Jev directory
Jev for Classification
2296 curated Jev resources. Assign a label or category to text or records, from intent and sentiment to topic and spam.
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All 2296 Jev AI model resources for Classification
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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
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
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
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
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
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
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
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
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
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
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
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
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
An episode of The Merge (by CodeRabbit) recorded at the first Jev hackathon 11 days after launch; TypeSafe DevRel Allie Laabs explains how Jev replaces prompt-parse cycles with typed choice, score and yes/no outputs.
Jev Guides & ArticlesArticle#community#press#podcast
Community
John Lindquist joins Claire Vo to walk through eight Jev use cases, from a real-time voice to-do app to data deduplication with confidence scores, on the episode billed as Jev week at How I AI.
Jev Guides & ArticlesArticle#community#press#podcast
Community
A pointer-network decision model from Keyush Nisar that reads one agent state document plus typed questions and returns calibrated tool-routing probabilities in a single forward pass, reporting 89.00% zero-shot tool routing.
Jev Models & Reimplementations#community#open-models#local
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
A DistilBERT prototype from akumar0205 that sorts historical email into legitimate and spam, reporting 0.987 accuracy and 0.983 spam F1 on a held-out SpamAssassin split; it is not a phishing or malware detector.
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
A small distilled sibling of Perplexity's pplx-decider-v1.2: a Qwen3.5-4B decider that reads a state plus typed choice, noul and score questions in one pass, keeping most of the 27B model's Decision Index.
Jev Models & Reimplementations#community#open-models#multimodal
Community
A 0.6B classification foundation model from the SocialJev project on Qwen3-0.6B: define a task and its answer options, and it classifies social-science documents through a typed interface that returns a choice plus probabilities.
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
mradermacher's static GGUF quants of Omartificial-Intelligence-Space/ara-jev-Faisal, an Arabic intent, policy and routing classifier; Q4_K_S and Q4_K_M are marked fast and recommended, under a non-commercial CC-BY-NC-4.0 licence.
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
Perplexity's 27B decision model rebuilt on Qwen3.8-27B with a joint request format that answers a whole request's questions in one sequence, reporting 68.09 on Decision Index 0.3 and 77.43 on vision.
Jev Models & Reimplementations#community#open-models#multimodal
Community
An MLX float16 package of jaredpalmer/kev-4b for Apple silicon: the backbone with its adapter merged, a float32 pointer head, the tokenizer, and a package.json carrying per-file SHA-256 digests.
Jev Models & Reimplementations#community#open-models#local
Community
A calibrated image-capable 27B decision model that fits an 8 GB GPU: the JEV LoRA-and-head recipe trained on Prism ML's ternary Bonsai 2, returning typed probabilities with no generated text.
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
WorkMagic's self-distilled churn decision model for Slack customer conversations: a LoRA on Qwen/Qwen3-8B trained with no human labels that returns churn-likelihood option probabilities through the AnyJev Tacit interface.
Jev Models & Reimplementations#community#open-models#local
Community
Start here with the Jev AI model
Featured entries for classification.
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
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 Hugging Face hub for Laya's open System 1 checkpoints: the 421M English model at the repo root, a 322M multilingual build in a subfolder, and the typed-decisions fine-tune, all Apache-2.0.
Jev Models & Reimplementations#community#open-models#local
Community
Jev AI model articles
Original guides that go deeper on Jev classification.
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: 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.
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 News