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#productivity
52 posts in the last 7 days
Local Eval Harnesses for New AI Coding Models
Developers are rejecting generic benchmark charts and building lightweight, reproducible test harnesses to evaluate new free and cheap AI coding models against their own specific codebases. This trend addresses the hidden costs of model adoption, such as broken diffs, silent failures, and increased retry rates, ensuring tools actually improve productivity before integration.
Key Areas of Focus:
- How can I build a fast, reproducible test harness for my own codebase?
- What historical bugs and edge cases should be included in model evaluations?
- How do I measure the hidden costs of cheap models, such as retry rates and hallucinations?
Active 43 minutes ago
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