If you are trying to commercialize something, a popular project with bad margins is a better spot to be in than an unsuccessful project with good margins. If it's a personal learning project, that might not be the case.
These cloud back and stacks are very cheap at low volume and honestly I expect them to remain so or even go down in price.
I've seen many colleagues bootstrap something - even if they're not themselves very technical - because they've leveraged these well integrated low cost platforms.
I do think it’s binary. The project either shows potential to meet your goal or it doesn’t.
I think it’s rare that fails to show potential because of the underlying technology that’s chosen.
Sure, Vercel is relatively expensive. But I just don’t see how you’d throw in the towel because the costs are too high without first evaluating how to lower them.
If you’re saying that the evaluation is likely to show that you’re stuck - I have never seen that be the case personally.
As a "real engineer" you'd likely use LLMs differently. I save my conversations, have chats and codebase exegesis summarized into .txt files, and constantly refactor LLM output. I have an increasingly reliable sense of when to dip in and write things myself and when to let the LLM rip. My LLM-assisted code is better than my hand-written code; how could it not be? I'd have to be committing raw LLM output without even reading it to end up somewhere worse. If I did that, how much of a "real engineer" would I be?
All this is to say: even if all progress on AI halted today, it would remain the case that, after the Internet, LLMs are the most impactful thing to happen to software development in my career. It would be weird if companies like Supabase weren't thinking about them in their product plans.
Have you found any good resources on how to get a good process going? That would be an interesting read.
I have two main issues, first the tooling is changing so rapidly that as I start to hone in on a process it changes out from under me. The second is verifying the output. I’m at like 90% success rate on getting code generated correctly (if not always faster than I could do it) but boy does that final 10% bite when I don’t notice.
An aside, I think the cloud ought to make your (perhaps especially your) list. At least for me that changed the whole economy of building new software enterprises.
For “real work” done by a “real engineer”, I approach it almost exactly as you say.
For side projects/personal software that I most likely would have never started pre-llms? I’ll just go full vibe code and see how far I get. Sometimes I have to just delete it, but sometimes it works. That’s cool.
