you also get to meet @hmartenjoyer which is probably the best part

One of my favorite parts of Jev blowing tf up is that everywhere we imagined it'd be an absolutely perfect fit, like with @n8n_io , it's been received tremendously well. Making individual components of code smart, while keeping them composable, is absolutely at the core of our design principles!

This office hours recording from @DSPyOSS is a gold mine of excellent Jev use-cases and explainers!

Context is now spending 1k/day on jev We may spend more on jev than any other company in the world Pretty cool

Favorite resources for good JX? Jeveloper eXperience of course.

Took a while, but here it is, a mega write-up on language models for text classification and, of course, Jev. It's basically a visual guide to RNNs, CNNs, transformers, and calibration, with hands-on experiments on accuracy and efficiency.

It’s easy to dismiss Jev as “just a classifier”. @rasbt initially thought he could build something similar himself, then found it worked better than he expected. What changed his mind? He’s put together a visual guide to text classification, from bag-of-words through RNNs, CNNs and transformers to Jev-like APIs and calibration, with hands-on experiments comparing accuracy and efficiency. One thing I really love about Sebastian is how committed he is to helping people understand all this wild technology we’re in the midst of, and how much care he puts into explaining it visually as well. Super cool stuff. Check it out! magazine.sebastianraschka.co… Figure: Sebastian Raschka.

this demo of codemode + jev in pi beautifully demonstrate both the power of codemode and general classification models well done w/ this example @badlogicgames, @mitsuhiko, and the rest of your team

Jev is a phenomenal reranker. At 75% of the previous reranking cost, it improved our main-app search by 30.64% in matches per 200 candidates on searches recruiters actually run. Most recruiting benchmarks measure precision and accuracy across just 10 candidates. That isn’t representative of real recruiting and sourcing workflows, where you may need to source hundreds of candidates to make a hire. We’ll release the full benchmark soon. We're excited to work with @hmartenjoyer, @mathfax , and the rest of the @typesafeai team to continue to bring Jev to more Wrangle features. Stay tuned 👀

The last time I launched on @ProductHunt , coding agents and of course Jev didn’t exist! Hypership looks like such a dream for tech nerds in the modern era.

begun, the clone war has jk, I love openai and think more competition and validation is great for developers! (assuming the model is good - plz make it good!) hopefully this is a sign for the future that building in a system one compatible way is the future

It's been almost 2 weeks since we've launched! And due to overwhelmingly popular request, we're making the datasets released with evals.typesafe.ai easier to work with! We do this in the spirit of openness, but I am still anti-public benchmarks. In that spirit, we deprecate all datasets we evaluate on (internally or publicly). (1/3)

TypeSafe AI's Diogo Almeida with a16z's Ben Horowitz and Martin Casado on Jev, the model built to live inside software: Diogo's elevator pitch for Jev is a simple question - where is all the automation? AI is unbelievably smart, but outside of chatbots and coding agents, it hardly touches any real work. His diagnosis is the industry built models that generate text for humans to read, and software can't consume that output. Jev reads natural language and returns a choice from a set of options with a confidence level assigned to each, so developers can build programs that reason about intent and make probabilistic decisions rather than relying on human interpretation. TypeSafe's philosophy is "We build prod, not God." 0:50 "Where the f**k is all the automation?" 2:50 Jev vs. Claude Code and Codex 6:55 Jev is a classifier and classifiers are sick 7:40 Chat vs. code: is Jev a slider? 9:00 Diogo: From mathlete to Kaggle to OpenAI 12:20 "We build prod, not God" 15:55 Reliability over demos 16:55 2021 thoughts: RLHF is AGI? 20:45 Optimizing for the wrong use case 21:50 Is the real world too messy to automate? 25:00 Nobody expected the Jev launch 26:35 Three kinds of reliability 28:05 Good at syntax, bad at architecture 30:00 The inverse SaaSpocalypse 33:40 Why coding agents automate so little 36:05 Probabilistic programming returns 38:45 Jev as the UDP-to-TCP layer for AI 40:20 The 5 stages of grief for embedding AI 41:30 Utopia: AI that actually does what you mean YouTube: piped.video/Ut3LOjKNJaE @CompleteSkeptic @typesafeai @bhorowitz @martin_casado

good news: ANYONE CAN SIGN UP 💪 bad news: we had to temporarily disable the free credits (just for new users) - we really want to figure out how to get people playing with jev, but a few bad actors were making it a bad time for everyone 😢

remember this old post of mine? Tried it with Jev---it seems to have a grasp of the globe on par with some of the best models a year ago due to the architecture & low cost, it was also feasible to extract a labeled map of continents and countries. lots of interesting details

Jev is a bona fide yapper 🥺

This is tracking to be the #1 podcast we've released in all of 2026. We asked Jev's creator @CompleteSkeptic to explain Jev, and why he does NOT call it a "Decision Model" — why it's called Jev available wherever the finest podcasts are sold! reviews help us a lot to keep us relevant in the rankings 🙏

It's picking up happiness. Start screeching incoherently.

The full Runtime agenda is live, including talks from: @ScottWu46, Co-founder & CEO @cognition @CompleteSkeptic, Co-founder & CEO @typesafeai @dylan522p, Founder & CEO @SemiAnalysis_ @sarahookr, Co-founder & CEO @adaptionlabs @ajratner, Co-founder & CEO @SnorkelAI