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
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
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
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 @CompleteSkeptichttps://t.co/C5D6lojjGH
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
🧵 I gave Jev $10,000 and let it trade BTC again. But this time, I gave it everything a trader would look at: market data, derivatives, macro, on-chain data, news and sentiment. 30 days https://t.co/6STcUk4ml1
Added a live eval system to my webmcp native component library, which I have been using for generative ui and consulting. It evaluates the component as soon as it's generated and if it's not valid, regenerates instantly. Left side is a chat agent with Claude, right side is the Jev Eval loop, webmcp tool calls.
Jev-style "typed decisions" without training anything. On a frozen Qwen3-4B: turn each schema field into a lettered question and read the option letters' logits. No output tokens. Same accuracy as grammar-constrained JSON on closed enums. 4× faster on short inputs, up to 2.4× on long ones with a shared-prefix cache. Strings and numbers still need generation. Write-up, figures, teaching bench: http
2075X posts·Tools & appstime4× faster on short inputs, up to 2.4× on long ones with a shared-prefix cacheOriginal source ↗
Built a pumpfun grading bot with Jev! Every new launch, Jev grades its chance at graduating. If it's over 10% it buys. Starting with a $10k paper account. https://t.co/4t9Xa6pAaLhttps://t.co/gJ6kUdPnLD
First quick test of Jev from @typesafeai Install via @vercel gateway Sending evaluation (input validation passed): { "model": "typesafe-ai/jev", "state": { "character": "2" }, "questions": { "kind": { "type": "choice", "instructions": "Is the character a number or a letter?", "criteria": { "number": "The character is a single numeric digit from 0 through 9.", "letter": "The character is an upperca
a binary event contract's price IS a probability. 0.62 = the market says 62%. so i pointed a @typesafeai jev at @dreamDEXSomnia to disagree with the book. @Somnia_Network reactivity pushes every event, whole loop under a second. 70k events tracked in 10 sec with offchain websocket. 0.212 brier vs the book's 0.311. testnet. mainnet next.
I built real-time Clippy with Jev. It quietly watches how you use the product and only wakes up when it thinks you’re struggling. Hesitating? Confused? Stuck? Clippy knows. Even its reactions are controlled by Jev. 👀 https://t.co/EgMhw8FPi1
I build a SQL extension that turns plain english into a WHERE clause with jev @typesafeai WHERE jev(people, 'could work from home') Every row gets judged individually, no index and no embeddings needed. Try it out (don't burn my wallet pls) https://t.co/4nYZVH9iXs
Can Jev bluff itself? I spun up two Jev agents, gave them 1,000 chips each, hid their cards from each other and let them play heads-up poker until one went broke. 20 hands. 153 Jev decisions. Biggest pot: 1,760 chips. Both got caught bluffing. Red Jev took all 2,000. The entire match cost $0.02. @typesafeai
FFirst Coin by Jev - Solana & Robinhood@jevcoinxyz𝕏
Jev made a coin. Jev is TypeSafe's System One model. It answers in types, not prose. So its first coin is typed too. One schema, validated once, sent to two chains in the same request. $JEVCOIN Solana, on Pump CA: A66FqchgzB8PCv9smWYvDctB4tWBefoEMnCecLvfpump Robinhood Chain, on Pons v2 CA: 0x8C0B9EaE5a2aF9680968c4d46fB43266491681cC
open sourced jev-lint, the linter i run on my own markdown wiki. one run over 99 pages: 16 flags for me to read, about 2 cents in input tokens. it never edits anything. agents wrote most of it, i set the constraints. #AIAgents#Obsidianhttps://t.co/5mrgk9AT8R
Dowse is a terminal-native web browser + answer engine. Search the web, read pages, follow links and generate cited answers — without leaving your terminal. I’ve added @typesafeai@CompleteSkeptic Jev as an opt-in System One layer in the answer pipeline: Search → Jev → LLM Before generation, Jev makes fast, structured judgments over the retrieved sources — relevance, usable evidence and prompt-inj
Jev only outputs probabilities. So I gave it the Snake board as text and asked one question every frame: up, down, left, or right? No training, no game code inside the model. It played fine for a while, then boxed itself with nowhere left to go. https://t.co/e6lGyVTrlk
I taught Jev to paint! Jev predicts a probability for each color of each cell, then we visualize the probability distribution: high confidence = big, flat strokes; low confidence = fine brush to layer different possibilities https://t.co/L99f5txwha
I just got access to Jev from @typesafeai and implemented it into https://t.co/U9dQ9zMz2T to replace the AI classifier I was using to clean the newsletters users are forwarding to read on their reMarkable or Kindle. The results are amazing and the newsletter content is now beautifully formatted. #jev#typesafeai
Wow it’s fast 🤯 Here’s Jev, in real time, scoring guesses and categorizing questions inside a tiny “Hot or Cold” game I threw together https://t.co/tJ0nKVsMxJ
I used Jev to make an audience of 100 personalities to yap to. Each blob has its own personality and makes its own Jev call every time you talk to decide if it’s bored of you yet. Each round costs <$0.01 in credits. Really cool model from @typesafeai ! https://t.co/nGlKuxxfAAhttps://t.co/YgRdcS5pfs
Jev is fun! One-click invoice finder for any website 🧾 - Automatically finds billing pages using @typesafeai's Jev - List/download all invoices with 1 click - Works with Stripe billing portals too - Remembers where invoices live for next time Should I open-source it? https://t.co/0pZmbrG7a4
This is absolutely crazy. I connected @typesafeai to my Icy Tower game and I’m shocked by how fast Jev reacts. Jev picks the landings, the game handles the jumps, and a live inspector shows its choices, probabilities, and inputs. Super excited to see how this evolves! https://t.co/wPWS9HrqxP
Just got access to Jev and built the most requested feature in quickinbox with it. It can label incoming emails as spam or any custom labels you set. You can classify the old emails from before this too, and it's actually fast. Quick walkthrough: https://t.co/h727bKMcBE
長長田英幸 | AWS Community Builder AI Engineering@nagata_hideyuki𝕏
Evangelion's MAGI, but real. 3 AI sages (powered by Jev, TypeSafe's System One model) deliberate your question and vote. Typed, calibrated, no hallucinations. Built entirely with Kiro — even this video. 🎬 👉 https://t.co/vWgK5vwqCQ#Jev#Kiro#AWShttps://t.co/YuEBHxbVku