Jev directory
Jev for Decision gating
723 curated Jev resources. Act on an answer only when confidence clears a threshold, otherwise confirm or escalate.
By format
723- Docs35
- Repos & sites571
- Videos3
- Posts7
- Articles103
- Playgrounds4
Updated
All 723 Jev AI model resources for Decision gating
Search the full list, or filter by category, format, and tag.
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
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
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
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
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
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
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
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
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
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
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
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 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
Celeris's launch post for Celeris-1-decision, a hosted diffusion language model for typed decisions. It reports 80.0% on the full jev-bench, the lowest Brier scores of five decision APIs tested, a 67 ms median and $0.04 per million input tokens.
Jev Media & TalksArticle#community#press#competition
Community
AICrier's news-feed summary of Celeris-1 Decision: a multimodal diffusion model for bounded structured decisions that Celeris says reaches 80% on JevBench, 7 points above Jev 1.13.0, with 67 ms median latency.
Jev Media & TalksArticle#community#press#competition
Community
YourStory's introduction to Jev, the transformer-based model that returns structured decisions with probabilities instead of paragraphs or code, covering the September 15 stealth exit, the $40M DCVC-led seed, and TypeSafe's pricing claims.
Jev Media & TalksArticle#community#press#explainer
Community
AIMag.no's week-in-review of Jev's waitlist-to-open-access sprint, Vercel and LangChain integrations, and Kognitos and GPTBots.ai partnerships, contrasting TypeSafe's 193.6x speed claim with a roughly 7x user measurement and one prompt-injection test.
Jev Media & TalksArticle#community#press#security
Community
ThursdAI's September 17 episode covers TypeSafe's Jev launch with founding DevRel Allie Laabs: choice, score and null primitives, $42-per-billion input pricing, and a live X timeline classifier running at 12 tweets a second.
Jev Guides & ArticlesArticle#community#press#podcast
Community
A LoRA plus 24-slot decision head over Qwen3.8-27B from Ming Xu's train-jev project: it serves TypeSafe's System One API with 0.851 Kev transfer-v4 accuracy and ECE 0.053, and needs an 80 GB GPU.
Jev Models & Reimplementations#community#open-models#local
Community
A 24 GB-GPU alternative from the train-jev project: Qwen3.5-4B plus a rank-16 LoRA and 24-slot decision head scoring 0.647 PK accuracy, 40.4 Decision Index and 43 ms p50 latency, ahead of Kev-4B at the same size.
Jev Models & Reimplementations#community#open-models#local
Community
The original OSS Jev Qwen2.5-0.5B decision checkpoint, trained 2,000 steps at learning rate 2e-05; the 10k sibling card warns that this overwrite tip may be the worse noul-fix variant and recommends sized siblings.
Jev Models & Reimplementations#community#open-models#local
Community
prithivMLmods' GGUF builds of autotrust/JEV-35B, the Qwen3.5-35B-A3B MoE decision model that returns calibrated choice, noul and score answers over text and images; files run from BF16 to Q3_K_L for llama.cpp.
Jev Models & Reimplementations#community#open-models#local
Community
A CPU-first decision model from Decision Models that answers the seven questions an LLM auto-router needs (category, difficulty, stakes, four flags) in one ONNX pass; MIT weights plus a Jev-compatible hosted API.
Jev Models & Reimplementations#community#open-models#local
Community
A Laya fine-tune for coding decisions: roughly 9k rows from SWE-Gym teach keep/drop file relevance, priority scoring and injection detection, reporting 0.833 accuracy on 1,150 held-out rows at 126 ms p50.
Jev Models & Reimplementations#community#open-models#local
Community
A community Q8_0 GGUF of Mapika/decider-0.8b, the smallest System One typed-decision model: one forward pass returns calibrated Choice, Score and noul answers, reporting 0.405 on JevBench hard-public at 920 MB resident.
Jev Models & Reimplementations#community#open-models#local
Community
A Laya fine-tune from Convai Innovations on the 1,200-case typed-decisions benchmark: it reports 0.777 accuracy on the official 400-case test set, above TypeSafe Jev 1.13.0 at 0.727 and the teacher ceiling at 0.735.
Jev Models & Reimplementations#community#open-models#local
Community
A 4-bit AWQ plus GPTQ build of AutoTrust's JEV-35B from AI Outfitters: INT4 weights with FP8 and INT8 activations, calibrated on all 256 experts, and served by vLLM on a single 24 GB GPU.
Jev Models & Reimplementations#community#open-models#local
Community
A 4-bit W4A8 AWQ plus GPTQ build of AutoTrust's JEV-27B-VL from AI Outfitters that keeps the rank-32 LoRA decision head and a bf16 vision tower, served by vLLM on a single 24 GB GPU.
Jev Models & Reimplementations#community#open-models#local
Community
The berkay123001/turnike-jev-9b LoRA adapter merged into Qwen/Qwen3.5-9B in bf16, so the bilingual Turkish and English decision model can be served without PEFT through the karar.py Karar interface.
Jev Models & Reimplementations#community#open-models#local
Community
A 9B bilingual Turkish and English Jev-class decision model from Berkay Hasret: a 173 MB LoRA adapter on Qwen/Qwen3.5-9B that answers up to 16 typed questions about one text in a single forward pass and reports 0.783 macro accuracy on full JevBench.
Jev Models & Reimplementations#community#open-models#local
Community
TurinTech's independent GGUF conversion of the 27B Kev decision model for llama.cpp: the default IQ4_XS file is 13.91 GB and keeps 97.8% of the BF16 original's answers over 2,000 test decisions, with zero changed confident answers.
Jev Models & Reimplementations#community#open-models#local
Community
The berkay123001/turnike-jev-4b LoRA adapter merged into Qwen/Qwen3.5-4B in bf16 so it can be served without PEFT: a bilingual Turkish and English decision model whose parent card reports 0.774 JevBench macro accuracy.
Jev Models & Reimplementations#community#open-models#local
Community
A bilingual Turkish and English 4B Jev-class decision model from Berkay Hasret: a roughly 130 MB LoRA adapter on Qwen/Qwen3.5-4B that answers up to 16 typed questions per text in one pass and reports 0.774 JevBench macro accuracy.
Jev Models & Reimplementations#community#open-models#local
Community
A specialist full fine-tune of mistralai/Ministral-3-3B-Reasoning-2512 by rony for the LocalLLaMA/typed-decisions benchmark: it reads a case, question and lettered options and softmaxes next-token logits, reporting 0.808 accuracy and 0.0773 KL on all/test.
Jev Models & Reimplementations#community#open-models#local
Community
Singularity Compute's fine-tune of Google's diffusiongemma-26B-A4B-it, a discrete-diffusion mixture-of-experts, served from an 18 GB NVFP4 checkpoint that answers choice, noul and score questions with calibrated distributions on one 48 GB GPU.
Jev Models & Reimplementations#community#open-models#local
Community
A PPO-trained reinforcement-learning decision brain for the CONNECT TYCOON company simulator from CONNECT AI LAB: it runs staffing, inventory, marketing, refund and security judgments and answers in the JEV request format with probabilities.
Jev Models & Reimplementations#community#open-models#reinforcement-learning
Community
An 8-bit MLX conversion by SirSahOl of chaoliangUNSW/Jev-Style-0.8B-Decision-v3 built for Apple Silicon native GPU inference: about 990 MB of active VRAM, a 262,144-token context and an average of 8.25 bits per weight.
Jev Models & Reimplementations#community#open-models#local
Community
Patronus Studio's 151M-parameter Jev-like decision model for AI-agent security: given a serialized agent scene, a security question and answer options it returns calibrated probabilities in one pass, reporting 92.8 balanced accuracy on 10,576 held-out decisions.
Jev Models & Reimplementations#community#open-models#security
Community
Start here with the Jev AI model
Featured entries for decision gating.
AutoTrust's bf16 System 1 decision model on Qwen3.5-35B-A3B with about 3B active parameters: Decision Index 0.3 public 59.73, ERQA 51.3% in one pass with zero robot training, 66.5 GiB of weights.
Jev Models & Reimplementations#community#open-models#local
Community
The vision sibling: the same text-trained System 1 head on Qwen3.8-27B's image encoder, taking images zero-shot, ranked first of 20 vision decision models on the community Jev Decision Index 0.3.
Jev Models & Reimplementations#community#open-models#local
Community
Autonomous agent platform with 187,504 stars for building and running agents from plain English or a visual builder. Its platform features first-class TypeSafe Jev decision blocks for typed routing, filtering, scoring and confidence-gated next-action dispatching.
Jev Integrations & Adapters#community#autonomous-agents#platform
Community
Significant-Gravitas/AutoGPTPython
Significant-Gravitas/AutoGPTPythonJev AI model articles
Original guides that go deeper on Jev decision gating.
Voice agents need decisions in milliseconds. How turn detection, intent, and escalation work in real time, and where a fast decision layer fits.
Jev Practices & Patterns#voice#realtime#agents
Read article
Synthetic labels can train a decision model, but they can also bake in a teacher's errors. How to generate and audit them.
Jev Practices & Patterns#synthetic-data#training#distillation
Read article
Model deprecations and price changes are operational risks. How to pin versions, read contracts, and plan a migration before you need one.
Jev Guides & Articles#operations#vendor-risk#sla
Read article
From the Jev community
Watch walkthroughs, read the threads, and see what builders are posting.
YouTube: Gary Explains the catch: Jev understands natural language but answers with structured values and confidence.
YouTube: Sam Witteveen on System 1 thinking, then Choice, Score, and Noul demos, and chained actions.
YouTube: Nate Herk compares speed and cost against ordinary models, then builds an X feed classifier and a paper-trading prototype.
Hacker News: Spoken-language learning with video scenes branching on TypeSafe Jev.
j_rcht1 pointShow HN: Worklaude – learn by speaking; video scenes branch on TypeSafe JEVRead the thread on Hacker NewsHacker News: OpenAI's Luna Decisions API framed as a fast follow to TypeSafe's Jev wave.
matt_d6 pointsOpenAI Answers TypeSafe's Jev with a Decision API Built on LunaRead the thread on Hacker NewsHacker News: Nikola Balić on what made the Jev launch easy to try and easy to share.
nkko2 pointsWhat TypeSafe Got Right with the Jev LaunchRead the thread on Hacker NewsHacker News: Pointer to a DiffusionGemma plus vLLM tutorial for a Jev-shaped classifier.
1 pointHow to build a Jev-style classifier with DiffusionGemma and vLLMRead the thread on Hacker News