Happy Friday. Week 0 of doing these roundups publicly. Let's see how it goes. This week was mostly about figuring out where AI actually saves time in an engineering workflow versus where it just creates a convincing illusion of speed. Here's the honest tally. ✅ What worked 1. Using AI for the first draft of boilerplate, not the logic. Generating scaffold code — config files, test stubs, README sections — is where I stopped fighting the output and started trusting it. The model doesn't need to understand your domain to write a Dockerfile or a pytest fixture skeleton. Ship the boring stuff fast, think harder on the real problems. 2. Prompt iteration as a debugging loop. Treating a bad AI output like a failing test — rather than a reason to give up — changed everything. Reframe, constrain, give an example. Three rounds of that usually gets you somewhere useful. The mental model shift is: you're not asking, you're specifying. 3. Writing documentation before the code. Sounds backwards but it works.…