- New spectral module: Laplacian eigendecomposition of the memory graph.
Commands: spectral, spectral-save, spectral-neighbors, spectral-positions,
spectral-suggest. Spectral neighbors expand search results beyond keyword
matching to structural proximity.
- Search: use StoreView trait to avoid 6MB state.bin rewrite on every query.
Append-only retrieval logging. Spectral expansion shows structurally
nearby nodes after text results.
- Fix panic in journal-tail: string truncation at byte 67 could land inside
a multi-byte character (em dash). Now walks back to char boundary.
- Replay queue: show classification and spectral outlier score.
- Knowledge agents: extractor, challenger, connector prompts and runner
scripts for automated graph enrichment.
- memory-search hook: stale state file cleanup (24h expiry).
Three new tools for structural graph health:
- fix-categories: rule-based recategorization fixing core inflation
(225 → 26 core nodes). Only identity.md and kent.md stay core;
everything else reclassified to tech/obs/gen by file prefix rules.
- cap-degree: two-phase degree capping. First prunes weakest Auto
edges, then prunes Link edges to high-degree targets (they have
alternative paths). Brought max degree from 919 → 50.
- link-orphans: connects degree-0/1 nodes to most textually similar
connected nodes via cosine similarity. Linked 614 orphans.
Also: community detection now filters edges below strength 0.3,
preventing weak auto-links from merging unrelated communities.
Pipeline updated: consolidate-full now runs link-orphans + cap-degree
instead of triangle-close (which was counterproductive — densified
hub neighborhoods instead of building bridges).
Net effect: Gini 0.754 → 0.546, max degree 919 → 50.
New command: `poc-memory triangle-close [MIN_DEG] [SIM] [MAX_PER_HUB]`
For each node above min_degree, finds pairs of its neighbors that
aren't directly connected and have text similarity above threshold.
Links them. This turns hub-spoke patterns into triangles, directly
improving clustering coefficient and schema fit.
First run results (default params: deg≥5, sim≥0.3, max 10/hub):
- 636 hubs processed, 5046 lateral links added
- cc: 0.14 → 0.46 (target: high)
- fit: 0.09 → 0.32 (target ≥0.2)
- σ: 56.9 → 84.4 (small-world coefficient improved)
Also fixes separator agent prompt: truncate interference pairs to
batch count (was including all 1114 pairs = 1.3M chars).
Reads a conversation JSONL, identifies experiential moments that
weren't captured in real-time journal entries, and writes them as
journal nodes in the store. The agent writes in PoC's voice with
emotion tags, focusing on intimate moments, shifts in understanding,
and small pleasures — not clinical topic extraction.
Conversation timestamps are now extracted and included in formatted
output, enabling accurate temporal placement of mined entries.
Also: extract_conversation now returns timestamps as a 4th tuple field.
`poc-journal tail 5 --full` shows full entry content with
timestamp headers and --- separators. Default mode remains
title-only for scanning. Also passes all args through the
poc-journal wrapper instead of just the count.
Sort key normalization ensures consistent ordering across entries
with different date formats (content dates vs key dates). Title
extraction skips date-only lines, finds ## headers or falls back
to first content line truncated at 70 chars.
Also fixed: cargo bin had stale binary shadowing local bin install.
Batch all non-deleted links (~3,800) into char-budgeted groups,
send each batch to Sonnet with full content of both endpoints,
and apply KEEP/DELETE/RETARGET/WEAKEN/STRENGTHEN decisions.
One-time cleanup for links created before refine_target existed.
Co-Authored-By: ProofOfConcept <poc@bcachefs.org>
Pattern separation for memory graph: when a file-level node (e.g.
identity.md) has section children, redistribute its links to the
best-matching section using cosine similarity.
- differentiate_hub: analyze hub, propose link redistribution
- refine_target: at link creation time, automatically target the
most specific section instead of the file-level hub
- Applied refine_target in all four link creation paths (digest
links, journal enrichment, apply consolidation, link-add command)
- Saturated hubs listed in agent topology header with "DO NOT LINK"
This prevents hub formation proactively (refine_target) and
remediates existing hubs (differentiate command).
Co-Authored-By: ProofOfConcept <poc@bcachefs.org>
Three Python scripts (858 lines) replaced with native Rust subcommands:
- digest-links [--apply]: parses ## Links sections from episodic digests,
normalizes keys, applies to graph with section-level fallback
- journal-enrich JSONL TEXT [LINE]: extracts conversation from JSONL
transcript, calls Sonnet for link proposals and source location
- apply-consolidation [--apply]: reads consolidation reports, sends to
Sonnet for structured action extraction (links, categorizations,
manual items)
Shared infrastructure: call_sonnet now pub(crate), new
parse_json_response helper for Sonnet output parsing with markdown
fence stripping.
Replace daily-digest.py, weekly-digest.py, monthly-digest.py with a
single digest.rs module. All three digest types now:
- Gather input directly from the Store (no subprocess calls)
- Build prompts in Rust (same templates as the Python versions)
- Call Sonnet via `claude -p --model sonnet`
- Import results back into the store automatically
- Extract links and save agent results
606 lines of Rust replaces 729 lines of Python + store_helpers.py
overhead. More importantly: this is now callable as a library from
poc-agent, and shares types/code with the rest of poc-memory.
Also adds `digest monthly [YYYY-MM]` subcommand (was Python-only).
- &PathBuf → &Path in memory-search.rs signatures
- Redundant field name in graph.rs struct init
- Add truncate(false) to lock file open
- Derive Default for Store instead of manual impl
- slice::from_ref instead of &[x.clone()]
- rsplit_once instead of split().last()
- str::repeat instead of iter::repeat().take().collect()
- is_none_or instead of map_or(true, ...)
- strip_prefix instead of manual slicing
Zero warnings on `cargo clippy`.
- Replace all 5 `Command::new("date")` calls across 4 files with
pure Rust time formatting via libc localtime_r
- Add format_date/format_datetime/format_datetime_space helpers to
capnp_store
- Move import_file, find_journal_node, export_to_markdown, render_file,
file_sections into Store methods where they belong
- Fix find_current_transcript to search all project dirs instead of
hardcoding bcachefs-tools path
- Fix double-reference .clone() warnings in cmd_trace
- Fix unused variable warning in neuro.rs
main.rs: 1290 → 1137 lines, zero warnings.
journal-write creates entries directly in the capnp store with
auto-generated timestamped keys (journal.md#j-YYYY-MM-DDtHH-MM-slug),
episodic session type, and source ref from current transcript.
journal-tail sorts entries by date extracted from content headers,
falling back to key-embedded dates, then node timestamp.
poc-journal shell script now delegates to these commands instead
of appending to journal.md. Journal entries are store-first.
Sections within a file have a natural order that matters —
identity.md reads as a narrative, not an alphabetical index.
The position field (u32) tracks section index within the file.
Set during init and import from parse order. Export and
load-context sort by position instead of key, preserving the
author's intended structure.
write KEY: upsert a single node from stdin. Creates new or updates
existing with version bump. No-op if content unchanged.
import FILE: parse markdown sections, diff against store, upsert
changed/new nodes. Incremental — only touches what changed.
export FILE|--all: regenerate markdown from store nodes. Gathers
file-level + section nodes, reconstitutes mem markers with links
and causes from the relation graph.
Together these close the bidirectional sync loop:
markdown → import → store → export → markdown
Also exposes memory_dir_pub() for use from main.rs.
load-context replaces the shell hook's file-by-file cat approach.
Queries the capnp store directly for all session-start context:
orientation, identity, reflections, interests, inner life, people,
active context, shared reference, technical, and recent journal.
Sections are gathered per-file and output in priority order.
Journal entries filtered to last 7 days by key-embedded date,
capped at 20 most recent.
render outputs a single node's content to stdout.
The load-memory.sh hook now delegates entirely to
`poc-memory load-context` — capnp store is the single source
of truth for session startup context.
node-delete: soft-deletes a node by appending a deleted version to
the capnp log, then removing it from the in-memory cache.
resolve_redirect: when resolve_key can't find a node, checks a static
redirect table for sections that moved during file splits (like the
reflections.md → reflections-{reading,dreams,zoom}.md split). This
handles immutable files (journal.md with chattr +a) that can't have
their references updated.
Faster serialization/deserialization, smaller on disk (4.2MB vs 5.9MB).
Automatic migration from state.json on first load — reads the JSON,
writes state.bin, deletes the old file.
Added list-keys, list-edges, dump-json commands so Python scripts no
longer need to parse the cache directly. Updated bulk-categorize.py
and consolidation-loop.py to use the new CLI commands.
