Evidence-Focused Research for Ergod

Ergod uses Jev to assess whether results are relevant and find useful evidence in long articles and wiki pages, keeping headings and tables in context.

View on X

Ergod@ergod_dev𝕏

What I’m doing with #Jev from @typesafeai: Making Ergod, my multiplayer AI agent, better at the work between “let me check” and actually getting it done. Better research. Clearer comparisons. Fewer dead ends. Small decisions. Bigger capabilities. 🧵 A result can match your keywords and still miss your question. Jev helps assess what’s relevant. Alongside better page reading, Ergod can focus on useful evidence in long articles and wiki pages - with headings and tables kept in context. The follow-up is where this gets interesting. “What about the exceptions?” “Where did that figure come f

Sep 19, 2026X postsView on X

The author says the integration aims to improve research and comparisons and reduce dead ends. They also point to follow-up questions as an area where Jev could help.

Also filed under Agents & browsers

Building a Harness with Jev
Building a Harness with Jev Learn all about Jev, a new System One model from TypeSafe AI, and how you can use it in your agent harness. Jev is up to 200x ...
How To Build A Harness With Jev | A LangChain x TypeSafe Conversation
How To Build A Harness With Jev | A LangChain x TypeSafe Conversation Hear from Sydney Runkle, Product Manager, OSS at LangChain, Allie Laabs, Founding Developer Advocate at TypeSafe AI, and ...
Jev-kit for Claude Code
Cadaverr’s Jev-kit integrates TypeSafe’s Jev model into Claude Code for tasks including tool-call guarding, sub-agent sizing, file search, and browser automation. Jev answers pick-one and yes/no questions in about 0.3s, according to the author.
Jev Browser Automation
jev-browser takes a task and URL, then uses Jev to choose one action per step from the page’s clickable, typeable, and selectable elements. It scores whether the goal is met or the run is stuck.