X posts

i used jev from typesafeai to build a model router cli you give in a task and current ai subscriptions you have, the jev tells you which agent/model should pick it up pair this with herdr and it will be a killer combo jev was the missing bit that makes this whole thing possible by being a super fast judge

one jev call on a polymarket question, 640 ms, and it hands back asset, direction and strike, each with a probability. ran it on 9,916 questions for $0.46 and it flagged 2,015 candidate misses in my regex parser. agents wrote most of it. #AIAgents #LLMEval https://t.co/h6NxFyAr3i

built a chrome extension that lets you filter the internet in plain english pick a post, tell it what you don’t want to see, and it filters similar content as you scroll. same rules across sites. powered by jev from @typesafeai . your feed, your rules. https://t.co/AWIxgqDc63

Gf approved the demo 🥳 Zero-shot robot tasks with the @typesafeai api Give it a goal in plain English. Jev chains a set of hardcoded primitives. The arm does the thing. No training. https://t.co/cTSJcVzJDf

Okay so Jev can actually do computer use really well Without any screenshots, or LLMs and no Pixels leave my mac I dont even read the Dom elements A local CoreML model segments every button and UI element on screen. On-device OCR reads the labels. That text is all Jev gets. It returns a probability across those elements and tells me the best one to click. Then it clicks, re-runs detection, and dec

Who said Jev can’t generate words? I gave Jev a 2048 word dictionary to have it work like an LLM by choosing the next best word. It ends up performing at 1.5 characters per second. Just need to benchmark it on ARC AGI next https://t.co/miCB90rBg6

Jev同士のぷよぷよ対決も作ってみました 落ちゲーのAI対決は一生見てられるな... 多分改善すればもう少し連鎖も作れる気がする 今の所５連鎖を観測できてない https://t.co/YSaLWnfCbi

Here is AXe using @typesafeai Jev with the instruction "Open Calendar app, go to Aug 16 2026, create a new event titled 'Cameron Birthday' and save." to control the iOS simulator. This cost only $0.006141. This is just an early prototype and I should be able to make this far more efficient too.

Announcing Jev Detector The world's fastest AI slop detector, built on jev from @typesafeai ~10,000 words scanned for slop in ~2 seconds Best part, its free, no sign up required, enjoy! https://t.co/8mlq6QpFrR https://t.co/glLFOIsPv5

I let @typesafeai's Jev play SameGame. No search, no lookahead. Each move, code writes down what every legal move does and Jev picks one. About a third of a second a move, a tenth of a cent a game. Watch it clear this board, then press ✨ Jev: https://t.co/p6s1oO3fqH https://t.co/PyfsUtLI2Q

Let me get in on the Jev hype train. Since I have no early access I tried recreating Jev's O(1) decision only architecture using a standard Causal LM to run a real-time Snake Game ~120ms per decision (8 moves/sec) with zero token generation https://t.co/jPRrIxIoyp

They say you’re six connections away from anyone on Earth. So I tried Jev from @typesafeai and built this: enter your X handle and someone you’d love to reach. It finds a chain of real follow connections that could get your message there, under 2 seconds! Apparently, Luke Skywalker is just two people away from me. Who’s your impossible person? I can drop the link below if you want to try. @MarkHam

I put Jev and GPT-6 Astra head-to-head in my new word game Yapello. Available here: https://t.co/di5kpYZEId Jev answered 10+x faster at a fraction of the price. Astra scored more. Both confidently submitted a word the game rejected. Final: Astra 105, Jev 72. Five decisions each: Jev 2.02s total API time; Astra 24.29s. API cost: Astra $0.32 · Jev <$0.01 (creator-provided billing figures). How this

视觉版 Jev 来了 https://t.co/x0kzeE3hdx 仿照着社区的思路，把候选结果映射成固定标签，prefill 完直接采样，限制输出 token，采用多模态的 qwen-3.5-0.8B 4bit 量化部署在本地，16g m4 mbp 顺利运行 叠甲：这只是在 infer 层小小的复现一下 jev 的形式，肯定不如各种成熟推理框架效率高，也肯定不是 jev 的正在原理，但是作为 inference 的小入门仍旧是不错的 demo 如下 200ms 实现《三色货品分拣》

Here’s a very alpha demo of a browser-use agent I hacked together today. It’s built around @typesafeai’s new Jev model, working alongside GPT 5.6 Luna for reasoning. A lot of the work is still being handled by Luna, and I need to figure out how to offload more of it onto Jev to get the iteration speed up. But it actually works. Right now, it’s basically a duct-taped Frankenstein of web search, Lun

TypeGPU + ruNNtime + Jev @typesafeai is a very fun combo :D ruNNtime gives me efficient local inference, TypeGPU lets inference and rendering share GPU resources directly with zero copy. That’s 3 separate NN inferences plus rendering, all happening in realtime Since we control the pipeline, Jev can just sit in the middle and add the semantic bit. camera + mic → Moonshine + YOLO26 + DepthART → Jev

Jev v1.13 dodges rockets with probability calculation 🚀 @typesafeai's new non-LLM model returns decisions instead of text so we had it calculate a safe tile every 330 ms while rockets fell and it survived 25 of 26 for under a cent Run Jev via API -> https://t.co/RbcCOIgVkj https://t.co/f2VmOEPFnO

hear me out… why are we sleeping on @typesafeai 's Jev for retrieval? tested 34 questions from QASPER. Jev went 17–3 against pgvector + OpenAI text-embedding-3-small, with 14 ties, on gold evidence coverage. small-to-medium doc RAG where accuracy matters might be Jev's playground

Built a bit of a proof of concept using @typesafeai jev. The most prosaic of enterprise AI use cases, a chat assistant for navigating your terrible mobile UI. Interprets your question and finds where you hid the menu option. No hallucinations. https://t.co/ylm3zycwKS

I got early access to Jev, so obviously I gave it $1,000 and let it play blackjack by itself. It made 243 hands in ~191 seconds, briefly reached $1,080, then ultimately depleted the $1,000 bankroll to $5. Turns out Jev is fast. Just not a very good gambler. @typesafeai https://t.co/NarPF9o3jB

I used @typesafeai's Jev to label spam comments on my YouTube channel. For the test, I picked 70 real comments, 12 of them spam. Jev missed 3. GPT-5.6 Luna missed 2. Jev: 25 seconds, $0.00114 GPT-5.6 Luna: 79 seconds, $0.00267 That makes Jev 3x as fast and about 2x cheaper. https://t.co/0BBpjKhlOY

2/ Cua Driver + Jev playing 2048. In this measured 2048 run, Jev was 5x faster and about 1,000x cheaper than Astra. That result is specific to this run, not a general claim about either system. Video attachment: 30 seconds, silent, 1920 x 1080. The final frame shows Jev at 44.9 seconds and about $0.00108 API-equivalent cost, versus Astra at 294.9 seconds and about $1.45. Demo by @injaneity

i made a @typesafeai jev+chatgpt instance race against a purely chatgpt instance on two super simple tasks to see the difference, 2-3x faster! :o on first-time tasks chatgpt will write a question set for jev on the fly, and then subsequent tasks get super fast https://t.co/gMBtV3ou3c

So I did a thing with Jev. Saw a few posts about training Jev to play video games. I figured, why not try to get Jev to play a song? Found a piano version of Bella Ciao. Put it through a piano transcription model. That gave 1,353 notes with their timing and loudness. The timing and loudness became the fixed level layout. The pitch itself was hidden from Jev. The task: At each note, Jev had to pick

I said I’d come back when I was done. I’m done. 😂 I took the Jev-powered experiment, rebuilt it with Jev + Grok Build, and turned it into parody. You can now use it to create parody versions of posts, comments, and basically any social interaction using the identity/context of an existing account. Search for an account → write your parody → generate it → share it. The whole point is to make fictio

Jev PoC on an iOS device remotely. @typesafeai great work, OCR is the bottleneck in speed. https://t.co/hwH4nTyJZu https://t.co/fGwQKh4SFq

Now we're cooking. Got @typesafeai's Jev + AXe controlling the iOS simulator now ultra fast at a fraction of the cost of using an LLM. This is game changing! https://t.co/JK5r23ySoI

Can Jev play Mario? Made this quick demo to check it, and from 10 tries it was able to get to the finish line only once! Most probably the issue is within the prompt that I am sending, as it is not being able to properly calculate the statistics from the information given. Will polish it further....lets see!

i used @typesafeai 's jev in a wine app i made, to check web search results. you ask where to buy a wine nearby. search finds "Steep Ridge Zinfandel" when you wanted "Ridge Zinfandel". Jev just answers: same wine, yes or no? 33 tricky ones. got all 33. little animation of it deciding 👇

I built a chat bot with jev, no LLM at all! Responses are instant, no hallucinations. I hooked it up to web search, wikipedia, weather, todoist and home assistant. Jev decides what tool to call and what args to use based on the prompt. Instant answers cite sources as well! https://t.co/owgeOwMFbZ

In my demo, Jev can play 2048 but makes some beginner mistakes without an advanced harness. It's so fun to watch https://t.co/HBqjIPNxqX

I built a trading agent with the all new @typesafeai Jev. You can try it here : https://t.co/e7bLjnCfTj. Just put your own API key and start playing or clone it from https://t.co/7nJeuB2H8t and test it locally. To my surprise the agent was able to take a trade of 2.405 $ETH and took a profit of roughly 0.23%. It is very low but testing a new model in town is genuinely crazy. @CompleteSkeptic did y

had to try something with jev so i tried what you'd expect from me: graph extraction - score every word with semantic significance (1-5) - also tag relevant words with ID - unique list of high scoring words - graph of high scoring words https://t.co/oJtI6py84H

some UI things this morning, character expressions driven by screen state and Jev (typesafe ai) https://t.co/LTzPvET9Fx

I gave Jev 3,282 of my X posts across 100M views and asked it to find what actually works for growth. 4,252,330 tokens $0.1282 for the full 8m 34s run! Each post got 8 questions about the topic, hook, tone, whether it teaches something, etc. How-to posts got 150 median likes vs the average median of 44. AI and coding was a 1.9x multiplier topic compared and SEO, despite recent posts, was right at

made a Jev-like classifier model + pathfinder to control my enemy AIs in my capture the flag minigame built in @threejs https://t.co/GkZujHExNG

Introducing jev board, short for jevin keyboard. Fast, reliable models unlock much subtler experiences for building ambient intelligence into interfaces. A small exploration at the edges of GenUI. With done @typesafeai and @CompleteSkeptic https://t.co/C5D6lojjGH

I made a toy CLI for talking to Jev npx @fernandoviac/judged https://t.co/cGTFFqj4xO

Jev for soccer ⚽️ Built a Chrome extension that watches live football with me. It scrapes the game page's commentary + stats, and every time something happens it sends Jev (@typesafe_ai) one batched request with 11 questions at once: who scores next, how it ends, is a goal/penalty/red card coming, who has momentum. Next step: adding an LLM on top to push the analysis further.

I gave an evolution simulation to Jev, a small model from @typesafeai that answers typed questions with odds instead of writing text. Two species with opposite DNA. 14 generations. An ice age. 432 creatures. Jev decided who survived, who mated, and what killed each one. https://t.co/Law8fyfy6d