Updated Sep 26, 2026
Projects, posts and guides about Jev, the System One model from TypeSafe AI. Each entry links to its source and shows the cost and speed its author reported.
Diogo Almeida
@CompleteSkeptic
After co-inventing ChatGPT, I kept asking myself: why have superhuman chat models not led to AGI? I’ve spent the last 2 years in stealth building a new way to train models (RLCD), and a new type of frontier AI model that we are releasing today: Jev • 20-200x faster • 40-400x cheaper (w/ output tokens free) • Frontier composable intelligence optimized for decisions AFAICT the shortest path to AI-based economic revolution
Most voted first, 112 of 901. All 901 in curated order
Marcel Pociot 🧪
@marcelpociot
Jev unlocks SO many awesome new ideas. I built a macOS app that monitors my Downloads folder along with a customisable set of rules. Is the downloaded file an invoice? Move it to a special folder with the correct filename. No other LLM calls involved - just Jev!
XTools and apps
Riley Brown
@rileybrown
Yeah Jev by @typesafeai is very cool. It classified 500 emails in seconds. And it costed 3.5 cents.
Matthew Berman
@TheMattBerman
jev is INSANE. in 40 seconds it broke down 724 live ads from 37 brands. every hook. every format. offer. cta. awareness stage. landing page mismatch. used 9 cents of tokens. (will be avail in @stealads + mcp)
Sponsored
One card in the feed, every sixteen, on every page of it. $100 a week, cancel any week.
Our guidemadewithjev.com
Our guide to the term: split an agent into an LLM that writes, Jev that decides and code that acts, with the rules the builds on this site have in common.
Gregor Zunic
@gregpr07
Breaking: Browser Use + Jev = Ultrafast ⚡ Findings flights took 7s and cost only $0.0039 🤯 > new action space every step > DOM state space > small LLM fallback to type (this video is at 1x speed btw) Built a tiny open source browser agent. try it below ↓
Marcel Pociot 🧪
@marcelpociot
I built a browser extension with Jev @typesafeai that can hide/collapse posts on X based on natural language. It's so fast that it's not noticeable and insanely cheap...this must be the future of "ad blockers" and content firewalls.
XSocial feeds
Tony Dinh
@tdinh_me
Just trying out Jev, I made a Chrome extension that: - Listens to your YouTube audio (optional) - Detects if it gets to a sponsor segment - Skips it ➡️➡️➡️ - All in real-time while costing ~$0.005 per video Prototype project, BYOK, open-source: github.com/trungdq88/yout…
Guideyoutube.com/@CalebWritesCode
Caleb Writes Code: what Jev is and how to use it, in seven minutes.
Justine Moore
@venturetwins
Jev can serve as better natural language search on websites. It can scan thousands of Zillow listings and classify properties by things you can't normally filter for - e.g. architecture, renovation status, proximity to freeways. This was done in <20 sec and costs $0.18 👇
Nailthy Tang
@nailthy62
jev is insane 🫣 it makes realtime virtual try-on hauls possible. built this experiment for Drape with @typesafeai > i talk > jev reads transcript + what i'm wearing > picks from my closet > changes my outfit in realtime cost: $0.0011 per decision time: ~620ms per decision imagine getting ready like this:
Ronak Malde
@rronak_
Jev + Astra beats the Ender Dragon in Minecraft in 8 minutes 43 seconds! ⏱️ Cost less than $1 ($0.01 Jev, $0.96 Astra) I open sourced the code and explain the harness setup below. This type of movement is only possible with Jev's near instant decisionmaking, and some continually learning skills from Astra.
Oursshippile.com
Guideyoutube.com/@GregIsenberg
Greg Isenberg and Ryan Vogel on the Startup Ideas Pod: what Jev is, the businesses it unlocks, and how to call it today through the Vercel AI Gateway.
Romàn
@romanbuildsaas
JEV is INSANE. We gave it 700 high-intent leads and personalised outreach messages. In 40 seconds, it predicted how each message would perform, assigned a confidence score and detected lead-message mismatches. All for just $0.09. JEV can also score leads, analyse buying signals, match each prospect with the best message and identify the campaigns most likely to perform based on data. Coming soon to @GojiberryAI+ MCP. Comment “JEV” for early access.
Moritz Kremb
@moritzkremb
I built a Sales Copilot using Jev → Listens to sales call live → Tells you what to say next → Helps you follow the script and handle objections → Shows you what stage of the call you're in → Gives you live signals and probability of closing Demo below on a recorded sales call:
XSales and leads
Faadil Shaik
@faadilhshaik
got @typesafeai’s new model Jev to play Super Mario Bros. fast inference + structured outputs makes it surprisingly good for real time use cases. I'm excited to see what can be done with these new models!
XGames and real time
Our guidemadewithjev.com
Our explainer: what a System One model is, the three question types, what it costs, what it cannot do, and the builds that show it working.
Hassan
@nutlope
I used Jev to classify 1,018 AI research papers. The result: $0.08 total cost and 256ms median end-to-end latency per paper. The pipeline was: 1. Summarize each paper with DeepSeek V4 Flash 2. Send the title + summary + 24 possible topics to Jev 3. Use Jev to classify each paper 4. Visualize everything on http://1kpapers.com The summaries cost $3.99 on @togethercompute. The classifications cost $0.08 on @typesafeai. So for just over $4 of inference, I ended up with a pretty useful way to explore the top AI research papers from the past year. I think this is where things are heading: different models for different parts of the workflow, instead of using one model for everything. I’m running evals on the Jev classifications before replacing the current ones, but the site is already live: http://1kpapers.com
GitHubAgents and browsers
PickBrowser Use’s browser agent, with Jev choosing each next step.
Browser Use
17.3k
Kyle Jeong
@kylejeong
we built blazing fast computer/browser use with Jev + @Stagehanddev. this task cost $0.001 and executed at near instant speed (in a remote browser btw) the loop: observe the page, send a11y tree as state + actions as questions, Jev decides the next action, then Stagehand executes it.
Matt Canham
@matthewcanham
I've had many people reach out saying they've read everything they can about Jev but still don't get it. Here's an explanation for the normies
Guidex.com
Matt Canham’s X article, written for everyone who read the launch posts and still did not get it.
SkillAgents and browsers
The official skill for building with Jev, installable with one command.
TypeSafe AI
1.7k
Takayuki Fukuda
@hedachi
Jevで𝕏をかわいくしてみた
XUI
Pick
Ian Nuttall
@iannuttall
I built a free internal linking tool using @typesafeai Jev for classifying and selecting the links. BYOK or pay $1 to use mine. It works for up to 500 pages and gives you a CSV or JSON to pass to an LLM to implement. ian.is/tools/internal…
Guidegithub.com
The companion list to this site: use cases, projects, SDKs, tools and learning resources, with pricing, limits and a quick start.
Desert Ant Labs
@desertantlabs
Jev + on-device models = results in seconds with no LLM in the loop. Quick demo app to show the possibilities. Drop in an audio file: Ear detects the language, Voz transcribes it and Redact removes PII. Then @typesafeai's Jev makes about 20 decisions in one call in milliseconds, and picks which of our on-device models to run. Voice memo to to-do list. Meeting to redacted transcript. Podcast to clips.
Meet Shukla
@meetshukla_
i’m probably going to regret this. 10,000+ videos were imported by our users into Ghostfeed in last 6 months. this weekend, we picked 1,066 reaction videos and analysed all of them with JEV + GEMINI. now you can search by structure, reaction, and who’s on camera. opening the library today which has been our months of data gathering, clone all videos to mass publish them on instagram, tiktok and youtube. comment "reactions" if you want the access to the library.
Gustav Ekerot
@designgurra
more magical UI with JEV: auto-tagging
GREG ISENBERG
@gregisenberg
Jev is HERE and this is the CLEAREST explanation of what it is and what NEW businesses it unlocks. (and at the end I'll tell you how to get Jev even if you're on the waitlist) WHAT IT IS You know how you open your inbox and have to decide what's junk, what needs a reply, and what can wait? Jev does that part. It looks at each thing and says "this is junk, I'm 94% sure." It doesn't write anything back to you. It just sorts. 1,700 emails for 18 cents, instantly. That sounds kinda trivial but the important part WHAT IT UNLOCKS My explanation of Jev sounds small until you realize HOW MANY jobs are exactly this. Someone reading a stack of applications. Someone deciding which support ticket goes to which team. Someone looking at inbound and deciding who's worth calling back. A few ideas on what it unlocks: 1/ Instant quotes that are actually instant. Every quote form on the internet says "we'll email you by end of day." Build the version that answers in under a second, for roofers, movers, insurance, legal intake. 2/ Lead scoring as a product. Every agency and service business has a contact form full of junk. Score every submission and send the real ones straight to the owner's phone. 3/ Support triage for companies with no support team. The ticket gets classified and routed before anyone opens it. 4/ Clipping tools. Pass in a transcript, get the best moments scored in three seconds. Every clipping product just got a cheaper engine. 5/ Application piles. Grants, permits, insurance claims, job apps, loan docs. Someone reads that stack one item at a time today. 6/ Marketplace matching. Someone types what they need and gets matched to the right local business instantly instead of waiting for callbacks. 7/ Browser agents that actually move FAST. That makes bulk browser work practical: pulling quotes from five carriers, filing the same form for 200 clients, checking supplier inventory in real time etc. TLDR; find an expensive queue and put Jev at the front of it. HOW TO GET IT I didn't realize you can skip the waitlist because Jev is live on the Vercel AI Gateway right now, so you can start calling it today. In this episode, we share how. Episode now live on @startupideaspod (thanks to @ryanvogel for coming on and spilling the sauce today) Watch: https://www.youtube.com/watch?v=4mTLpuQpB80 Jev is a big deal because this is a whole new way to do AI Really cool Happy Jev day.
Guidex.com
Greg Isenberg: find an expensive queue and put Jev at the front of it. Seven ideas, from instant quotes to lead scoring, plus a Startup Ideas Pod episode with Ryan Vogel.
Justin Schroeder
@jpschroeder
I rebuilt Tesla Full Self Driving with Jev in less than an hour. This model is a total unlock.
Rob Hallam
@robj3d3
Jev + SuperX = virality solved ✅ Every post gets 61 questions in ~1s for $0.0004 🤯 > fitted on 9,481 real posts from 207 creators > picks the viral post 2 in 3 times > never rewards reply bait So: write, score, rewrite, stop when it peaks. Free, no signup. try it below ↓
Jon Kraayenbrink
@kraayenJon
jev is INSANE. in 243 ms it checked a website for 35 tells of ai slop. purple gradients. emoji headers. "seamlessly". fake testimonials. bento grids. the works. used $0.00015 of tokens. paste any url, get a slop score. free: madewithjev.com/free-tools/ai-…
Ian Nuttall
@iannuttall
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 base median 1.0x - surprisingly. The recommended topic + angle + voice formula was: AI coding + teach something + provocative
Jon Kraayenbrink
@kraayenJon
Jev repos blowing up right now. I made a list of the repos people are using to build with jev. 👇
Riley Brown
@rileybrown
Just created this with Jev by @typesafeai. A live viral post analyzer. As soon as you stop typing for .5 seconds it analyzes the viral potential. Going to try and actually make this good, will need to scrape a lot of twitter data... Notice how it also categorizes the tweet live... I could have it surface similar tweets on the right side for inspiration... idk just experimenting.
XSocial feeds
Guidetechcrunch.com
TechCrunch on Jev: why developers see a cheaper and faster path to software intelligence.
Diogo Almeida
@CompleteSkeptic
We love how this doomo doomonstrates real-time intelligence and what can be doone with code + AI! ~10 calls/sec = ~$7/hour
Jarrod Watts
@jarrodwatts
I built a trading bot with Jev! Jev decides if it should "buy" or "sell", given the price feed of an asset pair, and executes real trades. It uses Monad to place the orders on Kuru's on-chain order book in every 300ms block. Demo link → jev-trader.vercel.app
tamara
@tamarajtran
found the perfect use case for @typesafeai Jev: instant compaction in 2026, why is compaction still a summarization prompt? Jev can make it instant by scoring every tool call and dropping what’s irrelevant
XContext and memory
PickGuideyoutube.com/@nateherk
Nate Herk compares speed and cost against ordinary models, then builds an X feed classifier and a real-time paper-trading prototype live.
Hassan
@nutlope
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 Jev is it will give you the confidence score for a classification, so I routed any prediction under 95% confidence to Kimi K3 to be fully sure. 31 emails fell below that threshold. After routing those to Kimi K3, the combined pipeline reached 96% accuracy. The full run took 16 seconds & ~$0.07 in inference costs: - $0.068 from Kimi K3 on @togethercompute - $0.003 (1/3 of a cent) from Jev on @typesafeai. I think this is a really interesting pattern: use a fast specialized model like Jev for the narrow task, then route the uncertain cases to a larger LLM. I feel like this kind of approach could be a game changer for use cases like fraud or anything realtime. You can use the speed & low cost of Jev while having a larger LLM as a fallback to ensure high accuracy.
XSecurity and abuse
Pick
Zachi
@iam_zachi
I think I just cooked something 🔥 jev(): a PostgreSQL extension that searches your whole database in natural language. No index, no embeddings, just one function. WHERE jev(people, 'could work from home') or WHERE jev(people, 'name sounds european') 129 rows judged in ~1s for $0.0009. Second run: 6ms from cache.
Milind S
@milindlabs
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 decides again. In a loop until the goal is done. ~90ms per decision. Faster than any LLM computer use I've tried. Blazing fast computer use, without any latency @typesafeai is building something really interesting
XAgents and browsers
Guideyoutube.com/@DavidOndrej
David Ondrej explains how to use Jev, the architecture behind it, and how to build a business on it.
Milind S
@milindlabs
got @typesafeai's new model Jev as a chief of staff for bots Jev reads the task, wakes the right teammates off the bench and gives each one the right model It is possible on OpenMausBot as it supports all the LLMs from your existing subscriptions Jev as a decision engine is great
XRouting and model choice
Max Blade
@_MaxBlade
jev is insane 🤯 Here is Jev playing subway surfers at super human speed, and also playing 50 games at once. cost less than a cent to do this run. Jev does not replace llms like astra or fable, but opens up an entirely new world of capabilities.
Mau Baron
@maubaron
jev is insane 🤯 here is jev playing smash bros against itself he is controlling all 4 different characters. and literally deciding whats the best move to play against itself all within a fraction of a second i used over 22 million tokens to play this match and it only cost me a couple of cents... jev does not replace gpt6 astra but the possibilities with its instant response time are endless
Guideyoutube.com/@RoboNuggets
RoboNuggets: how to put Jev to work inside Claude Code.
Zachi
@iam_zachi
I build an undetectable realtime adblocker extension with typesafe It checks every dom element and classifies as ad/non-ad and removes it if true Extremely fun to work with, expecting an incredible shift in how AI is being used in the future
XTools and apps
Farouq Aldori
@FarouqAldori
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?
XEcommerce
Hugo Duprez
@HugoDuprez
Jev can generate game levels in real time. Faster and cheaper structured output could be a big deal for game dev!
XGames and real time
Guideyoutube.com/@Raul-Marin-Calleja
Raúl Marín, in Spanish: what Jev decides against what an LLM writes, then closed judgments applied to a design system — variant choice, email classification next to GPT, fast UI rendering and screens generated from prompts. 21 minutes, with chapters.
Raihan Khan
@raihankhan_rk
I got access to Jev by @typesafeai today morning and I built a cool use case for it Introducing DiffJury - simply paste any public PR link and Jev tells you immediately if it's safe to merge or does it require review ✅ 🔗 Feel free to try it out here - http://diffjury.up.railway.app Imagine Jev being able to tell you if you should merge a PR with grounded context of your codebase. that's what we're building at @graphify 👀 It's fascinating how insanely fast Jev is... the model architecture in itself is quite interesting and this has opened up a plethora of new use cases and I'm sure the internet will pick up on it sooner than anyone'd expect
Live siteCoding and code review
Guideyoutube.com/@LatentSpacePod
Latent Space interviews TypeSafe’s Diogo Almeida on what System One models are for.
GitHubOpen source
A tiny Jev-like model on Qwen2.5-0.5B that trains and runs on a MacBook.
Jared Palmer
3k~160 ms
Marcus Lowe
@marcus_lowe
what if copy/paste was smart? powered by @typesafeai jev it feels like every computer interaction will get rewritten
XTools and apps
Utkarsh Upadhyay ⚒️
@utk2103
just shipped jev-studio v0.2.0 @typesafeai Jev in one pip install - MCP tools for Choice / Noul / Score - `jev` CLI now with dry-run provenance - ready-made prompt libraries + slash commands for every cookbook - Claude Code + Codex plugin manifests pip install jev-studio
XSDKs and integrations
Our guidemadewithjev.com
Every guide on this site in one page, grouped by the question it answers: what Jev is, what a decision costs, how to put it inside an agent, and what it is already being used for across the 729 builds catalogued here.
Muskan Paliwal
@PaliwalMuskan19
built this skill-picker for myself because apparently having 30+ agent skills also means remembering which one does what 😭 jev (@typesafeai), being the god that it is, ranks them for a given task and tells me and my agents which skills are actually worth using phew. one less thing for my brain to cache. https://github.com/MuskanPaliwal/skill-picker
GitHubRouting and model choice
Chris Brownridge
@chrisbrownridge
another @treg_ai /Jev demo to analyze data SUPER fast used treg to pull meta ads and then Jev to teardown the landing pages so you can understand where brands are sending traffic to categorizes the type of page, offers they're using and language used to sell also shows ad creative launch velocity, creative mix takes a few seconds to do it all end to end.
XAds and marketing
Our guidemadewithjev.com
What people actually use Jev for, from 729 catalogued builds: 100,000 posts scored in 20.4 seconds, 500 emails triaged for 3.5 cents, a flight search in about 7 seconds.
frevana
@frevana_ai
Jev made competitor ad research 30x faster and ~90% cheaper. Typed “game” as the category. 20 seconds later: 294 top TikTok ads across 68 brands analyzed. Jev: 1/ pulled the top ads from the past 180 days 2/ scored every ad on stop-scroll, hook, format + trust 3/ clustered them into creative patterns 4/ classified winners, watches + dogs 5/ built a leaderboard of what’s winning 6/ recommended what to make next 20 sec · ~$0.02 For gaming: Winning hooks: offer/promo, challenge accepted Winning format: gameplay + UGC This is what we’re building at @frevana_ai with Jev: decode your category’s ads → know what to make next, in seconds. Try it free. Link in the first comment 👇
XAds and marketing
keno
@kenonews
JEV can’t see images. So how do you give it eyes? What’s possible? What can run locally? How far can you get for free? The answers (and the catches) are in the full breakdown below:
XDocuments and OCR
ArticleBenchmarks and evals
A Spanish guide to what Jev is and how to try it.
Alex dc
193xOur guidemadewithjev.com
Our own: a Mac app that sorts, labels and marks your Gmail with a System One model running on your machine, so nothing is uploaded and nothing is installed in your browser. In build now, and the first 50 on the list pay $5.
Hassan
@nutlope
Just trained Tev1 0.8B, a tiny Jev-like classifier. Here it is running completely locally on my mac with @ollama & classifying some tasks. It's extremely fast: only ~50ms E2E latency. Video is not sped up! Releasing weights & benchmarks very soon so you can try it yourself :)
Jerry Liu
@jerryjliu0
DocJev is the fastest way to classify and split complex document packets ⚡️. (and the default is fully free and OSS!) I made a sick teaser video below. TY Opus 5.5 🙏 check it out: github.com/jerryjliu/docj…
Stefan
@heystefan_
when a designer gets access to Jev
Our guidemadewithjev.com
No: TypeSafe serves Jev from its API and has not published the weights. These are the open models that answer typed questions locally — Laya at 421M, von under 15 ms, kev on a MacBook.
OpenRouter
@OpenRouter
1/ Jev, a decision model by @typesafeai, sparked a burst of projects and discussion. We tested it using Ori Eval against popular LLMs on OpenRouter at judging. Jev was >5x faster than the next fastest model, and even its slowest requests beat every other model's median.
Cline
@cline
We built a plugin that gives Jev a browser in Cline, and have been blown away by the results. 1. Install it in our new desktop app: Customize > Marketplace > Plugins > search 'jev-browser' 2. Create a Vercel AI Gateway API key, then save it to ~/.cline/plugins/cline-jev-browser.config.json as {"gateway": {"apiKey": "..."}} and restart Cline. 3. Ask any browser task and it will launch Chrome in the background to complete it.
XAgents and browsers
Cua
@trycua
1/ Introducing CUA-S1: a family of System One Models, small, specialized, and built for computer use. Today we're open-sourcing CUA-S1-FORMS, the first in the family: github.com/trycua/cua
Our guidemadewithjev.com
Our own count: every public Jev build in week one, with the median published cost per decision, the median decision time, and the stars and languages of every repository created since launch. Free to cite, with the rows as JSON.
Sarah Drasner
@sarah_edo
🎇 I made a small Chrome extension: a side panel that drives any site's WebMCP tools with Jev! When you type, on every keystroke it picks the relevant page's tool, fills in the arguments, and tells you how sure it is. Here it is grocery shopping:
XRouting and model choice
Pick
idan levin
@0xidanlevin
We just ran Jev on our WebMCP benchmark. The result: basically broke the benchmark. Jev + Mercury 2.5 (a fast, low-cost LLM) using WebMCP solved 100% of the tasks at roughly 112× lower model cost than GPT-6 Astra using computer use with code execution. Compared to Astra using screenshot-based computer use, the model cost was 245× lower (!). We also compared Jev operating the browser with and without WebMCP. We used Browser Use’s open-source Ultrafast, with some improvements to the harness to make it more reliable across the benchmark. Jev’s browser-control accuracy on its own was not amazing - adding WebMCP nearly doubled the number of solved tasks, from 25/49 to 49/49, while reducing model cost by 18% (more on why below). The benchmark and methodology are fully open and reproducible. Full results: https://webmcp.com/benchmark A few words on how the Jev + WebMCP harness works and why this is exciting: Jev receives text as input and a set of discrete options it can choose from. With WebMCP, those options are the tools exposed by the website. At each step, Jev sees the task, the available tools and previous results, then picks what to do next. The limitation is that Jev can’t generate arbitrary text, which you need for tool arguments. For example, it can choose the search_products tool, but it can’t generate the search query itself. So we split the work: Jev picks the tool and Mercury 2.5 generates the arguments if needed. This works well because turns out most of the cognitive load in these tasks is around choosing the right action. The argument generation itself is relatively simple, so we can delegate to a small and very fast model. We used Mercury, which outputs 1,000+ tokens/sec and is very cheap. The result is a pretty simple combination: Jev for tool selection + Mercury for arguments + WebMCP for the interface. It ends up being very reliable, very fast, and very cheap. A few words about Ultrafast and why do we think it underperforms: Without WebMCP, Jev chooses from the page’s controls: which button to click, which field to fill, or which option to select. But choosing a valid button is different from choosing the right next step. The agent still has to navigate menus, understand forms, recover from errors and recognize when the task is actually complete. Our hypothesis is that WebMCP makes the decision space much simpler. Instead of figuring out a sequence of clicks through a website, Jev chooses explicit actions that directly advance the task. @typesafeai itself documents weaker accuracy on questions requiring multiple reasoning steps. WebMCP moves much of that complexity into the website’s tools, leaving Jev with clearer decisions and fewer opportunities to go wrong (in a sense WebMCP "compresses" a sequence of clicks into one tool call). Our modified Ultrafast setup solved 25/49 tasks - that is a result for our particular implementation and benchmark, not a universal limit on Jev or Browser Use. We are open to more harness optimization to get this result to perform better, feel free to directly contribute to the benchmark here: https://github.com/nekuda-ai/WindTunnel Browser-use ultrafast: https://github.com/browser-use/jev-ultrafast
Abol
@abolbuild
I gave Jev $10,000 and let it trade
Our guidemadewithjev.com
Your first Jev call, line by line: the state, the questions, the typed answer and what to do with the confidence score. Then the SDKs, the gateways and 56 video walkthroughs.
leo
@leojrr
rebuilt the X algorithm with Jev - uses real weights - simulates virality of your post - has a global feed (you see everyone) it's insanely accurate
XSocial feeds
Aayan
@aaayandev
Jev (@typesafeai) is so insane & cheap for search!! > 6000+ @ycombinator Startups indexed. > Sub 1 second search results. > 90M tokens & $2.7 in total testing costs. Search any startup in a second, in any way! - Color - Niche - Your Competitor - Age - Image - etc... > watch the entire video, it's so freaking cool omg! > this is the coolest thing i have ever built for fun! (worked on it for 2 days straight!)
Ian Nuttall
@iannuttall
Pro tip: You can use Jev to remove annoying reply guy comments that X seems to always miss. Takes 5 minutes in Astra with the docs and an API key.
XSocial feeds
Guideyoutube.com/@KodeKloud
KodeKloud’s short: what a System One model decides, and why it answers in milliseconds.
255 optionsChoice ceiling
TypeSafe AI
GitHubAgents and browsers
Computer use on macOS, with one Jev decision per step.
Andrew Levin
789~$0.0002
GitHubRobotics and devices
A simulated drone that asks Jev for a judgment 2.5 times a second.
Roman Slack
1232.5 Hz
Our guidemadewithjev.com
What Jev costs and what that buys: $0.042 per million input tokens, output free, and a median of $0.000068 per decision across 15 published runs. No free tier.
vogel
@ryanvogel
this model is actually insane at email classification i tested it on 1500 of my own emails to see how well it works and I am blown away
Dan Shipper
@danshipper
we almost never test new foundation models but we've been testing this for ~a week @every and it's pretty wild. the kind of things that will be obviously indispensible in 6-12 months it doesn't produce words as output, it produces probabilities. so it can efficiently act as a judge in cases where you'd need a Fable-level model—but in our testing was 25x faster and 600x lower priced excellent vibe check by @hammer_mt on @every: https://every.to/also-true-for-humans/mini-vibe-check-typesafe-s-jev-judged-everything-i-ve-written-in-0-7-seconds?utm_cta_source=home_main_a_3
ArticleBenchmarks and evals
Pick
ArticleGames and real time
Jev picks from the legal moves, compared with reasoning models.
Maxim Saplin
Our guidemadewithjev.com
No jargon: Jev answers multiple-choice questions about a piece of text, in about the time it takes to blink, for about a hundredth of a cent. What that changes, and what it cannot do.
paulwei
@coolish
昨天出的 typesafe.ai Jev 模型 适合高频决策，那岂不又是适合打游戏？ 之前用 GPT-6 Astra 代打杀戮尖塔2 能力强但速度慢。 我刚实测用 Jev 打，行动思考只需 0.7秒， 画面我都没看清它就操作完了。 这超人类游戏速度， 着实又让我震惊瘫坐了😅 语言很难描述这感觉，看视频⬇️
Ian Nuttall
@iannuttall
Cloudflare Workers has Jev now so I'm putting it to the test on keep.md - 7x faster search rerank compared to the current hybrid - 50x faster tagging of content vs GLM 4.7 Flash with no failures
Niaz Morshed
@niazmorshed_
built `jev-review` @typesafeai it's an experimental, local-first MCP plugin that gives coding agents a score quality feedback loop across different metrics. agents call jev while they work, get scored, make improvements, and repeat the loop try below 👇
XCoding and code review
Our guidemadewithjev.com
Same job, both ways: 100,000 posts for $0.67 against 214 for $0.98, 0.35 seconds a passage against 8.83, and the cases where the larger model still won. When to use which.
Riley Brown
@rileybrown
Building an agent with model router powered by Jev.
XRouting and model choice
Mormon Negro
@mormonnegro
Acabo de terminar la implementación de @typesafeai + Chromium Headless para que mis agentes puedan navegar por internet a una buena velocidad! En este ejemplo le pido al agente que entre a la página del término "Café" en Wikipedia y navegue por los hipervínculos hasta terminar en la página "Inteligencia artificial" Logra completar la prueba en 20 segundos. Creo que pocos humanos podrían hacerlo en ese tiempo.
XAgents and browsers
GitHubAgents and browsers
A browser you control by voice, from Moritz Kremb’s Jev tutorial.
Moritz Kremb
231
Guidetypesafe.ai
TypeSafe’s launch post: the model, the evals, and their caveats.
nader dabit
@dabit3
Also have been playing with @typesafeai Jev, insane! So many immediate use cases and new apps are possible. What a time to be a builder! Sharing some experiments here starting with: Keystroke oracle / predictive launcher: Your launcher ranks by aliases, fuzzy match, and habit. Jev reads intent: type "the pdf I just downloaded" and the newest PDF is already the top hit with a full confidence on every keystroke, in ~100 ms
XBenchmarks and evals
kitze 🛠️ tinkerer.club
@thekitze
i made a smart calculator notebook using jev it can calculate ANYTHING!! 😅
XTools and apps
Jack Cheng
@jackcheng
Jev is the future
XTools and apps
Our guidemadewithjev.com
SEO work as typed decisions: a client audit at a tenth of the old cost, 4,000 Reddit threads scanned for AI citations, 10,000 words checked for slop in 2 seconds.
ILIAS ISM
@illyism
Now using @typesafeai Jev in http://aiseotracker.com, http://linkdr.com, http://genppt.com, etc AI ends up vibe coding so much AI regex slop if you don't read the code, so I can finally move all this hard-coding to Jev and it's insanely fast! Also for regular LLM calls, it is around 10x faster, 50% cheaper
XBenchmarks and evals
Vincent Wang-Maścianica
@vinnylarouge
I reverse-engineered a jev-like architecture given its type. You can find the repo here to train your own jevlikes: github.com/vinnylarouge/j…
GitHubOpen source
Alan Daitch
@AlanDaitch
Claude me integró Jev con Playwright para buscar usados. Lee unos 26 artículos por minuto y decide qué hacer con cada uno en 406 milisegundos Descartó los que no encajaban con lo que busco, ofertó por los que sí y hasta les mandó un mensaje a los vendedores cuando faltaba algún dato en la publicación. Toda la búsqueda salió USD 0,00085. O sea: con un dólar revisás unas 26.000 publicaciones. Una IA que por fin puede navegar por internet más rápido que nosotros es un game changer
Our guidemadewithjev.com
Paid media as typed decisions: a 1,891-ad library labelled in 19 seconds for $0.12, and a 30-persona focus group over 723 ads — 21,690 judgments — for 22 cents.
CJ (Coding Garden)
@CodingGarden
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!
XRouting and model choice
nader dabit
@dabit3
Another crazy @typesafeai Jev example: Predictive spreadsheets Spreadsheets recalculate numbers, not meaning. Jev reads intent. Type "Urgency" at the top of a column and, as you type, it figures out you want each row rated from "no follow-up needed" to "urgent" in ~100 ms.
RaZaan
@razaanstha
I built a Chrome extension for agentic browsing using Jev by @typesafeai, fx.sh including AI Gateway by @vercel. Now agents can browse, click, and interact with websites directly in your browser. Cost effective and fassst. Decision-making by Jev.
XAgents and browsers
Avid
@Av1dlive
everything you need to start building with jev, in one article. code, architecture, diagrams... everything you need to follow the build and make it your own. x.com/i/article/2102…
Guidex.com
Avid’s builder’s guide, and the fullest public write-up of the pattern: where the decision layer sits in a coding harness, the five rules that keep it enforceable, and keel, the Rust app it was built in. Honest about the self-improvement part it has not proved.
One vote per person per card, counted by IP and nothing else, so this is a signal rather than a ballot. The order is recounted every hour. Entries nobody has voted for yet keep the curated order behind the ones that have, which is why the tail of this page reads like the home page.