The article discusses the evolution of large language models (LLMs), highlighting the shift in perception among researchers regarding their capabilities. It emphasizes the role of chain of thought (CoT) in enhancing LLM outputs and the potential of reinforcement learning to drive further improvements. The piece also touches on the changing attitudes of programmers toward AI-assisted coding and the ongoing exploration of new model architectures.

The article examines whether large language models (LLMs) can function like compilers, translating vague specifications into executable code. It argues that while LLMs may offer ease in programming, they also create risks by relying on imprecise natural language, which can lead to unintended outcomes. Effective specification becomes critical as development shifts toward iterative refinement rather than structured coding.

This article explores how large language models (LLMs) can evolve competitive assembly programs, known as warriors, in the game Core War. The Digital Red Queen (DRQ) algorithm drives an ongoing arms race, resulting in increasingly robust strategies and revealing patterns similar to biological evolution. The research provides insights into adversarial dynamics and the potential of AI systems to compete in real-world scenarios.

The article discusses the author's experiences with LLMs and coding agents over the past year. It highlights significant improvements in coding models, the issues with current IDEs, and the author's new approach to programming using agents instead of traditional environments.

The conversation explores the role of Large Language Models (LLMs) in software development, emphasizing the distinction between essential and accidental complexity. It argues that while LLMs can reduce accidental complexity, the true essence of programming involves iterative design, naming conventions, and the continuous evolution of programming language within a collaborative environment. The importance of understanding the nature of coding and the risks of over-reliance on LLMs for upfront design decisions are also highlighted.

The author evaluates various large language models (LLMs) for personal use, focusing on practical tasks related to programming and sysadmin queries. By using real prompts from their bash history, they assess models based on cost, speed, and quality of responses, revealing insights about the effectiveness of open versus closed models and the role of reasoning in generating answers.

Recipes are likened to programming languages, where ingredients and actions serve as inputs and instructions, respectively. Large language models (LLMs) simplify the process of creating compilers for various domains, empowering individuals to experiment with structured systems in cooking, fitness, business, and more. This shift democratizes the ability to translate intent into action, making complex processes more accessible to everyone.

Frontier LLMs like Gemini 2.5 PRO significantly enhance programming capabilities by aiding in bug elimination, rapid prototyping, and collaborative design. However, to maximize their benefits, programmers must maintain control, provide extensive context, and engage in an interactive process rather than relying on LLMs to code independently. As AI evolves, the relationship between human developers and LLMs will continue to be crucial for producing high-quality code.

Peter Naur's essay argues that large language models (LLMs) cannot replace human programmers because they lack the ability to build theories, a crucial aspect of programming. Naur emphasizes that programming involves the development of a deep understanding of the system, which LLMs, as mere consumers of textual data, cannot achieve. Consequently, to believe LLMs can effectively write software undermines the complexity and theoretical nature of programming work.

After years as a software engineer, the author created two card games, Truco and Escoba, using Go. The first game took three months to develop without LLMs, while the second game was completed in just three days with LLM assistance, showcasing the drastic improvement in development efficiency. The article also offers a guide on how to create similar games using Go and WebAssembly.

The article discusses the potential of large language models (LLMs) to function as compilers, transforming natural language into executable code. It explores the implications of this capability for software development, highlighting the efficiency and creativity LLMs can bring to programming tasks. The piece also examines the challenges and limitations of using LLMs in this role.

The article discusses the implications of integrating large language models (LLMs) with the Elixir programming language, evaluating whether this combination could lead to significant advancements or potential drawbacks in software development. It highlights both the opportunities for innovation and the risks that may arise from over-reliance on AI technologies.