A Python framework where an authoring LLM writes a task-specific Jev harness, then optionally evolves it using reward reflection and GEPA over full trajectories.
Needs an AI Gateway or TypeSafe key, Python 3.11 or newer, and a macOS native sandbox for functional Python nodes. The reported gain is on the selection eval set, not unseen games, and the repo ships no license.
Reactions & coverage
Posts, threads, and videos about this entry from around the web.
Jev + GrokBot is the best AI agent system I’ve built in my life
It just made my setup CHEAPER and FASTER than what 95% of people are running...
setup takes literally 7 minutes:
prompt → GrokBot → Jev decision → GrokBot execution → result
step 1 → open @typesafeai ,Show more
Jev is the "Internet" moment for the AI industry
It tells your agents and LLMs what to do next, in milliseconds and at almost zero cost
If you set it up correctly, you will have the AI engineer’s stack for 2028
In this article, I show you how x.com/i/article/2077…
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
thanks to @typesafeai jev I no longer have fill out all of those fields on prompt boxes. It picks the agent / model / computer / folder for me.
- For a major rewrite it uses Fable + Claude Code.
- Changes to an ios app run on one of my macs
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 toShow 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
A collection of Jev demos, workflows, and agent skills for coding agents: five installable skills and 108 scenarios, installed by pointing Codex, Claude Code, or OpenCode at an agent prompt.
A collection of 26 production-ready agent skills for Claude Code, Cursor, Kiro, Windsurf, and OpenCode; four skills call TypeSafe Jev for calibrated Score and Noul judgments and fall back to heuristics when it is unavailable.
A Stop hook that keeps coding agents from quitting early: rules written in plain language are scored by Jev in about 0.7 seconds, and a violated rule sends the agent back to work instead of letting it stop.
WTF is Jev by @typesafeai? Here’s the tl;dr ELI5:
Think AI multiple choice, not AI essay writing.
It doesn’t chat. It makes decisions your software can act on: “Spam or not?” “Which tool should this agent use?” “Does this need a human?”
The exciting part: roughly 200x fasterShow more
Tested TypeSafe’s Jev (no-text, probability-only model) as an AI agent safety monitor.
Checking each action first worked well caught most attacks with almost no false blocks, and much faster than Gemini.
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
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
cua open sourced a 706k param model that fills a whole form in one 50ms pass
the llm agent doing the same form took 23 turns and 39.6 seconds
the specialists are going to eat the generalists from the bottom
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