The Speed of Prototyping in the Age of AI

Because we have experience doing things pre-AI.

For example, most projects had no tests before AI because tests are very time intensive and take a lot of forethought since you also need to engineer the code to be testable. Yet now tests are trivial. Every project I delegate to AI has tests. Good tests too. How do I know? Because I have 20 years of experience and I looked.

Or, fixing a bug in my codebase is as simple as copy and pasting the user's bug report email into Claude Code. The LLM verifies that the bug exists, writes a red test to ensure the bug exists, then proposes the best fix that will turn the test green. Meanwhile it did all this while I was doing something else.

Or, the projects I've built that would have taken me a lot of time because they are in domains I don't have much experience in. I built a macOS app around libghostty which involved bridging Swift to Zig, something I've never done before. And when Ghostty has new versions, I ask AI to look at the diff and find new features and APIs I can take advange of in my own project. I didn't write any code in this project myself. It would have taken me a lot of time because everything takes a lot of time. It makes progress on the project while I'm doing something else, yet I use the project as my main terminal every day, it's so good.

It's very wishful thinking to assume that nobody knows whether AI is helping them or not, probably coming from an understandable place where you hope all of this a fever dream and we'll go back to the old way any day now. But you owe it to yourself to believe others here so that you can take it seriously.

I also think there are a few interesting things you can explore that go beyond a simple carbon copy of what's on the Internet. Ideas that I've implemented are things like automatic extraction of audio tracks, transcription, and summarization, loading a page or podcast transcript into the context window of a LLM to discuss the arguments or factuality of the claims being made, automatically turning articles to reader view using readability/trafilatura, etc.

Directions I'd like to explore would be things like multimodal search ("that page I read six months ago about computer security with neon green text on a black background", or give me a list of fitness related pages I've read in the last twelve months), personal statistics (how is the mix of topics I've been reading about changing over time), annotating pages instead of just passively reading them, maybe even P2P archiving or discussions about pages, and all kinds of other things.

But installing archivebox would be easier indeed.

I’ve felt that any non trivial amount of code not written myself tends to be hard to own. And like the author said, need to keep skills sharp also

One of the second order effects of AI collapsing the cost of building things is that product management is much more important now. A Product Owner/Manager who lacks the taste and insight (or data) to know what they should put in front of users and what they should just put in the bin will cause a company real harm, especially if the company moves to a "there's zero effort in building something, so we'll try everything!" model.

The only part that's really collapsed in effort is the translation from requirements into code. If you're using AI to generate requirements you're effectively building things based on what a 'random' requirements generator says. If that's as good as the requirements a Product Owner was writing then that person needs to improve.