On-device segmentation and OCR turn the screen into text for Jev.
Milind S runs computer use on a Mac without sending screenshots anywhere. A local CoreML model finds every button and UI element on screen, on-device OCR reads the labels, and that text is all Jev gets.
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 ↓
# Jev: AI Decisions as a Typed Function Call What I learned using TypeSafe's Jev for code review, and why its more surprising uses are browser control and generative UI.…
ResourceAgents & browsers
Chrome extension that labels every post you scroll past on X with six typed questions per post, and counts what it costs in the corner.
GitHubAgents & browsers
Chrome extension that scores draft posts and reply context before posting; optional media descriptions come from a separate vision model.
GitHubAgents & browsers
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. Show more
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
