mrr7337

Interesting. How are you handling sheer number of replays without missing anything important?

Not enough community around it. Shopify would have to pioneer a lot more than they would like to get it working for their use case.

These benchmarks are done with incorrectly and missing a lot of baselines. The article seems very vibe coded too.

Lol. Yeah, do you have a good example I can follow and learn from? That would probably help explain well.

Yep yep that's why I didn't like it when I was originally using MS Clarity. It was just summarizing stuff that was obvious (and more importantly after the issues happen). RJ here is built mainly for a single focus of being really automated. Of course, if you want the classic product analytics like replay, crashes, api, heatmaps, that also exists in the RJ dashboard but that's more for human validation of the issues spotted.

Wow Turing Scholars! you guys are like the navy seals of CS at UT lol. Yes I appreciate the insight and will actually hand write it (and copy some parts of this post, but it is a bit too goofy to just use the post straight up). One thing you mentioned, I've never heard of Readmes including a history of the project, because I thought Readmes were like a small intro doc with a bunch of hyper links only.

For example, I looked at the these two Readmes: https://github.com/rybbit-io/rybbit/blob/master/README.md <-- this one is lovely https://github.com/supabase/supabase/blob/master/README.md

Both include pictures, features, ect, but not really histories, unless I'm understanding your comment incorrectly.

Keep it up bro! Tell your kid to share it with his friends and family to get feedback and support!

Nothing Proton VPN can't solve

I didn't really understand anything...lgtm

Great Question! We have a few options when it comes to how we process a 100s of session replays that each can be a few MBs or greater. The main concept here is that we initially use a grouping system that groups the same signals (such as rage taps or dead taps) that occurred on the same app or website page for many sessions, and admit a random sample of that group into deeper analysis, which is an LLM of choice. We also have other tricks up our sleeves such as reusing session context we already put the effort into processing before among other things. We're working on improving this system though so let us know what suggestions you have!