v0.7.0

Performance, benchmark honesty, and the native-memory bridge.

Performance

• Embeddings stored as packed float32 blobs (~3.9× smaller than JSON text) with an
  in-process cache of unit-normalized vectors — dense search/dedup no longer
  json.loads every scoped embedding per query; cosine is a dot of two cached unit
  vectors. Measured A/B vs the previous engine: dense search() 10–29× faster and
  the speedup grows with store size (at 6k memories, 8.1 s/query → 0.28 s). Still
  zero-dependency (stdlib array, no numpy).
• Lexical search pushes ORDER BY bm25 LIMIT into SQLite instead of Python-scoring
  every term match.
• Batched reindex via Ollama /api/embed (32/req, per-item fallback) and startup
  warmup/reindex moved off the bind path (no lazy-spawn port race).
• MCP tool calls run in-process instead of a python ygg.py subprocess per call,
  with stdout/stderr separated so --json stays parseable.
• Index on (user_id, namespace, created_at) for the session-start hook's get_all.

Changed

• Embedding model is versioned per row. Switching YGG_EMBED_MODEL marks old vectors
  stale and reindexes them, instead of comparing vectors across models. ygg doctor
  counts model-mismatched rows.
• Benchmark reporting is credibility-first. eval/ygg_eval.py --report leads with
  holdout recall@1 (weights tuned on dev only): 0.93 within a project, 0.80 full-corpus,
  recall@3 = 1.00 in both. Discloses candidate pool sizes (min 2 / median 6 / max 35) and
  95% bootstrap CIs. README badge → holdout 0.93; BENCHMARKS.md + all 6 translations
  rewritten to the honest two-view framing.

Added

• ygg export-native --project P — the native-memory bridge: writes a curated,
  type-grouped digest of a project's memory into a managed block in AGENTS.md/MEMORY.md
  (idempotent; preserves hand-written content). Pairs with ygg seed (which imports from
  the native memory) so Yggdrasil is the layer above Claude Code's and Codex's own memory,
  feeding them both ways.
• ygg review [--apply] — work the governance queue from the CLI: consolidate exact/
  near duplicates (keep the oldest, archive the rest) and surface stale/conflict markers.
  Interactive on a TTY; --apply --yes auto-consolidates duplicates and flags stale markers
  for manual review. Everything is archived (reversible), never hard-deleted.
• Ranking parity — a pinned or frequently-recalled memory retrieved only by vector now
  gets its pin/usage boost, via the same channel lexical hits use.

Security

• Engine-side secret guard — a raw POST /add bypassing the CLI now also refuses obvious
  credentials (AWS keys, JWTs, GitHub/GitLab PATs, private keys, connection-string passwords).
  High-confidence structured tokens only, so memories that merely mention "password"/"secret"
  are unaffected.

Fixed

• Cross-platform hw() — Linux /proc/meminfo + /proc/cpuinfo (+ nvidia-smi), Windows
  PowerShell CIM; the model recommender no longer sizes off 0 GB off-macOS.
• First-hour polish — actionable port-conflict hint instead of a traceback; non-interactive
  install announces the lexical-only fallback.