olafura

Yeah, the limit was exhausted weirdly fast compared to openai and anthropic models. I checked the thread to see if more people had this issue on the 5x plan, but why pay for more when it's barely usable for real work.

Which is even weirder given that the api costs are less than for the other providers

Maybe they don't really care about antigravity users. Also, monitoring usage is horrible.

It keeps on being weird

Wonder if it's a Gemini model because it was getting stuck on a rebase command because of vim being a non-interactive editor

You can just use git to clone the code

If you want we can schedule a quick video call. Just curious about the problems you are facing.

I still don't understand this. If you are big enough then you get Amazon to make an official sdk, if you aren't then what exactly are you looking for?

The official aws cli used to talk to the soap interface and used regex instead of actually doing correct error handling and that was used by so many tools. Even though it used to break horrible.

It's quite a niche you are talking about, not big enough to debug open source code but still big enough to require SLA for SDK and not being able to talk Amazon into creating it. It's generated code, it's not rocket science.

What I have experienced is that software licence, where you are sending data to, where you are hosting it and having access to audit the code has usually been a bigger concern.

But then again big organisations often have really specific concerns. So I'm not doubting your statement it's just that I have never heard it before.

One example given in the article is Erlang VM which maps a lot better to modern processors.

We currently have a problem where we can't have thousands of cores because, even today, so much code is designed to be fast on one core.

We really have to move the asynchronous programming because synchronizing async hardware is both complex and inefficient.

RISC V is probably going to help since it allows for a lot of experimentation.

Very exciting, there are so many things happening in the Elixir Machine Learning space, with all of it focusing on being easy to deploy and test yourself.

I just love that you can create robust ML services. Which for me, isn't as easy to do in Python, even though I've created async service with core Tornado before asyncio was a thing.

I don't understand how this is a difference between stacked diffs and feature branches. Github allows you to use feature branches. I can see there might be some convenience with tooling

So the question is why a project created in a language that has been around since before the devices being emulated are more mature than ones in a language that is 8 years old?

Sorry to be so snarky. It probably depends on people's time working on it and their familiarity with async processes. This is an inherent problem with any media like that. With sound, it's primarily that it can't come before the image but can lag behind the image. Stuff like this.

I don't think any programming language is going help with that because it's how we perceive things, not just how correct your code is.