AI Coding
Building AI systems that watch, learn, and evolve with your codebase.
Overview
AI coding has evolved dramatically from simple autocomplete. Modern tools can understand your entire codebase, make multi-file changes, run tests, and iterate based on results. But getting the most out of them requires understanding how they work—and how they fail.
This section is for developers who want to move beyond copy-paste workflows to build genuinely integrated AI development systems.
Topics
Agentic Patterns
Harnesses, context systems, self-monitoring, and building AI that integrates with your environment.
Prompt Engineering
Effective prompting strategies specifically for code generation and debugging.
Tool Comparison
Honest comparison of Claude Code, Cursor, Copilot, Aider, and custom solutions.
The Agentic Difference
The shift from chat-based AI coding to agentic AI coding mirrors the difference between asking someone directions versus having a GPS that navigates for you.
Chat-Based (Old Way)
- • Copy code to chat
- • Describe what you want
- • Copy response back
- • Find it doesn't work
- • Repeat
Agentic (New Way)
- • AI reads your codebase
- • Makes changes directly
- • Runs tests automatically
- • Iterates on failures
- • You review final result
Last updated on Dec 18, 2025