Writings

How I used autoresearch to run 65 autonomous prompt optimization iterations on a production LLM agent, cutting it 28% while retaining 98% output quality.

AI agents produce better output when the codebase is ready for them. Here are the four dimensions of codebase readiness that account for most of the gap.

Claude Code runs terminal commands and asks you to approve them. This explains what those commands mean and when to pause before saying yes.

Ruby LSP now has official Claude Code support. Install the plugin to give Claude go to definition, find references, and call hierarchy across your Ruby project.

Claude Code's native worktree support handles file isolation, but Rails apps need database isolation too. Here's how to extend it with the WorktreeCreate hook.

A practical guide to building an AI agent with Mastra that researches contacts, schedules follow-ups, integrates with Slack, and uses layered memory.

AI agents are stateless by default. Here's how memory systems actually work, covering the storage patterns, lifecycle triggers, and architecture behind agents that remember you.

How I built a bash-based agent loop that pulls work from Linear, implements features, runs code review, and opens pull requests autonomously.

MCPs give Claude capabilities. Skills teach Claude workflows. Here's the mental model I use to decide which one I need.

LLMs don't have access to the current date, causing issues in time-based analysis. Here's how to fix date and time handling in production LLM systems with explicit context.