Recursive generation allows intent-based programming toolkits to ingest their own outputs and build entire applications from minimal specifications. This essay explores how recursive intent turns code generation into a compiler-like pipeline that scales small prompts into structured systems.
Traditional specifications failed because they required exhaustive detail. With LLMs, we can now embrace "Sufficient Specification" - providing just enough intent to guide generation without drowning in details. Learn how iterative specification specificity and generator patterns make intent-based programming practical for the first time.
Optimize your README.md files to empower both human developers and AI coding assistants alike.
AI coding assistants are excellent at generating tests with high coverage. But there is often a critical problem: they are testing that code behaves as written, not as intended. This "Test Theater" creates a dangerous illusion of quality while providing almost no real protection against bugs.
Exploring the simplest possible implementation of an agentic coder using just 200 lines of TypeScript, demonstrating foundational concepts clearly and concisely.
How to build an autonomous agentic coder that leverages advanced LLMs to transform software development. A talk given at the Ottawa Forward JS meet-up.
As AI dramatically reduces software development costs, we need to rethink our approach to code. Software is becoming ephemeral - easily replaced rather than maintained. This paradigm shift means optimizing for current needs instead of hypothetical futures.
AI agents are moving beyond IDEs into full automation of coding workflows. Discover how integrating agentic coding into GitHub Actions can streamline software development -- turning issues into PRs and automating documentation, tests, and even code reviews, all through simple GitHub comments.
Adding GitHub Mode to MyCoder has radically improved my productivity, making my agentic coder feel like a true team member. By leveraging GitHub for task tracking, pull requests, and CI/CD feedback, I’ve seen a 3x to 5x boost in development efficiency.
Adapting your codebase to match AI coder assumptions doesn't just help AI -- it uncovers hidden complexity, ultimately benefiting human developers.
As AI coding agents become increasingly capable, we need to rethink our code organization and documentation practices. Here's why traditional "human-first" coding approaches are becoming obsolete and how to structure your projects for optimal AI compatibility.