feat(8.0): brain.fillSubtypes migration helper + pre-RC1 gap closure

- brain.fillSubtypes(rules): idempotent subtype back-fill for pre-8.0 data.
  One rule per NounType/VerbType (literal default or per-entry function);
  fills only entries still missing a subtype through the public update()/
  updateRelation() paths; returns { scanned, filled, skipped, errors, byType }.
  Full unit suite in tests/unit/brainy/fill-subtypes.test.ts.
- Fix getNouns/getVerbs pagination hasMore (peek one past the window) —
  was permanently false, silently truncating every multi-page walk.
- find({ near }) without near.id now throws a teaching error instead of an
  opaque storage sharding failure; CLI --threshold without --near applies a
  plain score floor.
- CLI init/close audit: every one-shot command init()s, close()s, and exits
  explicitly; delete the unmaintained interactive REPL; replace the cloud-era
  storage subcommands with status/batch-delete; new types/validate commands.
- requireSubtype JSDoc now documents the 8.0 default-on contract; audit()
  recommendation points at fillSubtypes.
- Docs: data-storage-architecture rewritten to the real 8.0 on-disk layout;
  README storage section reflects filesystem+memory and snapshots; eli5 and
  SEMANTIC_VFS /as-of/ semantics corrected; internal tracker IDs and
  .strategy references scrubbed from published files.
This commit is contained in:
David Snelling 2026-06-11 10:42:34 -07:00
parent 9b0f4acd5b
commit c44678390e
30 changed files with 1517 additions and 3226 deletions

View file

@ -133,7 +133,7 @@ Brainy is the only row with every box checked. And it runs all of them in a sing
### One library, any scale
Brainy scales from a single laptop to billions of entities without changing a line of code. Small datasets live in memory. Larger ones spill to disk. At cloud scale, Brainy uses S3-compatible storage and automatically shards across nodes — the same API the whole way. Up to ten billion entities is fully implemented today.
Brainy scales from a quick experiment to serious production datasets without changing a line of code. Small datasets live entirely in memory. Larger ones spill to disk, where Brainy shards and compresses everything automatically. Need a backup or a copy? Snapshots are instant — the same API the whole way.
Add Cortex and you also unlock memory-mapped storage — aggregate state lives directly in the operating system's memory with zero serialization overhead, as fast as the hardware allows.