Preface
We’re at a tectonic shift moment in software development.
Over the past few years, generative AI tools have gone from novelty to necessity. What began as autocomplete suggestions in your IDE has evolved into full-blown copilots, design assistants, and production-grade agents that can debug code, ship features, or scaffold entire applications. The way we build software is changing—and it’s changing very fast.
As a fractional CTO, for the past decade I’ve been helping early-stage companies ship better software, faster. My job is to set high standards for development velocity and product quality, and to make sure my teams leverage the best tools and processes. That means I need to be ahead of the curve and try the latest tools as they become available. I evaluate which ones can improve our workflows, and I integrate them into our development processes and foster adoption. I’ve led engineering teams that have accelerated their velocity and increased their delivery quality by using tools like ChatGPT, Cursor, GitHub Copilot, and Lovable for their daily software development processes.
But it’s overwhelming. New models are released every month. New tools are launched every week. And every day, I see developers using them in wildly different ways: to code faster, to learn new stacks, or to go from zero to launch in a weekend. I knew I wasn’t alone in struggling to keep up. So I decided to take a step forward, test all the tools I could find, and distill the signal from the noise. ...
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