As a founder, you sell yourself before you sell your product — and in B2B, no one buys without trust. This is where I write in public: a daily story from twenty years of building, and a book on what engineering leadership looks like now that AI has changed the job. New entries publish regularly.
As a founder, you sell yourself before you sell your product — and in B2B, no one buys without trust. So I'm sharing one story a day from my career and entrepreneurship: leaving IBM, chasing a traffic-congestion startup, relearning to code from scratch, and the turnarounds in between. Honest, in public, one day at a time.
Today marks the start of my 365-day challenge—one personal story every single day....
Read the story →When I resigned in April 2015 to venture into entrepreneurship, I had no idea what business I was going to start. But one thing was clear—I did not want to do a job anymore....
Read the story →With my business idea finalized during my notice period—solving traffic congestion—the next challenge was:...
Read the story →New stories publish regularly — check back for the next entry in the series.
Building future-ready engineering organizations in the AI era. For the first time in the history of software, the activity that consumed most engineering effort — writing code — is no longer the primary bottleneck. So what does a future-ready engineering organization actually look like?
Written chapter by chapter, in public, from twenty years of engineering leadership.
I am writing this book because I believe we have reached the end of a long chapter in the history of software development — and the beginning of another for which we do not yet have a playbook.
Read the chapter →Most engineering organizations fall into two categories: traditional engineering orgs, and reasonably mature (but imperfect) agile organizations. Understanding where we are is crucial before we can evolve.
Read the chapter →The way most engineering organizations work today is not accidental. It is the result of years—sometimes decades—of practical learning under very real constraints. And for a long time, these models didn't just exist. They worked.
Read the chapter →20 of 32 chapters written so far. New chapters publish regularly as full pages.