I think Zig does, specifically because of its build system and it's C interopt story. Plus Zig has the added advantage that learning it teaches you about how the computer works, how memory gets laid out, etc. So even if it never gets wide adoption the skills are highly transferable.

The questions also had a tendency to be somewhat shallow. There were a lot of places where it felt like the interviewer was queuing Andrew to respond to criticism or explain controversial choices made for the language or tool chain, but the interviewer doesn't really follow up on them are point out what the issues might be.

It might have been expectations I guess. I was hoping for an interesting technical interview and instead it seemed like a fluff piece.

Anyone using Bun should consider migrating away immediately. Not because of the LLM angle, but because of how insanely irresponsible this is.

We have absolutely no need for it at work. We're writing micro services that run in K8s with no extreme performance requirements. Nobody on the team knows the language (I know it better than the people arguing for it, and I don't know more then the basics). And yet, every couple of weeks, I'm having to talk someone out of switching certain services over to it. It's like a damn disease.

But here's the thing, you almost never know what the architecture is up front. If you do you probably aren't the one writing the actual code anymore. Writing the code, with or without an AI is part of the design process. For most people it isn't until they've tried several times, fucked it up a bunch, and refactored or rewrote even more that you actually know what the architecture needs to be.

It would be awesome to read a blog post about the project. Your approach, lessons learned, unexpected stuff, etc.

People want to use stuff like this as somehow evidence for AI being able to write entire software systems in a few days. We saw the same shit with the "compiler" they made with a bunch of agents. Literally the only reason it's possible is because the hundreds of thousands of man hours and God knows how much money that was poured into the reference projects befoes the AI got anywhere near it.

To replicate this kind of thing with a green field project would take an absolute ton of spec work and requirements derivation, which will substantially eat into any savings from having AI generate it.

The accomplishment itself is interesting, and unlocks opportunities to do work no one would have bothered with before, but it doesn't represent what a lot of people desperately want it to.

Ignore the hundreds of thousands of hours put into the original architecture and test suite that made it possible in the first place.