Why are we here
I’ve spent two decades inside broken systems — first building them, then managing the teams duct-taping them together, now auditing and fixing them before they explode. Payments, fintech, crypto — if money moves through it, I’ve watched it fail in the dumbest ways possible (sometimes even by my hand).
When I took my (final?) leave from the corporate world last year, AI was the word in every executive’s mouth. Lots of wild predictions were thrown around, “Initiatives” were sponsored, then slashed by angry CISOs, everyone was vibe coding, no one had a clue.
So I decided to run an experiment to answer the questions on everyone’s minds: Is AI good enough to be used in production? If so, how? How will software engineering practices evolve? What are the skills that the engineers of the future should learn?
For the last 9 months, I have used AI in production systems, with real stakes, audits, regulators, real money. 0 to 1 MVPs, feature dev, infra, DevOps, SRE, accounting, governance, I have tried it all and lived to tell the tales.
I’m here to show you how to leverage AI coding tools as cognitive augmentation, to not only push software faster, but also safer, more secure and better than you’d ever be able within the normal constraints of the workplace. In my view, it enables engineering teams to achieve operational excellence at speed, by not taking the shortcuts they’d otherwise be forced into.
What you’ll find here:
Prompts and playbooks to grow from vibe-coder to master operator
Reflections on software engineering practices
Field notes from actual disasters and last-minute saves
Whatever else I feel like, this is a creative space
It feels SO GOOD to share this with you all. Thanks for reading.