How to Make an AI Coding Agent Actually Yours
I built Monet to fix the agent that forgets yesterday’s conventions. How an agent that writes its own memory, reads what mattered, and grows with use actually works.
Insights
Interpreting what changes mean and naming the problems that keep recurring — judgment frames, not information.
How Deep Does Your Coding Agent Setup Go?
Two developers, same tool, wildly different setups. How deep coding agent customization goes — from bare defaults to full pipelines — and what actually pays off.
Why We Need Behavioral Benchmarks for LLMs — Not Just More Knowledge Tests
What today’s LLM benchmarks actually measure, what they miss, and why we need behavioral benchmarks instead of more knowledge tests.
Token Economics: The Real Cost of AI Coding Agents
Two requests with near-identical input tokens, a 46x cost gap. How prompt caching really works, and why agents should be designed around structured state.
Claude Opus Prices Just Crashed 67%. Is Anthropic Still Making Money?
Opus prices fell 67% in a year. A weekend of napkin math on GPU costs, batching, and tokenizer overhead to estimate what Anthropic actually makes per token.
Does Your Coding Agent Need Memory?
Coding agents re-read the same files and re-ask the same questions every day. What memory actually does for an agent, how it differs from docs, and when it pays off.
How Are You Managing Your AI's Context Window?
Bigger context windows didn’t give better results. Notes on the context-management problems that keep recurring in real coding agent sessions.