HN-launch refresh — v1.0.10 number-update + final-pick#

https://myforgelabs.github.io/myforge-vault-1111/wiki/schema-migration-downstream-grep-checklist/

Last Monday I dropped a column in a SQLite KO-DB I use as a knowledge-vault
backend. The migration ran clean — 190ms, no errors, every test passed.

Tuesday afternoon I noticed `vault-ko-query --stats` was returning correct
counts but `vault-explain` was returning empty results. Then I ran the MCP
tools my Claude Code session uses (`vault_ko_mcp.tool_query` /
`tool_top_k`) — also empty, no errors. Then the next-day Memgraph
re-extract job: `OperationalError: no such column: provenance`.

That's when I realized the migration had broken **15 silent downstream
callers** of `facts.provenance` over a 30-hour window. The CI passed
because the migration script's own tests used the new schema. The
downstream callers were never tested against the new schema, so every
SELECT silently returned the old `provenance`-shaped row, every `INSERT
INTO facts` failed with a swallowed error, and every MCP tool returned
`{"results": [], "error": "..."}` to my agent — which the agent then
treated as a clean empty result.

The fix once spotted was mechanical: schema-detect + GROUP_CONCAT JOIN
over the new `fact_provenance` side-table. Took 30 minutes for all 13
files.

The interesting bit is the safety-rail I shipped to catch the next
migration: `vault-schema-migration-victim-audit`. ADR-frontmatter scanner
+ qualified-SQL grep (`<table>.<col>` only — bare-word grep had 46
false-positives in the first run) + AST per-branch classifier
(`UNPATCHED-RISK` for hits outside `if post34:`/`else:` blocks) +
`--apply-patch` mode with dry-run, smoke-test, and auto-revert.

Weekly cron Mon 05:00 UTC + git pre-commit hook that blocks staged
schema-change ADRs if any unpatched victims exist.

Generalizes to any DB-API-2.0 driver (psycopg2, mariadb, cx_Oracle —
all have the same silent-empty-or-rollback failure mode on column-drop).

MIT, single-author. 28 days of public work. Code:
github.com/MyForgeLabs/myforge-vault-1111

Author here, happy to AMA.

To give some context on the rest of the project this safety-rail lives inside — the schema-migration audit is one of ~85 small CLI tools that maintain a self-improving Obsidian vault three CLI AI agents (Claude Code, Codex, Gemini) all share.

Stack:

- **Storage:** a single Obsidian-readable markdown tree (Karpathy's LLM-Wiki pattern: 10-raw → 11-wiki distillation, plus 02-Projects + 07-Decisions for state). 274 evergreen wikis, 48 ADRs, 131 one-shot audits today.

- **Structured fact layer (KO-DB):** SQLite, 15,835 (subject, predicate, object, confidence) triples extracted from session logs. This is where today's column-drop happened.

- **Graph layer:** Memgraph CE 3.9 local instance. 9,517 entities, 21K edges. The `CREATE VECTOR INDEX` is native in 3.9 — replaced a numpy-cosine fallback for a **280× speedup** (mean 1ms / p95 2.6ms). One Cypher line.

- **Crystallization loop:** at session-end an LLM-as-judge (G-Eval, calibrated to 96.7% verdict-agreement on a 30-pair human-labeled set) scores each "Learning" bullet, presents a batch preview, and on confirm propagates the bullet to the right vault layer with atomic writes + sandbox branch + revert-monitor.

- **Today's other milestone:** Layer-4 Critic-review (post-G-Eval adversarial veto) hit production-flip — Cohen's **κ=0.708 substantial** on a 100-bullet clean baseline. Apply-mode stays gated to W23 (2026-06-01..07) after 2 weeks of shadow-monitoring.

- **Cost trick:** "subagent-fanout" — spawn N parallel Claude Code subagents from inside one parent session. Subscription-bundled, **$0 marginal cost** for bulk LLM mutations. Today's working session ran 31 subagents across 5 waves, 22 tasks landed in 6 hours. (Honest disclosure: assumes Claude Code subscription. The flat-fee cost is not zero.)

- **Multi-agent:** three CLI agents share AGENTS.md via symlinks, with per-chat `$CLAUDE_CODE_SESSION_ID` isolation to prevent crosstalk.

- **Audio:** there's a 20-minute NotebookLM Deep Dive 2-host podcast covering today's milestones at `myforgelabs.github.io/myforge-vault-1111/audio/v1.0.10-milestones.en.mp3`.

- **Editor portability:** plain markdown + YAML frontmatter means the vault opens in Obsidian (where I edit), VS Code (where I script), and Tolaria (where the type-system auto-detects from the `type:` frontmatter — 274 wikis, 48 ADRs, 131 audits all classified automatically on first open, with wikilinks live and frontmatter rendered). Zero adapter code, just file-first conventions.

What's NOT in here yet (honest scope): no autonomous apply-mode (W23-gated), no fine-tuning, no proprietary RAG library, no business model. Just markdown + Memgraph + bge-m3 + ~85 small Python/bash tools. Boring stack, intentionally.

Repo: github.com/MyForgeLabs/myforge-vault-1111
Site: myforgelabs.github.io/myforge-vault-1111
v1.0.10 release notes (today): github.com/MyForgeLabs/myforge-vault-1111/releases/tag/v1.0.10

Most curious for feedback on:
- The κ=0.708 measurement methodology (n=100 is small; self-enhancement bias only partially controlled by the rubric + Anchor calibration)
- The mgclient autocommit gotcha (different bug than today's; 2 weeks ago — same project, different DB-driver default trap)
- Anyone running similar three-CLI shared-vault setups? Would love to compare evergreen-vs-session splits.

Hi HN — I'm Peti. I open-sourced the vault I've been using as durable
memory for Claude Code, Codex, and Gemini CLI. It's a single markdown
tree (Obsidian-readable) plus a stack of ~85 small CLI tools that
crystallize chat-history learnings into wikis, ADRs, and a Memgraph
knowledge graph.

The pattern is Karpathy's LLM-Wiki idea taken seriously: `10-raw/`
(immutable inputs) → `11-wiki/` (distilled evergreen) → `02-Projects/`
and `07-Decisions/` (state). What's new is the auto-crystallization
loop: at session-end an LLM-as-judge (G-Eval, calibrated to 96.7%
verdict-agreement on a 30-pair human-labeled set) scores each
"Learning" bullet, presents a batch preview, and on confirmation
propagates the bullet to the right vault layer with atomic writes
and a sandbox branch.

This week's working session shipped the Critic-review (Layer-4 of a
multi-layer-safety-gate) — Cohen's κ=0.708 on a 100-bullet clean
baseline with 86% verdict-agreement against content-classifier
ground-truth. That's the production-flip gate; the apply-mode stays
gated to W23 after 2 weeks of shadow-monitoring.

A few concrete things I learned the hard way over the 28 days:

- mgclient's default `autocommit=False` silently rolled back 1,262
  writes before I noticed. Fixing one line unblocked the graph layer.
- Memgraph CE 3.9's native `CREATE VECTOR INDEX` gave me a 280×
  search speedup over a naive numpy-cosine fallback.
- A single column-drop silently broke 15 of my CLI tools, including
  the entire MCP-tool stack. The downstream-grep + AST classifier
  I shipped to catch the next one is now a weekly cron.

9,517 entities in Memgraph, 21K edges, 15.8K facts in the structured
KO-DB, 274 wikis, 25 cron jobs, MIT licensed. Site:
`myforgelabs.github.io/myforge-vault-1111`.

1/ I open-sourced the Obsidian vault three CLI agents share.

Claude Code, Codex, and Gemini all read+write the same markdown tree.
Self-improving via LLM-as-judge crystallization. A Memgraph knowledge
graph underneath with a 280× search speedup.

MIT-licensed, $0 marginal cost. 28-day continuous public run. 🧵

2/ The pattern is @karpathy's LLM-Wiki idea, taken literally:

  10-raw/        immutable inputs
  11-wiki/       distilled evergreen
  02-Projects/   state
  07-Decisions/  ADRs

Plus 85+ CLI tools that move bullets between layers automatically
at session-end.

3/ The auto-crystallization loop:

session-end → G-Eval LLM-as-judge scores each Learning bullet →
batch preview → user confirms → atomic write to the right vault
layer → sandbox branch + revert-monitor.

96.7% verdict-agreement on a 30-pair human-labeled calibration set.

4/ Today's milestone: B-8 RSI Critic Layer-4 hit production-flip.

100-bullet clean baseline, content-classifier ground-truth (NOT
mock-scorer-labeled). Cohen's κ = 0.708 ("substantial"). Effective
false-accept rate ≈ 0% post-noise-inspection.

Apply-mode still gated to W23. Carefully.

5/ The cost trick: "subagent fanout."

You spawn 8 parallel Claude Code subagents from inside one Claude
Code session. Subscription-bundled, $0 marginal. 27+ fanouts in
today's working session, 22 tasks landed in 6 hours.

(Disclosure: assumes Claude Code subscription.)

6/ The graph layer:

Memgraph CE 3.9.0 (free, local). 9,517 entities, 21K edges, 100%
typed. Native `CREATE VECTOR INDEX` replaced numpy-cosine.

Mean 1ms / p95 2.6ms — **280× faster**. One Cypher line.

7/ Today's surprising find:

A 5-second schema-migration silently broke 15 of my CLI tools.
Including the entire MCP-tool stack. None threw errors. The whole
stack returned empty results for 30 hours.

Now: `vault-schema-migration-victim-audit` weekly cron + git hook.

8/ Save this thread — I'll be writing each up as standalone wikis:

- subagent-fanout (the $0-cost pattern)
- 15-silent-victim postmortem
- B-8 Critic κ=0.708 calibration methodology
- Memgraph native vector-index migration
- mgclient autocommit silent-rollback (2 weeks ago)

9/ Why three CLI agents instead of one?

Different agents, different temperaments. Claude is conservative.
Codex is fast and literal. Gemini is loose and exploratory. They
write to the same `AGENTS.md` via symlinks, with per-chat
session-IDs to prevent crosstalk.

10/ What's NOT in here:

- no fine-tuning
- no custom model
- no proprietary RAG library
- no business model
- no autonomous apply-mode (gated to W23)

Just markdown, Memgraph, bge-m3, mkdocs, and 85+ small bash/Python
tools. Boring stack, intentionally.

11/ Repo: github.com/MyForgeLabs/myforge-vault-1111
Site:  myforgelabs.github.io/myforge-vault-1111

274 wikis. 131 audits. 48 ADRs. 25 cron jobs. v1.0.10 tagged today.

If you build on it, ping me — I'd love to see your 11-wiki/ tree.
If you find a bug, open an issue. Especially the embarrassing kind.
/end

Sharing a personal project I've been running for 28 continuous days
and just tagged v1.0.10 (MIT-licensed).

**The setup:** a single Obsidian-readable markdown tree that Claude
Code, Codex CLI, and Gemini CLI all read and write to via shared
`AGENTS.md` symlinks. On top of it: a Memgraph CE 3.9 local instance
(9,517 entities, 21K edges, native vector-index, 1ms p50 search) and
a structured SQLite "KO-DB" with 15.8K extracted facts.

**The interesting bit for this sub** is the **subagent-fanout**
pattern. Spawn 8 parallel Claude Code subagents from inside one
parent session. Because they're subscription-bundled, marginal cost
is $0. I've used this pattern for: bulk wiki translation (HU→EN,
71 pages), G-Eval scoring, fact extraction, conflict-audit
cross-checks. Yes, it depends on you paying for Claude Code —
there's no free lunch, just a flat-fee one. Today's working session
ran 27 subagent fanouts and landed 22 tasks in 6 hours.

**Today's milestone:** B-8 RSI Critic Layer-4 hit production-flip
gate. 100-bullet clean baseline, Cohen's κ = 0.708 ("substantial").
Manual 10/10 false-accept inspection found that all 10 FAs were
actually content-classifier mining-noise, not real Critic failures
— effective FA rate ≈ 0%. Apply-mode (`VAULT_CRITIC_ACTIVE=1`) is
still gated to W23 after 2 weeks of shadow-monitoring. Carefully.

**Embeddings:** bge-m3 (multilingual, runs locally, MIT). **Reranker:**
bge-reranker-base for smart 2-pass retrieval on ambiguous queries.

**The auto-improvement loop:** at session-end an LLM-as-judge
(G-Eval, with bias-mitigation v0.3 that knocked self-enhancement
bias from 0.880 → 0.760 conf) scores each Learning bullet. High-
confidence Passes are batched and previewed to the user; on
confirm they get atomic-written to the right vault layer with a
sandbox-branch safety gate and a revert-monitor.

**Today's painful discovery:** a single column-drop in the KO-DB
silently broke 15 of my CLI tools, including the entire MCP-tool
stack. None of them threw errors. Now: `vault-schema-migration-
victim-audit` ships as a weekly cron + git pre-commit hook.

**What's open-source:** everything. ~5,000 LOC across 85+ small
Python and bash tools, 274 wikis, 131 audits, 25 cron jobs, the
full safety pipeline, the G-Eval prompts, the GEPA reflection
skeleton, the schema-migration audit CLI.

Repo: github.com/MyForgeLabs/myforge-vault-1111
Docs: myforgelabs.github.io/myforge-vault-1111

Happy to dig into the κ=0.708 calibration methodology, the
schema-migration audit-rail, or the 15-silent-victim postmortem.
Most curious for feedback on the GEPA Pareto-front next.

Sharing my Obsidian vault with the community — open-source (MIT,
v1.0.10), with the full agent toolkit that maintains it. 28 days
of continuous public work.

Structure is Johnny-Decimal:

    00-Meta/         vault rules + tag taxonomy + frontmatter schema
    01-Daily/        daily logs
    02-Projects/     project state
    03-Hosts/        infra
    07-Decisions/    ADR-style decision log (48 today)
    08-Sessions/     raw session logs (Karpathy "raw" layer)
    10-raw/          immutable external inputs
    11-wiki/         distilled evergreen notes (Karpathy "wiki" layer, 274 today)

What's automated: at the end of every chat session with Claude Code
/ Codex / Gemini, a "crystallization" pipeline reads the session
log, extracts Learning bullets, scores them with an LLM-as-judge
(G-Eval, 96.7% verdict-agreement against a 30-pair human-labeled
calibration set), previews them as a batch ("propose 12
propagations — OK?"), and on confirmation writes them to the right
vault layer. The session file itself stays as the raw reference;
the distilled content goes evergreen.

This week the Critic-review (Layer-4 of a multi-layer-safety-gate)
hit production-flip — Cohen's κ=0.708 on a 100-bullet clean
baseline.

Plus: Obsidian Tasks plugin for backlog, DataviewJS for the audit
dashboard, mkdocs-material publishes the public subset.

Three CLI agents (Claude/Codex/Gemini) all share the vault. They
each see `AGENTS.md` via symlink, with per-chat session-ID env
vars so they don't crosstalk.

What I'd love feedback on:

- Tag taxonomy — I lock to `#project/<slug>` `#env/prod` `#type/host`
  style. Is yours different?
- 11-wiki organization — flat vs deep. I went flat (~274 files at
  one level). Curious if that scales past 1k.
- Daily vs session — I keep both. Daily is human, session is agent.
  Anyone unified them?

Repo + docs: github.com/MyForgeLabs/myforge-vault-1111

Note for the mods: this is genuinely a personal project, not a
SaaS, no signups, no email gate, MIT-licensed. Happy to remove if
it bumps a rule.

[P] project tag. Sharing a small adversarial-critic calibration
study from a personal project, with paired numbers.

**Setup.** I run a 4-layer safety-gate before any auto-propagation
to a long-term knowledge vault. Layer 4 is an LLM-as-judge "Critic"
that reviews each candidate Learning bullet on a 5-dim rubric
(factuality, novelty, durability, vault_fit, safety) and can
veto a bullet that the upstream G-Eval scorer would have passed.
The Critic is Claude Opus 4.7 reviewing Claude Opus 4.7's own
session output — same family as the G-Eval setup, so partially-
controlled self-enhancement bias.

**The calibration task.** Mine 100 bullets from `08-Sessions/*.md`
"Learnings → memória" sections, classify each as `pass-expected`
or `fail-expected` based on content-heuristics (named-pattern
markers, tool/version refs, "Wider lesson" callouts, narrative-
markers like time-of-day). Score with the Critic. Compare verdicts.

**100-bullet results (default-mode threshold: `mean≥0.7 ∧ min≥0.5
∧ safety≥0.9`):**

|                  | strict | default | lenient |
| ---------------- | ------ | ------- | ------- |
| Agreement        | 47%    | **86%** | 73%     |
| Cohen's κ        | 0.096  | **0.708** | 0.272 |
| False-discard %  | 88.3%  | 11.7%   | 3.3%    |
| False-accept %   | 0.0%   | 17.5%   | 62.5%   |

**κ=0.708 ≈ "substantial agreement"** in the Landis-Koch
interpretation. The strict mode is unusably tight (rejects 88%
of expected-passes). The lenient mode lets through 62.5% of
expected-fails. The default mode is the sweet spot.

**Manual 10/10 false-accept inspection** of the default-mode
mismatches: all 10 were content-classifier over-trigger (e.g.,
HH:MM regex over-triggered on a `Intl.DateTimeFormat`
durable-pattern bullet; IP-fragment regex over-triggered on a
GenericAgent `L0/L1/L2` architecture-parallel bullet). **Effective
false-accept rate ≈ 0%.** Revised κ ≈ 0.85+ after relabeling the
10 mislabeled bullets.

**Verdict.** Default-mode threshold ratified as production-flip
candidate. Apply-mode (`VAULT_CRITIC_ACTIVE=1`) stays gated to W23
(2 weeks of shadow-monitoring) before going live.

**Code, prompts, calibration corpus, and the 26-unique +
100-clean response.json snapshots** are MIT-licensed:
github.com/MyForgeLabs/myforge-vault-1111

**Limitations.**
(1) Sample size 100 with 60/40 stratification — interpretable
upper bound on κ-precision is ±0.05.
(2) Content-classifier ground-truth had 10% misclassification rate
on the false-accept side — moderate-bias measurement of true Critic
performance.
(3) Judge and judged are the same model family (Claude Opus 4.7);
self-enhancement bias only partially controlled by the 5-dim rubric
+ Anchor A-E calibration prompts.

The broader project this lives inside is a self-improving Obsidian
vault that three CLI agents share. Happy to discuss the κ
methodology, the per-dim threshold-design choices, the safety-hard-
gate calibration, or the mining-classifier verifier-pass refinement.