redox99

Not surprising considering Grok 4.7 is a 2T model, so Sol/Opus class, not Astra/Fable class.

That's outdated and doesn't fully include the multiple billion per month contracts.

Anthropic: 1.25B/month

Google: 0.92B/month

Unnamed customer starting in december: 1.1B/month

Starlink monthly revenue is ~1.5B/month

xAI is the most profitable part of SpaceX by far.

A dense 27B doesn't really make sense for the Mac. A MoE makes way more sense when you have modest bandwidth but lots of memory.

I mean tokens for internal use.

Yeah it's a bit ambiguous, but in any case I'd think simply using that idle capacity to generate tokens would be more valuable than finding a basically meaningless number.

You're underestimating how good models are. I'm pretty sure you could in fact prompt that and have it work.

> To what end? The biggest benefit of tinkering with 40-year-old hardware is the experience of doing so.

The biggest benefit to you. Other people, most people in fact, just like the game and want to enjoy it in new ways. They don't care about the code or reverse engineering itself. That people now benefit that it's easy to decompile because otherwise outside of really big titles like sm64, probably nobody is going to take the effort to manually decompile a less popular game.

That's the egoistical part.

Nobody is forcing you to use AI. And it's not really any different to how people would build their own NES emulators to learn instead of just grabbing whatever best NES emulator already exist.

> the whole point of the endeavor is the process of getting that knowledge and becoming an expert in the system

There's nothing stopping you from making an emulator without using AI or whichever way you think is best for your own learning.

AI allows players to have decompiled and in turn ports of games to new platforms, enhanced versions of the game, etc.

Seems pretty egoistical when nobody's forcing you to use AI in any way, and it provides an objective benefit to other people.

Quite bearish on Anthropic if they had nothing better to do with 2048 GPUs for 10 days than finding an RSA number with already existing algorithms.

It's pretty sad when you realize the average person is just kind of... unaware. They are fine with whatever, they don't really notice most stuff that goes around them.

It's a monorepo and they're at over 1 million PRs. There's surely some juicy stuff there.

It's crazy that we still rely on these unsafe C dependencies, in an era where migrating code to Rust (or other languages) is so easy.

There's really no excuse.

I didn't claim you wouldn't discover things in pure math that would decades later turn out to be useful in other fields. I'm sure you would. My claim was that the ROI is lower compared to applied math, which matters when funding is limited.

I think most of the long hanging fruit has been discovered, and even then you'll have better ROI focusing funding on applied math instead of pure math.

You can trivially run 131k on 24GB 4bit, and there are repos with tweaks that allow you to get the full 262k but idk if there's degradation with their approach.

I tried their WebGPU version and it immediately started looping. Yeah "near lossless" my ass. Plus the reasoning that it looped on was clearly wrong and unlike the non quantized 27B

Not everyone uses laptops. I can run Qwen 3.8 27B (which is a REALLY capable model) in the background coding for me while I'm simultaneously browsing the web and playing VALORANT without any performance impact, and that's on a 6 year old GPU.

> But I also think that the state of the art in small LLM and user device capabilities aren't there yet to put a "good enough to be actually useful" local-only LLM as a prepackaged thing in a mass market distributed browser.

Then just allow it to be enabled on high end devices? But it must be local only. As hardware advances and people upgrade, more people will be able to turn on the feature.

Targeting byte code or asm instead of high level would be silly for everyday tasks. You blow up the number of tokens, reduce your effective context, and there's just more places for it to make a mistake, which most likely won't be caught by the assembler (unlike compilers).

I'm so happy for the two used 3090s I bought for $500 each after Ethereum mining ended. I even saw them for like $430 at some point lol.

The math is wrong, the tok/s is at least 2x that, at least with MTP and Q8 KV which you should always use. And the default tokens a day is ridiculously low at least for coding.

Having said that, it will never pay for itself. A simpler more absolute math is, if I buy a Mac and use it to sell tokens on OpenRouter, will I make a profit? And the answer is no.

Terminal bench 4 is good largely because it's recent so it hasn't been benchmaxxed yet. It's more of a sysadmin/devops benchmark than a coding benchmark though, but still a decent proxy.

https://artificialanalysis.ai/evaluations/terminalbench-v4-0

They may allocate different number of resources every year based on market conditions but they'll never give up on gaming, that would be extremely silly.

I think in a few decades when there are more humanoid robots than humans we'll likely have skynet, so I'm pessimistic in a way lol.

But in the near future and on a personal level I think we'll need to adapt and pivot but we'll manage.

I think it will be like how a lot of people know how to code in python but have zero understanding of assembly or how a cpu works. As a researcher you'll accept there's this low level stuff that if you want you can dig into but isn't worth your time, like looking at the generated ASM isn't.

You're exaggerating the LLM progress a bit but yeah progress has been crazy. Yet not much has changed right? We mostly have the same jobs, just code wayyyy more than before because things that wouldn't be worth it now are worth it.

10 years from now I have no fucking idea. But I'm sure in the meantime those who leverage AI will do better than those who yell at a cloud.

Of course you're not going to get rich with the kind of software that LLMs can one shot these days. But that kind of software like to-do lists or basic CRUD have been saturated for over a decade, way before LLMs. People overestimate how much you can one shot, yeah a good prompt can get you 90% there but that 10% remaining often takes months of extra work.

Software has always progressed this way, lots of devs back then would work on business websites that have been 99% replaced by wordpress, squarespace and instagram.

I'm sure it's the same with research, you're going to tackle problems that would have not been worth the effort or outright impossible without AI. The old stuff that you'd work for months, yeah that's going to be a prompt away.

This has always happened, way before AI. You'd spend months or years building and growing your business, and then Google would release a feature or product that would kill your business overnight because they can throw way more money at the problem, plus their branding. That's life.

> Many graduate students (I know) are having a crisis if any of their research worth it? If AI can (or will) do everything, what's the point of doing experiments and all? This will eventually deter a whole generation of curious minded students from research.

Those who think it's me or the machine will fail.

Those who realize how much you can accelerate your research with the help of AI will succeed.

The two times I tried to use fiverr I literally got ghosted.