nikonyrh

Does that take into account the efficiency of transforming the gas to electricity first? Ah, well we have other power sources as well. But if you were to use gas, it doesn't seem to make much difference (if the gas power plant doesn't utilize the extra heat for district heating etc.).

At least in Finland's Nordea bank you can order a physical code calculator, they used to be small enough to keep on your wallet but the new one is the size of an old small phone. It even has a QR scanner. So I just keep it at home.

Wood / metal working, MTB and Factorio. Or did you mean within the IT sector? Well ML is interesting ;)

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-for

He should have asked what is the price for 1 MB, or 1000 kB. 2 cents, right? insert meme here

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.

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.

This reminds me of the meme where you read up instructions of good sleep, at 4am before getting ready to go to bed.

"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.

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.

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.

- Photo editor

- In-memory database

- Data compression algorithm

- 2D or 3D physics engine / game

- Game engines in general

Aah that makes sense, thanks!

I'm not familiar with this library, how does `text("(now())")` evaluate and why there are extra parentheses? And should that be a lambda expression as well, so that `create_date` isn't just the timestamp when the python process was started?

I suppose all SW engineers have experience with data structures, when you are choosing whether to store the data into a dictionary, array or linked list. It is a bit more stretch to ask whether you've implemented a database before or not. On a more advanced level you'd dive into topics such as copy-on-write and software transactional memory (hobby-wise I come from Clojure background).

As an example, do you know how git works?

Edit: Oh an other great example of data structures is torrent files (and magnet links). Hint: https://en.wikipedia.org/wiki/Merkle_tree

Wow, very cool and easy to set up!

Building a custom wall mount for my downhill bike. It requires some extra engineering and planning, the space is tight since there is already one bike on the hallway. It is also important to estimate the forces involved, so that there is sufficient safety margin.

That is what I usually do, I don't find very interesting to just load pre-existing models and feed data into them. But sometimes it is a necessity, if the network is too large (like Stable Diffusion, ResNet and its variants, ...).

I remember not being able to read Pandas parquet files using for example Vaex (https://pypi.org/project/vaex/). And it is a bit disgenuine not to enlist all supported arguments of `pandas.read_parquet`'s `*kwargs`. But I agree that Parquet is already 100x better than CSV.

I thought this was 17% of all online sales! Like all of the stuff that Amazon sells.

These are just my favorites, and in the order of which the popped into my mind.

- Office Space, 1999 (especially for people who work at offices) - The Matrix, 1999 - Fight Club, 1999 :)

Umm, what else? - Spirited Away, 2001 (and many others from Miyazaki) - Requiem for a Dream, 2000 (Very Heavy addiction-related film! I actually got watery eyes just by typing this, I've seen it too many times.) - Joker, 2019 - Full Metal Jacket, 1987 (and many others from Kubrick, such as Dr. Strangelove and The Shining) - Django Unchained, 2012 (and many others from Tarantino, at least Pulp Fiction) - Alien, 1979 - The Silence of the Lambs, 1991 - Saw, 2004

I made a bit longer list, so that I can share the link to my friends.

Actually there are many quirky ones which came to my mind, such as Borat, the South Park movie, Team America, Airplane! (alternatively titled Flying High!), Idiocracy and Hangover, but I wouldn't say these are in the must-see category. Actually I wouldn't even recommend these to most people, if I didn't know what kind of humor they like.

Very interesting, it must feel great to get to apply CS knowledge at work, rather than writing basic CRUD apis / websites.

Educate me, what did they build?

One option is to go for Dell's Alienware line, depending on your needs you need to choose how much RAM you need on the GPU card. RTX 4000 generation seems to have at minimum 8 GB, which is sufficient for most problems for now.

https://www.reddit.com/r/MachineLearning/comments/17a31qb/r_...

Oh interesting, I thought this was going to be a .gitignore file we could use in all projects :D Do you have any thoughts on rather than listing all the file extensions you don't want to track: - https://github.com/tensorflow/tensorflow/blob/master/.gitign... - https://github.com/redis/redis/blob/unstable/.gitignore - https://github.com/elastic/elasticsearch/blob/main/.gitignor...

You'd list the file extensions you want to track? Here is an example for a PHP project:

* !/ !.php !.md !.xml !.gitignore !composer.json

Or Clojure:

* !/ !.clj !*.md target/ !.gitignore

If you have exceptions on these rules, you can always force-add the files or add the extension to .gitignore, to configure it not being ignored.

A doctor came over to review the test results, saw that the CT scan was the previous day, and yelled at me for several minutes about how I risked my life by listening to the front desk staff.

Has he tried yelling at the front desk staff yet? I hope you know his name and contacted him afterwards.

I asked if I should have forced my way back to find an empty bed and just jumped into it.

I assume that would get you arrested, and maybe even dragged to jail. :S

Could you start with a 2 - 6 months paid sick leave, due to burnout? Or at least, maybe you don't need to quit the job, just take a long leave and return in the next 6 - 12 months?

I think that kind of thinking leads to a lot of unhappines and burnout to many of talented & gifted people. But I was thinking this more in the context of academics and careers.