Adam Engst explains why AI software development should be approached as an evolution, starting with safe, verifiable data analysis, advancing to simple scripts, and eventually building applications. He distinguishes programming from development, arguing that non-programmers can act as product managers who direct AI, demand security checks, define requirements, and create software tailored to problems existing apps do not solve. (3)
Fruit flies aren't exactly famous for their brainpower; you've probably drowned more than one in a wine glass left too long on the patio table. And yet, working with roughly 140,000 neurons—a brain smaller than a poppy seed—Drosophila can sort through a huge range of smells in a fraction of a second, and then retain the memory of that scent for a long time.
In this, they do much better than current "electronic noses." Even the most advanced ones on the market tend to be expensive, painfully narrow in what they can detect, and quick to forget an odor the moment they learn a new one.
So why not just copy the fly? That's the question a growing number of researchers have been asking—including Kevin Max and Yang Shen at the Okinawa Institute of Science and Technology, whose new algorithm, Spi-Fly, is described in a paper recently published in the journal Neuromorphic Computing and Engineering.
Adam Engst looks back at HyperCard as an early tool that democratized software creation, then connects its legacy to today’s AI-assisted development. He outlines how Claude helped turn an idea for tracking macOS setting changes into a working Mac app, SetShot. Adam discusses Xcode, GitHub, rapid iteration, and the value of visual feedback in his development process, and how AI lowers programming barriers without eliminating the need for thought, judgment, and effort.
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