This article critiques the use of structured outputs in large language models (LLMs), arguing that they often compromise response quality. The author provides examples, showing that structured outputs can lead to incorrect data extraction and limit reasoning capabilities compared to freeform text responses.

This article reviews key developments in large language models (LLMs) throughout 2025, highlighting trends such as reasoning, coding agents, and the rise of CLI tools. It details significant releases like Claude Code and the impact of agents on coding and search tasks. The author also discusses the implications of using LLMs in YOLO mode and the evolving landscape of AI applications.

The article discusses the importance of data activation in enhancing the performance of large language models (LLMs), particularly in the healthcare sector. It highlights recent advancements in transforming structured medical data into usable formats for LLMs, emphasizing the need for effective reasoning methods to fully leverage the potential of healthcare data.

The article reviews significant trends and developments in the LLM space throughout 2025, highlighting breakthroughs in reasoning, the rise of coding agents, and the increasing use of LLMs in command-line interfaces. It notes the evolution of tools and models, including the impact of asynchronous coding agents and the normalization of YOLO mode for improved efficiency.

The article discusses the potential of large language models (LLMs) when integrated into systems with other computational tools, highlighting that their true power emerges when combined with technologies like databases and SMT solvers. It emphasizes that LLMs enhance system efficiency and capabilities rather than functioning effectively in isolation, aligning with Rich Sutton's concept of leveraging computation for successful AI development. The author argues that systems composed of LLMs and other tools can tackle complex reasoning tasks more effectively than LLMs alone.