Isn't it showing a problem with an academia?

"I don't want to live in a world where someone else makes the world a better place than we do."

The problem is a lack of funding, which leads to excessive competition and ties continued employment to sustained contributions.

Many results are obvious in retrospect, and such results are often the best ones. The difficult part with such results is framing the problem in the right way and asking the right questions. If you manage to do that, the result simply follows. You may still need funding and hard work to confirm your finding, in which case someone with more resources can claim your result, if they are aware of the idea.

I think the GP was suggesting that their reluctance was more about someone taking their idea. I still think it might suggest a problem with academia, but the summary would be closer to

"I don't want to live in a world where someone else follows through with my ideas without giving me credit"

It's still a problem because a lot of academics aren't especially well equipped to follow through with their ideas, which can create information silos that lead to ideas never being implemented. Still, I don't know if this is the biggest fish to fry: you have other silos like IP law and NDAs etc.

Academics have to eat and they're judged on the quality of the research they produce.

They're more likely to share their research then big tech once it's ready and they can get the credit they deserve.

This can then be used to succeed in future grants or if your institution is particularly strict, meet your publish quota to keep your position.

This feels like an unwarranted strawman. There are plenty of reasons for researchers to share openly at times and plenty of times it makes sense to wait until the meal is ready to serve before publishing.

grants are competitive

I think the sentiment is misplaced here (there is a legitimate concern for IP protection), but this is my absolute favorite line from Silicon Valley - small correction though: “… makes the world a better place better than we do”

I have M5, but diffusion is a bad fit for metal: https://eamag.me/2026/why-parallel-diffusion-llms-are-slow-o...

That's mostly correct, but I believe the INT8 support on M5 changes the equation pretty drastically. I also believe that diffusion models generally output _better_ results if they have shaping for code (they often will miss delimiter matching inside of a canvas).

US only, right?

for now, yes

by a third (it was 50% increased)

cowork was 100%

If you could add all known routes to the map in the beginning, people would've used it. But OSM doesn't allow automatic contributions...

Crazy how the most liked comment under the video as of now discusses AI video generation

The music also gets talked about quite a bit.

My immediate reaction to a "what is with this loud music, I can barely hear the actual audio" comment on YouTube was to assume the channel was afraid of being copystruck by the iceberg's lawyers.

That's a very silly comparison, there are many startups working on RSI, karpathy is just a basic version to try the concept (similar to his gpt work)

Google's stock dropped 5% right away, 200billion of value

It's weird for an announcement like this to drop in the middle of a trading day. I wonder if it got leaked, forcing the timing.

And? I bought more. This is excellent news.

This over-reaction is why people can't see over a long time horizon.

This is great news for Google as they realize that Sundar is the problem and he will soon leave Google for Demis to be the new CEO of Alphabet (Google) which I am predicting. [0]

[0] https://news.ycombinator.com/item?id=39868160

AI is critically important to Google, but there's a lot more to Google than just having a frontier AI model. Do Demis skills line up with what the whole company needs? It's going to be tough to beat Sundar's 1200% increase in stock price.

I sold out of my position. I can imagine a story where it works out in the long term, but I don't see how this doesn't cause terrible retention problems in the short to medium term. I felt a pull to launch a startup when I heard Jeff Dean was leaving, and I'm a long time big corp employee who hasn't been at Google in over a decade.

Why would they do this extra step if that were the case?

In theory I think the stock should be going up because his tenure has found Google getting left behind.

Cool project! I looked into it recently and thought that running diffusion models locally doesn't really make sense: https://eamag.me/2026/why-parallel-diffusion-llms-are-slow-o...

What are your thoughts on this?

TBH, I think there's some truth to that. I spent _ages_ tuning the kernels to match the tested FLOP count of my M3's processor. I only have an M3 though and wasn't able to push int8 very far on it, but I think there's a chance that M5-class machines and higher might have more capability in this regard.

What I also learned is that MLX/vLLM is probably within ~20% or so of the absolute max perf on Mac. I found some improvements over what they were doing, but we're at the point where it's challenging to optimize without per-stepping kernels.

I found a few improvements over stock DiffusionGemma along the way, like using top-k attention, which drastically improves perf on my mac without sacrificing any of the benchmarks I was able to throw at it.

FWIW some of the issues with Gemma being slow on Mac are specific choices they've made in the architecture that make it challenging to make use various optimizations that have popped up recently. I think a Kimi K3-style network hybrid with the diffusion bits of DiffusionGemma could have some serious sway.

I think that diffusion still has an edge locally, but with some architecture tweaks and CPU improvements it would actually be a winner (ie: training the network for smaller token batch sizes or flexibility in attention heads, a less expensive attention mechanism, and others).