This article analyzes the strengths and weaknesses of GPT-5.1 Pro and Gemini 3 as AI tools for coding and problem-solving. While GPT-5.1 Pro excels in backend tasks and detailed research, Gemini 3 is preferred for speed and frontend work. The author emphasizes the need for better integration of GPT-5.1 Pro into development environments.

This article explains the differences between skills, commands, and rules in AI coding tools. It emphasizes how skills provide optional expertise for agents, while commands are explicit user instructions, and rules are fixed and always apply. The author also discusses effective strategies for organizing these elements to optimize performance.

The author missed the word game Wordiest after switching to iOS, so they used AI tools to reverse engineer the game and create a new version called Wordiest Classic. After overcoming various technical challenges, they received approval from the original developer and launched the game on the Apple App Store.

This article outlines how designers can leverage AI tools like Cursor and Claude Code to build web applications without needing extensive coding knowledge. It provides a step-by-step approach to creating projects, from setting up the tools to deploying live websites.

This article discusses the excitement surrounding Claude Code, an AI coding tool from Anthropic that simplifies coding for both developers and non-developers. It highlights its potential to accelerate human productivity and democratize access to coding capabilities, positioning it as a key player in the AI landscape.

The article details various ways to utilize Claude Code for coding projects, both personal and professional. It covers essential features like the CLAUDE.md file, custom commands, and context management strategies. The author shares insights on best practices and anti-patterns they've encountered.

An ex-founder of PSPDFKit is innovating in AI-powered developer tools, creating a suite of applications that enhance productivity and streamline workflows for developers. With a focus on rapid prototyping and efficiency, the tools range from command-line interfaces to automation features, all designed to improve coding experiences.

The author expresses frustration with AI coding tools, feeling that despite their hype, they often lead to underwhelming results and time wastage compared to traditional coding methods. They struggle to reconcile their experiences with those of others who claim to achieve significant success using these tools, leading to a sense of inadequacy.

The article discusses the benefits and applications of Claude, an AI tool that can assist with coding tasks, enhancing productivity and efficiency for developers. It emphasizes how Claude's natural language processing capabilities streamline the coding process by generating code snippets and providing assistance in debugging. Ultimately, the piece advocates for broader adoption of Claude in the software development community to leverage its potential.

After two weeks of using Claude Code, the author shares their experience with the AI tool, highlighting its strengths in code generation and context management. They discuss challenges faced with rate limiting and performance issues, as well as tips for maximizing efficiency while using the tool in various coding environments. The article includes insights into the author's workflow and preferences for different AI models.

Claude Sonnet 4.5 from Anthropic demonstrates significant improvements in speed, reliability, and steerability compared to its predecessor, Opus 4.1. While it excels in day-to-day coding tasks, it still falls short against GPT-5 Codex for complex production issues, making it a valuable tool for specific applications.

The author shares insights from a month of experimenting with AI tools for software development, highlighting the limitations of large language models (LLMs) in producing production-ready code and their dependency on well-structured codebases. They discuss the challenges of integrating LLMs into workflows, the instability of AI products, and their mixed results across programming languages, emphasizing that while LLMs can aid in standard tasks, they struggle with unique or complex requirements.