> but not contemplating that it's going to be out in the wild for 10 years either way
I think there are a lot of developers working in repos where it's almost guaranteed that their code will _not_ still be there in 10 years, or 5 years, or even 1 year.
> People may not know that the reason they like your product is because the code is so good, but everyone likes software that is mostly free from bugs, performs extremely well, helps them do their work quickly, and is obviously created by people the care deeply about the quality of the product they produce (you know, the kind that acutally read bug reports, and fix problems quickly).
I would classify all of those as "capabilities and limitations of your product"
I read OPs "good code" to mean "highly aesthetic code" (well laid out, good abstractions, good comments, etc. etc.), and in that sense I agree no customer who's just using the product actually cares about that.
Another definition of "good code" is probably "code that meets the requirements without unexpected behavior" and in that sense of course end users care about good code, but you could give me two black boxes that act the same externally, one written as a single line , single character variables, etc. etc. etc. and another written to be readable, and I wouldn't care so long as I wasn't expected to maintain it.
I really don't see how this can be possible unless they're accepting abysmal recall? Perhaps I'm missing something fundamental here, but the idea that AI and non-AI assisted text can be separated with "nearly 0 false positives" just says to me that it's really just a filter for the weakest, most obvious AI generated text. Is that valuable?
We use playwright for interacting with the browser, so while it's not available by default, we do support bulk exporting tests as playwright to move off our platform or to customers who want to run deterministic versions of the tests on their own infra (you can also run them on ours!)
This is interesting, I think we've shied away a bit from security-ish use cases since it's outside of our personal core competencies, do you have examples of what tools exist today for catching things like that? Or is it totally adhoc?
> can the agents check their email? other notification methods?
Yes to email (for paying customers agents spin up with unique addresses), no to other notifications, but as soon as a paying customer has a use case for SMS, etc. we'll build it.
> Are your agents good at testing other agents? e.g. I want your agent to ask our agent a few questions and complete a few UI interactions with the results.
I'd say this is one of our strong suits I think, specifically the UIs tend to be easy to navigate for browser agents, and the LLM as a judge offers pretty good feedback on chat quality and it can inform later actions. (I'd be remiss not to mention though that a good LLM eval framework like Braintrust is probably the best first line though)
> How do you handle testing onboarding flows?
We can step through most onboarding flows if you start from logged out state & give the context it'll need (i.e. a stripe test card, etc.) That said though, setting up integrations that require multi-page hops is still a pain point in our system and leaves a lot to be desired.
Would love to talk more about your specific case and see if we can help! [email protected]
Hey I'm Matt! Really excited to answer any questions.
To elaborate a little bit on the "canary" comment --
For a while at Airtable I was on the infra team that managed the deploy (basically click run and then sit and triage issues for a day), One of my first contributions on the team was adding a new canary analysis framework that made it easier to catch and rollback bugs automatically. Two things always bothered me about the standard canary release process:
1) It necessarily treats some users as lower value, and thus more acceptable to risk exposing bugs to (this makes sense for things like free-tier, etc. but the more you segment out, the less representative and thus less effective your canary is). When every customer interaction matters (as is the case for so many types of businesses) this approach is harder to justify
2) Low frequency / high impact bugs are really difficult to catch in canary analysis. While it’s easy to write metrics that catch glaring drops/spikes in metrics, more subtle high impact regressions are much harder and often require user reports (which we did not factor in as part of our canary). Example: how do you write a canary metric that auto rolls back when an enterprise account owner (small % of overall users) logs in and a broken modal prevents them from interacting with your website.
I view what we’re building at Propolis as an answer to both of these things. I envision a deploy process (very soon) that lets us roll out to simulated traffic and canary on THAT before you actually hit real users (and then do a traditional staged release, etc.)
I think there are a lot of developers working in repos where it's almost guaranteed that their code will _not_ still be there in 10 years, or 5 years, or even 1 year.