codexon

It most likely will be quantized. A cerebras wafer only has 44gb ram, and linking them together vastly reduces the speedup.

I don't know about recently, but for many years paypal automatically sided with the customer.

We had many customers that would buy a 1 month sub then chargeback the whole amount after using it heavily for a few days or right before renewal, with a nonsense reason like "item not as described" or "unauthorized charge".

We lost every single case.

Why does it feel silly? There are already billionaires, and now Elon Musk is a trillionaire, and they still want more even though they have enough money to spend for several lifetimes.

Some people always want more. And defending against others like that will result in infinite demand.

I've held this view and talked about it many times here before.

It seems like an obvious conclusion to me that the end result will be a few AI owners trading among themselves should AI develop in what seems to be likely: recursive self improvement, robotics allowing it to displace manual labor and combat.

Then the owners will be trading for land, AI tech, minerals, energy, which will likely be owned by the other AI conglomerates, and maybe the odd thing that can't be replaced by AI like human entertainers that would make up 1% of the economy.

This is similar to what I experienced when I tested mimalloc many years ago. If it was faster, it wasn't faster by much, and had pretty bad worst cases.

Agreed mostly. Going from standard library to something like jemalloc or tcmalloc will give you around 5-10% wins which can be significant, but the difference between those generic allocators seem small. I just made a slab allocator recently for a custom data type and got speedups of 100% over malloc.

It was mainly pushed as easy to adopt, easy to use, easy to statically link, etc.

That is true of basically every single malloc replacement out there, that is not a uniquely defining feature.

I've benchmarked them every few years, they never seem to differ by more than a few percent, and jemalloc seems to fragment and leak the least for processes running for months.

Mimalloc made the claim that they were the fastest/best when they released and that didn't hold up to real world testing, so I am not inclined to trust it now.

It still beat mimalloc when I checked 4-5 years ago.

I've been using jemalloc for over 10 years and don't really see a need for it to be updated. It always holds up in benchmarks against any new flavor of the month malloc that comes out.

Last time I checked mimalloc which was admittedly a while ago, probably 5 years, it was noticebly worse and I saw a lot of people on their github issues agreeing with me so I just never looked at it again.

I'm not arguing about growth. I was addressing this statement which seems to presume that AI has no effect if the job can't be removed.

If AI can't do 100% of a job then you can't remove the job.

Claude code will prompt you and explain to you what practice fits a situation. It might not do it perfectly, but the foundations are there.

AI is already aware of the best practices. It does not just blindly do what you ask of it in the simplest way.

I'm not sure that's entirely true. For most things, checking if a solution is correct is much easier than implementing it (page looks wrong, can't login etc...)

You can replace it with a much lower paid employee though.

I think some of it might be genuine. For people that don't code (like management), going from 0 to being able to create a landing page that looks like it came from a big corporation is a miracle.

They are not able to comprehend that for anything more complicated than that, the code might compile, but the logical errors and failure to implement the specs start piling up.

Check the link to the study. It has been updated for Opus 4.5.

This paper creates a new benchmark comprised of real remote work tasks sourced from the remote working website Upwork. The best commercial LLMs like Opus, GPT, Gemini, and Grok were tested.

Models released a few days ago, Opus 4.6 and GPT 5.3, haven't been tested yet, but given the performance on other micro-benchmarks, they will probably not be much different on this benchmark.

https://arxiv.org/abs/2510.26787

Testing the top llms on wework, the highest performing one only succeeded with a rate of 2.5%

Can you imagine not being fired when you can only do 2.5% of all tasks?

This study is dated October 30th, very recent.

You don't need to be a genius or rocket scientist to write code, but llm don't even reach the bar for anything but the most simple things. Take a look at the video I posted earlier for an example.

And specialised models for programming HAVE plateaued.

https://livebench.ai/#/?sort=Agentic+Coding+Average

From Claude 4.1 to 4.5 was only an 18% gain, and from 4.5 to 4.6 it even DECLINED. Codex 5.1 to 5.2 also shows a decline.

Top AI researchers like Yann LeCunn have said that LLMs are a dead end.

It seems to me that LLM performance is plateuing and not improving exponentially anymore. This recent hubbub about rewriting a worse GCC for $20,000 is another example of overhype and regurgitating training data.

You don't know for sure if it is going to "snow" (AI reaches general intelligence) Snow happens frequently, AI reaching general intelligence has never happened. If it ever happens, 99% of jobs are gone and there is really nothing you can do to prepare for this other than maybe buy guns and ammo, and even that might not do anything to robotic soldiers.

People were worried about AI taking their jobs 60 years ago when perceptrons came out, and anyone who avoided a tech career because of that back then would have lost out majorly.

Even for coding, it seems to still make A LOT of mistakes.

https://youtu.be/8brENzmq1pE?t=1544

I feel like everyone is counting chickens before they hatch here with all the doomsday predictions and extrapolating LLM capability into infinity.

People that seem to overhype this seem to either be non-technical or are just making landing pages.

Didn't this paper demonstrate that you only need 1.58 bits to be equivalent to 16 bits in performance?

https://arxiv.org/abs/2402.17764

Has anyone noticed a lot of polymarket posts on their X (formerly known as twitter) feed claiming to be making a fortune? It makes me feel like its some kind of coordinated guerilla marketing scheme.

Recent update proving my point

https://www.notebookcheck.net/Steam-updates-AI-disclosure-fo...

That's not really the same as stores outright banning AI code.

An apt analogy would be like a shared drawing taking merge requests and having to spend 30 minutes looking at every single merge request zoomed in to see if there was a microscopic phallus embedded somewhere.

It is completely fair for an open source project to have their own standards, and you are also free to fork it so you can accept as many AI PRs as you want.

None of these options are available for someone that wants to sell AI generated music. There are really only 2 marketplaces to sell your own music and if both of them banned AI, then you are effectively locked out of the entire market.

They are obviously different things, but aren't the people who spent thousands of hours honing their coding and releasing their code spending just as much time and effort if not more than the people who made non-ai images and music?

Yes and it was probably only done because of people complaining about AI art, not AI code.

AI music from suno sounds indistinguishable to non-ai generated music to me.

In terms of how well it works, the quality of AI music is far better than art or code. In art there are noticeble glitches like multiple fingers. For code, it can call non existent functions, not do what it is supposed to do, or have security issues or memory leaks. From what I can tell, there is no such deal breaker for AI music.

Sure, I am one of the people who will say that. But where are the people calling for it to be banned? Where are the stores and websites that are banning AI generated software?