Jev and the models people use with it
Six reports bring together X posts, follow-up discussions and project documentation. They distinguish working arrangements reported by users, side-by-side model tests and cases where an LLM helped write the application.
Jev + GPT / Codex
GPT revising Jev game prompts, plus a separate Codex desktop experiment. What the users reported, including failed runs.
Jev + Claude
A plugin scores old tool calls before pruning context. Its implementation and a user report show both the approach and its limits.
Jev + Gemini
Two published comparisons cover business emails and app reviews. Gemini leads on the reported quality checks; Jev costs less in those runs.
Jev + Grok
A 1,000-post selection workflow and a separate action-router setup, with source links and no inferred speed or cost claims.
Jev + Kimi
A measured email handoff sends uncertain cases to Kimi K3. A separate developer uses Kimi to build a Jev-powered comment filter.
Jev + DeepSeek
Developers report intent-recognition changes and a train-search demo. The browser project documents how decisions and text generation are separated.
Compare measurements within the same task and dataset. The benchmark reports preserve those boundaries; the source notes record what each author actually reported.