zumachase

We don't but it helps with marketing and has synergies with other paid products

Most of the traffic is P2P. We operate a couple of geo distributed relays for NAT'd traffic. But everything is 100% e2e encrypted thanks to webrtc.

It's mostly a tool for us and something that helps sell our other products, but we decided to split it out as a separate app so that it could be used independently. All we collect is a name and email, and even those can be pseudonymous.

Wow...an S-1 with positive net income.

Also there's a large selection bias: people using less common or more esoteric/demanding languages tend to be better engineers who have invested the time in learning something off the beaten path. That absolutely does not mean it's the right decision since most employees will statistically be average and you want to have a stack that appeals to a large enough talent pool.

We've played around with that and it's on the roadmap. But it just adds another set of fixtures to herd.

This is what we do but it's too manual for my taste.

We've got a core product running on Django, and one thing the author doesn't mention is testing migrations. The migration system is wonderful, except for the difficulty in testing migrations. There's no sane/official way to do this. And it's such an important thing that I don't get why the Django crew haven't tackled it.

Hi Vlad - your anecdote about ship tracking is interesting (my other startup is an AIS based dry freight trader). You must know the Vortexa guys given your BP background.

How does QuestDB differ from other timeseries/OLAP offerings? I'm not entirely clear.

The prompt contained a few react examples with annotated English descriptions that were similar enough. You’re not seeing the entire prompt and as impressive as this is, it’s likely that you’d end up writing more code trying to get it to do what you want than if you just wrote it yourself. Future GPTs may of course be better, but there’s a bit of “magic” to these demos that the author isn’t super upfront about.

We're incredibly excited about GPT-3. I think there is a fair bit hype exhaustion, especially from the likes of OpenAI ("our AI is too dangerous to release"). So this is completely understandable.

However I think what's missing here is our benchmarks (a la Turing test) are about negation as opposed to affirmation. We tend to evaluate AI on whether or not we can discern the fact that it's AI. We seek to negate it as human, as opposed to affirming it as human (or close to). And this is not the right mindset when it comes to AGI because the gap between "obviously not human" and "human-like" is enormous. These are all definitely steps in the right direction, and the applications for even robotic process automation will be huge. But we're not even close to having nets that can reason about even the most basic things.

I’m not really sure what containers/k8s has to do with JVM? Containers are just name spacing, not virtualization. If you’re running in the cloud, either way you slice it you’ll have two virtual machines: hypervisor and JVM.

I’d argue that being able to herd your JVM procs like cattle makes them good candidates for k8s because you can always just set resource limits so they get purged when the heap becomes too large.

I think this is pretty myopic. As someone who straddles both sides of this, the world is not that simple.

If the "business side" as you say isn't viable, then nothing matters. This has nothing to do with late stage capitalism...this is capitalism 101. If you're not hitting critical mass, you're dead.

This take also continuously misses that most people, and thus must customers, are willing to trade privacy/performance for convenience. If that's not your cuppa, then hopefully there's an alternative.