u/syntax_error_again

Gonna be in a tough spot with the new Mac studios dropping at much much higher bandwidth. Pricing gonna be awkward for them.

Ah so you are a vibe coder then eh? Prob use max effort for everything too then complain your usage is draining too fast on socials?

I think there is always going to be a place for frontier intelligence but it's only in a few nodes of the graph. If you are suggesting you are using Opus/Sol/Fable for everything then you are using it wrong. You need a few key things to be done by those models and the rest can be some variation of lesser models. About 1/4th of the nodes in my graphs run on Qwen3.8 27b and the work I do (AI researcher) is entirely novel and very complex. A mixed model will always be the best way to work and just like people diversity is required to get the best outcomes.

I mean... it's still limited to your overall throughput since you don't have unlimited time.

Btw I agree that this may point at a convergence not necessarily explained by distillation. I could see a ton of reasons for it too so not sure what the root causes are. I also think without a deep dive on the methodology we can't say if the matrix is right in the first place. Still interesting.

Lol what? Are you color blind? There is far less correlation between fable and Gemini. I don't have any issue with the Chinese researchers but you definitely can't read this matrix... and I have no idea how you are getting upvotes. 😂

Not really if you understand how I/O works on the models and where time gets sunk. M3U is slower on much of what you would actually use the advantage is sheer size (256/512) but those are discontinued and OP is looking at tiny RAM. Honestly wouldn't bother at $3500 I would get a Spark all day. If they are really price sensitive they could go Bosgame and get 96gb useable on a AMD.

If your whole use case is local LLMs then 64/512 is just not enough for anything serious. Especially for that kind of $$ - you might as well save a little more and get a Spark.

Congrats man - I have a similar config and it's a great machine.

I've got an M4 16gb, m4 pro 48gb, m4 pro 24gb, and m3 24gb if you've not got enough tests on these let me know.

Edit: forgot I've got a first gen m1 mbp too if that's interesting.

He's obviously right in the long term but we conveniently skip the dystopian mid-term we'll have to go through to get there.

I have been coding for 25 years and was super stuck on a personal project. Fable partially solved it the first time around but didn't finish before the rugpull. I'm fully unstuck now thankfully but just like OP I've burned all the usage on 20x.

Doesn't one more dollar inherently mean they did go over? Lol

Exactly. People are purposefully projecting onto these statements because they don't like Dario - not because the statements are unreasonable.

Nothing he said is unreasonable unless you project onto it.

It's absolutely true that IF the concern is misuse then open source models encourage this by design. We already know this is true when one of the first things people did with open source image models was FT to let them create nudes from photos of real people or the first primary use people found for Grok (a less guarded model) was child porn.

It's obvious he is right unless you bias your view of the statement he made.

I think my m4 pro 48gb is gonna be my primary for a while. Prices are wild these days.

Strongly disagree with this overstatement. First, clustering any mini ai workstation is exceptionally slow. Second, most people doing it were actually just paying a few hundred a month to subscriptions not API costs. Third, 90%+ frontier? Really? For what use case because absolutely none of the open weight models that can be reasonably run locally come close where it matters. Sure, some obscure bench that isn't reflective of anyone's actual workflow or was released by the labs that created the model may claim it's close but anyone using these things for real work know it's not.

Sounds like bad management to me. I run data, analytics, and now AI teams and I've seen the opposite - abstracting the boring bits to AI frees us up to think about more complex things. If your teams are doing the opposite I'd strongly question their leadership.

We had to leave after nearly a decade in London. I wish it wasn't so but the UK gov is super allergic to people with money!

UK taxes are out of control man. Nobody serious is gonna move ops there.

We thought that too til this last round. Opened a Senior Analyst role and got about 5k applicants and the top 100 ended up having some fake candidates that had excellent LI's. I think AI making it a lot easier to do.

If you've hired recently you know fake LI is already a thing. However, LI is still my first stop when reviewing what recruiters send me and if it's shit they don't get through.

Some good points here but I strongly disagree with much of it still.

First, B2B isn't subsidized much on the Anthropic side anymore. Most movement has been to standard API pricing. Tools like cursor still partially subsidize but contractually Anthropic is positive on those deals. Generally, B2B is highly profitable right now.

Second, I don't think serious devs switch much now. Reddit is not a proxy for real use. The teams I interact with are not switching on a whim anymore. They've built against fixed forms and don't want to change them.

Third, very marginal gains have been made in model performance in the last few quarters. This isn't hindering growth on the business side. The opposite is happening - it's meant models are more stable and building them into processes is easier. That's not something that comes from dramatic leaps. Stable means enterprise can properly start to adopt which deepens the margin and lowers risk of change.

Now, Mythos will be a jump but I think generally people will be underwhelmed. It's better in the edge cases where most people don't work. So they will see it as a marginal upgrade for their use cases which is exactly what they want. However, marginal gains for the masses can be had much cheaper through updated weights not broad training of new model classes.

Finally, I'll say that I believe you are just plain wrong when you say "wrappers" have to be model agnostic. So wrong in fact that this is the opposite of true. If you attempt to build model agnostic it means you aren't leveraging the differences in how harnesses work across these providers. Which means your thing will work poorly.

Not every company is doing it and I don't think the horizon for human growth is close enough to be considered. It's highly possible that AI growth scales to a point we don't need human growth outside of a baseline required to orchestrate well. It's unknown though right now. The only examples of rehiring are narrow and there is a large amount of "ai washing" right now to obfuscate the real reasons for firing.

Definitely, which bridge are you selling?

We don't really need any internals to understand the basics of their economics though do we? It is fairly straightforward if you abstract the noise.