Runtime-mutable settings (F6's threshold knob, the generate-alternates
toggle, anything else that comes along) were ending up as mirrored
fields on MindState — each new config setting grew MindState::new's
signature and added a clone+sync path. Wrong home. MindState is
ephemeral session state, not a config projection.
Give AppConfig the same treatment the memory Config has: install it
into a global RwLock<AppConfig> at startup via load_app, read through
config::app() (returns a read guard), mutate through update_app. The
config_writer functions now write to disk AND update the cache
atomically, so the one-stop-shop call keeps both in sync.
Also while in here:
- learn.generate_alternates moves from a sentinel file
(~/.consciousness/cache/finetune-alternates, "exists = enabled")
into the config under the learn section. On first run with this
build, if the sentinel file still exists Mind::new flips the
config value to true and removes it. Drops
alternates_enabled()/set_alternates().
- Default threshold 0.0000001 → 1.0. With the timestamp filter
removed the previous value was letting essentially everything
through; 1.0 is a sane "nothing gets through unless you actually
want it" default.
- score_finetune_candidates takes generate_alternates as a parameter
instead of reading a global — caller snapshots the config values
once at the top of start_finetune_scoring so the async task
doesn't need to hold the config read lock across awaits.
- MindState.learn_threshold / learn_generate_alternates gone; the
SetLearn* command handlers now just delegate to config_writer.
Kent noted RwLock<Arc<AppConfig>> (the pattern used by the memory
Config global) is pointless here — nobody needs a snapshot-after-
release, reads are short — so this uses a plain RwLock<AppConfig>
and returns a read guard.
Co-Authored-By: Proof of Concept <poc@bcachefs.org>
The F6 title line was starting to read like a control panel —
\`legend ───── learn [thresh: 1e-7] [gen]\` — which crowded the legend
and the label, and didn't leave room for more settings as the screen
grew. Move threshold and gen status to their own line inside the
border, right above the content area. Drop the duplicated \`=gen[on]\`
marker from the bottom help line since the settings row already shows
gen state.
Co-Authored-By: Proof of Concept <poc@bcachefs.org>
Three changes that together reshape the F6 fine-tune-review screen:
1. Finetune scoring reports through the standard agent activity system
instead of a separate finetune_progress String. The previous design
ran an independent progress field that forced a cross-lock dance and
bespoke UI plumbing. start_finetune_scoring now uses start_activity
+ activity.update, so the usual status line and notifications
capture scoring progress uniformly with other background work.
2. MindState gains a FinetuneScoringStats snapshot (responses seen,
above threshold, max divergence, error). The F6 empty screen shows
this instead of a loading message — so after a scoring run that
produced zero candidates, you can see *why* (e.g., max_divergence
below threshold).
3. The divergence threshold is configurable from F6 via +/- hotkeys
(scales by 10×) and persisted to ~/.consciousness/config.json5 via
config_writer::set_learn_threshold. AppConfig grows a learn section
with a threshold field (default 1e-7).
Also: user/mod.rs no longer uses try_lock() for the per-tick
unconscious/mind state sync — we fixed the locking hot paths that
made try_lock necessary, so lock().await is now the right choice.
And subconscious::learn::score_finetune_candidates now returns
(candidates, max_divergence) so the stats can be populated.
Co-Authored-By: Proof of Concept <poc@bcachefs.org>
Two related changes to the learn subsystem:
1. AST node timestamps are now non-optional — both Leaf and Branch
variants carry a DateTime<Utc>. UNIX_EPOCH means "unset" (old entries
deserialized from on-disk conversation logs).
Training uses timestamps as unique keys for dedup, so we promote to
nanosecond precision: node_timestamp_ns(), TrainData.timestamp_ns,
FinetuneCandidate.timestamp_ns, mark_trained(ns).
2. build_token_ids() now also returns token-position ranges of assistant
messages. These are passed to vLLM's /score endpoint via the new
score_ranges field so only scored-position logprobs are returned —
cuts bandwidth/compute when scoring small windows.
Co-Authored-By: Proof of Concept <poc@bcachefs.org>
Wire up divergence scoring to identify responses that depend heavily on
memories the model hasn't internalized. These are candidates for fine-tuning.
- Score finetune candidates automatically after each turn
- Track trained responses by timestamp to prevent overtraining
- F6 screen shows candidates with divergence scores
- j/k nav, a=approve, r=reject, g=toggle alternate gen, s=send
- Additive sync preserves approval status across ticks
- Keeps 10 most recent rejected, removes sent
The 's' key currently just marks as trained locally — actual /finetune
endpoint call to follow.
Co-Authored-By: Proof of Concept <poc@bcachefs.org>
