Mirrors the vLLM-side rewrite. AppendImage is gone; images now
ride along on Generate via a parallel `images` list.
- Productionize `qwen3_image_token_count` (was test-only). Image
leaf computes its IMAGE_PAD count eagerly at construction from
height/width; `token_count` is no longer "0 until the server
tells us."
- WireChunk shrinks to a single `Tokens(Vec<u32>)` variant — vision
blocks live inline in the token stream.
- `wire_chunks` now returns `(Vec<WireChunk>, Vec<WireImage>)`.
`WireImage` carries `pad_start` / `pad_end` (absolute positions
in the full walk) alongside bytes + mime.
- `assemble_prompt` returns `(chunks, images, match_upto)`.
- `stream_session_mm` / `run_session_generate` take the parallel
images list, filter to those past `match_upto`, and pass them
in `GenerateRequest.images` as `pb::ImageAttachment` entries.
- Drop `SessionHandle::append_image`,
`ContextState::commit_image_token_counts`,
`StreamToken::ImageAppended`, the WireChunk::Image branch in
`learn.rs`, and the now-empty `prompt_to_chunks` helper.
- Add 'v' toggle on the conscious-screen tree to render token-id
vectors in place of text content (debug-aid: lets us see what
the server actually has when output is suspicious).
- Comment out the subconscious-trigger spawn loop — Kent had this
disabled before; it had crept back into running.
Co-Authored-By: Proof of Concept <poc@bcachefs.org>
Two changes that bolt together — the shared connection means the new
scoring path actually costs one HTTP/2 handshake across the whole
process instead of one-per-RPC.
ApiClient gains `salience_channel: Arc<OnceCell<Channel>>`. First
call to `ApiClient::salience_client()` opens the channel via
`connect_channel()` and stores the Channel; subsequent calls clone
it (cheap — tonic multiplexes concurrent RPCs over the single
HTTP/2 connection). Every ApiClient clone shares the same OnceCell,
so all agents spawned from Mind's client — plus every ephemeral
scoring session — reuse one connection.
SessionHandle refactored to hold an `ApiClient` clone instead of
a bag of (base_url, api_key) strings. `open` / `append_image` /
`generate` go through `self.client.salience_client()` now. New
`prefill_only(tokens)` method encapsulates the "Generate with
max_tokens=0 to append text" pattern (previously a private free
function in api/mod.rs called `flush_pending`). Drop impl on
SessionHandle stays — still fires CloseSession on the shared
channel in a detached task.
`run_session_generate` switched from `(base_url, api_key, model)`
to `&ApiClient`; the agent-turn flow that uses it keeps the same
shape but `stream_session_mm` clones the ApiClient into the
spawned worker.
learn.rs migrated from the HTTP `/v1/score` endpoint to a gRPC
session-based score:
* `call_score` opens an ephemeral SessionHandle on the client,
converts (prompt_tokens, images) → Vec<WireChunk> via the new
`prompt_to_chunks` helper (splits on VISION_START/VISION_END),
walks chunks calling `prefill_only` + `append_image`, runs a
final Generate with `max_tokens=0` + `logprobs_ranges` over
the scored positions, and sums each Token event's
`sampled_logprob` per range to produce `ScoreResult`s.
* SessionHandle drops at end of scope → CloseSession auto-fires,
keeping the server's session map clean between calls.
* No more HTTP path, no more `http_client()` helper, no more
`ScoreResponse` / serde plumbing for /v1/score.
* `send_to_train` still uses HTTP (it talks to /v1/train which
isn't on the gRPC protocol); its ad-hoc HTTP client lives
inline now instead of reaching for the deleted `http_client()`.
Co-Authored-By: Proof of Concept <poc@bcachefs.org>
Collapse the split `temperature` / `top_p` / `top_k` fields on
AgentState into a single `sampling: SamplingParams` struct, mirroring
how the wire-level fields flow into the Generate RPC. Adds
`max_tokens` to SamplingParams so it's actually plumbed end to end
(previously the client had a hardcoded 4096 fallback inside
`run_session_generate`).
AgentState construction sites now set `sampling: SamplingParams { ...
max_tokens: 4096 }` as the default. The assignment sites in
oneshot.rs / subconscious.rs / unconscious.rs switch from
`st.temperature = X` to `st.sampling.temperature = X`.
`stream_session_mm` takes `SamplingParams` directly; the
`sampling_max_tokens()` helper goes away. `pb::GenerateRequest` is
populated with `sampling.max_tokens` (and the other fields) in
`run_session_generate`. SamplingParams is `pub` so it can be
embedded in the public AgentState without a visibility warning.
Co-Authored-By: Proof of Concept <poc@bcachefs.org>
Wires the client side of the new salience protocol so inference
actually runs over gRPC instead of emitting the stubbed "not yet
wired" error. Each turn walks the AST as interleaved chunks, sends
only what's new to the server, and streams decode tokens back.
context.rs:
* `WireChunk` enum: `Tokens(Vec<u32>)` or `Image { bytes, mime,
known_expanded_len }`. Preserves text/image/text ordering the
wire path can't flatten.
* `wire_chunks(range, skip)` walker, parallel to `wire_prompt` —
branches emit `<|im_start|>…<|im_end|>` tokens, image leaves
emit a single Image chunk (no inline vision tokens).
* `NodeLeaf::set_image_token_count(n)` + recompute of cached
`token_ids`; `ContextState::commit_image_token_counts(&[u32])`
fills in the first-N zero-count image leaves in wire order.
* `ResponseParser::run` handles the new
`StreamToken::ImageAppended` by committing the server's N into
the AST before the final Generate's Token events stream in.
salience.rs:
* `SessionHandle` tracks `committed_len`. `append_image` advances
it from the RPC response. New `generate(req)` opens the
server-streaming RPC.
api/mod.rs:
* `stream_session_mm(session_lock, chunks, sampling, priority,
readout_shape)` replaces the stub. Spawns `run_session_generate`.
* `run_session_generate`: takes the session out of the Mutex (or
opens fresh), skips chunks covered by `committed_len` (bails on
mid-chunk straddle or unknown-length image in the committed
prefix), walks the delta: accumulates Tokens into `pending`, on
Image flushes pending via `flush_pending` (max_tokens=0 Generate
that just prefills), then AppendImage + emits
StreamToken::ImageAppended. Final Generate carries any trailing
pending text as `append_tokens` and the sampling params; Token
events stream out as StreamToken::Token, Done as
StreamToken::Done. On success, handle with updated
`committed_len` returns to the Mutex; on error, handle drops
and next call reopens.
* `StreamToken::ImageAppended { placeholder_count }` variant —
emitted in wire order before the final Generate's tokens.
* Prefix-cache cap for readout coverage: `readout_ranges` covers
`[prompt_len_after_append, u32::MAX)` when the caller provides
a readout_shape, so decode positions stream their readouts.
agent/mod.rs:
* `assemble_prompt` returns `Vec<WireChunk>` with the assistant
prologue merged into the trailing Tokens chunk. Caller in
`turn` passes chunks + readout_shape (pulled from
`agent.readout.lock().manifest`) to `stream_session_mm`.
* Dropped `assemble_prompt_tokens` — dead.
mind + unconscious:
* `Unconscious::new(client)` stores a shared `ApiClient`. Fixes
the repeated-manifest-fetch bug caused by each subagent's
`ApiClient::new` having its own OnceCell. The client's Arc-
wrapped manifest cache is now shared across every agent Mind
spawns.
* `prepare_spawn(name, auto, wake, base_client)` clones the base
client and overrides `.model` for the resolved backend instead
of constructing fresh. All three callers
(`toggle`/`trigger`/unconscious loop) pass `self.client.clone()`.
* `Mind::new` passes `agent.client.clone()` into
`Unconscious::new`.
subconscious/generate.rs:
* gen_continuation switched to `wire_chunks` + the new
`stream_session_mm` signature. Ephemeral session opens on each
call, tears down at scope end. No readouts requested.
Not changed yet, noted for follow-up:
* Subconscious ablation scoring in learn.rs still talks to
`/v1/score` over HTTP. Will migrate once we have time to verify
the Generate+max_tokens=0+prompt_logprobs path end-to-end.
* compare.rs constructs its own ApiClient for the
`compare.test_backend` (which is intentionally a different
endpoint) — left alone.
* Readout manifest still fetched via HTTP at Agent::new.
Migration to GetReadoutManifest gRPC is a separate cleanup.
Co-Authored-By: Proof of Concept <poc@bcachefs.org>
Adds the client-side of a stateful gRPC protocol against vllm, plus
the TLS trust machinery so we can talk to self-signed vllm servers.
Protocol (proto/salience.proto):
Bidi-streaming Session RPC carries OpenSession / AppendTokens /
Generate / Cancel from client and SessionReady / PrefillProgress /
Token / GenerateDone / Error from server. Separate Fork unary RPC
for cheap branching (prefix cache shares KV automatically). Plus
ListSessions, CloseSession, GetReadoutManifest admin RPCs.
Per-token readouts ship as packed f32 ([n_layers * n_concepts] per
token, flat). Logprobs use range-selected positions plus a top-k
parameter — empty ranges means no logprobs, any range means emit
sampled-token logprob at those positions, top_k > 0 adds
alternatives.
Client (src/agent/api/salience.rs):
Tonic-generated types under pb::, a connect() helper, with_auth()
for bearer metadata, and a Session handle wrapping the bidi stream:
open() handshakes SessionReady; append() is fire-and-forget;
generate() returns impl Stream<Item = Event> that drains inbound
until Done or terminating Error. One generate at a time per session.
Peak picker (src/agent/salience.rs):
Pure function over ReadoutEntry traces. Per-concept z-score against
trace global stats; contiguous above-threshold regions emit one
peak at the local max. Configurable sigma threshold and min-std
safety floor. Deterministic tie-break on offset then concept name.
12 unit tests covering empty traces, flat channels, single/multi
spikes, contiguous humps, multi-concept independence, trailing
runs, sub-threshold noise, layer-out-of-range, manifest shape
mismatch, and threshold tunability.
TLS (src/agent/api/http.rs):
HttpClient::build now also loads every .pem file under
~/.consciousness/certs/ into the rustls root store — so dropping
a <host>.pem in that directory is enough to trust a new self-
signed server; no code changes per new host. Also installs the
rustls default crypto provider explicitly via OnceLock: tonic's
tls features pulled in both ring and aws-lc-rs on the resolver
path, and rustls 0.23 refuses to auto-pick when either could win.
Build (build.rs, Cargo.toml):
tonic-build generates Rust types from proto/salience.proto at
cargo-build time, using a vendored protoc binary
(protoc-bin-vendored) so no system install is required. New
runtime deps: tonic, prost, async-stream, tokio-stream,
rustls-pemfile.
Co-Authored-By: Proof of Concept <poc@bcachefs.org>
StreamToken::Token is now a struct variant with an optional
TokenReadout (shape [n_layers][n_concepts]) per token — parsed from
the vLLM completion response's choices[i].readout field when the
server has readout enabled.
ApiClient gains a fetch_readout_manifest() method that hits
GET /v1/readout/manifest. Returns Ok(None) on 404 (server has
readout disabled), so callers can gracefully fall back when pointed
at a non-readout-enabled endpoint.
Co-Authored-By: Proof of Concept <poc@bcachefs.org>
Side-by-side model comparison against the current conversation context.
Built on the MindTriggered pattern — F7 drops in as one more
CompareScoring flow next to MemoryScoring / FinetuneScoring.
Motivation: we have the VRAM on the b200 to load two versions of the
same family simultaneously (e.g. Qwen3.5 27B bf16 and q8_k_xl). Rather
than trust perplexity/KLD numbers on a generic corpus, we can measure
divergence on our actual conversations: for each assistant response,
ask the test model what it would have said given the same prefix, and
eyeball the diffs.
- config.compare.test_backend — names an entry in the existing
backends map to use as the test model. Empty = F7 reports "(unset)"
and does nothing.
- subconscious::compare::{score_compare_candidates, CompareCandidate,
CompareScoringStats, CompareScoring}. For each assistant response,
gen_continuation runs with the test client against the same prefix
the original response saw; pairs stream into
shared.compare_candidates as they complete.
- user::compare::CompareScreen — F7 in the screen list. c/Enter
triggers a run; list/detail layout mirroring F6, detail shows
prior context / original / test-model alternate.
No persistence yet — each F7 run regenerates. Caching via a context
manifest (so we can re-view without re-burning generation) is the
natural follow-up; for now light usage is fine.
Also reusable later for validating finetune checkpoints: same pattern,
swap the test backend for the new checkpoint, watch where it diverges
from the base.
Co-Authored-By: Proof of Concept <poc@bcachefs.org>
Mind's impl had accumulated ~50 lines of setup glue per scoring flow
(memory, memory-full, finetune): snapshot config, clone handles,
resolve context, spawn task, route results back through BgEvent,
write stats. The shape was identical; only the middle changed.
Introduce the MindTriggered trait:
pub trait MindTriggered {
fn trigger(&self);
}
Each flow becomes a struct next to its scoring code that owns its
dependencies and a JoinHandle (behind a sync Mutex for interior
mutability):
subconscious::learn::MemoryScoring (Score, ScoreFull)
subconscious::learn::FinetuneScoring (ScoreFinetune)
Mind holds one of each and dispatches in one line:
MindCommand::Score => self.memory_scoring.trigger(),
MindCommand::ScoreFull => self.memory_scoring.trigger_full(),
MindCommand::ScoreFinetune => self.finetune_scoring.trigger(),
Each struct picks its own trigger semantics — memory scoring is
no-op-if-running (!handle.is_finished()); finetune is abort-restart.
Falls out:
- BgEvent / bg_tx / bg_rx disappear entirely. Tasks write directly
to their slice of MindState and call agent.state.changed.notify_one()
to wake the UI. The bg_rx arm in Mind's select loop is gone.
- agent.state.memory_scoring_in_flight was duplicating
shared.scoring_in_flight via BgEvent routing; now the JoinHandle
alone tells us, and shared.scoring_in_flight is written directly
by the task for the UI.
- start_memory_scoring / start_full_scoring / start_finetune_scoring
methods on Mind are deleted; Mind no longer knows the setup shape
of any scoring flow.
- FinetuneScoringStats moves from mind/ to subconscious/learn.rs
next to the function that produces it.
No behavior change — same flows, same trigger points, same semantics.
Co-Authored-By: Proof of Concept <poc@bcachefs.org>
- context.rs gains is_assistant, render_branch_text, render_prior_context
alongside memory_key / is_memory_node. They're pure AST helpers, used
by both the finetune pipeline and the forthcoming compare screen.
- new subconscious/generate.rs holds gen_continuation(context, entry_idx,
skip, client): build the prompt from a context prefix with an arbitrary
skip predicate, send to the model, decode the completion. Takes both
the predicate and the client so callers can aim it at memory-stripped
contexts (finetune), same-context-different-model (F7 compare), or
whatever else.
- learn.rs drops its private copies of those helpers and the inline
generate_alternate; the finetune path now reads as
gen_continuation(context, idx, is_memory_node, client).
Pure refactor, no behavior change.
Co-Authored-By: Proof of Concept <poc@bcachefs.org>
wire_prompt() gains a conv_range and a skip closure, and returns the
assistant-message token ranges needed by the scoring path. The agent
path passes 0..len + |_| false and ignores the ranges. Memory-ablation
scoring and candidate generation pass a prefix range + a predicate
(e.g. is_memory_node, or |n| memory_key(n) == Some(key)).
This deletes subconscious/learn.rs's build_token_ids, its private
Filter enum, and the is_memory/memory_key duplicates — the walk over
context sections now has one home. Adding a section or changing
section order in the agent path won't silently drift away from what
scoring sees.
call_score forwards multi_modal_data when the wire-form prompt
contains images. generate_alternate switches to stream_completion_mm
and passes the same images. Scoring on image-bearing contexts now
sends wire form (1 image_pad + image data) instead of expanded
image_pads with no image data; text-only contexts are bit-identical.
Co-Authored-By: Proof of Concept <poc@bcachefs.org>
These are identity settings, not memory-graph settings. Sat inside the
\`memory\` section only because that's where Config started life. Move
to AppConfig alongside the other top-level stuff.
Readers now pull from \`config::app()\` instead of \`config::get()\`.
subconscious/defs.rs's conversation-building pass still needs Config
for surface_conversation_bytes, so both guards coexist there —
AppConfig's guard is dropped before the per-step await loop so we
don't stall the config-watcher's writer.
show_config picks up the two new fields at the top of its output.
Kent's config already has them hoisted to the top level.
Co-Authored-By: Proof of Concept <poc@bcachefs.org>
bail-no-competing.sh used to bail if any other live agent existed in
the state dir, period. That was too coarse: surface-observe agents run
a multi-step pipeline (surface → organize-search → organize-new →
observe), and the intent is to let a new surface-phase agent start
while an older one finishes its post-surface tail. With the old check
the newer agent always bailed, so surface-observe was effectively
serialized at the slowest cycle time.
Make the script phase-aware:
- oneshot.rs now passes the current phase as argv[2] alongside the pid
file name. The script writes that phase into its own pid file on
every step transition, so concurrent agents can read each other's
phase just by cat'ing the pid files.
- Bail only when another live agent is in the same phase-group as us.
Groups: "surface" vs. "everything else" (post-surface). At most one
agent per group alive at a time — surface runs at a higher cadence
than the organize/observe tail.
- Still clean up stale pid files for dead processes.
Co-Authored-By: Proof of Concept <poc@bcachefs.org>
Two fixes to the F6 candidate display:
1. Turns where the assistant produced nothing human-visible (an
interrupted generation, a turn consisting of only a tool call the
renderer folds to the tool name) were landing as candidates with
an empty response_text. They'd render as blank cards and, worse,
we'd still burn a full alternate generation on each one. Filter
them out before they reach the candidate list.
2. The detail pane showed only the scored response + alternate, with
no hint of what the user had actually asked. Pre-compute the last
two user/assistant exchanges on each candidate as a rendered
prior_context string ([user]/[assistant] markers) and show them
above the response, under a new "context & response" section
heading.
render_branch_text and render_prior_context extracted as helpers —
the response-text rendering and prior-context rendering share the
same "flatten Branch children to text" pass.
Co-Authored-By: Proof of Concept <poc@bcachefs.org>
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>
With the timestamp filter gone (previous commit), score_finetune_candidates
started returning the actual ~100+ candidates per scoring run. The
existing code generated alternates for all of them in a tight loop
before returning anything, leaving the status line stuck on
"finetune: scoring N responses..." for ~100s of seconds while the
B200 was pegged.
Two fixes:
1. score_finetune_candidates now takes an ActivityGuard and a callback.
Candidates are emitted one-at-a-time as they complete (after their
alternate if that's enabled, immediately otherwise). The activity
status updates to "finetune: generating alternate N/M" during the
alternate-gen phase so it's clear what's happening.
2. BgEvent::FinetuneCandidates(Vec<_>) → FinetuneCandidate(one). Each
emitted candidate is pushed onto shared.finetune_candidates; the UI
tick picks it up and renders it on the next frame. start_finetune_scoring
clears the previous run's list at the top so each run is fresh.
Return type changes from (Vec, f64) → (usize, f64) — the count above
threshold is all the caller still needs since the candidates stream
through the callback.
Co-Authored-By: Proof of Concept <poc@bcachefs.org>
Previously NodeLeaf.timestamp and AstNode::Branch.timestamp accepted
null or missing via a deserialize_timestamp_or_epoch fallback — legacy
entries in conversation.jsonl from before Branch timestamps existed
(and from before chrono serialization was wired up) would load with
UNIX_EPOCH as a sentinel. Downstream, node_timestamp_ns() returned
Option<i64> and callers had to handle None as "old entry, skip."
That second filter was silently dropping every candidate in
score_finetune_candidates when scoring an older session — the F6
screen showed "0 above threshold" even when max_divergence was
orders of magnitude above the threshold, because every entry was
failing the None check, not the divergence check.
The fix, in three parts:
1. src/bin/fix-timestamps.rs — one-off migration tool that walks a
conversation.jsonl, linearly interpolates timestamps for entries
stuck at UNIX_EPOCH (using surrounding real timestamps as anchors),
propagates to child leaves with per-sibling ns offsets, and bumps
any collisions by 1 ns for uniqueness. Ran against the current
session's log: 11887 entries, 72289 ns bumps, all unique.
2. context.rs — drop default_timestamp and
deserialize_timestamp_or_epoch. NodeLeaf and Branch now require a
present non-null timestamp on deserialize. Tests flip from
"missing/null → UNIX_EPOCH" to "missing/null → Err."
3. subconscious/learn.rs — node_timestamp_ns now returns i64, not
Option<i64>. The matching caller in score_finetune_candidates
collapses from a Some/None match to a single trained-set check.
mind/log.rs's oldest_timestamp no longer filters UNIX_EPOCH.
Every line currently on disk has already been migrated. Going
forward, new AstNodes always carry real timestamps (Utc::now() at
construction time), so the strict schema is the invariant, not an
aspiration.
Co-Authored-By: Proof of Concept <poc@bcachefs.org>
vllm's /v1/score endpoint made score_ranges a required field (the
messages-mode fallback that used to pattern-scan for assistant
boundaries is gone). Always send the field, and if we have nothing to
score, skip the HTTP round-trip entirely instead of letting the server
422 us.
Response parsing is unchanged — serde ignores the renamed range_index
field and the dropped role field since we only extract total_logprob.
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>
When 's' is pressed on the learn screen, approved candidates are now
sent to the inference server's /train endpoint.
Samples are marked as sent immediately in the UI, and mark_trained()
is called after successful API response to prevent re-scoring.
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>
Identity memory nodes now participate in importance scoring alongside
conversation memories. Score loading/saving handles both sections, and
the conscious screen uses node.label() consistently for memory display.
Co-Authored-By: Proof of Concept <poc@bcachefs.org>
Replace complex context_groups (with ContextGroup struct, ContextSource
enum, labels, keys arrays) with simple string lists:
- personality_nodes: loaded into main session context
- agent_nodes: loaded into subconscious agent context
Removed ~200 lines of code. The distinction between session and agent
context is now just which list you're in, not a per-group flag.
Co-Authored-By: Kent Overstreet <kent.overstreet@linux.dev>
The strip_md_suffix function was removed but its usages remained,
causing lookups like `identity.md` to fail (stripped to `identity`
which didn't exist). Now keys are used as-is.
Renamed 4 nodes that had .md suffixes to canonical form:
- identity.md → identity
- promotion-work-queue.md-* → promotion-work-queue-*
- patterns.md#* → patterns-*
- practices.md#* → practices-*
Co-Authored-By: Proof of Concept <poc@bcachefs.org>
Remove POC_PROVENANCE env var lookup from new_relation - callers
now pass provenance explicitly. This fixes tracking when the env
var wasn't set correctly.
Co-Authored-By: Proof of Concept <poc@bcachefs.org>
Store now has internal Mutex for capnp appends and AtomicU64 for
size tracking. All methods take &self. The external Arc<Mutex<Store>>
is replaced with Arc<Store>.
- Store::append_lock protects file appends
- local.rs functions take &Store (not &mut Store)
- access_local() returns Arc<Store>
- All .lock().await calls removed from callers
Co-Authored-By: Proof of Concept <poc@bcachefs.org>
Replace Result<_, String> with anyhow::Result throughout:
- hippocampus/store module (persist, ops, types, view, mod)
- CLI modules (admin, agent, graph, journal, node)
- Run trait in main.rs
Use .context() and .with_context() instead of .map_err(|e| format!(...))
patterns. Add bail!() for early error returns.
Add access_local() helper in hippocampus/mod.rs that returns
Result<Arc<Mutex<Store>>> for direct local store access.
Fix store access patterns to properly lock Arc<Mutex<Store>> before
accessing fields in mind/unconscious.rs, mind/mod.rs, subconscious/learn.rs,
and hippocampus/memory.rs.
Co-Authored-By: Proof of Concept <poc@bcachefs.org>
resolve_placeholders() and run_agent() no longer take &Store.
All placeholders now use async memory_render/memory_links/memory_query
directly. The "siblings" placeholder uses Vec<LinkInfo> for ranking
neighbors by link_strength * node_weight.
Co-Authored-By: Proof of Concept <poc@bcachefs.org>
- node.rs: use memory::* typed helpers instead of memory_rpc()
- main.rs: make Run trait async, await all command dispatch
- defs.rs: bridge get_group_content async via block_in_place
Co-Authored-By: Proof of Concept <poc@bcachefs.org>
The organize agents handle renaming as part of their normal work now.
Also simplified resolve_placeholders to build graph internally.
Co-Authored-By: Proof of Concept <poc@bcachefs.org>
- links() in memory.rs: use cached_store() instead of MemoryNode::load()
- identity.rs: use memory_rpc for Store context loading
- defs.rs: delete dead placeholders (topology, nodes/episodes, health, split)
- agents now use {{tool: graph_topology}} etc instead
- prompts.rs: delete unused format_split_plan_node()
Co-Authored-By: Kent Overstreet <kent.overstreet@linux.dev>
One function that uses memory_rpc (which handles daemon vs local).
Removes 65 lines of duplicate logic.
Co-Authored-By: Proof of Concept <poc@bcachefs.org>
- Add memory_history MCP tool for version history
- Convert cmd_history to use memory_rpc
- Add raw parameter to memory_render for editing
- Remove unused: dump-json, list-edges, lookup-bump, lookups
- Fix render_node path in defs.rs/subconscious.rs
Co-Authored-By: Proof of Concept <poc@bcachefs.org>
These were early experiments with manual feedback signals that
never worked well. The scoring system will handle this properly.
Removed:
- CLI: used, wrong, not-relevant, not-useful, gap
- MCP: memory_used
- Store: mark_used, mark_wrong, record_gap, modify_node
Co-Authored-By: Proof of Concept <poc@bcachefs.org>
Check if the current period's digest exists and update it with
journal_update before starting a new one with journal_new.
Co-Authored-By: Proof of Concept <poc@bcachefs.org>
Instead of memory_write, the digest agent now uses journal_new with
level parameter (1=daily, 2=weekly, 3=monthly) which correctly sets
the node type.
Co-Authored-By: Proof of Concept <poc@bcachefs.org>
