devwithjev
🔮 Built a real-time click prediction right over the DOM using JEV that feels like a crystal ball.
Powered by jev-latest to predict the next clickable element and intent in 345ms per check, highlighting candidate elements on screen.
It's extremely cheap to use and helps predict user intention, hitting 83% accuracy in testing.
Usages that come to my mind:
- Web performance optimization via predictive resource load
- UX Research and testing
- Progressive UI loading based on intention
- Accessibility for reduced motor precision (highlight based on intention allowing to click, for example)
- An
Tools & apps
229 builds · page 5 of 6
.@jonptaylor recorded a detailed walkthough of Jev vs GPT-5.6 Luna as the "operator" element of a Pipecat speech interface pipeline.
GPT-5.6 Luna:
- 81.3% command accuracy
- 1,008 ms median latency
Jev
- 92.6% command accuracy
- 296 ms median latency
A few notes here ...
1) We expected to see a big latency benefit. But the higher accuracy is maybe more interesting. Jev (with a bunch of code wrapped around it) is much better at turning messy transcriptions from a noisy speech environment into correct command structs.
The very hand-wavy explanation here is something like: LLM too
Can we estimate a book’s literary quality and how enjoyable it is to read by using Jev to perform a multicriteria classification of its prose?
I wanted to investigate that, so I built Salomón, a tool designed to do exactly this.
I analyzed 32 books blind using Jev, and this is the map I got.
Infographics, details and links in the thread.
Opencode sends every MCP tool schema to the model on every step.
With 18 common MCP servers, that's ~90,000 tokens before the model even reads your request.
I built a plugin using Jev that cuts it to ~7,300. (-92%) 🧵
0180X posts·Tools & appscost~90,000 tokens before the model even reads your request. I built a plugin using Jev that cuts it to ~7,300. (-92%)Original source ↗
Built a chrome extension that covers distracting youTube videos using Jev https://t.co/5TVn6cnLjD
I have a script running that's going through all of my magi-code sessions, extracting every bash command and then categorizing them by similarity using Jev.
It's going to end up being about ~$0.60-$0.70 to do this for 4200 bash commands and take ~10 minutes with a very inefficient python script
My goal here is to identify instances where the model keeps repeatedly writing python or bash scripts to perform very similar actions - and provide the agent with a small set of scripts that do those things for it so it doesn't keep wasting output tokens on writing repeated code.
I swear this is the last Jev demo I'm doing... 🙏🏻
I'm using Jev by @typesafeai to get a third person opinion on my vibe coded projects... 👀
Checkout FirstScreen 🔗 https://t.co/nZnEsgxl1n
As usual, it's again open source so feel free to star the repo if you want : )
For the past three days, I've had access to Jev, and I'm having so much fun playing around with it 🔥
I built Diffjury and JevArena in the past 2 days and shared with you guys on here, and today I built FirstScreen, where Jev basically takes a look at the website and quickly gives a verdict whether it's ready to ship or need
Jev can tell if your resume is good enough for a job.
I added a simple resume scorer using Jev by @typesafeai
1. You give your resume (Doesn't get saved)
2. PDF to Markdown conversion
3. Jev, does the analysis, across on 5 Dimensions
4. Your resume score
Jev is quite fast btw. but he is very reasonable, so if the score is low, it is what it is.
#jev #typesafe #flutter #mobile #test
Using Jev from https://t.co/wVCGdOU10g, I created a flutter package that allows you to write integration tests for Flutter apps using natural language.
https://t.co/hG0mtW2FGp
https://t.co/OkXWEkObbJ
We did a test using Jev 1.13.0 on official LoCoMo data benchmark.
Jev makes AtMem better at putting the right memory first, but it does not yet help AtMem find memories it missed entirely.
The significant increase is on how often the correct memory was the very first result. Improved from 33.99% to 54.23%
AtMem used about 1,593,522 token and costed about $0.0583
Jev remains as option in the next releases since AtMem Position is local first.
We continue to explore more use cases for Jev
