I have setup a rustdesk server with docker, it was surprisingly easy to get started. It was for a friend who is managing the IT services of a small factory, the completely switched from TeamViewer and they are satisfied. More importantly their users, who are worse than your average windows user, found the transition relatively painless.

While the consumption for AI train can be large, there are arguments to be made for its net effect in the long run.

The article’s last section gives a few examples that are interesting to me from an environmental perspective. Using smaller problem-specific models can have a large effect in reducing AI emissions, since their relation to model size is not linear. AI assistance can indeed increase worker productivity, which does not necessarily decrease emissions but we have to keep in mind that our bodies are pretty inefficient meat bags. Last but not least, AI literacy can lead to better legislation and regulation.

Gitea and forejo are doing some amazing work

It depends on the field you are studying. I was into CS, using Linux was recommended because the machines they used to test our code were also running linux.

Most fields are going to be okay with linux, the only exception being fields that rely on specialized software like architects, engineers, and audio/video editing. Also, some software like MatLab are possible to run on Linux but it’s a pain to set them up.

They made meow meow beans?

While I agree with most of what you say, I have a personal anecdote that highlights the importance of performance as a feature.

I have a friend that studies economics and uses python for his day to day. Since computer science is not his domain, he finds it difficult to optimize his code, and learning a new language (C in this case) is not really an option.

Some of his experiments take days to run, and this is becoming a major bottleneck in his workflow. Being able to write faster code without relying on C is going to have a significant impact on his research.

Of course, there are other ways to achieve similar results, for example another friend is working on DIAS a framework that optimizes pandas in the runtime. But, the point still stands, there are a tonne of researchers relying on python to get quick and dirty results, and performance plays a significant in that when the load of data is huge.

Has anyone gone through the documents? Any interesting findings? Is it time to create a website?

Jack Dorsey, good or bad?

(bonus points if you get the community reference)

pain

Lemmy’s ActivityPub library needs some help.

Maybe something nexus project related? Working with the largest research paper database is definitely going to provide you with a decent challenge.

If you use a recent release of Fedora (last 2-3 years). Try disabling WiFi and/or Bluetooth before suspending. There is an issue with some hardware, especially adapters. It doesn’t happen everytime, and it’s hard to accurately reproduce. Also, the symptoms can vary from black screen to sudo being stuck.

It’s a wild guess, but try to disable Bluetooth or WiFi before suspending.

It’s doesn’t happen with all hardware, but it is a knowing issue.

WOW! https://github.com/modularml/mojo

Been looking for something like this, thanks a lot!!!

Edit: Had a quick look at the docs. Mojo’s initial build was published Sep2022, it’s fairly young, but seems to be getting a lot of attention (on GitHub they have the same number of stars as mypy 🤯).

For anyone interested, their roadmap is an interested read. They seem to be taking a step-by-step approach, trying first to nail down core features first before moving to stuff like python inter-op and syntactic sugar.

Mojo still doesn’t support classes, the primary thing Python programmers use pervasively! This isn’t because we hate dynamism - quite the opposite. It is because we need to get the core language semantics nailed down before adding them. We expect to provide full support for all the dynamic features in Python classes, and want the right framework to hang that off of.

The “why mojo” section give a lot of background too. They are implementing an ML-IR compiler, which is really promising for optimization (think all the goodies we could use from LLVM).