Wood / metal working, MTB and Factorio. Or did you mean within the IT sector? Well ML is interesting ;)
Readit Newsnikonyrh commented on Ask HN: Does anyone have interests in anything besides AI? · Posted by u/drsalt
I’m not enough of a tech person to understand this, to be honest. I just see investment articles talk about CUDA and keep thinking why others don’t make something compatible but cheaper. Maybe that is naive? It sounds like you are saying AMD could do this in theory even though Intel or Apple can’t?
AMD already has Composable Kernels[1], and supports for example Triton[2]. Then there is also HIP[3], and there are tools to automatically convert from from CUDA to HIP. But since CUDA is the de-facto standard, there is always friction to use something else (unless you need to support also AMD stack).
Making something just CUDA-compatible is non-trivial, and since Nvidia decides its direction and new features then the alternatives would always be lagging behind. Currently there are also major hardware differences between Nvidia and AMD, which may make highly optimized CUDA code inefficient or even buggy.
[1] https://github.com/ROCm/composable_kernel?tab=readme-ov-file#composable-kernel
[2] https://github.com/triton-lang/triton?tab=readme-ov-file#triton
[3] https://github.com/ROCm/HIP?tab=readme-ov-file#what-is-this-repository-fornikonyrh commented on The Most Frustrating Customer Service Call of All Time youtube.com/watch?v=nUpZg... · Posted by u/mafuy
He should have asked what is the price for 1 MB, or 1000 kB. 2 cents, right? insert meme here
nikonyrh commented on Modded-NanoGPT: NanoGPT (124M) quality in 3.25B tokens github.com/KellerJordan/m... · Posted by u/ocean_moist
It's not.
I suppose you aren't a fan of the https://en.wikipedia.org/wiki/Hutter_Prize .
> The goal of the Hutter Prize is to encourage research in artificial intelligence (AI). The organizers believe that text compression and AI are equivalent problems.
A few things here - 1) TFA is referencing household use, and you're referencing individual use, and 2) the linked source from TFA doesn't actually give the 1135L/day stat. What it gives is this: "Each American uses an average of 82 gallons of water a day at home" which is ~310 liters.
Also, it's not clear if your source is including water use for home irrigation and recreation (lawns and pools?), whereas the US source is.
I don't think the distinction between indoors or outdoors usage is relevant, in the end we are still talking about the volume of drinkable water people use. Whether they use it for drinking, cooking, washing, or watering the lawn.
I always found it interesting that you cannot have a domain name without publishing your address and phone number, or paying for an extra service.
i'm not a great sleeper, but the folowing has helped me quite a bit:
earlier bedtime (no later than 11:00 pm unless super necessary).
earplugs. I seem to sleep deeper. I think sometimes I'd be woken up and not remember from random sounds outside or even my air conditioning going on.
melatonin/cbd/ashwaganda gummy. Very very small dosage of all of the above.
Not eating for an hour+ before bed. I used to love snacking right before bed, but I don't sleep as well.
Coffee finished before 10:00 am.
This reminds me of the meme where you read up instructions of good sleep, at 4am before getting ready to go to bed.
nikonyrh commented on Notebooks Are McDonalds of Code yobibyte.github.io/notebo... · Posted by u/sebg
REPL stands for Read–eval–print loop https://en.wikipedia.org/wiki/Read%E2%80%93eval%E2%80%93prin...
A REPL, by its name, is a very narrow version of the broader paradigm of interactive computer programming environments. But Notebooks are not REPLs, unless you use REPL to mean "interactive programming environment" and not REPL. Notebooks are much broader than a REPL! In a notebook, you can go back and edit and run individual lines of the notebook without re-running the whole notebook from the start and without re-computing everything that depends on what you just edited. Behavior like this makes it super hard to track the actual state, and super easy to lose track of how things are how they are. That's pretty terrible!
The parent article links this great talk that goes into more detail than the parent post and is much easier to understand: https://www.youtube.com/watch?v=7jiPeIFXb6U
"In a notebook, you can go back and edit and run individual lines of the notebook without re-running the whole notebook from the start and without re-computing everything that depends on what you just edited."
Isn't this a standard on REPLs as well? You can select the code you wish to run, and press Ctrl+Enter or what ever. I must admit, I've programmed Python for about 10 years in Spyder and VS Code now, but I haven't used notebooks at any point. Just either ad-hoc scripts or actual source files.
My definition of a "notebook" is an ad-hoc script, split into individual "cells" which are typically run as a whole. On my workflow, I just select the code I wish to run. Sometimes it is one expression, one line, 100 lines or 1000 lines depending what I've changed on the script.
nikonyrh commented on Why today's phones are so boooooring? andreyor.st/posts/2024-06... · Posted by u/I_like_pigeons
I bought Unihertz Pocket Titan, at least it was different! Quite thick and heavy, but the battery lasts about 6 days even without any power-saving mode.
nikonyrh commented on Ask HN: As someone with obscure CUDA knowledge, how to become AI developer? · Posted by u/cromka
Since with CUDA you are programming so close to hardware, and hardware (and CUDA itself) has advanced so much, I recommend you to go trough very carefully all the major CUDA versions and see how it has evolved. Well, strictly speaking I'm talking about different versions of "compute capabilities". Of course Wikipedia has a good summary: https://en.wikipedia.org/wiki/CUDA#Version_features_and_spec...
An other point is that you don't need to write any CUDA code to be able to utilize GPU computing. If you need ML models, you have frameworks like PyTorch and Tensorflow. You just need to express your mathematical problem, and the framework will take care of the rest.
Even if you needed to write custom GPU code, you don't need to do it in C anymore! For example you can JIT Python, using Numba or Triton.
Usually writing custom code is only required when:
- You are doing something novel, like PhD level stuff
- You must optimize the ML project for performance and trough-put at interference time
- You need to brute-force solutions (be it crypto-hashes, passwords, NP-complete problems, ...)
My last point to you is that do you want to learn to use these pre-existing frameworks and libraries, or learn to develop them or maybe even create new ones? What ever your answer is, I'd say that the first option is a great stepping-stone to advance to the second one.