Jev (@typesafeai ) not only can be used as judge, but it's confidence signal can be used to find eval problems.
Don't just throw away your LLM-as-judge results to Jev, but make Jev give additional information for your evals:
BLOG: https://t.co/aAztOxx7y1
Found totally awesome Jev use-case that changes how I think about evals:
TLDR: Use Jev in LLM-as-judge to give confidence on the PASS/FAIL answer.
I'll try to gather my thought on this to blog, but I recommend others to try. Early evidence very strong. @typesafeai
GLM 5.3 > Opus 5.
Maybe not in benchmarks,
but I rather read GLM 5.3 output than Opus 5 explanations.
Maybe need to add some instructions on the output format for Claude models, so hard to read
Some thought on the principles of loop engineering in new blog: https://t.co/DZhqoZBtpi
Better model helps on agent autonomy, but designing your environment for the looping agents is at least as much important.
You changed one prompt. Now you're manually re-testing everything.
Write down what your agent must be capable of as tests. Then let your coding agent improve the system in loop: real agent runs, pass or fail with evidence, improve, repeat.
https://t.co/rISeThhfMq
@ZixuanLi_ Make it learn from tool errors. When flash uses tool and the tool responds woth error message on what went wrong, currently it just retries the same command over an over again, not learning from previous mistakes.
@buildwithhassan How to game it even more:
- Use zcode for additional 50% more tokens in the 5 hour window
- Use outside of peak hours
- Heavy use of idle-tasks for free tokens.
- zcode for usage resets (Have used alresdy 2 times last week, it's really frequent)
Loop engineering in the harness?
Actually possible!
Creating smart interfaces for the agent to run your system, trace your system. judge your system, and modify your system, all through CLI.
https://t.co/FH4PN2hNB5