This is a personal interview preparation and learning repository focused on large language model (LLM) algorithm positions. It contains extensive interview question logs from 2024-2026, organized by company, covering topics such as fine-tuning, RAG, RLHF, agent design, model architecture, and deployment. The repository also includes learning notes on transformer, CNN, RNN, LoRA, PEFT, and other LLM-related concepts, along with code implementations: a decoder-only transformer trained on Chinese text (红楼梦), a reinforcement learning-based code optimizer using PPO, and various PyTorch examples. The utility scripts (DuckDB cache decorator, JSON extraction, logging, proxy management) are well-crafted and potentially reusable. However, the code components are explicitly unmaintained (the author states only interview records will receive future updates), and many paths are hardcoded. The interview notes are unstructured personal logs, not reusable as data assets without significant cleanup. The repo serves as a portfolio of technical capability and study material rather than a production system or reproducible data pipeline.