There is no bubble bursting in the near future.

We are just entering a stage when AI is becoming a cyber security threat. It can become a weapon. What that means is:

1. There is a large military budget that is going to be invested in it
2. There is an order of magnitude larger cybersecurity budget to be spent by everyone not wanting to become a target of those mentioned in point 1

AI is a weapon and the only way of defending against it is AI. Do not expect budget cut.

Guys, please do not downvote this post just because you do not agree who actually solved this problem. The title of the post is correct, unbiased. It says that it’s OpenAI that claim to solve this problem. It doesn’t say that OpenAI has indeed solved this.

I haven’t even noticed how useful it was to me until they disabled it. Glad they came back

If the benchmark is solved, then why official arc prize page on X https://xcancel.com/arcprize does not mention it? And instead they post results of models that are not even close to 100%?

Any chance most of this traffic is just bots hosted on Linux?

lol, so it came to the point when developers need to explain what tools are they using 😆 I don’t care how it’s done as long as it works as intended

Being used to modern journalism I was not expecting even a link to schematics, but instead I was given list of all required components and even re-upload of a video showing how to assemble it. What a treasure this website is

For now, finding collections is very manual, primarily through word of mouth. In the future, we are planning to introduce some ways to browse and discover popular collections.

ok, so any recommendations of interesting collections?

Why not? Why Microsoft develops it’s .NET ecosystem? Why Google develops Go/Dart? It costs them lots of money and they give it for free.

The answer is: they don’t earn money on it directly, but these tools are a way to tie programmers to their cloud services. If you use .NET you’ll probably end up on Azure. If Go - probably you’ll use GCP.

So I suspect the same will be with LLMs. At some point they will say: “hey, you can use this LLM however you want, but as you are already using it, then you may want to know our platform is optimized for it”

So are we assuming here that LLMs won’t become more efficient over time? GPT-3 has been a frontier model just a few years ago and it’s performance blew everyone’s mind at that time. I can now run equivalent LLM on my personal computer. Why can’t we expect that after a few years Claude Sonnet level of capability won’t be possible to accomplish locally?

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Hopium administered