I’ve been diving deep into AI-assisted development this summer, and I’ve collected some of the best articles I’ve found on the topic. This reading list focuses heavily on Claude Code and practical AI coding workflows, featuring insights from developers who are actively using these tools in their daily work. Whether you’re just getting started with AI coding assistants or looking to refine your approach, these articles are a good jump start.

Claude Code

Claude Code is My Computer

Peter Steinberger describes using Claude Code with a dangerous permissions flag, transforming AI into a universal computer interface that can perform complex tasks, such as system migrations, content generation, and code management. By granting broad system access, he has found that Claude can automate numerous computing tasks, saving him significant time and operating at a fundamentally higher level of abstraction compared to traditional development tools.

How I Use Claude Code

Philipp Spiess shares his experiences and strategies for effectively using Claude Code, an AI coding assistant that has transformed his programming workflow. He emphasizes techniques like starting new threads frequently, creating precise prompts, breaking complex tasks into smaller steps, and maintaining a balance between AI automation and human oversight to maximize productivity.

Basic Claude Code

Harper Reed discusses his workflow for using Claude Code, an AI coding assistant, which involves generating project specifications, using AI to create and implement prompts, and leveraging test-driven development (TDD) and pre-commit hooks. He emphasizes the importance of defensive coding practices like testing, linting, and creating project-specific configuration files to improve code quality and AI coding efficiency. Reed shares his team’s experience using Claude Code, noting increased test coverage and more effective development processes.

Field Notes From Shipping Real Code With Claude

The article explores how to effectively use AI, specifically Claude, in software development by establishing clear boundaries, documentation, and workflows. The author emphasizes that AI is a powerful tool for generating code, but human oversight, particularly in writing tests and maintaining code quality, remains crucial. The piece provides a comprehensive guide to vibe-coding, a collaborative approach where humans direct AI to enhance productivity while maintaining rigorous engineering standards.

My First Open Source AI Generated Library

Armin Ronacher created a sloppy XML parsing library entirely using AI (specifically Claude), which generated around 1,100 lines of code, 1,000 lines of tests, and handled package configuration. The project was an experimental exploration of AI’s capabilities in software development, resulting in a functional library that solved his immediate technical need of parsing imperfect XML output from language models.

AI Development Workflows

My LLM codegen workflow atm

Harper Reed describes a comprehensive workflow for using Large Language Models (LLMs) to generate code, which involves three main steps: idea honing, planning, and execution. The workflow can be applied to both greenfield (new) and existing code projects, utilizing tools like Claude and Aider to iteratively develop software with minimal manual intervention. Reed emphasizes the importance of careful planning, testing, and maintaining context to effectively leverage AI in software development.

An LLM Codegen Hero’s Journey

Harper Reed describes a progressive journey of adopting AI-assisted coding, starting from basic autocomplete to increasingly sophisticated AI coding tools like Cursor and Aider. The article outlines a step-by-step evolution of using Large Language Models (LLMs) for code generation, emphasizing that developers should embrace the technology incrementally and be open to its potential, ultimately reaching a point where AI agents can handle significant coding tasks with minimal human intervention.

How I program with Agents

The article explores how Large Language Model (LLM) agents, defined as a for loop that contains an LLM call, can significantly improve programming by providing environmental feedback and tools like bash commands, web searching, and compiler interactions. The author argues that agents transform LLMs from whiteboard-style code generation into powerful programming assistants that can navigate codebases, fix errors, and complete tasks more efficiently despite current limitations in speed and cost.

AI Industry Perspectives

AI Changes Everything

Armin Ronacher explores the profound impact of AI on work, creativity, and society, describing how AI tools like Claude are transforming his programming workflow. He argues that AI represents a revolutionary technological shift comparable to electricity or the printing press, and encourages embracing this change with curiosity and optimism rather than fear, believing AI can dramatically increase human agency when used well.

Builder.ai did not “fake AI with 700 engineers”

The viral claim that Builder.ai used 700 engineers to manually pretend to be an AI system was false. In reality, the company built a code generation platform called Natasha using AI models like GPT and Claude, with a small team of about 15 engineers. The company collapsed due to alleged accounting fraud and misrepresentation of revenue to investors, not because of a fake AI scheme.

The Future Is Now (And It’s Iterative)

What strikes me most about these articles is how quickly we’ve moved from “Can AI write code?” to “How do I optimize my AI coding workflow?” The common thread isn’t just about technology getting better but about developers becoming smarter about how they collaborate with AI. Whether it’s Harper’s methodical approach, Armin’s experimental libraries, or Peter’s all-in computer replacement strategy, AI coding is changing how we develop applications.

Written by Jeff, typos fixed by Grammarly, summaries, feedback, and heading suggestions via Claude Code.