Practical guide covering four main LLM evaluation methods: multiple-choice benchmarks, verifiers, leaderboards, and LLM judges, with code examples and analysis of their strengths/weaknesses. Essential reading for engineers comparing models, interpreting benchmarks, and measuring progress on their own projects.

Deep dive into Qwen3 architecture implementation from scratch in PyTorch, covering the open-weight model family's design choices and building blocks. Provides practical code examples and architectural patterns directly applicable to understanding modern LLM internals and building custom variations.