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This might be the craziest thing I’ve built with TypeSafe’s Jev yet. I wired it into Red Alert 2, and now it scouts with dogs, optimizes its economy and base placement, decides when to expand, attack, defend, or use superweapons—and somehow split-pushes tanks, micros its army during fights, and routes around enemy defenses. A lot of this behavior just emerged from Jev making decisions in real time

あんまJevでやる必要はないかもだけどおためし。飲食店の人流シミュレーション。トークン量多くなるとやっぱ1.5秒くらいかかるなあ...十分速いけど https://t.co/Zk2XDEMsX6

introducing Unclutter: a smart ad + slop blocker with Jev 🤓 it auto cleans up pages from slop elements: ⬖ ads ⬖ cookie banners ⬖ upsells ⬖ bs dialogs BYOK. open source + free, download below 👇 https://t.co/u3ippda11z

Just used Jev to edit videos and generate shorts. As a demo took this video from the amazing @Danieldalen and turn 26 min into 3 with smooth cuts and transitions plus shorts. All for $0.02, insane the amount of use cases for this https://t.co/FM79tB2U5d

Made Jev play against Fable and Astra in Tetris to showcase its usability Where it wins. Decisions that are frequent, bounded to a schema, answerable from the state you hand it, and where consistency beats depth Here are some examples - Gates and routers: should this alert fire, which pipeline gets this document, does this need the expensive model. The firehose gets judged in full instead of sampl

I used jev to make AI worms in my snake game They are not too bad! https://t.co/X7YTW8b7yj https://t.co/7IhrPCwyUz

I asked @typesafeai's new model Jev to play N Wordle boards at once! Each board hides a different word, but every guess applies to all unsolved boards simultaneously. Every move has to balance solving one board with revealing useful information across the others. Jev only needed 7 model calls across the entire game. Its shared guesses progressively narrowed the candidate set for every board until

🚨 Open Source Jev BS meter you can use this to analyze any debate / investor call / interview / sales pitch / podcast video fact check live , for example this dario interview cost 60 Jev calls / 111K tokens / $0.0047 https://t.co/3JYfoz8vgL https://t.co/oeXcGmZrVV

Using @typesafeai Jev to pull of details of the coffee beans from it's product page for the bazaar on @caffeineletter Jev is super fast, and costs ~1 cent for 20 coffees. Crazy https://t.co/syVzx3lvfJ

Jev + Kimi K3 for fraud detection! TLDR: Jev classified 100 emails in 1.42 seconds, then I routed the uncertain cases to Kimi K3. The full pipeline got 96/100 correct for only ~$0.07. Video is not sped up, check out the live run! Here was my process: I gave Jev 100 emails to classify (a mix of 50 legit & 50 fraudelent emails). It classified all of them in 1.42 seconds. An underrated feature about

I built a Jev harness to play Gmoku. How it works - instead of asking to evaluate all 225 moves, my harness does the tactical work first. Every turn it: • detects wins, blocks, forks & broken fours locally • shrinks 225 moves to ~40 candidates • ranks them into tactical tiers: S = forced win/block A = critical threats (open/broken fours, forks) B = strong attacking/defensive builds C = positional

Jevで音声対話のターンエンド判定やってみた。発話終了後0.5秒後にJevでターンエンドしたかどうかを判定して、スコアに応じて追加ホールド。 timeout=3.0とか出てるのが追加ホールド時間。Noneは追加なし。elapsed=0.224とか出てるのがJevの処理時間。 まだまだ追い込みがいるけど可能性を感じる https://t.co/Zg9sTgYAnv

Is your "for you" page overobsessed with a single topic like mine? I added Jev (@typesafeai @diogoalmeida) to my Tweet classifier chrome extension to find out! Previously I used Cerebras and the fastest LLMs i could find for the analysis. Jev is 6X faster and about 40X cheaper than the fastest/cheapest LLM I could find on this task! You can try it yourself here: https://t.co/cV9cXQuy4O

I just built Easy-Jev, a Jev demo anyone can try right now! Change the inputs and watch the classifications change in real time. I did my master's thesis on classification models so it's fun to see a modernized version of a very useful alg Typesafe are correct in that LLMs are just one approach to intelligence, and I think as time goes forward we’re going to see a hybrid of dif models splitting up

@typesafeai (and System One models in general) have a lot of uses, and fun to play with. New paradigm open to public now. This is a self-driving sim I spun up to test Jev today. Structured sensor state in, typed driving decisions out, steer, brake, overtake, pedestrians, speed limits. Prompt: https://t.co/KxMMNb9ME8

I built a little JEV bot to trade on @Kalshi across 15m & 1h #BTC, #ETH, and #SOL markets 🤖 Pretty fun building this while still putting that $5 credit to work instead of burning a hole in my wallet 😂 Built with @EffectTS_ + @cursor_ai, powered by @typesafeai https://t.co/6L4VyEGYUH

I quickly added Jev by typesafe to Rankhog. It can detect in less than 5-min opportunities to promote your product on Reddit or outrank Reddit posts already ranking on Google & ChatGPT for your keywords. Insane and so precise 🤯 https://t.co/ub2WTcqR77

Playing around with Jev and completely automated Minecraft in a couple hours. Token costs under $1 for hours of gameplay. What a time to be alive :) https://t.co/BJ7gn8T2DI

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