Always moving targets.

They never surrender.

"They're moving the goalposts" is increasingly the autistic shrieking of someone with no serious argument or connection to reality whatsoever.

No one cares about how "AGI" or whatever the fuck term or internet-argument goalpost you cared about X months ago was. Everyone cares about what current tech can do NOW, and under what conditions, and when it fails catastrophically. That is all that matters.

So, refining the conditions of an LLM win (or loss) is all that matters (not who wins or loses depending on some particular / historical refinement). Complaining that some people see some recent result as a loss (or win) is just completely failing to understand the actual game being played / what really matters here.

It doesn't work for international users.

these links are ok:

https://www.reddit.com/r/cars/comments/1r04f0x/official_ferr...

https://www.ferrari.com/en-EN/auto/ferrari-luce

On android, these don't work: Firefox Chrome Firefox focus :-(

Thanks anthropic

doesn't work.

Do you have any DNS blocking settings?

He should have done more of these simple videos, instead he aimed for a bigger target... it has been two years, and still nothing.

Forever grateful for this series anyway.

Very good arxiv paper, I wish there where some updates on that.

All my notes are in latex.

Any way to use them, or do I have to go through markdown format?

What

Bad, bad architects.

They have a reason for this choice. I remember studying it at university—the professor said that when people have intimacy, there's no need for doors.

But what if you have a guest? And what if your poop stinks?

Incredibly low-IQ people.

Kindly suggest some books about RL?

I've already studied a lot of deep learning.

Please confirm if these resoruces are good, or suggest yours:

Sutton et al. - Reinforcement Learning

Kevin Patrick Murphy - Reinforcement Learning, an overview https://arxiv.org/abs/2412.05265

Sebastian Raschka (upcoming book)

...

I believe Kochenderfer et.al.'s book "Algorithms for decision making" is also about reinforcement learning and related approaches. Free PDFs are available at https://algorithmsbook.com

Scaling is not over, there's no wall.

Oriol Vinyals VP of Gemini research

https://x.com/OriolVinyalsML/status/1990854455802343680?t=oC...

He didn't say it's over, just that continued scaling won't be transformational.

Oriol Vinyals said that.

https://xcancel.com/OriolVinyalsML/status/199085445580234368...?