Large Language Models (LLMs) producing hallucinated code methods is considered a minor issue since compiler errors immediately expose these mistakes, unlike prose hallucinations which require careful fact-checking. The author emphasizes that manual testing and code review remain essential skills, as LLM-generated code's professional appearance can create false confidence.
An exploration of using LLM-driven development tools like Cursor reveals potential for enhanced code development through self-modifying rules and automated error detection. The combination of LLMs with tools like Semgrep could enable automated vulnerability detection and bug fixing through closed-loop systems that learn from runtime errors.
A comprehensive exploration of reasoning LLMs focuses on four main approaches: inference-time scaling, pure reinforcement learning, supervised finetuning with RL, and pure supervised finetuning with distillation. The article analyzes DeepSeek R1's development pipeline and compares it with OpenAI's o1, highlighting how reasoning capabilities can emerge through different training methodologies. Practical insights are provided for developing reasoning models on limited budgets, including alternative approaches like journey learning and small-scale implementations.