Thanks! I don't know much about it, but from what I can tell from their site, it does differential testing based on screenshots and behavior, and mocks out network IO, focusing on the client side. I'm not sure what the AI part is, if it's for extracting information from the page, for generating actions, for smarter diffing, or some combination of those.
Bombadil doesn't mock the network (although I'm working with a contributor to add something like Playwright's Route system for manipulating responses). It runs on your full stack (if you want) and if you need determinism for perfect bug reproducibility, you should run it all in Antithesis.
On the AI front, my view is that Bombadil should be optimized for using LLMs to generate and refine specs over time (properties and action generators), but that it should be an offline process. It's not like a "browser testing agent". It's analogous to having the LLM generate a program, rather than having the LLM itself perform actions. For some problems, the latter makes more sense, but for many problems the former is better, and I believe that PBT (and things like proof systems, but that's not my domain) are suited to the former strategy.
I have some sketches on how the refinement-with-human-in-the-loop thing should work with Bombadil and LLMs, but it's only a roadmap item for now.
Bombadil doesn't mock the network (although I'm working with a contributor to add something like Playwright's Route system for manipulating responses). It runs on your full stack (if you want) and if you need determinism for perfect bug reproducibility, you should run it all in Antithesis.
On the AI front, my view is that Bombadil should be optimized for using LLMs to generate and refine specs over time (properties and action generators), but that it should be an offline process. It's not like a "browser testing agent". It's analogous to having the LLM generate a program, rather than having the LLM itself perform actions. For some problems, the latter makes more sense, but for many problems the former is better, and I believe that PBT (and things like proof systems, but that's not my domain) are suited to the former strategy.
I have some sketches on how the refinement-with-human-in-the-loop thing should work with Bombadil and LLMs, but it's only a roadmap item for now.