$jevwiki.ai#an LLM wiki about Jev, written for agents rather than people
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Jev Wiki

An unofficial, agent-maintained knowledge base about Jev, TypeSafe AI's System One model. Give it to your AI agent (Claude, Codex, Cursor, Kimi, DeepSeek, anything that can fetch a URL) so it can (1) tell you whether and where Jev fits your project, and (2) build with it: the exact HTTP contract, models and prices, SDK signatures, the three question primitives, confidence semantics, known failure modes, architectural patterns, all official cookbooks, and what the community has built.

If you are not technical, three things to know. Jev does not write text; it reads something and picks an answer from options you define, with a probability. Your agent will read this wiki and hand you a ranked shortlist of places in your project where that helps, and where it does not. Nothing gets installed or changed in your project unless you paste the install prompt below.

It follows the Karpathy "LLM Wiki" pattern as used by agentwikis.com: refreshable raw sources in raw/ (never hand-edited), LLM-written pages in wiki/, a schema in CLAUDE.md, and every page served as plain Markdown with YAML frontmatter.

Ask your agent

Paste one of these into Claude Code, Codex, Cursor, or any agent that can fetch URLs.

"Could Jev help my project?"

Read https://jevwiki.ai/raw/wiki/ideas/consult.md and follow it exactly
(it tells you which one or two further pages to open). Then look at my project (this repo / the description
below) and give me the ranked shortlist it asks for: which decisions Jev fits, the primitive and criteria
sketch for each, estimated cost, caveats, and what you would NOT use Jev for.
Stay within about 15k tokens of wiki reading. Do not load llms.txt or llms-full.txt for this.

"Audit my existing code for Jev opportunities" — this one INSTALLS TypeSafe's agent skill into your project first (npx skills add ...); the community prompt is via @k2sbhai

npx skills add typesafe-ai/skills --skill typesafe-ai

Use /typesafe-ai to audit this project. Find every place where we make a slow or expensive LLM call
that is really a yes or no decision, a ranking, or a classification. For each one, tell me what Jev would
replace, what it would cost, and what would break if the answer is wrong. Then list 3 new features this
project could add if judgments were instant and nearly free. Don't change any code yet. Show me the list first.

"Build this with Jev"

Read https://jevwiki.ai/llms.txt and follow its "Build something with Jev" route
(agent playbook, HTTP API, jaggedness, then one SDK page and the closest cookbook). Then implement: <task>.
Use exact field names from the reference pages and gate actions on confidence.

For agents: start here

curl https://jevwiki.ai/llms.txt
URL (under https://jevwiki.ai) What
/llms.txt Task routing table, then an index of every page with a one-line summary and an estimated token size
/llms-full.txt The entire wiki in one fetch (wikilinks resolved to absolute URLs)
/index.json Same registry as JSON (slug, title, type, tags, sources, raw/html URLs)
/raw/wiki/<section>/<slug>.md Any page as exact on-disk Markdown, frontmatter included
/wiki/<section>/<slug>.md The same page as HTML for humans (send Accept: text/markdown to get redirected to raw)
/raw/CLAUDE.md The schema: page format, inventory, rules
/raw/MAINTAINING.md Procedures for maintaining agents: health check, add content, refresh, retire, cold test, restyle, deploy, handoff; permission scale
/raw/MANIFEST.json Every upstream source, its URL, fetch date, and repo commit

Recommended reading order for a coding task:

  1. guides/agent-integration-playbook — decision tree, checklist, code templates
  2. reference/http-api or reference/python-sdk / reference/javascript-sdk — the contract you will code against
  3. concepts/jaggedness-jev-1-13 — what Jev gets wrong and how to design around it
  4. the closest cookbooks/* page — real decompositions with verbatim instructions and criteria

Assessing whether Jev fits a project at all? Start at ideas/consult instead (community tier: a pattern index plus domain pattern pages, field reports, a repo index plus domain repo pages).

Wikilinks in raw pages look like [[concepts/confidence]] and resolve to /raw/wiki/concepts/confidence.md.

Trust semantics

Layout

CLAUDE.md / AGENTS.md   schema, page inventory, workflows (ingest / query / lint / refresh)
raw/                    immutable sources: docs pages (.md), OpenAPI, SDK repos, site, blog, evals, press
wiki/                   the knowledge base (concepts, reference, patterns, cookbooks, guides, ideas, entities, syntheses)
scripts/build.mjs       lint + build dist/ (HTML, raw copies, llms.txt, llms-full.txt, index.json, sitemap)
scripts/refresh.mjs     re-fetch sources, diff, list wiki pages that need re-ingestion
site/                   Cloudflare Worker (routing + content negotiation), stylesheet (theconsigliere.ai Brand Standards v2.0), mark and favicon (generated by scripts/make_brand_assets.mjs)

Maintaining it

npm install
npm run check      # lint: frontmatter, dangling wikilinks, orphans, required sections
npm run build      # produce dist/
npm run refresh    # re-fetch upstream, print a change report (raw/LAST_REFRESH.md)
npm run deploy     # build + wrangler deploy (Cloudflare Workers, static assets)

Point any agent at MAINTAINING.md and name the procedure: "run the health check", "add this post", "refresh upstream", "cold-test the consult route". CLAUDE.md is the schema it reads first. The permission scale in MAINTAINING.md §0 says what an agent may do unasked; npm run deploy refuses to ship a page that fails lint.

Credits

Status

Unofficial. Not affiliated with TypeSafe AI, Inc. Source material © TypeSafe AI and the cited authors; wiki text is derived from it for reference use. Report mistakes by opening an issue.