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First seen 3 September 2026
Hamel Husainx.com · 20 September 2026
Can you use Jev for Evals? Yes! Remember that a LLM Judge is also classifier*. Make sure to test your classifiers against human labels and don't overfit. Hope this helps! * https://t.co/dKYUiOnNDl https://t.co/8FhY6bQX7L
LangChain bloglangchain.com · 20 September 2026 · 2 posts
Today, agent evals come in two flavors: code-based and LLM-as-judge. Both have their own limitations: code-based evaluators can only be used for a narrow set of problems with set inputs, while LLM judges can be slow, expensive, and unreliable. With the popular release of TypeSafe AI’s Jev, we wanted to see whether the “System One” model might be a new third form of agent evaluator, and the impact it could have on agent engineering.What is Jev?Jev is a new model released by TypeSafe AI. Jev is actually not a traditional LLM; it doesn’t generate text. It’s what the TypeSafe AI team calls a “System One” model:📖 System One models are a class of AI models built to make fast, structured decisions that software can use directly. A System One model evaluates a state and returns typed answers and probabilities.How an autoregressive LLM and a System One Model (Jev) answer the same question.According to TypeSafe AI, this makes Jev faster and cheaper than LLMs, up to 200x faster inference and 400x lower cost than comparable LLMs on classification tasks.If you want to learn more about building agents with Jev, we're hosting a livestream with the TypeSafe AI
Andrew Chen, a16zx.com · 20 September 2026
Jev is going to change the prosumer/consumer AI landscape by unlocking a specific thing: Ad-supported + free AI native apps this is going to usher in a generation of new AI-native marketplaces, social networks, photo apps, messaging, calendars, email, collab tools, and much more. Why? Previously, if you wanted to have an AI-native app with multiple/fast LLM calls on every screen, the math just didn’t work - inference costs just couldn’t be paid back from throwing a few video ads or affiliate links. So there were really just two solutions: 1) be a massive company and subsidize AI costs 2) charge a subscription fee to cover inference (or both) So what happens when you take a different approach
Maggie Appleton, GitHub Nextx.com · 20 September 2026
Can we agree to call Jev a "Decision Model"? I feel like they buried this in the explainations and docs. Language models output language. Decision models output decisions. https://t.co/4IIHss00va
r/LLMDevsreddit.com · 19 September 2026 · 5 threads
I’ve spent the last couple of days going through open-source projects built around TypeSafe Jev. The list started with 14 projects. It’s now at **287**, and I’ve been checking the public source for each one to make sure Jev is actually integrated, not just mentioned in a README. After look
Justine Moore, a16zx.com · 19 September 2026 · 3 posts
The price & speed here are the difference between something that ~works~ as a consumer app vs. something that’s completely infeasible. Excited to see the apps that Jev unlocks. Also I’m kind of surprised it didn’t need a deeper reasoning model for this! But I’ll take it 😅
Jerry Liu, LlamaIndexx.com · 19 September 2026
with jev, it's nice to see builder energy back on X, reminiscent of the early days of building with LLMs (back when gpt_index/llama_index/langchain first started) before jev, most of the 2026 AI demo hype has revolved around the end-to-end capabilities of frontier models. The art of how to build something started taking a backseat to having agents automate as much work as possible. jev is a lego block. it puts the joy of building back in the hands of humans. it forces you to think about how to build something as a system, instead of one-shotting a prompt through astra/fable.
r/AI_Agentsreddit.com · 19 September 2026
I set Jev up as a "subconscious" filter for my coding agent to handle tiny checks before the main model sees anything. I wanted to share a couple things I've done with it so far. For terminal logs, I got Jev to check the output first. If a test passes, it collapses 80 lines of logs into a 1-line badge, and if it fails, it only passes the e
Tobi Lütke, Shopifyx.com · 19 September 2026
LCM for Large Choice Models, or what are we going to call the Jevs?
Lobstersdev.to · 19 September 2026 · not counted as a party
From our March 2025 arXiv paper on RL conversion trajectories to building a sub-40ms open-weight System 1 decision engine with RLCD. Everyone in AI right now is talking about a new kind of model: an architecture that is not auto-regressive, does not generate text, and gives lightning fast probability predictions over a structured JSON schema. Seeing the hype online feels both validating and deeply frustrating. I worked on this literally one year back in March 2025. I spent months of hard work, sweat, and sleepless nights building it, published an arXiv paper (arXiv:2503.23303), released the model weights on Hugging Face (sales-conversion-model-reinf-learning), published the open dataset (saas-sales-conversations), built a PyPi package, and posted the whole approach on Reddit (r/LocalLLaMA post). Then in September 2025, I published a second paper (arXiv:2510.01237), laying out the exact framework for schema-based decisions guided by reinforcement learning. For anyone curious, the guiding brain in my system was always reinforcement learning, not just an embedding model or an a
Hassan El Mghari, Together AIx.com · 18 September 2026 · 3 posts
Jev vs GLM 5.3 at chess! Results: ◾ GLM 5.3 won by checkmate in 29 moves ◾ Jev: ~0.3s and <$0.0001 per move ◾ GLM 5.3: ~5.8s and ~$0.008 per move ◾ The whole game cost 24 cents My main takeaway is that it's often useful to use each one to their strengths: ◾ Fast, well-defined classification → specialized models like Jev ◾ Classifications that need reasoning or lookahead → LLMs like GLM 5.3 ◾ Real classification pipelines → hybrid. Jev handles the easy calls, an open model handles the hard ones. The future is multi-model!
Tony Dinhx.com · 18 September 2026 · 3 posts
Jev is fast and cheap but is not very smart. No reasoning, no thinking. I don’t have benchmark but there are many questionable decisions in this Tetris play 😂 https://t.co/aGKMcKHqVD
Aaron Levie, Boxx.com · 18 September 2026 · 2 posts
Jev will be super helpful for agents to make split second decisions in workflows, data classification, judgment calls, and hundreds of other use-cases in the enterprise. Here's a quick demo with Box and Jev to make that real. The demo pulls an incident report from Box, asks whether it's customer-facing and how severe it is, moves the file into escalate, monitor, or review folders, and sets a metadata template instance with the result. This all happens nearly instantly and at almost no cost. You can imagine this in insurance claims, contract management, loan processing, security reviews, customer log analysis, and so on. Definitely a great new class of AI use-case.
Jared Palmer, VP Eng at Cognitionx.com · 18 September 2026 · 2 posts
Kev-0.5B: A tiny open source Jev-like decision model with a TypeSafe-compatible API based on Qwen2.5-0.5B that you can train and run on a MacBook Pro. Model card and weights are available on GitHub https://t.co/53wZQYhhmu https://t.co/6BXJQ8rSHt
Marc Köhlbruggex.com · 18 September 2026 · 2 posts
building a computer assistant with Jev local whisper listens to everything I say which then gets classified by Jev to determine what actions to take it uses a small Swift app to provide the full accessibility tree to Jev (i.e. tell its what's on my screen, what can be clicked, etc) still super early, but promising and all real-time which Jev was also local though. then it would be completely private
Packy McCormick, Not Boringx.com · 18 September 2026 · 2 posts
i can't believe they made a super fast cheap ai that can't write before they made a slow expensive one that can
Vercel changelogvercel.com · 18 September 2026 · 2 posts
Within 24 hours of launching on AI Gateway, Jev from TypeSafe AI reached more than twice as many paid teams as any previous model launch, making it the fastest-adopted model in gateway history.Jev passed every other comparison model in its first twelve hours and continued to widen its lead for the rest of the day. By hour 24, nearly 13% of paid teams were using it. That's 2x the GPT-5.6 family and more than 6x Fable 5.1's share.Jev’s launch shows how quickly a specialized model can find a place in production. Its first-day adoption was unmatched among recent launches; the next test is whether that early adoption lasts. Copy link to headingAbout JevJev was introduced on September 15 as a probabilistic decision model designed to support structured decision-making within software. An application sends it context and a set of questions. Jev evaluates those questions in parallel and returns typed choices, scores, or true-or-false answers, along with probabilities.Unlike the text produced by a general-purpose language model, Jev’s answers come in a format the code can use directly. Developers can use it to:choose an agent’s next tool
Arize blogarize.com · 18 September 2026
This week the AI community was in uproar about Jev from TypeSafe, not just a new model but a new kind of model: one that classifies, scores, and routes but cannot write a sentence. The reason for the fuss is simple: it’s radically faster and cheaper than using an LLM to perform the same task, up to 200x faster and 400x cheaper if TypeSafe’s numbers are to be trusted. In one small independent test, a general-purpose model spent about 910 output tokens reasoning its way to each yes-or-no answer while Jev spent 85, and it doesn’t even bill for them. That’s potentially a really big deal. An enormous share of LLM-powered components in AI applications today are being asked to make decisions: pass or fail, route A or route B, which of 5 labels to pick. In particular, that’s something that LLM-as-a-judge evaluations are doing all the time, so it really made our ears perk up at Arize AI. This post is about how we got here, what this new kind of model buys you, what you lose, and what choices you should be making about your application’s architecture as a result. What is TypeSafe’s Jev, and how does it work? Jev is TypeSafe AI’s first S
Bilawal Sidhu, spatial/world modelsx.com · 18 September 2026
Jev feels like the leap that only happens when you push towards efficiency over extravagance. And yet when you use the damn thing it feels so magical - like doing a google search for the first time after using excite and altavista forever.
Guillermo Rauch, Vercel CEOx.com · 18 September 2026
The data and the anecdata on Jev's adoption are shocking. Everyone is adopting it. I think it's a great product, but this is also downstream of the "AI is too expensive/slow" zeitgeist. People are eager to optimize and put AI in even more places! https://t.co/tK86PNGgNs
Jon Yongfookx.com · 18 September 2026
First Jev use case published live on Bannerbear! Instant field mapping between template and source, when names are slightly different eg template - photo - name - company_name data source (eg Airtable) - avatar - full_name - business Jev figures it out in one click. https://t.co/2fxRQJ2qEh
Nikunj Kothari, FPVx.com · 18 September 2026
Jev scoring 3000 kid snacks with multiple criteria on https://t.co/KSiodPdZHT in 28 seconds for $0.11 🤯 https://t.co/c9O6uTyiUK
TechCrunch startupstechcrunch.com · 18 September 2026
ChatGPT broke Diogo Almeida’s heart. Almeida was an OpenAI researcher who helped build the chatbot and then invent reinforcement learning from human feedback (RLHF), the model-training technique perhaps most responsible for our current age of AI. But despite its capabilities, he was disappointed. “We have lightning in a bottle, and yet it is not useful,” Almeida told TechCrunch. “I’ve been battling that problem since then. It took me a while to come to the conclusion: The problem is we are optimizing for human language … We have been super good at human language for four years, but it’s not useful for automation because computers speak a different language.” Two years ago, Almeida left OpenAI to start TypeSafe AI, a startup trying to fix that problem. This week, the company released a new transformer-based model, Jev, that is not a large language model (LLM). It doesn’t output text, but instead produces probabilities, or what the company calls “calibrated decisions.” Eschewing language does a few things: It makes the model incredibly cheap and fast, and because users define the outputs in advance, it
Shreya Shankar, incoming CMUx.com · 17 September 2026
Yes! AI functions are popular in databases because no one wants to do the MLOps work of training their own classifier ☺️
minimallysufficient.comminimallysufficient.com · 17 September 2026 · not counted as a party
LLM Classification Is Feature Engineering
Amjad Masad, Replitx.com · 16 September 2026
This is cool, but if your output domain is known in advance, why not just train a model to produce logprobs over enums?
Hugging Face bloghuggingface.co · 3 September 2026
This guide is a fully public, inexpensive recipe for making a small model substantially better at structured-output compliance. We fine-tune LFM2.5-350M with Group Relative Policy Optimization (GRPO) using the TRL library and evaluate it on the IFStruct benchmark. The full run takes around 500 samples and 100 training steps, small enough for a free-tier Colab or Kaggle GPU, and is available on GitHub. The results show that even a light fine-tuning procedure improves performance from 22.6% to 29.7% on the IFStruct benchmark. Structured output is one of the most common real-world tasks for LLMs, yet most benchmarks fold it into broader reasoning or extraction scores rather than measuring it on its own. Whether a model reliably returns valid, parseable output in the requested format and shape — schema compliance — is often what decides whether it can be wired into a downstream system at all. Note that the training pipeline described here is not the one used to train the RL model described in the IFStruct blog. This notebook doesn't aim to recreate the IFStruct benchmark score, but to show how task-specific fine-t