Let’s say it is, so what? The actual question to ask whether the text conveys useful information to the reader or not. Whether LLM was used to make the content more readable is completely irrelevant.

Does it identify people like Kid Starver who are murdering the disabled on an industrial scale?

What specifically isn’t true?

‘democracy’

For sure, having mature and battle tested libraries is really hard to beat.

Clojure has a lot better story in that regard living on the JVM, but the overhead of using the JVM is a downside of its own. It’s a good platform, but definitely not what you’d call lightweight.

I briefly worked with Hasura which does this sort of magic to produce GraphQL API on top of Postgres. Incidentally, also written in Rust. I do like Leptos approach, it sounds similar to HTMX approach where you just treat the DOM as a dumb terminal.

Being a Lisper, the idea of waiting for the compiler is very jarring. :)

It’s great to hear things mostly worked out. Stuff like scaling bottlenecks is definitely tricky to catch until you have serious loads on the system, but sounds like the fixes very mostly trivial validating overall design. It also looks like you managed to get a way with a fairly simple stack by leveraging Postgres and Rust. I’ve had really good experience with using pg myself, and really don’t see a point in using anything else now. You can use it both as a relation db and a document store, so it’s extremely flexible on top of being highly performant. Keeping the stack simple tends to be underappreciated, and projects often just keep adding moving pieces which end up adding a lot of overhead and complexity in the end.

What have been the biggest challenges with the project over the years, both in terms of technical and non technical aspects. I’d be interesting to hear a bit of retrospective on how has the stack’s been working out, and what surprises you might’ve run into in terms of scaling and federation. What recommendations you’d make based on that and what you would’ve done differently knowing what you know now.