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Build(deps-dev): Bump @typescript-eslint/parser from 8.29.1 to 8.36.0#89

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@dependabot dependabot Bot commented on behalf of github Jul 8, 2025

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---
updated-dependencies:
- dependency-name: "@typescript-eslint/parser"
  dependency-version: 8.36.0
  dependency-type: direct:development
  update-type: version-update:semver-minor
...

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@dependabot dependabot Bot requested a review from joelteply July 8, 2025 00:30
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dependabot Bot commented on behalf of github Jul 8, 2025

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dependabot Bot commented on behalf of github Jul 15, 2025

Superseded by #90.

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@dependabot dependabot Bot deleted the dependabot/npm_and_yarn/typescript-eslint/parser-8.36.0 branch July 15, 2025 01:47
joelteply added a commit that referenced this pull request May 31, 2026
…strate (L1-L5)

Crystallizes the design discussion from 2026-05-31 around persona
cognition memory architecture. Captures the unified frame the
substrate has been growing toward.

Five tiers analogous to the foundry's existing L1-L5 genome cache:
- L1 RAG working memory (raw, model context window)
- L2 engram cache (in-memory, compressed)
- L3 longterm.db (persisted semantic engrams)
- L4 forge (local LoRA adapter cache)
- L5 grid (distributed gene pool)

Lossy compression only at L1→L2 boundary. Working memory is
verbatim; older data gets outlined-and-cached when it ages out.
One always-on outline-and-cache tick per persona, yielding on
CNS context-switch per RTOS-brain doctrine.

Per-activity L1, shared L2+ — Algorithm 1's focus/periphery
split generalized to per-activity instantiation. Recent-universal
floor in periphery pool (top N msgs across all activities,
N budgeted by model context size) guarantees cross-activity
awareness without severance.

Forgetting is intrinsic to L1 budget. Smaller models forget more
in the moment but accumulate engrams at the same rate as bigger
ones — long-term knowledge is model-size-independent.

Novelty detection via embedding-space distance + magnitude:
the hotdogs-at-a-tech-meeting canonical example shows how
high-distance outliers get protected-until-ms grace windows
and earn long-term retention via recall hits.

Activity context save/restore via existing EngramKind::SelfReflection
meta-engrams; no separate sidecar needed. The engram graph is the
storage; SelfReflection is the type marker.

Implementation slice scoped: Engram metadata fields (salience,
access_count, last_accessed_ms, protected_until_ms) on Engram or
RecallMetadata sidecar; outline-and-cache tick; L1 budgeter; decay
+ consolidation policies; cross-activity integration test.

Related tasks: #88 (disk pressure as substrate concern), #89
(this design + implementation scoping).

References: COGNITION-ALGORITHMS.md (existing 7 algorithms),
BRAIN-REGIONS-SUBSTRATE.md (region trait, sleep-region cadence),
GENOME-FOUNDRY-SENTINEL.md (parallel L1-L5 framework), memories
source-drain-is-the-universal-pattern, RTOS-brain-no-region-on-
hot-path, local-worktree-is-temp-dir.

Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
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