What people are building with Jev

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

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:

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-…

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.

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

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

Jon Kraayenbrink

@kraayenJon

Jev repos blowing up right now. I made a list of the repos people are using to build with jev. 👇

Abol

@abolbuild

I gave Jev $10,000 and let it trade

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

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

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!)

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…

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.

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.

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

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.

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

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

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