Historically in a lot of niches such as search marketing etc, people would not name their successful projects because the barrier to entry is low.
It someone can use AI to make a $50,000/year project in three months, then someone else can also do so.
Obviously some people hype and lie. But also obviously some people DID succeed at SEO/Affiliate marketing/dropshipping etc. AI resembled those areas in that the entry barrier is low.
To get actual reports you often need to look to open source. Simon Willison details how he used it extensively and he has real projects. And here Mitchell Hashimoto, creator of Ghostty, details how he uses it: https://mitchellh.com/writing/my-ai-adoption-journey
Update: OP posted their own project however. Looks nice!
This is definitely the case. I have a project that while not wildly profitable yet, is producing real revenue, but that I will not give details of because the moat is so small. The main moat is that I know the potential is real, and hopefully not enough other people do, yet. I know it will disappear quickly, so I'm trying to make what I can of it while it's there. I may talk about it once the opportunity is gone.
It involves a whole raft of complex agents + code they've written, but that code and the agents were written by AI over a very short span of time. And as much as I'd like to stroke my own ego and assume it's one of a kind, realistically if I can do it, someone else can too.
Maintaining a codebase with mongodb db is already hard enough considering 99% of the time you need a relational db. It always end up as a mess.
But letting an llm doing this as well, ouch. I fear for the maintainability of the codebase in the long term.
Both are hard for complex apps regardless, which is why we’re making sure that the framework has all the necessary guardrails to prevent devs from using it incorrectly.
Relational dbs just have more built-in guardrails, but in our case we prefer to have the same guardrails in the framework.