OP wanted to set up a nested copmany structure. Instead of Person -> LLC it goes Person -> Limited Partnership --> LLC. The in between company is only for tax-efficiency and has nothing to do with limited liability.

Even then you'd usually go with a Person -> Holding UG -> Startup UG structure. That's a few hours and less than a thousand Euros.

The question is, is that really only due to data center geo? I am always amazed how low latency and high quality Facetime between Europe <-> Australia is. Seems like good engineering can overcome less optimal geographics.

I find that hard to believe. Are you implying that Apple is running their own fiber network providing low-latency connection between Europe and Australia? Or what kind of "good engineering"?

I can vouch for GP's exact experience. Facetime does feel much smoother than other videocalling apps for Aus<>Europe. Of course they don't run their own fiber network. The good engineering is making it feel smooth and good despite that. At its core, nothing about computing is smooth. Everything is based on making it feel that way, using countless techniques.

What "techniques"? Audio/video over high-latency connection is not a computer game where there all all kinds of latency compensation techniques - several meeting participants start speaking at the same time, realize they do only after RTT, stop, then awkwardly wait for a moment and repeat hoping for no "collision", rinse-repeat. Everyone who often has meetings with participants connecting from different continents knows what I'm talking about. But you can have this in beautiful high-definition "smooth" 4K if bandwidth is high enough, yes.

the reply here is .. any software can really perform badly.. it takes some effort to not perform badly. the default gravity is to be buggy and bad performance. The parent-post is right there are hundreds of small parts and they all have to do well to accomplish "live video and audio across half the globe"

Very unrelated: first time I see someone with the same last name as me in the tech community, it's somewhat odd :)

The sound of it has something very gracious (or maybe it's the Italian vibe) :D

Async presumably happens in the JS runtime that bun calls into. Just need 1 thread to host that

If this works `git clone me@github.com:me/mine.git release_01 && ln -s release_01 /var/www/me/mine/current` then your Docker builds should also be extremely quick. Where I have seen extremely slow docker builds is with Python services using ML libraries. But those I reallly don't want to be building on the production servers.

"ECS would have worked for 99% of these apps, if they even needed that."

I used to agree with that but is EKS really that much more complicated? Yes you pay for the k8s control plane but you gain tooling that is imho much easier to work with than IaC.

Wait a minute, if this is on AWS then what are we talking about? On-prem k8s sounds fine to me but you don't have the ECS option.

I always feel like I am taking crazy pills when I read these threads. The k8s API and manifests config feels like a create standardardized way to deploy containers. I wouldn't want to run a k8s cluster from scratch but EKS has been pretty straightforward to work with. Being able to use kind locally for testing is amazing and k9s is my new favourite infra monitoring tool.

Even if you just run on 2 nodes with k3s it seems worth it to me for the standardized tooling. Yes, it is not a $5 a month setup but frankly if what you host can be served by a single $5 a month VM I don't particularly care about your insights, they are irrelevant in a work context.

Do those use cases need LLMs? Probably not. but if good results can be had with a day of prompting (in addition to the stuff mentioned in the article, which you have to do anyway) and a smaller model like Haiku gives good results why would you build a classifer before you have literally millions of customers?

The LLM solution will be much more flexible because prompts can change more easily than training data and input tokens are cheap.

> Do those use cases need LLMs? Probably not.

One of the points of the article is the importance of gathering data to support your conclusions.

> prompts can change more easily than training data

Training data is real, and prompts are not. I don’t think this is an apples to apples comparison.

I don't disagree that very numerical tasks like revenue forecasting are not a good fit for LLMs. But neither did a lot of data scientist concerns themselves with such things (compared to business analysts and the like). Software to achieve this has been commoditized.

As others have mentioned, one big issue is that every company does these things differently and just because someone texts you a link doesn't mean it's phishing, even though it feels shady. In Australia I have had calls by immigration officers on supressed numbers that wanted PII over the phone without being able to tell me what the purpose of the call is.

Wow, this is tricky. Even though you can look up the official number you will likely not get through to the same person.

The average person self hosts literally nothing, why would it be different for inference? Which benefits severely from economies of scale and efficient 24/7 utlization

Ok but "Business Email" wasn't exactly invented yesterday...

Which is my point. They did basically nothing new internally and will be able to capture what...10-20% of overall business suite market?

That’s genius