Explores how natural language, like English, is becoming a key interface for software development with AI tools, lowering barriers to participation.
Explores Andrej Karpathy's concept of Software 2.0, where AI writes programs through objectives and gradient descent, focusing on task verifiability.
A response to a blog post about refining AI-generated 'vibe code' through manual refactoring and cleanup.
A weekly collection of articles on software architecture, AI's impact on programming, engineering metrics, and legacy code strategies.
Martin Fowler shares thoughts on LLMs in software development, discussing usage workflows, the future of programming, and the AI economic bubble.
A humorous exploration of using AI to generate Python scripts that identify and mock outdated C++ code patterns, comparing outputs from Claude Sonnet and GPT-5.
Explores the shift from traditional coding to AI prompting in software development, discussing its impact on developer skills and satisfaction.
A developer's personal reflection on struggling to integrate AI coding tools effectively, feeling left behind despite the hype.
Explores Claude Code as a transformative AI-powered programming interface, comparing its potential impact to Smalltalk-80's historical influence on software development.
A developer explores building simple and tool-using chatbots from scratch using the DSPy framework, reflecting on practical vs. theoretical learning.
Explores the common practice of developers assigning personas to Large Language Models (LLMs) to better understand their quirks and behaviors.
A guide to the GitHub Copilot Adventures repository, featuring hands-on coding challenges to learn AI-paired programming across multiple languages.
A developer explains why they limit AI use in programming, citing the difficulty of verifying AI-generated code without a proper mental model.
A developer's monthly digest covering tech books, industry commentary, AI debates, and personal reflections on walking and productivity.
The author discusses the current state and future potential of AI in software development, focusing on bug fixing, documentation, and the need for developers to stay sharp.
