docs: karpathy-pattern wiki built from the spec, plan, design contract and decisions ledger
kb/ is its own wiki root: 34 pages across 7 categories, index.md catalog, log.md, AGENTS.md schema layer, and raw/ holding copies of README, the spec, the 13-task plan, the design master and the SDD ledger. Every page cross-references 3 to 6 others and all 107 wikilinks resolve except two [["list"]] artefacts inside JSON snippets in the copied plan. Kept separate from the repository root on purpose: its generated graph lands in kb/.ua/ instead of overwriting the code graph. Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
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ahmed mohammed
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Claude Opus 4.7
Claude Opus 4.7
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# SQL Server qAI
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Paste a SQL Server ShowPlan XML (or drop a `.sqlplan`), get a report with several ranked remediation options and
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rewritten queries. Two models, one job: a **generative analyst model** proposes the options and writes the report, and
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**Jev** (TypeSafe System One) scores every option and decides which one to run first. Jev never writes SQL; it returns
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typed scores, a choice, and confidences. The report renders as OpenUI elements, not a markdown blob.
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## Pipeline
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1. `lib/digest.ts` parses the XML in code: top operators, missing indexes, warnings, waits, estimate-vs-actual row
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errors, tables, statement text, subtree cost, parallelism. Raw XML never reaches a model.
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2. The analyst model receives the digest JSON and proposes 2–4 genuinely different options (index, rewrite, statistics,
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parallelism, schema), each with ordered action steps and an optional rewrite.
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3. Jev answers four questions per option (`bottleneck_fit`, `semantic_safety`, `ease` `score`; `root_cause` `noul`) and
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one cross-option call (`first_to_run` `choice`, `anything_worth_running` `noul`).
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4. `lib/jev.ts` combines them with visible weights (0.40 / 0.30 / 0.15 / 0.15 — four numbers in that file, the only
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tuning knob) and prints a verdict. Jev's pick leads only when its confidence clears 0.5; otherwise the report says
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the order came from composite scoring.
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5. The analyst streams the final report as OpenUI Lang; `<Renderer>` paints it as it arrives.
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Getting the XML: SSMS → **Ctrl+M** (include actual execution plan) → run the query → right-click the Execution Plan tab
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→ *Save As .sqlplan*. No SSMS: prefix the batch with `SET STATISTICS XML ON;` and copy the XML result cell.
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## Run it
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This repo runs as a plain Node process. `Dockerfile` and `docker-compose.yml` are ready, but the image has **not
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been built or run on this machine** (Docker is not installed here); treat it as untested until
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`bash scripts/smoke.sh --docker` says otherwise. Local work is `npm run dev`.
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```bash
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npm install
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cp .env.example .env.local # then fill in APP_SECRET and DATA_DIR
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npm run dev # http://localhost:3000
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bash scripts/smoke.sh # healthz, login page, signed-out redirect, analyze needs a session
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```
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`.env.local`:
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| key | why |
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| --- | --- |
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| `APP_SECRET` | ≥16 chars. Encrypts API keys at rest (AES-256-GCM). Changing it makes stored keys unreadable. |
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| `DATA_DIR` | where `qai.db` (SQLite, `node:sqlite`) lives. Default `./data`. Docker uses `/data`. |
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| `COOKIE_SECURE` | set to `1` only when the app is served over https without a proxy that sets `X-Forwarded-Proto`. The session cookie is `Secure` when that header says `https`; forcing it on plain http makes Safari refuse to store it. |
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Model keys are **not** config. Each user types them in the app (first-run guide or `/settings`), so a second user
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brings their own endpoint and their own spend. Blank key field = keep the stored key.
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- Analyst: any OpenAI-compatible base URL + key + model, plus optional extra JSON params.
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A local endpoint such as `http://192.168.88.131:8888/v1` works; use key `EMPTY` when the server has no auth.
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For a reasoning model (Qwen3 on vLLM) put `{"chat_template_kwargs":{"enable_thinking":false}}` in **Extra params**:
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with thinking on, the model spends the whole context budget reasoning and the report arrives empty.
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Both model calls default to `max_tokens: 2048`; the same field overrides it.
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- Jev: a TypeSafe API key from `console.typesafe.ai/keys`, model usually `jev-latest`.
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## Tests
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```bash
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npm test # node --test on tests/*.test.ts, no test framework installed
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npm run build # production build + type check
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npm run e2e # builds first, then Playwright against the production server
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```
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The unit suites run offline: the analyst and Jev are exercised through injected `fetch`, so no key and no network.
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`npm run e2e` is the opposite — `pree2e` builds, then Playwright drives `npm run start` (the production server,
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not `next dev`: cookie flags and hydration differ, and that difference once hid a sign-out bug) against the real
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models. It reads `JEV_API_KEY` from `.env.local`. Expect it to be slow; that is the models, not the harness.
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Projects: `chromium` (everything except the session spec, reusing one signed-in session), `webkit` (Safari —
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session survival only, because it is the one engine that rejects a `Secure` cookie over `http://localhost`),
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and `mobile` (Pixel 7, layout only, so no model run is paid for twice).
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`tests/digest.test.ts` skips unless a genuine plan exists at `fixtures/real.sqlplan`. Drop one there from SSMS and the
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skip becomes the real check on ShowPlan attribute spellings.
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## Git remote (Gitea)
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Origin points at the self-hosted Gitea:
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```bash
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git remote -v # http://192.168.88.39:3000/ahmed/sql-server-query-ai.git
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git push origin feat/sql-server-qai
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```
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The remote URL deliberately contains **no password**. Git prompts for the username (`ahmed`) and password the first
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time, or you can keep them out of prompts:
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```bash
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git config credential.helper osxkeychain # remember them locally
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git push origin feat/sql-server-qai
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```
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Never commit the password into `.git/config`, a remote URL, or this README — a stored credential in the repo is
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readable by whoever you hand the repo to. If you want it non-interactive in CI, create a Gitea token
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(Settings → Applications) and use `http://192.168.88.39:3000/ahmed/sql-server-query-ai.git` with the token as password.
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## Layout
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```
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lib/digest.ts XML → bounded digest (the only shape models see)
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lib/analyst.ts propose 2–4 options; stream the final OpenUI report
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lib/jev.ts questions, weights, ranking, verdict, fallback
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lib/report-ui.tsx OpenUI component library — the same module builds the prompt and renders the answer
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lib/db.ts node:sqlite: users, sessions, settings, analyses
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lib/vault.ts AES-256-GCM seal/open/mask for credentials
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lib/auth.ts scrypt passwords, hashed session tokens (DB stores the SHA-256, never the token)
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app/api/analyze SSE pipeline: digesting → proposing → judging → writing
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tests/ offline unit suites tests-e2e/ Playwright
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docs/superpowers/ spec + the 13-task implementation plan this was built from
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```
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Design: dark terminal console, JetBrains Mono for data, IBM Plex Sans for prose, one phosphor accent.
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Print stylesheet flips to paper white and drops the rail, buttons and chrome — that is the Export to PDF path.
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