A list of things built with Jev, TypeSafe's model for typed decisions, with the numbers behind them: who posted each demo, how many followers they have, how many likes it got, and what the limits of the model are. This list is open source (CC0), free to copy and reuse, and sponsored by AY Automate. It is unofficial and is not affiliated with TypeSafe.

Every entry links to the original post or repository. Ideas that nobody has shipped are in their own section and marked as ideas.

The 30 most-liked demos, ranked. Click a card to open the original post. Each card shows the demo's rank, area, author, likes, reposts, and reach (likes divided by the author's followers). Preview frames are low-resolution stills from the builders' own videos and belong to them. If an author wants one removed, open an issue. Full metrics for every demo are in docs.

• Top 30 popular demos
• FAQ
• Start here
• Browse by area
• Try Jev free right now
• Numbers at a glance
• The first week in numbers
• Most-liked demos
• Small accounts, big results
• More demos by area
• Open source
• Search demand
• Full tables
• By the maintainer
• What Jev is
• Cookbooks from TypeSafe
• Patterns
• Limits of Jev 1.13
• Reported cost and latency
• Ideas nobody has shipped yet
• Tools
• How this list was made
• Contributing

Short answers to the questions people ask most, each with a source.

What is Jev? Jev is a model from TypeSafe AI that answers typed questions instead of writing text. Each question is a Choice, a Score or a Noul (yes or no with a probability), and the answer comes back as a number or a pick with a confidence. It does not generate text. See What Jev is.

Is Jev the same as the "Jev" that searches show for Jevons paradox or Deltarune? No. The word has other meanings. Search for "TypeSafe Jev" or "Jev AI model".

How do I call the Jev API? Send a POST to https://api.typesafe.ai/v1/systemone with a bearer key. A full curl example is in docs/api-quickstart.md.

How much does Jev cost? TypeSafe lists $42 per billion input tokens, and output tokens are free. Vercel says Jev is free on AI Gateway until Sept 25. See Reported cost and latency.

What can I build with it? Routers, classifiers, judges, guardrails, triage and game agents. The Top 30 demos and Browse by area show real examples.

What are the limits of Jev? It reads literally, is weak at math, counting and dates, and accuracy drops with irrelevant state. See Limits of Jev 1.13.

Is Jev better than an LLM? It is a different tool. Use Jev for fast typed decisions and an LLM for writing. Many demos pair them.

Which open-source Jev projects exist? More than 150 repositories. See Open source and Long tail.

Is this list official? No. It is unofficial, open source under CC0, and sponsored by AY Automate.

Short on time? Read in this order.

1. Most-liked demos shows what people actually built.
2. Limits of Jev 1.13 tells you what it cannot do, so you do not build the wrong thing.
3. Patterns shows how the working demos are put together.
4. Reported cost and latency has the price and speed numbers.
5. Contributing shows how to add yours.

Every tracked demo, grouped by what it does and ranked by likes inside each group.

• Real-time slop detector as you scroll by @RBilgil, 7,180 likes.
• 724 competitor ads, broken down by @TheMattBerman, 6,348 likes.
• 700 leads scored in 40 seconds by @romanbuildsaas, 3,138 likes.
• Every’s editorial vibe check by @danshipper, 1,818 likes.
• Post scoring with SuperX by @robj3d3, 1,341 likes.
• Doomscroll Filter by @robj3d3, 1,213 likes.
• The X algorithm, rebuilt with Jev by @leojrr, 1,167 likes.
• SEO and GEO fixes, 90% cheaper by @irabukht, 1,146 likes.
• Live viral post analyzer by @rileybrown, 915 likes.
• Ad creatives from filtered assets by @higgsfield_ai, 824 likes.
• 3,282 posts, eight questions each by @iannuttall, 740 likes.
• TypeSafe Typewriter by @stevekrouse, 587 likes.
• Lurk by @mxfp4, 559 likes.
• Jev Detector by @jozef_gherman, 296 likes.
• A filter for reply-guy comments by @iannuttall, 250 likes.
• X timeline labeler by @the_cyw, 79 likes.
• 700 live ads in 40 seconds by @Yarilo7brigada, 59 likes.
• AI slop detector by @kraayenJon, 0 likes.

• Instant compaction for Claude by @tamarajtran, 10,435 likes.
• A canvas you control by pointing and speaking by @jackcheng, 4,797 likes.
• A real-time ad blocker by @iam_zachi, 3,872 likes.
• jev() for PostgreSQL by @iam_zachi, 2,738 likes.
• Keystroke oracle by @dabit3, 2,368 likes.
• An always-on assistant with no wake word by @_MaxBlade, 1,538 likes.
• Predictive spreadsheets by @dabit3, 1,169 likes.
• Simple Jev by @picocreator, 1,105 likes.
• A Downloads folder that sorts itself by @marcelpociot, 1,092 likes.
• Hide posts on X in plain language by @marcelpociot, 1,090 likes.
• YouTube sponsor skipper by @tdinh_me, 1,078 likes.
• askjev.ai by @waynesutton, 511 likes.
• jev-review by @niazmorshed_, 492 likes.
• Website to App by @chddaniel, 382 likes.
• Jev Calc by @thekitze, 318 likes.
• Real-time Clippy by @sotak, 175 likes.
• One-click invoice finder by @FarouqAldori, 43 likes.

• Flight search with Browser Use by @gregpr07, 8,723 likes.
• Voice-controlled computer use on a Mac by @instantricecook, 5,016 likes.
• Computer use without screenshots by @milindlabs, 1,823 likes.
• A chat bot with no LLM by @CodingGarden, 1,115 likes.
• End-to-end tests run by agents by @o_kwasniewski, 1,012 likes.
• A second-hand shopping agent by @AlanDaitch, 873 likes.
• Stagehand on a remote browser by @kylejeong, 743 likes.
• An agent with a Jev model router by @rileybrown, 281 likes.
• Headless Chromium agent by @mormonnegro, 213 likes.
• Agentic browsing in Chrome by @razaanstha, 194 likes.
• A chief of staff for bots by @milindlabs, 182 likes.
• A Slack agent, twice as fast by @johnyeo_, 148 likes.
• jev-job-hunter by @hqmank, 75 likes.
• Jev as an agent safety monitor by @isNickMa, 1 likes.

• 500 emails for 3.5 cents by @rileybrown, 3,853 likes.
• Triage across 1,500 emails by @ryanvogel, 3,538 likes.
• A model router on Jev by @ephraimduncan, 1,858 likes.
• 400 companies matched to one candidate by @sarvagya_kul, 1,688 likes.
• Intent-based search in Gmail by @dabit3, 886 likes.
• Fraud detection with Jev and Kimi K3 by @nutlope, 846 likes.
• 900 images in 40 seconds by @fayazara, 433 likes.
• A prompt box that fills itself in by @sawyerhood, 193 likes.
• DiffJury by @raihankhan_rk, 81 likes.
• Expo recovers hallucinated docs URLs by @davidmokos_, 43 likes. When an agent requests a docs path that does not exist, a Jev Choice question over the docs catalog picks the closest real page or returns no match. The author reports replies in under 200 ms and warns that a wrong redirect can mislead an agent, so keeping the 404 is sometimes right.

• Jev plays Doom by @CompleteSkeptic, 4,890 likes.
• Jev plays Subway Surfers by @_MaxBlade, 3,956 likes.
• Jev plays Smash Bros. against itself by @maubaron, 3,620 likes.
• Jev plays Super Mario Bros. by @faadilhshaik, 2,860 likes.
• Game levels generated in real time by @HugoDuprez, 2,614 likes.
• Jev plays Slay the Spire 2 by @coolish, 1,137 likes.
• Jev plays Tetris by @AlanDaitch, 48 likes.

• jevlike by @vinnylarouge, 2,018 likes.
• 1kpapers by @nutlope, 1,962 likes.
• openjev-sglang by @ekzhang1, 972 likes.
• openjev on Qwen 4B by @justALEXWORTEGA, 774 likes.
• A visual reference finder by @albicodes, 654 likes.
• A local Jev by @wmoto_ai, 387 likes.
• Which outreach signals book demos by @pierreeliottlal, 113 likes.

• jev-trader by @jarrodwatts, 4,913 likes.
• $10,000 in Jev’s hands by @abolbuild, 1,606 likes.

Vercel Developers announced on 2026-09-19 that Jev is free on Vercel AI Gateway until Sept 25. The model page is vercel.com/ai-gateway/models/jev. Outside that window, the price from TypeSafe is $42 per billion input tokens, and prices can change, so check before you build on it.

Vercel's earlier post says Jev was adopted faster than any other model in AI Gateway history. It reports about 13% of teams in the first day, 2x the GPT-5.6 family and 6x Fable 5.1. Those are Vercel's figures and I have not verified them. The free-window post had 2,043 likes and 552,601 views when I read it.

To use anything in this list, copy the pattern, get a key, and start with one question type. The Patterns section shows how.

Last refreshed 2026-09-26: repository stars, licenses and last-push dates were re-read from GitHub, 12 community pull requests and 3 issue suggestions were reviewed and merged, and 3 repositories that no longer exist were removed. A second GitHub sweep added 33 repositories and listed 507 more in a CSV. Demo likes and views are still the 2026-09-19 snapshot.

Demos by area: Content and growth 18, Apps and tools 17, Agents and computer use 14, Triage and routing 9, Games and real time 7, Research and data 7, Trading and markets 2.

Snapshot of 2026-09-19.

• 74 demo posts with video, published between 2026-09-15 and 2026-09-19, with 127,162 likes combined.
• 102 builder accounts checked. The median has 6,615 followers. 47 have under 5,000 and 26 have under 1,000.
• 37 open-source repositories below, with 21,456 GitHub stars combined. Each one mentions Jev or TypeSafe in its own README.
• The four most-liked demos are a Claude Code plugin, a browser agent, an ad teardown and a Mac voice assistant. None of them generates text with Jev.

The 15 demo posts with the most likes, with the follower count of whoever posted them. Numbers are a snapshot of 2026-09-19.

Likes divided by followers, for demos with at least 1,000 likes. A high ratio means the post traveled far beyond the author's own audience.

The next tier by likes, grouped by what the demo does.

• Computer use without screenshots by @milindlabs. On-device segmentation and OCR convert the screen into text that Jev can read.
• A chat bot with no LLM by @CodingGarden. A chat bot without an LLM. Jev chooses the tool and its arguments, so replies arrive instantly.
• End-to-end tests run by agents by @o_kwasniewski. Open-source framework where agents run end-to-end tests for web and mobile apps.
• A second-hand shopping agent by @AlanDaitch. Reads about 26 second-hand listings per minute and decides on each one in 406 ms.
• Stagehand on a remote browser by @kylejeong. Runs Stagehand browser tasks on a remote browser at about a tenth of a cent per task.

• Game levels generated in real time by @HugoDuprez. Game levels get built on the fly while you play.
• Jev plays Slay the Spire 2 by @coolish. Jev plays Slay the Spire 2 at 0.7 seconds per move, where GPT-6 Astra ran slowly.

• A model router on Jev by @ephraimduncan. Router that chooses the model best suited to each request and forwards it there.
• 400 companies matched to one candidate by @sarvagya_kul. Predicts which jobs one candidate is most likely to land across 400 companies, at $0.0005.
• Intent-based search in Gmail by @dabit3. Gmail search that matches what you mean instead of the exact words you type.
• Fraud detection with Jev and Kimi K3 by @nutlope. Jev classifies 100 emails in 1.42 seconds and passes the uncertain ones to Kimi K3.
• 900 images in 40 seconds by @fayazara. Image classifier by Fayaz Ahmed. OCR reads each image and Jev sorts it, 900 images in 40 seconds.

• $10,000 in Jev’s hands by @abolbuild. Abol handed Jev $10,000 and let it place trades on its own.

• Every’s editorial vibe check by @danshipper. Runs 21 questions over each of 37 documents. That is 1,709 judgments for under one cent.
• Post scoring with SuperX by @robj3d3. Answers 61 questions about a draft post in about one second.
• Doomscroll Filter by @robj3d3. You choose a niche and Jev sorts new X posts into Read, Skim or Pass.
• The X algorithm, rebuilt with Jev by @leojrr. Rebuilds the X algorithm to estimate how far a post will reach, with a global feed of everyone's posts.
• SEO and GEO fixes, 90% cheaper by @irabukht. Ryze AI agents that use Jev to audit a client's SEO and GEO and apply fixes at 90% lower cost.
• Live viral post analyzer by @rileybrown. Gives a draft post a score half a second after you stop typing.
• Ad creatives from filtered assets by @higgsfield_ai. Jev filters content and selects assets, then DeepSeek and Higgsfield turn them into ads.
• 3,282 posts, eight questions each by @iannuttall. Ian Nuttall ran eight questions on each of 3,282 posts in his X archive, for $0.1282.
• TypeSafe Typewriter by @stevekrouse. Asks sixteen judgments about your text again on each keystroke.
• Lurk by @mxfp4. Finds and tracks Reddit threads so your content can get cited by AI, at no cost.
• Jev Detector by @jozef_gherman. AI slop detector that scans about 10,000 words in 2 seconds.

• 1kpapers by @nutlope. 1,018 AI papers grouped by topic for $0.08, then published as a website.
• A visual reference finder by @albicodes. Returns 100 reference images from one prompt, drawn from sources like Cosmos and NASA.

• jev() for PostgreSQL by @iam_zachi. A jev() function for PostgreSQL that searches a table in plain language, without an index or embeddings.
• Keystroke oracle by @dabit3. Keystroke oracle, a predictive launcher and the first experiment in Nader Dabit's Jev series.
• An always-on assistant with no wake word by @_MaxBlade. Always-on assistant with no wake word that separates commands for the computer from ordinary conversation.
• Predictive spreadsheets by @dabit3. Name a column and Jev fills each row in about 100 ms.
• A Downloads folder that sorts itself by @marcelpociot. macOS app that files your downloads by rules you set, with Jev as the only LLM.
• Hide posts on X in plain language by @marcelpociot. Browser extension that hides or collapses X posts based on a rule you write in plain language.
• YouTube sponsor skipper by @tdinh_me. Chrome extension that finds sponsor segments in YouTube videos and skips past them.
• askjev.ai by @waynesutton. Website where you ask Jev anything and it gives a judgment instead of an answer.
• Website to App by @chddaniel. Paste a URL and Jev decides how to rebuild that site as a native mobile app.
• Jev Calc by @thekitze. A notebook-style calculator that understands terms written in plain language.

Repositories with a working project and a README that mentions Jev or TypeSafe. Unless an entry says otherwise, stars are a snapshot of 2026-09-26.

• browser-use/jev-ultrafast. Browser agent that picks an operation and element per step, with a small LLM writing text only when typing is needed. 20,513 stars, Python, MIT.
• awlevin/typesafe-computer-use. Drives a Mac toward a plain-English goal for about $0.0002 a step. It reads the screen with OCR and sends no screenshots. 1,001 stars, Python, MIT.
• wy-coliney/jev-browser-use. Browser skill where Jev handles navigation and clicks while Codex keeps text input and final checks. Built at EZCollegeApp. 555 stars, JavaScript, MIT.
• jkudish/jev-browser. Runs a real headless browser through an MCP server, a CLI or a library. It picks one action per step from the clickable elements on the page. 274 stars, TypeScript, MIT.
• moritzkremb/jev-voice-browser. Node app controlling a headed Chromium window by voice. Jev picks intent and target on each partial transcript and Playwright acts. 324 stars, JavaScript, MIT.
• droidrun/mobile-jev. Navigates a live Mobilerun phone with Jev. The demo opens Uber and enters a route in about 21 seconds for 9 actions. 405 stars, JavaScript, MIT.
• socai-io/jev-social by @IRONICBo. Jev uses Choice questions to select the platform and each bounded read-only operation; SocAI runs the operation in a logged-in Chrome session and preserves source-linked Instagram, TikTok, or LinkedIn evidence for a cited report. 47 stars as of 2026-09-23, JavaScript, MIT.
• realZachi/typesafe-adblock. Chrome extension that asks Jev whether a DOM element is an ad and removes it. A side project that needs your own key. 81 stars, JavaScript, MIT.
• kitze/unclutter. WXT browser extension that removes page clutter using Jev, with reusable template rules. 304 stars, TypeScript, MIT.

• tamaratran/fast-jev-compaction. Claude Code plugin that scores each tool call and result at compaction, drops or truncates stale ones, and keeps the rest verbatim. 6,951 stars, TypeScript, MIT.
• tamaratran/jev-pruner. Claude Code plugin that trims noisy Bash output with Jev after a command runs and before the main model sees the result. 151 stars, TypeScript, MIT.
• devagrawal09/jev-review. Staged code-review workflow and local dashboard that reviews a Git diff or a whole codebase using focused Jev calls. 621 stars, TypeScript, MIT.
• thruwire/foreman. Places Jev above slower coding agents to judge whether a Codex worker's implementation is complete for a ticket, spec or bug report. 572 stars, Python, MIT.
• kitze/skillbox. Self-hosted, versioned skills library for AI agents with MCP, scoped clients and optional Jev recommendations. 248 stars, TypeScript, MIT.
• DevMortimer/pi-warden. Guardrails for Pi that feed problems back to the agent. It judges irreversible or off-task tool calls, stuck loops and unverified done claims. 146 stars, TypeScript, MIT.
• vinilana/jev-eval-agent. Compares how many steps an agent with 100 mocked tools needs to finish a task when the LLM picks tools versus Jev. 105 stars, HTML.
• mrnugget/jev-shell-history. Zsh autosuggestions that have Jev rank your last 100 distinct history entries and show the best match in grey with its score. 112 stars, TypeScript.
• vercel-labs/ai-cli. Terminal tool that generates text and media through AI Gateway and can also evaluate typed questions. 816 stars, TypeScript.
• RafalWilinski/vibecheck. Chrome extension that rates a draft X post for virality and clarity before you publish. 47 stars, JavaScript.
• kaustav1996/reflex by @kaustav1996. Coding agent on Pi where each state-changing tool call goes through one Jev request of five Noul risk checks plus a risk Score, and code allows, asks or blocks according to the user's risk setting; protected paths always ask. Jev also picks the model tier for each prompt, sends back "done" claims that ran no verification, and drives a browser loop ported from jev-ultrafast. The author reports about 400 ms per decision. TypeScript, MIT.

• gargpratyush/jev-router. Per-turn router for Claude Code and OpenAI Codex. Simple work goes to a fast tier and hard work to a strong tier. 431 stars, JavaScript, MIT.
• BillionsBobby/JevRouter. Router that picks among models, subagents and tools from one candidate set. Jev answers a Choice question. Code enforces permissions. 246 stars, TypeScript, MIT.
• Codex Jev Router by @suenot. Jev answers Choice and Noul questions about a short task summary; local code chooses a Codex subagent model and reasoning effort, with a Sol fallback when the decision is uncertain.

• GPTCache by @zc277584121. Optional Jev Noul checks judge whether a cached answer can be reused for a new query; application code combines compatibility scores, with a public reuse-compatibility benchmark.
• DeepSearcher search-stopping evaluation by @zilliztech. Standalone experiment comparing Jev Noul stopping decisions with a generative baseline over shared search trajectories, with recorded results and replay instructions.
• MemSearch by @zilliztech. Markdown memory retrieval for coding agents with optional Jev Noul reranking of retrieved chunks and a published Chinese/English evaluation.
• realZachi/pg-jev. PostgreSQL extension for asking your tables questions in plain language. 364 stars, Shell, NOASSERTION.
• giuliosmall/pg_typesafe. Pre-alpha PostgreSQL extension that calls Jev from SQL for categorical classification. Tested on PostgreSQL 16 and 17. 87 stars, C, MIT.
• superagents-lab/jev-search. Web search where Jev picks sources, time ranges and search terms, then ranks Search1API results with visible scores. No generated answers. 469 stars, TypeScript, MIT.
• pithings/advocaat. Small type-safe client for asking questions about your data and getting typed answers in one request. 93 stars, TypeScript, MIT.
• Eliot5566/JEV-Paper-Radar. Daily arXiv and bioRxiv radar: every new paper is judged against plain-English interests, one Noul per interest, and the picks are published as a page and an RSS feed from GitHub Actions. 501 papers judged in 33 seconds for $0.0196 in a measured run. Live output.

• fhshaik/typesafe-mario. Experimental controller where Jev picks NES inputs for Super Mario Bros. from compact emulator telemetry instead of screenshots. 402 stars, Python.
• vinnylarouge/jevlike. Trains a small model that takes text plus N options and returns one probability per option in a single pass. 1,307 stars, Python, MIT.
• hr98w/jev-visual. Educational Apple Silicon project using Qwen3.5-0.8B with MLX to answer several questions about one image via browser UI, CLI or HTTP API. 287 stars, Python, MIT.
• jarrodwatts/jev-trader. Trading bot that asks Jev for a buy or sell decision each Monad block on the Kuru MON-USDC order book and posts a post-only limit order. 2,461 stars, TypeScript, MIT.
• RomanSlack/jev-drone. Simulated quadrotor flying a five-station MuJoCo obstacle course by camera alone, with Jev judging the situation at about 2.5 Hz. 198 stars, Python, MIT.

• TheoLeeCJ/SemIf. Semantic if-statements from open models on a home 3090 GPU, with a browser demo. Independent, not affiliated with TypeSafe. 4,384 stars, Python, MIT.
• jaredpalmer/kev. LoRA adapter and small readout head on a Qwen base that answers many typed questions about a document in one prefill pass. 7,185 stars, Python, Apache-2.0.
• razorback16/openjev. Open-source decision server that answers typed questions with probabilities and a confidence in tens of milliseconds. 445 stars, Python, Apache-2.0.
• logan-markewich/jeff. Self-hosted stand-in for the Jev API on a 400M-parameter GLiFormer, usable with the official SDK. Less accurate on reasoning-heavy tasks. 251 stars, Python, MIT.
• Mapika/decider. Language model fine-tuned from Qwen3.5-2B that returns calibrated probabilities for typed questions in one forward pass and generates no text. 504 stars, Python, Apache-2.0.

• itsmostafa/typesafe-mcp. MCP server that lets coding agents such as Claude Code and Codex call Jev and get probabilities to branch on. 312 stars, Go, MIT.
• jkudish/jev-mcp. MCP server giving agents ten Jev judgment tools, such as verifying claims against evidence and screening content before it enters context. 384 stars, TypeScript, MIT.
• dbreunig/building-with-jev-skill. Agent skill for writing and improving Jev programs. It teaches question design and how to diagnose wrong answers. 134 stars.

Found by searching GitHub for the model and company names on 2026-09-19. Stars are a snapshot. Each description comes from the repository's own summary and I did not open every README.

• ReallyArtificial/jev-by-example. Ten runnable examples of small Jev judgments between agent steps: memory reconciliation, tool-result contracts, retry or reconcile, context selection, handoff readiness and an option-reordering stress test. It runs offline on 34 authored cases, and the author says live model behavior is not yet verified. 2 stars, JavaScript, MIT.
• ReallyArtificial/stuntdouble. A proxy between an app and the Jev API. Jev still answers every request, while local models answer the same request in the shadow and a report says whether they would have decided the same, with a suggested threshold for routing traffic locally. 2 stars, JavaScript, MIT.
• y0usaf/pi-jev. TypeSafe Jev as a decision layer for the Pi coding agent: a measured tool-call gate plus jev_ask for typed, calibrated answers. 148 stars, TypeScript, MIT License.
• uehaj/jev-semgrep. grep by meaning, across languages. TypeSafe Jev scores every line against a meaning; combine meanings with AND/OR/NOT. 意味で探す grep。日本語で英語を、英語で日本語を検索できる. 144 stars, JavaScript.
• Dicklesworthstone/skillranker. Rust CLI powered by Jev from TypeSafe.ai that ranks agent skills for the next step using live session context. Includes Claude Code hooks, structured JSON, abstention, and local feedback. Requires a TypeSafe API key. 121 stars, Rust.
• obie/ruby_decision_model. Ruby client for decision models such as Typesafe Jev. 51 stars, Ruby, MIT License.
• devagrawal09/stanley-code. Bounded TypeSafe Jev workflows for coding agents. 117 stars, TypeScript, MIT License.
• wfzyx/von. The open-source System One decision model. Sub-15ms, non-autoregressive, local drop-in alternative to TypeSafe Jev. 700 stars, Python, Apache License 2.0.
• tacticocc/Jevbridge. ACP and MCP adapter that bridges TypeSafe Jev with any LLM , computer use and typed decisions alongside Codex, Claude, Grok, and OpenCode. 44 stars, TypeScript, MIT License.
• kylemclaren/jevql. Semantic SQL for Postgres, powered by Jev. A psql-style CLI adds jev(), jev_prob, jev_choice and jev_score to queries against vanilla PostgreSQL with no extension, and it also ships an MCP server and Go, TypeScript and Python SDKs. 13 stars, Go, MIT License.
• AboveColin/HA-Jev. Ask your house a question, get a number back. Home Assistant integration for TypeSafe Jev: typed answers as sensors, four actions for automations, and a conversation agent for Assist. 65 stars, Python, MIT License.
• peternguyen777/ai-icon-generator. WoofAI is an AI-powered micro-SaaS application allowing users to generate, share and download pet icon art. The project is built using the T3 stack (Typescript, TRPC, Tailwind), allowing end-to-end typesafe APIs and fast front-end prototyping. Under the hood, the project leverages OpenAI’s DALL-E2 and images hosted on Amazon S3. 20 stars, TypeScript.
• TheoOliveira/pi-jev. Semantic tool routing and typed System One decisions for the Pi coding agent using TypeSafe Jev. 51 stars, TypeScript, MIT License.
• dannote/jev. TypeSafe Jev for OTP: reply to Jev from a GenServer and pattern match on its answer. 33 stars, Elixir, MIT License.
• phyous/tsai-sc. TypeSafe Jev controls original StarCraft shareware through keyboard and mouse with recorded action probabilities. 26 stars, Python, MIT License.
• zhihz/openjev. Local bilingual probability decisions from context, questions, and candidate answers. Independent research preview inspired by TypeSafe Jev. 34 stars, Python.
• kavehmz/typesafe-playground. Interactive experiments with TypeSafe Jev, from support routing to 3D driving simulations with real AI decisions and visible sensor inputs. 14 stars, JavaScript.
• chopratejas/invalidate. The invalidation layer for AI memory. Every fact gets a lease; new evidence ends it. Built on TypeSafe Jev. 21 stars, Python, Apache License 2.0.
• blakestone-x/jev-mcp. MCP server for TypeSafe Jev: typed classify, score, check, match and screen for any agent, with confidence on every answer. 23 stars, Python, MIT License.
• AkashPriyadarshii/jev-seo. 100% free ₹0 agent-first SEO & GEO CLI suite and MCP server in Rust replacing Semrush and OpenSEO via DuckDuckGo and TypeSafe Jev System One. 77 stars, Rust.
• yikangy873-gif/jev-desktop. TypeSafe Jev action selection inside Codex Computer Use. 72 stars, JavaScript, MIT License.
• HyunjunJeon/pi-quiet-ask. TypeSafe Jev as the pi coding agent's quiet decision layer. 12 stars, TypeScript, MIT License.
• anpicasso/hermes-jev-approvals. PoC: TypeSafe Jev as the reviewer for Hermes Agent smart command approvals. 8.7x faster, 4.4x fewer prompts, measured on 153 real commands. Approvals only. 19 stars, Python, MIT License.
• khordoo/jev-reflex-autonomy-lab. Multi-drone autonomy lab demonstrating TypeSafe Jev reflex decisions with optional System 2 strategy guidance. 18 stars, TypeScript.
• GodsBoy/jev-agent-skill-router. Typed, confidence-aware agent skill routing with TypeSafe Jev. 20 stars, Python, MIT License.
• matthewp/flue-jev-demo. Flue agent routing with TypeSafe Jev through Cloudflare AI Gateway. 11 stars, TypeScript.
• abhixhek/jevcal. Stop guessing confidence thresholds: calibrate, threshold, and drift-check typed decision models (TypeSafe Jev) against an LLM teacher. 11 stars, Python, MIT License.
• DECRUX9812/typesafe-skill-router. TypeSafe (Jev) skill routing for Hermes Agent: names the one skill worth loading, before the model call. Opt-in, stdlib only, ~$0.001 per routed turn. 15 stars, Python, MIT License.
• keeltrace/hermes-jev. Typed System One decisions, ranking, verification, and an opt-in Hermes tool gate using TypeSafe Jev. 26 stars, Python, MIT License.
• tumf/jev-cli. Small dependency-free CLI for TypeSafe Jev. 14 stars, Python, MIT License.
• bohutang/sift. Chrome extension that labels every post on X (Substance · Humor · Chit-chat · Promo · Junk · AI-written) with TypeSafe Jev, and hides the ones you don't want. 12 stars, JavaScript, MIT License.
• joelhooks/pi-fast-jev-compaction. Pi extension: verbatim context compaction with TypeSafe Jev decisions. 13 stars, TypeScript, MIT License.
• Kevthetech143/super-jev. A small, extensible decision-to-action harness for TypeSafe Jev. 13 stars, Python, MIT License.
• Heman10x-NGU/openJev-verdict-2.0. Calibrated 151M Non-Autoregressive Decision Engine beating TypeSafe Jev & Laya on LocalLLaMA/typed-decisions (77.10% acc, 0.0636 Brier, 0.0144 ECE). 290 stars, Python.
• altryne/jevify. An agent skill to discover TypeSafe Jev opportunities, design typed questions, and learn from recent community experiments. 34 stars, Python, MIT License.
• raihankhan-rk/diffjury. DiffJury , TypeSafe Jev PR risk router + code review coach. 8 stars, TypeScript.
• AkashPriyadarshii/jev-curate. High-throughput synthetic & pretraining dataset sifter powered by TypeSafe AI Jev (api.typesafe.ai). Stream, filter, and score Parquet & JSONL datasets at 1,500+ rows/sec using System One typed decisions (Choice, Score, Noul). 77 stars, Rust, MIT License.
• xingwudao/OpenJev. OpenJev: an independent Jev-inspired System One decision API based on TypeSafe.ai concepts. Choice, score and noul primitives, local mock server, Python and TypeScript SDKs. Real inference planned; not affiliated with TypeSafe AI. 6 stars, Python.
• FFatTiger/new-api-plugin-typesafe. TypeSafe AI System One (Jev) task plugin for QuantumNous/new-api , native /v1/systemone, synchronous evaluation, token billing. 4 stars, JavaScript, Apache License 2.0.
• simota/tenbin. MCP server and agent skill for the TypeSafe AI System One API (Jev): decompose a judgment into Choice / Score / Noul questions, lint them, measure on labelled data, and put calibrated thresholds in code. 4 stars, TypeScript, MIT License.
• kylemclaren/jevsearch. Site search that understands the question, ranked by TypeSafe's Jev model. A shadcn/ui ⌘K block shows keyword hits on the first keystroke, then one Jev request re-ranks the top 20 with a noul per page and a choice over all of them; the author reports Hit@1 of 83% against 41% for its keyword pass alone on 41 queries over the TypeSafe docs. 7 stars, TypeScript, MIT License.
• chrishan17/claude-jev-mod. Typed decisions in Claude Code: adds $.jev over TypeSafe's Jev, through OpenRouter, Vercel AI Gateway, Cloudflare Workers AI, LiteLLM or the TypeSafe API. 1 stars, TypeScript, MIT License.
• YIZY-API/yizy-web-app. Document and Build Typesafe JSON APIs with Instant Code Generation. Boost Development Productivity with AI. 1 stars, Svelte, GNU General Public License v3.0.
• JacquesGariepy/ORIGIN-CIVILIZATION. AI life-and-civilization simulation: TypeSafe Jev makes every decision (typed, probabilistic, auditable); LLMs plan , OpenAI-compatible APIs, local models (Ollama, LM Studio), Claude Code, Codex. 3 stars, HTML.
• everyinfra/jev-radar. 📡 全网最全 · The world's most full tracker of the Jev (TypeSafe AI System One) ecosystem , 220+ documented cases · 108 confidence-graded entries · verified & rescanned every 3 hours · API access guide included. 29 stars.
• kylemclaren/jevpdf. Ask a PDF in your own words and watch the matching lines light up. pdf.js extracts each line in the browser and Jev answers one noul per line, 16 lines to a request, so highlights stream in page by page ranked by probability. 4 stars, TypeScript, MIT License.
• oceanByte/tsai-cli. Unofficial CLI for the TypeSafe AI System One API. 0 stars, TypeScript, MIT License.
• xinyao27/jevonian by @xinyao27. Local OpenAI, Anthropic, and Responses-compatible proxy that puts one Jev call per turn between a coding agent and its providers: it answers both the model route and the thinking level for the virtual model jevonian/auto, from session state, quota health, candidate capabilities, and cache-switch penalties. Code filters candidates by protocol, context window, effort floor, and spent quota windows before Jev is asked, a pinned model or explicit route skips Jev entirely, and every turn is logged with the serving model, the reason, real token usage, cache reads, and an estimated cost. TypeScript, AGPL-3.0-only, published to npm as jevonian.
• jujumilk3/jev-calibration-audit. Independent API-only calibration audit of TypeSafe AI's Jev decision model. 1 stars, Python, MIT License.
• twilwa/pi-typesafe. Pi coding-agent extension built on the TypeSafe AI System One API (Jev). 1 stars, TypeScript.
• Olli0103/openclaw-typesafe-ai. Optional typed TypeSafe AI Jev decisions for OpenClaw, with SecretRef credentials and strict API validation. 1 stars, TypeScript, MIT License.
• harshithsunku/learn-jev-end-to-end by @harshithsunku. Free course of 12 Python notebooks that puts Jev inside an LLM agent loop as router, tool-call guard (a Choice of allow, ask or block plus Noul checks), done gate and judge across 13 use cases, with every tool read-only or dry-run. The author reports, from two runs on 2026-09-23, that Jev matched a frontier LLM (92%) on a 40-email 8-way Choice task at 7-8x lower median latency and 87-88x lower cost, while the LLMs were more confident than Jev on an easy 40-SMS scam task.

A second GitHub search a week after the first found 507 more repositories created since 2026-09-10 that mention Jev or TypeSafe in their description. The 33 below have at least 25 stars, and I checked that each README mentions Jev or TypeSafe. I wrote the descriptions from each repository's own summary and did not read every README, so verify before you depend on one. All 507 are in data/new-repos-2026-09-26.csv.

• kerpopule/hermes-jev-skills. Jev-powered skills for the Hermes agent: model routing, memory, compaction, skill selection, and computer and browser use. 850 stars, Python as of 2026-09-26.
• anishfn/shapeshift. One text box that morphs into the right UI as you type, with Jev deciding what the input means. 676 stars, TypeScript as of 2026-09-26.
• 0xNatoshi/jev-codex-router. Per-turn routing for Codex: Jev picks the model and reasoning effort for each turn. 274 stars, JavaScript as of 2026-09-26.
• allebee/jevk5. An open-weight alternative to Jev that returns typed decisions with probabilities in one forward pass. 109 stars, Python as of 2026-09-26.
• Ying-Kai-Liao/jev-browser. Browser automation where an LLM plans and Jev decides each step. Ships as a library, a CLI and an MCP server. 90 stars, JavaScript as of 2026-09-26.
• Bodila51/grok-bot-jev. Connects Jev to Grok Bot as a cheap decision layer, with usage gates, a skill template and examples. 83 stars, Python as of 2026-09-26.
• hyperspaceai/jevcache. A decision cache for Jev-class models that memoizes decisions so repeated questions cost nothing. 73 stars as of 2026-09-26.
• 1Panel-dev/laya-server. A self-hosted API and web interface for Laya structured decision models, compatible with the Jev API format. 72 stars, TypeScript as of 2026-09-26.
• mode-io/vllm-jev. Native vLLM serving for Jev-style decision models. 49 stars, Python as of 2026-09-26.
• intikhab49/open-jev-typed-decision-engine. An open reproduction of Jev: a 150M typed decision engine answering noul, choice and score in one non-autoregressive pass. 44 stars, Python as of 2026-09-26.
• JoshuaSP/open-jev. Typed JSON inference with DiffusionGemma, with benchmark results against Jev. 41 stars, Python as of 2026-09-26.
• PerryLink/jevcore. Jev for the DeepSeek harness, the Model Context Protocol and plain Node: typed judgments from one package. 40 stars, TypeScript as of 2026-09-26.
• shantanugoel/ask-jev-skill. A skill that lets Hermes and other agents ask Jev questions. 40 stars, Python as of 2026-09-26.
• emrickgarrett/OneVOneJev. A 1v1 quickscope arena game in Three.js where Jev makes the opponent decisions. 39 stars, TypeScript as of 2026-09-26.
• danvega/jev-spring-boot-starter. A Spring Boot 4 starter for Jev built on Spring MVC and RestClient. 37 stars, Java as of 2026-09-26.
• spring-ai-community/spring-ai-typesafe. A Java SDK for the TypeSafe API plus Spring AI integrations. 37 stars, Java as of 2026-09-26.
• klauswg/jev-guard. A real-time risk triage gateway for exchange deposits and withdrawals, with Jev doing the triage. 37 stars, Java as of 2026-09-26.
• kyu1204/jgrep. Semantic code search that greps for what code does, not what it is called, scored by Jev. 36 stars, TypeScript as of 2026-09-26.
• kiwi0719/jev-edge. Typed-judgment admission control at the traffic edge, with a three-layer filter for prompt injection and abuse. 36 stars, Lua as of 2026-09-26.
• safzanpirani/pi-jev-skill-picker. Ranks Pi agent skills for the current task with Jev. 35 stars, TypeScript as of 2026-09-26.
• win4r/jev-skill-suggester. Bounded recommendations of installed skills with Jev, in Python. 33 stars, Python as of 2026-09-26.
• zhangcy122/OpenJev. A self-evolving decision engine positioned as a Jev alternative. 33 stars, Python as of 2026-09-26.
• bytelabs-oss/clash-jev. A Clash Royale bot with no trained policy, where Jev makes every in-game decision. 33 stars, Python as of 2026-09-26.
• klauswg/jev-suite. Four decision-quality tools built on Jev that answer structured questions and check the answers. 33 stars, Java as of 2026-09-26.
• VGabriel45/polymarket-btc5m-jev-trading. A trading agent for 5-minute BTC up/down markets on Polymarket that uses Jev as the decision layer, with a terminal UI. Not financial advice. 31 stars, TypeScript as of 2026-09-26.
• PromptEngineer48/laya-vs-jev-arena. Laya (open source, local) against Jev (API): two models race in Snake and fight in a Mortal Kombat-style game. 30 stars, JavaScript as of 2026-09-26.
• mattn/go-jev. A Go SDK and CLI for Jev returning yes/no, choice and score decisions. 30 stars, Go as of 2026-09-26.
• chy4pro/jev-for-chrome. Drives the tab you are looking at with Jev, using its sub-second decisions. 29 stars, TypeScript as of 2026-09-26.
• shaharia-lab/jev-cli. A command-line tool for Jev: yes/no, multiple-choice and rubric questions. 29 stars, Rust as of 2026-09-26.
• AkashPriyadarshii/jev-superpowers. A software development framework for AI coding agents, upgraded with Jev. 27 stars, HTML as of 2026-09-26.
• jomatsu/pi-jev-auto-mode. An auto mode for the Pi coding agent where Jev semantically decides what to auto-approve. 27 stars, TypeScript as of 2026-09-26.
• keeltrace/hermes-nerve. A supervisory layer for Hermes agents that adds typed Jev decisions. 26 stars, Python as of 2026-09-26.
• buberlo/jev-trader. A market-making system around Jev decisions with deterministic state. Not financial advice. 25 stars, Python as of 2026-09-26.

Unofficial libraries for calling the TypeSafe API from other languages.

• burnigtm/jev-mcp. MCP server that puts TypeSafe Jev on the coding loop in Cursor, Codex, and any MCP client. 57 stars, TypeScript, MIT License.
• Tangerg/typesafe-sdk-go. Go SDK for the TypeSafe AI API , typed questions in, probability distributions out. 9 stars, Go, MIT License.
• saibimajdi/typesafeai-dotnet-sdk. Community .NET SDK for the TypeSafe AI System One API , typed noul, choice, and score questions with structured, confidence-scored answers. Not affiliated with TypeSafe AI. 8 stars, C#, MIT License.
• gilljon/typesafe-ai-rs. Independent async and blocking Rust SDK for the TypeSafe AI System One API. 6 stars, Rust, MIT License.
• codeitlikemiley/typesafe-sdk-rust. Rust SDK for the TypeSafe AI API. 4 stars, Rust, MIT License.
• fgn/jevgo. Go client for TypeSafe AI's System One API (Jev), with optional Langfuse instrumentation. 3 stars, Go, MIT License.
• chez-shanpu/typesafeai-go. Go SDK for TypeSafe AI API https://docs.typesafe.ai/api. 2 stars, Go, Apache License 2.0.
• zhirschtritt/typesafe-go. Idiomatic Go SDK for the TypeSafe AI API. 3 stars, Go, MIT License.
• abeldzan/jev-rs. Async-first Rust SDK for the TypeSafe AI API. 3 stars, Rust, MIT License.
• yunusey/typesafe-sdk-cpp. Unofficial C++23 client for the TypeSafe AI API. 1 stars, C++, MIT License.
• guillemus/jev-go. Unofficial Go SDK for TypeSafe AI's Jev API. 2 stars, Go.
• lu-zero/systemone. Rust client for the TypeSafe AI systemone API. 0 stars, Rust, MIT License.
• nirgal-soft/typesafe-rs. A rust client for the TypeSafe AI API. 0 stars.
• mattneel/typesafe.zig. An idiomatic Zig client for the TypeSafe AI API. 2 stars, Zig, MIT License.
• mattneel/typesafe. An idiomatic Elixir client for the TypeSafe AI API. 3 stars, Elixir, MIT License.
• hnegishi/typesafe-ai-ruby. Ruby client for the TypeSafe AI(Jev) System One API. 1 stars, Ruby, MIT License.
• community-ports/typesafeai-sdk-rust-community. Community-built Rust SDK for the TypeSafe AI API (System One / Jev). A port of typesafe-sdk-python. 0 stars, Rust, MIT License.
• Shubham510/typesafe-go. Unofficial Go SDK for TypeSafe AI's System One API (Jev). 1 stars, Go, MIT License.
• T-moz/typesafe-ai-dart. A pure Dart sdk wrapper around Typesafe AI API and JEV. 0 stars.
• valksor/typesafe-sdk-go. Unofficial Go SDK for the TypeSafe AI System One API , 1:1 parity with the official JS and Python SDKs. Not affiliated with TypeSafe AI. 1 stars, Go, MIT License.
• valksor/typesafe-sdk-php. Unofficial PHP SDK for the TypeSafe AI System One API , 1:1 parity with the official JS and Python SDKs. Not affiliated with TypeSafe AI. 1 stars, PHP, MIT License.

A second GitHub sweep on 2026-09-19 found 111 more repositories that mention Jev or TypeSafe in their description. Those with at least one star are below, grouped by what they do. All 111 are in data/more-repos.csv. Descriptions come from each repository's own summary and I did not open every README.

• yusukebe/hono-jev-router. Route HTTP requests by meaning. A semantic router for Hono powered by Jev. 51 stars.
• mejiasd3v/pi-jev-router. Automatic model routing for Pi using TypeSafe's Jev through Vercel AI Gateway. 15 stars.
• andrelandgraf/safer-with-jev. Neon Function proxy for the Neon AI Gateway with TypeSafe Jev routing. 6 stars.
• nexibeo/jev-cookbook. Practical, tested recipes for TypeSafe's Jev decision model on OpenRouter: support triage, database indexing, file organizing, tagging, taxonomies, dedupe, PII detection, extraction, search re-ranking and a browser agent. 28 stars.
• hamakyo/jev-starter. Typed, policy-driven decision workflows on top of TypeSafe AI Jev: confidence routing, fallbacks, evaluation, and RAG patterns for TypeScript apps. 2 stars.
• rajdhakad9826/jev-router. Cost-aware LLM router that picks the cheapest model capable of handling a query, using TypeSafe's Jev for fast classification instead of an LLM call. 10 stars.
• ajensenwaud/hermes-jev-plugin. TypeSafe Jev (System One) decision tools for Hermes Agent: jev_check / jev_route / jev_score / jev_evaluate. 7 stars.
• iJ03l/jear. Jev-routed client for NEAR AI Cloud inference and IronClaw agents. 3 stars.
• adarshmishra07/jcm-router. Local proxy that picks the Claude model and effort per message using TypeSafe Jev. Routes subagents, leaves your cached main chat alone. 6 stars.
• prismhq/jev-router. Open-source LLM router that uses TypeSafe's Jev to pick a model, on top of LiteLLM. 13 stars.
• Mandrilsquad1441/jev-model-router. Pick the best AI model and reasoning effort for any task in ~1s. Plugin for Claude Code, Claude Desktop and Codex, powered by TypeSafe's Jev decision model and live OpenRouter pricing. Balance intelligence, speed and cost, or choose your priority. 1 stars.
• TokenTrim/jev-routing-experiment. Benchmarking TypeSafe's Jev decision model as a cost-efficient LLM router on RouterArena. 3 stars.
• rsdkrasen/hermes-jev-router. TypeSafe/Jev router plugin for Hermes Agent , compact tool results, suppress duplicate tools, skip unnecessary main-model calls. 3 stars.
• TheEleventhAvatar/triage-bot. Real-time support triage + response bot Jev routes the ticket to a specialist agent (general / account / billing / technical) and decides whether a human should take it instead , all as typed data, no text to parse. Cerebras then drafts the reply using whichever agent Jev picked. The script times both calls separately so you can see the split. 1 stars.
• vinilana/jev-gateway-bench. Benchmark for jev-gateway: real coding agents on chess engine tasks, with Jev routing on and off. 4 stars.

• arunav25/jev-mcp. Connect JEV to MCP clients and compare its judgments against general-purpose LLMs using shared datasets and measurable accuracy. 8 stars.
• harshil1712/slidepilot. Voice-driven semantic auto-advance for Slidev, powered by Cloudflare Agents and TypeSafe AI Jev. 5 stars.
• rashedInt32/jev-mcp. MCP server exposing TypeSafe Jev as typed, calibrated judgment tools: classify, score, check, batched ask. Ships as a Claude Code plugin. 8 stars.
• samtay32/jev-system-architect. System-architecture skill for TypeSafe AI Jev/System One , find fuzzy semantic judgment and turn it into small Choice/Score/Noul primitives. 3 stars.
• abhishekashokvkumar/jev-mcp-dispatcher. Natural-language MCP tool dispatcher powered entirely by TypeSafe's Jev , no general-purpose LLM. Discovers a simple MCP server's tool signatures at runtime and uses Jev's typed primitives (Choice/Noul) to pick the right tool and extract its arguments straight out of the sentence. 4 stars.
• forvela/jev-agent-browser. Fast, bounded browser agents powered by Jev and agent-browser , typed actions, research, classification, and safe orchestration. 13 stars.
• raihankhan-rk/jevarena. JevArena , two Jev agents duel in click-only browser games (Browser Use + TypeSafe Jev). 3 stars.
• siddicky/omp-typesafe. TypeSafe AI (Jev) adversarial reviewer and typesafe_ask tool for the omp coding agent. 2 stars.
• BYK/jev-mcp. An eval-first MCP server for TypeSafe's Jev, a System One model that returns typed judgments (noul, choice, score) with probabilities instead of generated text. 3 stars.
• TokenTrim/jev-agent-failure-benchmark. Benchmarking Jev (Typesafe.ai) against a strong LLM on the Who&When Pro agent-failure-attribution benchmark (text subset). 3 stars.

• Olti1947/jev-java. Idiomatic Java SDK for TypeSafe AI Jev System One decision engine. 6 stars.
• Stumble/jev-go. Community Go SDK for TypeSafe AI Jev / System One. 5 stars.

• Heman10x-NGU/Verdict-open-jev. Non-autoregressive decision engine on ModernBERT (151M) with calibrated uncertainty (RLCD), TypeSafe AI Jev benchmark audit, and in-browser WebGPU playground. 104 stars.
• vinilana/live-jev. 2D autonomous car simulation in the browser, driven by TypeSafe's Jev decision model. 19 stars.
• Dimesio/typesafe-chess. FUn little experiment with Typesafe AI Jev Model playing chess against stockfish :). 2 stars.
• tedliou/decision-model-playground. A local playground for comparing Laya and Jev decision models with article recommendations. 1 stars.

• keltokhy/jgrep. grep, but the pattern is a description. Filters lines by meaning with TypeSafe's Jev decision model: ~200 ms and a thousandth of a cent per line. 104 stars.
• gtaras7/typesafe-jev. Screen a folder of CVs with the TypeSafe Jev decision model: typed judgments, an editable policy, free re-scoring. 8 stars.
• noetion/dsh-jev. DSH bundle that registers jev_ask for TypeSafe Jev noul, choice, and score answers. 4 stars.
• thezem/jev-one. A vocabulary-driven TypeScript runtime for safe, stateful applications powered by TypeSafe AI Jev. 1 stars.
• ItisShikhar/gg-friggin-ez. Fast, drop-in profanity and toxicity screener for Node.js, powered by TypeSafe AI Jev. Catches leetspeak, character spacing, and romanized profanity across languages including Kannada, Telugu, Tamil, Hindi, and Bengali. ~50-500ms latency. 7 stars.
• AkashPriyadarshii/jev-scout. Zero-hallucination open-source repo and crate scout powered by TypeSafe AI Jev System One scoring. 5 stars.
• AkashPriyadarshii/jev-git. Sub-second Git pre-commit & pre-push semantic reflex gate powered by TypeSafe AI Jev. 5 stars.
• kitze/pagegrade. Grade page sections for clarity, writing and on-page SEO. WXT + TypeSafe AI Jev. 7 stars.

• sontakey/awesome-jev. Unofficial list of insanely useful TypeSafe AI Jev / System One projects. 2 stars.
• AbdelStark/awesome-typesafe-jev. A source-backed field guide to Jev with SDKs and live demos. 528 stars as of 2026-09-26.
• wuyoscar/jev-skill. A collection of Jev use cases, workflows and agent skills. 501 stars as of 2026-09-26.
• AnotiaWang/awesome-jev. A curated list of Jev applications, libraries and resources. 491 stars as of 2026-09-26.
• yzfly/awesome-jev-zh. A Chinese-language list of Jev resources, apps, agent tools and reproductions. 76 stars as of 2026-09-26.

Deeper keyword research, live results-page checks, a forecast from X and YouTube data and a content plan are in docs/keyword-research.md. Views for 65 Jev YouTube videos are in data/youtube.csv. The full list of 112 keywords is in data/keywords.csv.

Google search volume in the US, from a keyword-data provider called through treg on 2026-09-19. These are monthly averages for the last 12 months.

What this says: people who look for the company by name are few but growing fast, and nobody yet searches for the model as "Jev" in a way that isolates it. Terms that describe the job, such as router and classifier, carry the volume. A page that ranks for those and shows a Jev example has more room than a page fighting for "jev". Volume for the newest terms is blank because the provider has no data yet, not because it is zero.

Suggested repository topics: jev, typesafe, system-one, llm, classification, routing, awesome-list.

Every entry with its source. Sorted by likes and by stars. Snapshot 2026-09-19.

• will-it-hit. A live LinkedIn draft scorer. One call asks eight Score questions (hook, specificity, emotion, clarity, repostability, authority, algorithm fit, expected engagement) and one Choice question for post type. The rubrics carry real engagement numbers from LinkedIn posts. A separate LLM writes rewrites, and Jev scores each rewrite again so you see both numbers. The source is in a private repository for now.
• linkedin-slop-blocker. A browser extension that scores every post in your LinkedIn feed as you scroll. Each post gets a "Slop" or "Not slop" pill with a percentage, and slop posts get a fading text treatment and a rotated SLOP stamp you can click away. That scroll style follows the real-time slop detector demo by @RBilgil. A free pattern scan runs first, then one Jev call per scroll returns a spam probability and a quality score for each post. It keeps your own API key in the browser and learns from posts you hide or dismiss. The source is in a private repository for now.
• jevkit. A CLI and MCP server with eleven tools for agents: route, triage, guard, grep, rank, compact, judge and more. It ships six runnable use cases (a guard hook for Claude Code, a build-log compactor, a skill router, issue triage, semantic log search, a PR ranker) and reports what was measured against the real model, including the weak results. Zero dependencies. Built by the list maintainer.
• Jev tagger (inside linkedin-slop-blocker v0.5). A floating corner panel where you write your own categories, each with a name and a plain-English description, then pick colors. As you scroll LinkedIn, one Jev Choice question per post tags it with the best-fitting category, or leaves it untagged when confidence is low. Each category can be shown, dimmed or hidden, and clicking a counter chip focuses the feed on that category. It follows a demo by @nateherk, who tags X posts as breaking, golden nugget or slop. Two presets ship with it, "Signal or slop" and "By intent". The source is in a private repository for now.

Jev answers typed questions about a piece of context. You send one state (a string, an object, or an array) and a map of named questions to a single endpoint. There are three question types.

• Choice picks one option from a set you define and returns the probability of every option.
• Score rates the state on an ordered rubric and can land between two levels.
• Noul answers yes or no as a probability.

Jev does not generate text. Output tokens are free, and questions asked over the same state run in parallel. For anything that needs prose, pair it with a normal LLM.

The smallest request:

curl https://api.typesafe.ai/v1/systemone \
  -H "Authorization: Bearer $TYPESAFE_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "state": "Help! My payouts have been failing for 3 days.",
    "model": "jev-latest",
    "questions": {
      "is_urgent": {"type": "noul", "instructions": "Does this convey urgency?"}
    }
  }'

The API reference shows this response for that request:

{
  "model": "jev-latest",
  "answers": { "is_urgent": { "type": "noul", "noul": 0.92 } },
  "usage": { "input_tokens": 312, "output_tokens": 48 }
}

The models page lists Jev 1.13 at $42 per billion input tokens, a limit of 250,000 tokens per second and 1,200 requests per minute, and a cap of 32k tokens on the state plus the longest question. The model value jev-latest currently points to jev-1.13.0.

TypeSafe's own worked examples. Numbers below are the ones TypeSafe reports in each cookbook.

• Parallel questions. Runs a 13-question regulatory briefing over one document. Batching every question into one call is reported as 12.2x cheaper and 10.0x faster with no change in answers.
• Re-ranking. Asks one question per query and candidate pair on 30-passage shortlists for 40 legal queries. Top-1 accuracy goes from 5% to 18% and top-10 from 38% to 62%.
• Line-by-line search. Semantic search over GitHub's Terms of Service. One request scores 218 line ids against a plain-language query.
• Structure recovery. Rebuilds Markdown from plain text that lost its formatting, in two requests.
• Function calling. Turns natural-language trading requests into calls to ordinary typed functions.
• Skill suggestion. Picks at most one skill for an agent turn out of the 182 in Nous Research's Hermes catalog, and can reject every candidate.
• Knowledge graph entity alignment. Decides which of 450 candidate pairs from two beer catalogues are the same product. One Score question carries the decision, and its levels are merge, leave unlinked, and hand to a curator.
• Classifying RAG passages. Scores each retrieved passage in one request, then code decides which ones reach the answering model. A passage carrying a hidden instruction can be dropped.
• Double-checking citations. One Choice question decides whether the quote's context supports the claim, and low confidence flags the citation for review.
• Guardrails for LLMs. Screens every message going into and out of an LLM app with one request that scores hazards such as jailbreak attempts. Your code applies the thresholds.
• SDE cascade. A two-stage structured-data-extraction cascade that gets most of a large reasoning model's quality at a fraction of the cost.
• Date extraction. Asks Jev for the parts of a date named in a document, then resolves and validates them in code.
• Pre-parsed value extraction. Regexes find candidate emails, phone numbers, and amounts, then Jev selects the requested span so code can normalize it.
• Hierarchical classification. Classifies documents through deep patent, retail product, biomedical, and source-code hierarchies with parallel beam search over Choice probabilities.
• Autoresearch feature discovery. A loop that proposes questions, turns free text into numeric features, and uses model errors to improve a supervised CatBoost regressor.
• Classification using confidence. Classifies SEC annual reports into 75 industry groups with one Choice each, then reads the answer's confidence to decide whether to report the group or the broader division above it.
• Self-consistency with nouls and with choices. Route uncertain probabilities to human review, and add an uncertain outcome to moderation decisions.

From TypeSafe:

• Speculative fan-out. Send many questions in one call. Speculative ones are fine, and your code decides what is relevant.
• Confidence-gated routing. The answer tells you what, and confidence tells you whether to act.
• Composite scoring. Break a complex judgment into atomic scores and combine them with weights you control in code.
• Intent routing. Classify incoming requests and send each to a deterministic handler, a specialist LLM, or a human.

Found while building the projects above:

• Generate with an LLM and judge with Jev. In 1kpapers the summaries cost $3.99 and the classification cost $0.08, so judging 1,018 papers cost about 50 times less than summarizing them. In will-it-hit, a separate LLM writes the rewrite because Jev cannot write text, and Jev then scores the result.
• Put many items in one state with one question per item. Send an array as the state, key the questions like post_0 and post_1, and refer to items by path such as posts[0] in the instructions. One request covered a full feed scroll. The cost is accuracy, because TypeSafe's docs warn that unrelated material in the state lowers it. Keep batches small and check results by hand.
• Write rubrics from real outcomes. Score levels should describe concrete situations, and the instructions can carry observed numbers, such as how many reactions a flop and an outlier received, so scores do not drift upward.
• Do the arithmetic in code. will-it-hit combines its eight Score answers with fixed weights in code and uses thresholds for the verdict labels, which matches the advice on TypeSafe's limits page.
• Send requests from the extension's service worker. On linkedin.com a fetch from the page context to localhost failed in testing. A background service worker is not bound by the page's Content Security Policy.
• Select by test ids on LinkedIn, not class names. The feed uses hashed class names that change on every deploy. In September 2026 these worked: div[role="listitem"][componentkey^="update-card-focus"] for a post card, [data-testid="expandable-text-box"] for the body text, and the aria-label of button[aria-label^="Open control menu for post by "] for the author name. Expect them to break when LinkedIn changes its markup.

From TypeSafe's jaggedness page, last reviewed 2026-09-17, and the models page. Read the source pages before you build.

• It reads instructions literally. Write the exact condition and put boundary cases in the criteria.
• It is not a calculator. Keep math in code, which includes counting and date comparison, and use Jev to extract the parts.
• Use Score outputs for thresholds and ranking. Do not interpolate an exact number between two levels.
• Accuracy falls when the state carries content unrelated to the question. Filter first.
• Text in the state can steer the answer. An injected instruction or a misleading framing can move it, so test edge cases before deploying to many users.
• Separate questions are not guaranteed to agree. A Noul and a yes or no Choice about the same thing can return different numbers, so do not carry a threshold from one type to the other.
• It does not generate text. Use a generative model for that.
• English works best. Test other languages on your own content.
• The state plus the longest question is capped at 32k tokens and a whole request at 64k. Input is text only.

Except where a row names TypeSafe, these are numbers from the builders themselves and have not been independently verified.

These are hypotheses from a brainstorm on 2026-09-19, not products. Where a documented limit applies, the note says so.

Consumer and content:

• A tone meter for any text box. Score clarity and tone as you type in Gmail, Slack, or X. The hard part is attaching to other sites' inputs without breaking them.
• Live chat moderation. Classify each message in a fast Twitch or Discord chat with Noul questions for toxicity and spam. Messages written to fool the classifier are the main risk.
• A feed reranker that asks only when unsure. Score every item in an infinite feed for interest fit and ask the user a question only when confidence is low.

Developer tools:

• A model router. Score prompt complexity and choose a model tier before the call, so the router costs less than the call it routes. Watch for flapping between tiers on near-identical prompts.
• CI test selection. Choose which test tier to run from a commit diff. Diffs are large, so filter to changed paths and hunks before sending them.
• Abuse scoring at an API gateway. Ask a Noul question about request text. Rates and timing must be computed in code. Request text is controlled by the attacker, so this is the riskiest idea here.

Browser extensions:

• A job board scam filter. Score each listing card as real, ghost, or scam. Selectors differ by site and change often.
• A marketplace price-trap detector. Flag likely bait listings, and compare prices in code instead of asking the model.
• A fake review flagger. Score each review as the list loads. Text alone is a weak signal for reviews written to look organic.

Operations:

• Support ticket triage. A Choice for department, a Score for urgency, and a Noul for refund requests. Calibrate the criteria on the client's own ticket history.
• Inbound lead scoring on form submit. Needs closed-won and closed-lost examples to calibrate the rubric.
• An outbound message compliance check. One Noul per policy rule in one request before send. Tune the confidence thresholds so false positives do not push people to turn it off.
• A CRM hygiene sweep. Score record quality and possible duplicates. A duplicate check needs both records in the state.

• TypeSafe skill for Claude Code. Install with claude plugin marketplace add typesafe-ai/skills, then claude plugin install typesafe@typesafe-ai. It points the agent at TypeSafe's live docs.
• Docs index for agents. Every docs page with a one-line description.
• Python SDK and JavaScript SDK.
• Use case map. TypeSafe's own list of use cases by industry.

Snapshot taken 2026-09-19. Post metrics (likes, reposts, replies) come from the posts themselves, read with yt-dlp. Follower counts come from treg. Repository stars, languages and licenses come from the GitHub API, and every repository was opened to confirm its README mentions Jev or TypeSafe. Descriptions are written from the original posts and READMEs and checked so that no number appears that the source did not contain. Metrics change by the hour, so treat them as a dated snapshot. Followers are counted today, after most of these posts went out, so the follower ratios understate how small the accounts were at the time.

The two CSV files in data hold the same numbers, and docs has one page per demo.

This list grows by pull request. Three ways to help, from smallest to largest.

• Report a broken link, a wrong number or a dead repo. Open an issue with the "Fix an entry" form.
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