro-steps, constrained JSON array)
- extract_tasks (optional-array; empty when no explicit commitments)
post_process_segments now takes an Option<&LlmEngine> and, when loaded and
format_mode != Raw, joins segments → cleanup → replaces segments with the
cleaned text (first segment span). Rule-based path still runs first; LLM
errors log and keep rule-based output.
Tauri commands: recommend_llm_tier, check_llm_model, download_llm_model,
load_llm_model, unload_llm_model, delete_llm_model, get_llm_status,
cleanup_transcript_text_cmd, extract_tasks_from_transcript_cmd,
decompose_and_store (LLM-backed subtasks).
Settings: AI tier toggle (off / cleanup / tasks), model picker with
downloaded/loaded status, download progress events via
kon:llm-download-progress.
Dictation: ensureLlmModelLoaded on mount, cleanupTranscriptIfEnabled after
stop when tier != off and format_mode != Raw, LLM task extraction when
tier=tasks (regex fallback on failure).
Interim: both llama-cpp-sys-2 and whisper-rs-sys statically link their own
ggml, so src-tauri/build.rs emits -Wl,--allow-multiple-definition on Linux.
Replace with a system-ggml shared-lib setup as a follow-up.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
Major quality pass on top of Phase 2. Five substantive changes plus
cross-cutting touches across audio, hotkey, transcription, and Tauri
command layers.
Transcription quality
- Long-audio chunking in commands/transcription.rs: Parakeet and large
file transcription now chunk-and-recompose with overlap trimming, so
the live-path chunking advantage extends to file-based workflows.
- Stateful live speech gate in commands/live.rs on top of the earlier
duplicate-boundary filtering — distinguishes start-of-speech from
mid-speech and holds state across chunks.
Auto-learning corrections
- New crates/ai-formatting/src/correction_learning.rs: extracts user
text corrections from viewer edits and proposes additions to the
active profile's vocabulary.
- src-tauri/src/commands/profiles.rs bridge for frontend-driven
confirmation of learned terms.
- src/routes/viewer/+page.svelte hooks the learning path into the
segment-edit flow so corrections feed profile_terms without a
separate 'train this profile' UX.
Transcript profile provenance
- Migration v8 (crates/storage/src/migrations.rs) adds profile_id to
transcripts, defaulting to DEFAULT_PROFILE_ID so existing rows stay
valid.
- crates/storage/src/database.rs: TranscriptRow + CRUD carry profile_id.
- src-tauri/src/commands/transcripts.rs: add_transcript accepts and
persists profile_id.
- DictationPage.svelte + FilesPage.svelte send activeProfileId on
capture so learned corrections are attributed to the right profile.
Cleanup prompt contract
- crates/ai-formatting/src/llm_client.rs hardened: the CLEANUP_PROMPT
now specifies concrete do/do-not rules, ready for a real model-backed
cleanup pass. The llm_client is still a stub — kon-llm remains unwired
— but the prompt shape is final.
Cross-cutting polish
- Minor touches in audio (capture/decode/resample), hotkey (lib/linux/stub),
core, transcription (concurrency/model_manager/local_engine/whisper_rs),
and the rest of src-tauri/src/commands/*: error-path tightening, log
clarity, TS-migration follow-ups (@ts-nocheck additions for incremental
typing).
Verified locally: npm run check, cargo test -p kon-ai-formatting,
cargo test -p kon-storage, cargo test -p kon --lib commands::live::tests,
cargo check — all green.
Scope boundary: kon-llm crate is still a stub; task extraction remains
rule-based. Bundled local-LLM runtime is the next clean step and is not
in this commit.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
