A guide to systematically evaluating and testing AI agent skills, covering success criteria, building an evaluation harness, and improving skill performance.
Technical report on Qwen3-VL's video processing capabilities, achieving near-perfect accuracy in long-context needle-in-a-haystack evaluations.
Argues that reading raw AI input/output data is essential for developing true intuition about system behavior, beyond just metrics.
Explains core prompting fundamentals for effective LLM use, including mental models, role assignment, and practical workflow with examples.
Explores counterfactual evaluation as an alternative to A/B testing for offline assessment of recommendation systems.
