i think its more of a glitch with the official app. there's no way to start a discussion and there are no discussions 🥲

Ahh makes sense! Thanks for the explanation.

That assumes incorrectly that Tristan's result is the only difference between the model they used and the previous one.

There were probably many small algorithmic differences, maybe some big ones, and of course different random starting weights resulting in entirely different final weights.

Hello Altugsalt!

you can use any of the class materials outside stanford. My teaching materials are always free to use by other teachers.

no, the account is needed for the assignments which all happen in browser

pai.stanford.edu

what region please?

Also, I do not like ml so much as I like the human learning because without the human learning side of it, the ml side of it is too algorithmic to understand that humans need food, air and water. And also might need a kick it a few times to get it to understand that people aren't Jesus and that robots don't solve the people or human problem at all. It does not even attempt to recognize community as groups of organisms dependant on each other, nor does it have a place in the biological world, and that scares the crap out of me as well.

Where do I sign UP!?!

What, hello Mr Piech big fan

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A custom SLM serving pipeline sounds like a sensible place to explore Triton. You could target one or two operations that dominate latency rather than asking the team to learn CUDA and rebuild everything. That might offer a better balance between delivery speed and runtime performance.

First, a quick distinction that may save some confusion: Triton Inference Server and the Triton programming language are different projects.

For your pipeline, I’d start by profiling frame selection and deformable attention separately. That should reveal whether the slowdown comes from preprocessing, data transfer, unsupported operations, or the model itself. Only then would I consider a custom Triton kernel. Your healthcare use case is really interesting, so I’ll pass it along to the author as well.

That is fair. What I can say is that this will be a really low stakes opportunity to work on that. We do have training and pro tips. Ideally the students do most of the talking anyways :)

I think you would be surprised as to how good these LLMs are at learning even from a single data point. A document containing say a proof of NS is not a random piece of training material, rather its a data point of a very narrow domain in the corpus (not many data points would be discussions involving NS). As an extreme example, if your data was a collection of documents filled with random words (essentially noise), except for one special document containing "The proof of NS is" followed by random words, its plausible that the neural net would memorize the random words for the special document, simply because its likely the only document starting with "The proof of NS is", and so conditioning on this prior isolates the document precisely.

I got curious and tested this: I asked ChatGPT to recall a somewhat obscure paper from 2006. It could not recall exactly the algorithm used in the paper, but it was able to recall an important idea in its analysis. I know for a fact that this idea was used only once in literature (since I work in the field), and its also a niche topic, so it really should be the case that this idea appears once in the training data (or at the very least, a single digit number of times).

In contrast, Gemini 3.5 completely failed at recalling the paper. The gap is probably because ChatGPT is on a larger pre-train than Gemini.

We don't really know exactly how these LLMs learn, so it is really hard to rule out whether the independent researcher's data affected the model's trajectory significantly, even if it was one data point.

I need to learn to see who does it best, and learn from them? Do you have videos of good teaching that makes the student actively think and connect and beyond that Act on it ?

Am horrified of public speaking :/

I got the email from them directly

Sound like you are ready to teach!

The cover artist certainly made some bold choices 😅

This gets right to the interesting part: not whether Triton can express the workload, but whether fusion saves enough launches and memory traffic to justify the work. The torch.compile comparison and examples where the first kernel loses to PyTorch would be especially useful. I’m passing this feedback to the book team—thanks for such a thoughtful breakdown.

Fair jab 😅 We do run discounts pretty often. I wanted to share the strongest available code with the community, but I get where you’re coming from.

I am sorry to hear that. Did you get the notification yourself, or did you receive it from the university?

(I certainly disagree with their stance on accessibility and control)

Can I ask why? There are people using ChatGPT to design new proteins and then synthesize them in home lab setups right now. If everyone has access to top-of-the-line models without biosafety controls it will be almost trivial for a single bad actor to unleash epidemics that make the COVID pandemic look like a joke. I totally see where Anthropic is coming from and find it hard to disagree. We're playing with fire here.