160 karma · joined February 8, 2018
meet.hn/city/40.7146220,-73.9534500/Williamsburg
Socials: - github.com/mpwhite
https://support.claude.com/en/articles/9517075-what-are-proj...
https://collusion.wiki/#different-from-hf
>The main reason we believe this was a distinct swarm is because these agents explicitly had internet access as part of their task—the whole point was web browsing. The Hugging Face agents were in a sandbox without internet access and had to hack their way out by exploiting the Artifactory package manager.
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.
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?
It's certainly not perfect, but similar to what you mention.
> 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.
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! founders@propolis.tech
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.)
Is any of your/their work published?
> This video is a feat of human intellect and ingenuity.
I couldn't agree more! Anyone reading this who can spare 20minutes - It's certainly worth it, and only escalates the longer you watch.
Formal language is certainly the most effective mode of communication for some things, but don’t discredit the value of other forms.
Also, this comment seems to discount the spirit of the post without offering any real reasons why.
I run a service that tracks work/sleep/exercise/meditation (along with a couple other data points that don't fit the duration model) via an SMS interface.
I text it when I start/stop these activities and then it sends me reminders based on when I normally do those things during the day if I haven't already.
It would be great to be able to push the data I collect into your service to have a unified view of how I spend my time, and also to pull events from your service to send reminders to do certain things via my bot.
Kudos to you!
My (possibly useless) .02 -- I would not pay for this as is. What I would pay for though is an API that reliably converts internet articles to markdown of the same quality your pdf generation seems to have. A couple years ago I poured 3 or 4 months into a site that let users select time-boxed streams of articles clustered by topic (i.e. "give me a 25 min list of content about Glacier National Park for my subway commute") By far the most challenging part of this project was formatting content in a readable way from various sources, which it seems like you have done VERY well here.
Anyway, congrats on shipping something, regardless of customer base that's very impressive!