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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
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
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.
Tobi Lütke, Shopifyx.com · 19 September 2026
LCM for Large Choice Models, or what are we going to call the Jevs?
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
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
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 ☺️
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?