DEMO / RECORDING
From the original post
Another insane Jev use case!
Jev is making it dramatically cheaper to evaluate what actually happened inside an agent run.
And finally, someone open-sourced a self-improving memory layer that can put that signal to work across agent harnesses:
- Claude Code
- Codex
- Cursor
- OpenCode, and 20+ more
Beacon by @asymptotelabs continuously captures your agent history across harnesses and uses Jev to identify which runs are actually worth learning from.
It then turns the highest-signal workflows, corrections, and debugging patterns into reusable skills.
GitHub repo:
How Jev fits in the loop
- Agent collects runtime context or input state from external sources.
- Bounded decision options are formatted into Jev System One questions.
- Jev scores each candidate option and returns a probability for it.
- Deterministic code executes the selected action or routes to specialized models.
How to reproduce
- Read the author's original post on X at https://x.com/_avichawla/status/2101966536798040332.
- Inspect the described workflow architecture and bounded decision points.
- Test Jev System One decision queries against the provided task context.
Why this build matters
Demonstrates real-world Jev Skills experimentation by @_avichawla with verified community reach of 266.3k views.
Limitations
- Implementation details are based on the author's public post on X.
- Performance figures and benchmarks are author-reported community claims.
- Production deployments may require custom calibration and policy thresholds.