The full directory
All Jev AI model resources
Entries for TypeSafe AI's flagship System One model — official docs and SDKs alongside community clients, tools, and write-ups.
- Entries
- 3,739
- Categories
- 11
- Official
- 102
- Community
- 3,637
- Source sites
- 292
- Deep dives
- 3,739
By format
3739- Docs169
- Repos & sites2980
- Videos73
- Posts103
- Articles344
- Playgrounds70
Updated
Browse the Jev AI model by category
11 ways into the ecosystem. Each category page leads with its own shortlist of starting points.
Browse the Jev AI model by use case or industry
The same directory, sliced by what you are building and who you are building it for.
All 3739 Jev AI model resources
Search the full directory, or filter by category and tag.
Showing 1–48 of 60 resources
TechCrunch reports TypeSafe AI raised $870 million at a $7.5 billion valuation, led by Andreessen Horowitz with Sequoia and DCVC, weeks after Jev's September 15 release. The startup claims a third of Fortune 500 companies already use the model.
Jev Media & TalksArticle#community#press#funding
Community
Per WSJ's search excerpt, the three-week-old Jev has set Silicon Valley abuzz with talk of alternatives to large language models and is already sparking copycats. It classifies inputs to predetermined outputs: yes or no, a numerical score, or answers on a list.
Jev Media & TalksArticle#community#press#competition
Community
Fortune reports Jev went viral among developers, with nearly 13% of Vercel's paid teams trying it within a day of its gateway arrival. Three weeks after Jev's launch, OpenAI rolled out a competing Decisions API built on GPT-6 Luna.
Jev Media & TalksArticle#community#press#competition
Community
InfoWorld covers a HiddenLayer report finding Jev more vulnerable than traditional LLMs to adversarial text and prompt injection, with analysts calling the methodology sound but debating whether flaw reports this early are premature.
Jev Media & TalksArticle#community#press#security
Community
Bloomberg Law reports TypeSafe AI raised about $870M at a $7.5B valuation in a round led by Andreessen Horowitz, weeks after the Jev launch video went viral. The startup says a third of Fortune 500 companies use Jev but declined to name any.
Jev Media & TalksArticle#community#press#funding
Community
SiliconANGLE reports TypeSafe closed $870M at a $7.5B valuation and explains the model's pitch: three request types, yes/no answers, picking from a list, or a score, return structured output and a confidence number instead of text.
Jev Media & TalksArticle#community#press#funding
Community
The Economic Times profiles Diogo Almeida, who left OpenAI in 2024 and spent about two years developing the idea behind TypeSafe AI. It cites WSJ on the roughly 25% Fortune 500 adoption and a $40 million DCVC raise.
Jev Media & TalksArticle#community#press#explainer
Community
The Register covers HiddenLayer's tests of Jev and a decision-tuned Qwen model: a planted ad stole a browser agent's click on a simulated store page, and tokenbreak edits snuck toxic prompts past verification. TypeSafe did not immediately respond.
Jev Media & TalksArticle#community#press#security
Community
Adrian Bridgwater's September 21 report on TypeSafe's launch pitch: a model that does not write but decides, a new architecture and RLCD training, and Vercel's Jev adoption numbers through its AI Gateway.
Jev Media & TalksArticle#community#press#explainer
Community
Andreessen Horowitz announces it is leading the investment in TypeSafe AI, calling Jev the fastest growing model it has seen at 1 trillion tokens generated in 3 days. The firm claims 1/100 to 1/500 frontier-model cost and 100x speed for classification, and says 25% of Fortune 500 enterprises have integrated Jev.
Jev Media & TalksArticle#community#press#funding
Community
HiddenLayer researchers tested Jev and an in-house System One-style model: on a simulated checkout page an attacker-controlled ad made the browser agent click away from the purchase, and TokenBreak-style edits lowered toxicity scores for prompts both models had flagged.
Jev Guides & ArticlesArticle#community#press#security
Community
HiddenLayer's insight post walks security teams through its Jev tests: on a simulated checkout page Jev gave the attacker ad's button 91% against 9% for Place order, and TokenBreak edits cut one prompt's toxicity score by more than two thirds so Jev stopped flagging it.
Jev Guides & ArticlesArticle#community#press#security
Community
Pillar Security analyzes Jev and System One models: a smart-home demo answered three typed questions in about 200 ms with no language model in the loop, while prompt scanning, output filtering and chain-of-thought monitoring all have nothing to inspect. TypeSafe's docs say state is not treated as hostile by default.
Jev Guides & ArticlesArticle#community#press#security
Community
Rogue Security's post uses Jev and System One models to frame agentic security as a decision problem: the same send_file action should be allowed, blocked or reviewed depending on who requested it and where the data goes, illustrated with policy case files rather than product results.
Jev Guides & ArticlesArticle#community#press#security
Community
The Injection's brief on HiddenLayer's tests: a browser agent powered by jev-ultrafast followed instructions planted in a web page and picked an attacker's ad over the real checkout button, and TokenBreak edits let toxic prompts slip past Jev's scoring. The post records no fix or response from TypeSafe.
Jev Media & TalksArticle#community#press#security
Community
Ian Maurer's ThinkThen analysis argues Jev's faster and cheaper are measurable today while its better is a bet on RLCD: unlike RLHF, which pleases humans, and RLVR, which optimizes benchmarks, Jev is trained to be reliable for programmatic use, in Diogo Almeida's own framing.
Jev Guides & ArticlesArticle#community#press#explainer
Community
Same-day reaction to Jev from four engineers: Mario Zechner called it basically a general purpose classifier, Thorsten Ball praised the onboarding, Malte Ubl said it reached AI Gateway, and Armin Ronacher expects fast followers. Wes Bos measured $0.000232 a call and there were no evals yet.
Jev Media & TalksArticle#community#press#adoption
Community
A podcast episode on Jev as a decision model rather than a chat model: hosts Luis Calderon and Carina discuss roughly 20x faster Coach testing, why that preliminary figure differs from TypeSafe's vendor benchmarks, and why type safety does not guarantee a correct decision.
Jev Guides & ArticlesArticle#community#press#podcast
Community
A BIG CHANGE analysis of the Diogo Almeida Latent Space interview: Jev's most consequential proposition is the unit of automation, a bounded judgment inside an existing process, and the launch claim that Jev can't hallucinate needs a narrow reading because schema matching does not establish truth.
Jev Guides & ArticlesArticle#community#press#explainer
Community
Simon Willison released llm-typesafe 0.1a0, an LLM plugin for TypeSafe's Jev: install with llm install llm-typesafe, set a typesafe API key, then ask noul yes/no questions, Choice questions and Score questions from the command line. A demo refund question returned noul 0.99.
Jev Media & TalksArticle#community#press#explainer
Community
Bloomberg News reports Jev went viral with the promise of a cheaper, faster alternative to large language models, its launch video nearing 40 million views on X. Per the Financial Times, investors have offered funding valuing TypeSafe AI above $10 billion.
Jev Media & TalksArticle#community#press#funding
Community
Business Insider's Stephen Council reports from a Jev hackathon at CodeRabbit's office, where developers said they had woven the model into agents and search tools. The piece cites nearly 40 million launch video views and a Discord server past 100,000 members.
Jev Media & TalksArticle#community#press#adoption
Community
VentureBeat's Sam Witteveen argues a large share of enterprise AI only needs a label, not generated text. Per TypeSafe, Jev returns choices, scores and yes/no answers in 70 to 500 ms at $0.042 per million input tokens, with output tokens free.
Jev Guides & ArticlesArticle#community#press#explainer
Community
VentureBeat's Louis Columbus reports Jev is spreading into agent infrastructure faster than enterprises can audit it. In one Octomind test a planted field cut a block probability from 0.76 to 0.48, and TypeSafe and Pydantic both warn inputs can steer answers.
Jev Guides & ArticlesArticle#community#press#security
Community
Forbes' Rashi Shrivastava profiles TypeSafe's $40 million DCVC-led seed at a $200 million valuation, and CEO Diogo Almeida's argument that software needs models reporting calibrated confidence rather than confident-sounding paragraphs.
Jev Media & TalksArticle#community#press#funding
Community
TNW reports Jev reached nearly 13% of Vercel's paid teams within 24 hours, the AI Gateway's fastest adoption: twice the GPT-5.6 family's share and more than 6x Fable 5.1's, at $0.042 per million input tokens with output unmetered.
Jev Media & TalksArticle#community#press#adoption
Community
Vega's proof of concept put Jev in front of its alert triage agent on two tenants: it gated off 15% of alerts on the busy tenant and a third on the quiet one, about 98% correct by three LLM judges verified by its own analysts.
Jev Guides & ArticlesArticle#community#press#adoption
Community
TWIML episode 779 features TypeSafe co-founder and CEO Diogo Almeida on Jev, the company's model for bringing fast, reliable intelligence directly into software, and why text-generating models are poorly suited to many real-world automation decisions.
Jev Guides & ArticlesArticle#community#press#podcast
Community
On The MAD Podcast, Matt Turck talks with TypeSafe CEO Diogo Almeida about why intelligence per dollar matters more than enterprise adoption, and why RLVR benchmark gains still leave models 95% Einstein, 5% Mr. Bean.
Jev Guides & ArticlesArticle#community#press#podcast
Community
In Law://WhatsNext's return episode, guest Chris Bridges of AI-native law firm Tacit Legal demonstrates Jev returning structured contract answers in 0.3 seconds where a chat model took 9.6 seconds.
Jev Guides & ArticlesArticle#community#press#podcast
Community
The Rundown's news brief reports TypeSafe's September 15 early access launch of Jev, a model that answers preset questions inside software, and flags that the company's can't-hallucinate guarantee concerns format, not correctness.
Jev Media & TalksArticle#community#press#calibration
Community
DigitalToday's Korea report relays The Information's September 24 story: TypesafeAI is seeking $1 billion, with some unnamed investors proposing valuations above $10 billion, while the talks remain early and terms could change.
Jev Media & TalksArticle#community#press#funding
Community
An analysis of TypeSafe's $870 million Series A at a $7.5 billion valuation that corrects the 24-days framing, showing the $40 million seed was priced more than a year earlier, and notes the close came in below September's $10 billion talk.
Jev Guides & ArticlesArticle#community#press#funding
Community
INXEN's syndication of Financial Times reporting says TypeSafe, last valued at $200 million, is fielding offers that would value it at $10 billion or more days after launching Jev, with Vercel citing record paid-developer interest.
Jev Media & TalksArticle#community#press#funding
Community
Braintrust's provider page covers two ways to use TypeSafe Jev: instrumenting System One calls as decision spans, and running Jev as an LLM judge that turns typed choices into scores or labels.
Jev Guides & ArticlesArticle#community#press#explainer
Community
Langfuse's guide to decision-model evaluators runs OpenAI's gpt-6-luna or TypeSafe Jev over observations, where each Choice, Score or Yes/no question becomes one Langfuse score.
Jev Guides & ArticlesArticle#community#press#explainer
Community
Langfuse's notebook integration traces TypeSafe Jev System One calls through the OpenInference instrumentor, which patches the Python client in place and emits OpenTelemetry spans.
Jev Guides & ArticlesArticle#community#press#explainer
Community
Langfuse's September 18 post presents Jev as a decision model for routing, classification and eval verdicts, and walks through scoring production traces with Jev-as-a-judge in Langfuse.
Jev Media & TalksArticle#community#press#explainer
Community
LangChain's September 25 post argues Jev's typed decisions fit LangGraph's deterministic orchestration, with a litigation review example and a Stagehand browser case where act() latency fell to 0.46 seconds.
Jev Media & TalksArticle#community#press#explainer
Community
A builder's walkthrough of System One, a dual-process browser agent that resolves routine steps with four parallel Jev questions in 70-150ms and falls back to Gemini or Claude below 0.55 confidence.
Jev Media & TalksArticle#community#press#explainer
Community
Tom Joseph's analysis walks through Jev's typed primitives and finds three claims that survive scrutiny: the parallel sampler, the price and speed, and the RLCD training whose calibration plot is unpublished.
Jev Media & TalksArticle#community#press#explainer
Community
Octomind 0.54 hands eight supervisor judgments to Jev, from risky tool-call checks to skill routing and done verification, reporting 0.5-1.3 second round trips at $0.00001-$0.0001 per decision.
Jev Media & TalksArticle#community#press#adoption
Community
A Spanish-language analysis of Jev's five-day rise to critical infrastructure, the documented injection finding that moved a block verdict, and the six controls it says to add before production.
Jev Media & TalksArticle#community#press#security
Community
A community roundup of every public Jev evaluation: TypeSafe's own workflow evals plus at least twenty independent builder benchmarks. The site reports speed medians of 127-422 ms per call leading every LLM compared, while accuracy splits by task.
Jev Media & TalksArticle#community#press#calibration
Community
Blackwell Systems' adversarial technical due diligence on Jev, built only from public material and live API calls. The author reproduces TypeSafe's published addressee numbers within a point and finds a real but noise-type-specific robustness moat.
Jev Guides & ArticlesArticle#community#press#competition
Community
An independent hands-on evaluation that ran Jev live on 16,379 benchmark requests and reports it is no frontier model but genuinely useful for cheap real-time sub-frontier judgement, with 82.7% MMLU-Pro and 76.5% GPQA Diamond.
Jev Guides & ArticlesArticle#community#press#competition
Community
A LessWrong pilot testing Jev as a trusted monitor in the ControlArena APPS backdoor setting: one yes/no question reaches AUROC 0.976 and catches 90% of backdoors at a 2% false-positive rate for about $0.04 per thousand submissions.
Jev Guides & ArticlesArticle#community#press#security
Community
A playground operator's audit of every published Jev number: TypeSafe's four-workflow evals, the favourable homepage demo, independent tests and its own calls. It concludes the price and speed are real and the intelligence claim is roughly mid-tier frontier on decision workflows.
Jev Media & TalksArticle#community#press#explainer
Community
Start here with the Jev AI model
Hand-picked entries the maintainers recommend first, from official primitives to community tools.
The Jev Day release post: Jev 1.14.0 under jev-preview plus three danger-zone features — composable images on both models, the box question type on 1.14 only, and opt-in 262k context.
Jev Guides & ArticlesArticle#official#release#vision
Official
The box question type: one bounding box per question, returned as four edges that are fractions of the image, plus confidence, with no criteria field and no not-found value.
Jev Practices & PatternsDocs#official#docs#vision
Official
LiteLLM's Day 0 post for TypeSafe Jev 1.14: the proxy adds box questions, image input and long-context mode on both POST /v1/systemone and POST /v1/decisions, forwarding TypeSafe-Danger-Zone headers only to TypeSafe deployments.
Jev Integrations & AdaptersArticle#community#litellm#gateway
Community
Newest Jev AI model resources
The latest additions to the directory, newest first.
AI SDK TypeSafe ProviderJev Integrations & Adapters
- Flow Steward Extension for TypeSafe AI (Jev)Jev Integrations & Adapters
Jev Arts & Human Taste Embeddings (Qwen3.6-35B-A3B-FP8)Jev Models & Reimplementations
- Jev Bookmark TriageJev Demos & Experiments
- Jev Consensus GateJev Agents & MCP
- Jev Game AgentJev Demos & Experiments
From the Jev community
Watch walkthroughs, read the threads, and see what builders are posting.
YouTube: KodeKloud's three-minute primer: what a System One model decides, and why it answers in milliseconds.
YouTube: Caleb Writes Code explains what Jev is and how to call it, in seven minutes.
YouTube: Greg Isenberg and Ryan Vogel on the businesses Jev unlocks, and a path in through the Vercel AI Gateway.
YouTube: CJ from Syntax demos browser use, classification, code review, a model router, and a chat bot with no LLM.
YouTube: Ryan Vogel runs Jev on 100 and then 1,000 emails: category, priority, spam, and reply predictions.
YouTube: AICodeKing tests support routing, refund detection, prompt-injection resistance, and browser automation.
Hacker News: Show HN: Ad Spend Guard gates budget before the auction, not the creative after.
Show HN: Gating ad budget with a decision modelRead the thread on Hacker NewsReddit: Reranking rebooking options with a decision model, ranked with reasons.
Hacker News: Pick Verify: a single decision at the warehouse pick station catches confusable picks.
Show HN: Verifying robot picks with a decision modelRead the thread on Hacker NewsHacker News: Decision Index 0.2.1 scores open reproductions of Jev on a frozen suite.
jackbravo4 pointsDecision Index 0.2.1 – 70 open reproductions of TypeSafe Jev's Decision ModelRead the thread on Hacker NewsHacker News: Software Bombe and live Enigma demo where Jev ranks cribs and judges plaintext.
ag_212 pointsShow HN: Jev as the Bletchley Park Analyst: A Software Bombe on EnigmaRead the thread on Hacker NewsHacker News: Show HN for the decide CLI: brew install, file context, and multi-question shell scripts.
vsekhar2 pointsShow HN: Decide – Jev decisions in the shell, scripts, and agent skillsRead the thread on Hacker NewsHow entries are chosen
Every entry is reviewed against the same bar, whether it comes from TypeSafe or the community.
- Public and working — no paywalls, sign-up walls, or dead links.
- Concrete — it has to be about Jev and the System One API, not generic LLM content.
- Useful today — something a developer can learn from or use right now.
- Distinct — if a similar entry exists, the description has to say what makes this one different: a different runtime, a cheaper path, a measured result, or a narrower focus.
- Documented — repos state their license, and docs are in English or carry an English summary.
Spam, affiliate links, and thin AI-generated content are rejected. New entries are expected to ship with a deep dive: two to five highlights, caveats, a quickstart snippet where one helps, and a verification date. Star counts are refreshed when an entry is touched.
Common questions
Short answers about Jev and how this directory works.
What is the Jev AI model?
Jev is TypeSafe AI's first System One model — an AI model that takes a state plus typed questions and returns one structured answer per question: a choice, an ordered score, or the probability of yes, with calibrated confidence on Choice and Score answers. Code can branch on the result without parsing generated text.
What is the difference between Choice, Score, and Noul?
They are the three question primitives. Choice picks one of up to 255 options, Score rates on 2 to 10 ordered levels, and Noul answers yes or no. Choice and Score answers carry a confidence between 0 and 1; a Noul answer is the probability of yes.
Which Jev SDKs and client libraries are available?
TypeSafe maintains official JavaScript/TypeScript and Python SDKs plus a Python adapter. The community has built clients for Go, .NET, PHP, Laravel, Rust, Elixir, Ruby, and Scala, along with framework integrations such as DSPy, LlamaIndex, and Home Assistant. All of them are listed in the Repos & SDKs category.
Is the directory free, and is it affiliated with TypeSafe AI?
The directory is free to browse: every entry is reviewed, and spam, affiliate links, and sign-up walls are rejected. JevDirectory.org is community-run and not affiliated with TypeSafe AI — Jev and System One are TypeSafe AI trademarks.
How often is the directory updated?
Entries are added as new resources appear and re-checked when they are touched. The directory currently holds 3739 entries from 292 source sites, last updated Oct 25, 2026.
What is a verified deep dive?
Most entries include a deep dive: two to five highlight bullets someone would only know from using the resource, a caveats note about limits and requirements, an optional quickstart snippet, and the date the link was last checked. 3739 of the 3739 entries carry one today.
For the canonical details, read the official TypeSafe docs and the API reference.