I build tools, design languages, and write about the things that quietly break in prod.
Four months ago I asked ChatGPT a dumb question while walking my dog. It told me building a programming language wasn’t that hard. So I built one — a compiled language with explicit error handling, generics, structured concurrency, and a garbage collector. Here’s what happened.
· cli
Defining the ‘Gold Standard’ for production-ready CLIs: TTY-aware behavior, clean stdout vs stderr contracts, JSON output for automation, and idempotent mutations.
The pitch is irresistible — per-PR environments, live links, instant feedback. Then someone needs twenty restarts to get a multi-service stack running, and the pitch meets operational reality.
Your CI pipeline is building the same Docker image three times per run, a dependency bot just DDoS’d your runner fleet, and nobody’s going to fix it — because nobody owns it. How shared pipelines corrode without a single bad decision.
Production has dashboards, alerts, and structured logging. Your test environment has SSH. Your CI runners have a re-run button. That gap is quietly eating your engineering velocity alive.
Most languages ship tooling years after the compiler. I built the Yar IntelliJ plugin in parallel with the compiler itself — 19 commits tracking language changes in real time. Here’s why DX-first language development changes how you think about design.
Implementing a garbage collector for the Yar compiler meant scanning the stack without knowing what’s a pointer. Mark-and-sweep, conservative scanning via setjmp, heap growth targeting, and the fun part: making it work when threads are running.
When the Yar compiler couldn’t import code across project boundaries, I had to build a package manager from nothing. Git-based fetching, alias-based imports, content-addressed caching, transitive resolution — and zero registry. Here’s every decision that mattered.
· music
I thought streaming was the ethical upgrade from piracy. Turns out it just made music disposable. Now I buy CDs, pirate FLACs, and actually listen to albums again.
· go
Why I built mokapot — a single Go binary that speaks SQS and SNS well enough for real AWS SDKs to connect without configuration changes. Persistence, filter policies, and the tricky parts of faking a cloud service locally.
Four real examples from recent Go CLI projects — bracketed paste handling, a scripting DSL, safe symlink restoration, and diff truncation — that show how small decisions separate a script from a tool people trust.
What happens when you give an AI coding agent memory that persists across tasks? It stops repeating the same mistakes. Here’s how lgtm and snap use persistent findings, shared context, and self-recovery to close the loop from planning to merged PR.
