Fatbobman's Swift Weekly #145

Photo by Skye Studios on Unsplash

When Inspiration Outruns the ResultsRead on web →

Over the past few months, I’ve been refining my own AI-assisted workflow. Real progress has been made, but for long-running tasks, I’ve consistently lacked any quantifiable benchmark data. Thanks to competition among AI model providers, OpenAI recently lifted its 5-hour usage cap — which finally let me test my ideas without interruption, and compare the results and performance of different approaches head to head. Last week I burned through 6 or 7 accumulated limit resets across my two Pro accounts, and genuinely got a taste of “token freedom.”

Since each test run typically takes at least several dozen minutes, I found myself flooded with ideas while waiting and observing — new optimizations and tweaks kept popping into my head. This produced a strange phenomenon: while the old version was still running its test, the new version’s code was already written. My iteration speed had completely outpaced my test results. Some of those changes did turn out to work well, but once I calmed down, I had to face an uncomfortable truth — many of my “brilliant ideas” were really just assumptions I hadn’t thought through carefully.

At the time, caught in the false “flow state” that vibe coding produces, I didn’t notice this at all. It wasn’t until the test results showed a clear regression that I slammed on the brakes and actually stopped to reflect.

AI genuinely shortens the time it takes to validate an idea — but that convenience makes it easy to fall into a blind spot, skipping the deep thinking and logical groundwork that should happen before you start coding. Cheap, exhaustive testing shouldn’t become a substitute for real thought. Looking back, the intuition and experience we’ve built up are often far more efficient than AI’s blind trial-and-error at ruling out or refining ideas that were never going to work.

AI is an extraordinarily powerful tool — but where it should really earn it…