I think it had all the pieces (api,cli,etc.) already that it would've still be very useful in an AI world without deeply integrating AI things (copilot, etc.). I'd take higher availability over AI features any day.
DRMacIver, can you comment on how this fits into the existing property-based testing ecosystems for various languages? E.g., if I use proptest in Rust, why would/should I switch to Hegel?
The short answer to how it fits into existing ecosystems is... in competition I suppose. We've got a lot of respect for the people working on these libraries, but we think the Hypothesis-based approach is better than the various approaches people have adopted. I don't love that the natural languages for us to start with are ones where there are already pretty good property-based testing libraries whose toes we're stepping on, but it ended up being the right choice because those are the languages people care about writing correct software in, and also the ones we most want the tools in ourselves!
I think right now if you're a happy proptest user it's probably not clear that you should switch to Hegel. I'd love to hear about people trying, but I can't hand on my heart say that it's clearly the correct thing for you to do given its early state, even though I believe it will eventually be.
But roughly the things that I think are clearly better about the Hegel approach and why it might be worth trying Hegel if you're starting greenfield are:
* Much better generator language than proptest (I really dislike proptest's choices here. This is partly personal aesthetic preferences, but I do think the explicitly constructed generators work better as an approach and I think this has been borne out in Hypothesis). Hegel has a lot of flexible tooling for generating the data you want.
* Hegel gets you great shrinking out of the box which always respects the validity requirements of your data. If you've written a generator to always ensure something is true, that should also be true of your shrunk data. This is... only kindof true in proptest at best. It's not got quite as many footguns in this space as original quickcheck and its purely type-based shrinking, but you will often end up having to make a choice between shrinking that produces good results and shrinking that you're sure will give you valid data.
* Hegel's test replay is much better than seed saving. If you have a failing test and you rerun it, it will almost immediately fail again in exactly the same way. With approaches that don't use the Hypothesis model, the best you can hope for is to save a random seed, then rerun shrinking from that failing example, which is a lot slower.
There are probably a bunch of other quality of life improvements, but these are the things that have stood out to me when I've used proptest, and are in general the big contrast between the Hypothesis model and the more classic QuickCheck-derived ones.
I know it and do like it! Even for those that follow Diataxis, in my experience the "Explanation" sections are often lacking, especially compared to the "How-To" or "Tutorial" ones.
Python is one example that comes to mind. They do have explanations here: https://docs.python.org/3/howto/index.html. And, to be fair, they are generally excellent in my opinion! But they're far from front and center and there's much less overall content compared to the other Diataxis types I think (granted, I haven't rigorously checked).
Maybe a hot take: no, it didn't.
I think it had all the pieces (api,cli,etc.) already that it would've still be very useful in an AI world without deeply integrating AI things (copilot, etc.). I'd take higher availability over AI features any day.