developer experience

A client's engineering team adopted AI coding tools across the board. Individual velocity soared. But their DORA metrics flatlined — and the reasons had nothing to do with the tools themselves.

The SpaceX-Cursor deal forced a lot of teams to rethink their AI tooling strategy overnight. But the real problem isn't the acquisition — it's that most teams never had a strategy to begin with.

Last quarter I tracked my time across three consulting engagements. I spent 11 hours a week reviewing AI-generated code and under 8 writing my own. The ratio surprised me, and it changed how I think about developer productivity.

A client's deployment kept failing in staging but not locally. The root cause wasn't code — it was sixty-seven environment variables spread across five files with no documentation and no single source of truth.

A client's platform had 23 AI agents built by a team of 8. Nobody could tell me what half of them did. Agent sprawl is the new microservices sprawl, and the cleanup looks depressingly similar.

A client's staging environment had drifted so far from production that developers stopped using it. Tests passed in staging and failed in prod. Tests failed in staging and passed in prod. Eventually the team just stopped looking.

A 20-person team was running 14 microservices with three full-time engineers just keeping the infrastructure alive. We consolidated six of them into a modular monolith and cut their deploy time by 70%.

A client's team was drowning in a 200K-line TypeScript monolith. When we finally measured what was actually running in production, we found that almost a third of it was dead code nobody had touched in over a year.

A client's on-call rotation was burning people out — not because of real incidents, but because of noise. Here's how we cut alerts by 87% without missing anything that actually required a human.

A client's team tripled their PR output with coding agents. Six weeks later, their cycle time had doubled and nobody could explain why.

A 47-minute build pipeline had become sacred infrastructure. When we finally opened it up, we found cargo-culted steps, redundant checks, and a team afraid of their own tooling.

A tool comparison from a consulting engagement — what Biome actually delivered, where it fell short, and the decision I'd make again (and the one I wouldn't).

A consulting story about a platform engineering initiative that checked every box on paper but collected dust in practice — and the uncomfortable reasons why.

AI code review tools are everywhere now. After rolling one out on a real project, I learned more about our team than about the tool itself.

How a 45-minute end-to-end test suite trained an entire team to ignore test failures — and what we did about it.

The latest DORA report confirms what I've seen on consulting engagements — AI tools amplify existing conditions. If your processes are solid, AI accelerates you. If they're not, you just create technical debt faster.

A consulting engagement where we measured where engineering hours actually went — and discovered the biggest productivity killer wasn't what anyone expected.

A debugging deep dive into replacing wall-of-text logs with structured logging and trace IDs — and how it cut our mean time to resolution from hours to minutes.

Practical insights from months of using AI-assisted coding in real client projects — what works, what doesn't, and how it changed my workflow.