Early on as in age of people, not the country. There also have to be opensource alternatives to get people with. If most of the stuff that's easily available is closed source, people will use closed source.

Furthermore, access to computers with which to make games still can grow a lot in Asia - I'd say more than in Europe or the US. You can look at numbers of device usage per country provided by Cint. India, Malaysia, Myanmar (well there's a war there now), Indonesia, Nepal, Pakistan, Afghanistan, Bangaladesh, and more countries in Asia have less than 30% of PC usage. Those numbers hover around 45-50% for European countries, North America, Australia, Russia, and a few others I'm probably forgetting . I'm sure you can find more up to date numbers with a possible separation between laptop and PC, but regardless, it wouldn't surprise me if the numbers were about the same or climbing.

Combine that with the direction Macroslop is going, exposure to Linux and other OSS will probably increase. Yes, new generations are very mobile phone heavy, but game development will probably happen more on beefier machines, which Asia is slowly getting access to at an increased rate.

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  • [–] 11 points 1 day ago* (2 children)

    With Tencent as both a strategic partner and an investor, we have an opportunity to accelerate the adoption of enterprise-ready Godot technologies, particularly in Asia, while continuing to strengthen the open-source ecosystem that sits at the heart of everything we do.

    Emphasis mine. I like this. Asia is extremely populous. Getting them started on opensource early on is a great way to get more contributors and users.

    This new round of funding that was just announced was led by "Tencent, with support from other investors including OSS Capital, LUX, Naval Ravikant and Tobias Lutke’s family office"

    If this is the same guy who owns Shopify and added DHH to the board of directors, fuck him.

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    Inb4 somebody says "wHy Is ThIs A vIdEo???"

    Thanks for sharing. I like these kinds of videos too.

    Edit: I bet the problems he ran into were due to CachyOS (and NVIDIA). I don't know why people keep recommending some arch linux stuff to beginners.

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    Code examples that use this convention are in my opinion terrible for comprehension - especially for domain specific examples.

    Say you have a library called libprefix and you want to demonstrate how to use it

    foobar example

    import prefixer
    
    foo = "foo"
    bar = prefixer.prefix(foo, "bar")
    print(bar) # foobar
    

    vs representative

    import prefixer 
    
    to_prefix = "to prefix"
    prefixed = prefixer.prefix(to_prefix, "not ")
    print(prefixed) # not to prefix
    

    The variables tell you what they represent

    • to_prefix a string that has to be prefixed
    • prefixed contains a string that has been prefixed

    This is just a small example, but some example have 10, 20, 50 lines of code and when I start reading and it uses this foobarbaz crap, it makes me want to close the tab and move on. Naming things is hard, but come on...
    Sometimes there are even domain specific examples where the effort is made to convert it to foobar convention just for the sake of it.

    import traffic_light
    
    foo = traffic_light.intersection("cross", 2) # 2 roads intersect
    bar = { "north": 4, "east": 1, "south": 10, "west": 0} # vehicles at each light
    baz = foo.simulate(bar) # creates a simulation object
    
    print(baz) # print out the state of the intersection (bar in this cas)
    quux = baz.step() # state of intersection after one step
    print(quux)
    

    We have all seen worse than this. Seriously, why?

     

    I was curious about opencode and tried to run it with mistral:7b as well as qwencode3:8b, but it simply couldn't execute a single command.

    It was necessary, I think, to be able to execute commands because it kept attempting to write to /home/user. So I told it to run pwd to find out where it was. That always failed.

    Was j using a wrong model? Or is opencode just incapable of running commands?

     

    It's that time again and I'd like to make my life easier. I have multiple CVs for different positions and in different languages. Recruiters and companies want the CVs in PDF.

    My current workflow is to sequentially edit the .odts (wording then layout because things move around depending on length), maybe the style, then save and export to PDF. That can take a while, so here I am, wondering if it can be sped up by simply editing a text file and generating CVs with certain themes.

    Or what your workflow is. Maybe this isn't worth automating...

    Solution

    Finally went with RenderCV (thank you @bert@sh.itjust.works), which also runs locally. YAML in, PDF, PNG, HTML, etc. out. It comes with 10 themes or so. Haven't looked at what it take to create my own (I'm lazy).

    Contender was JSON Resume, but the site is down and I tried it years ago but never got it to work properly.

     

    Equivalents because VSCode and its forks are not an equivalent.

    TL;DR Jetbrains is getting worse, more like VSCode. I want out to something as powerful but not VSCode like (which is worse).

    Jetbrains has been chasing VSCode's popularity to the detriment of its own product.

    The modal commit dialog is now opt-in, behind a nearly hidden, advanced parameter and will be removed in the not so distant future. This was part of their effort to make the UI "leaner" and more like VSCode so now Jetbrains IDEs look more like it.

    Some keyboard shortcuts have also had to be changed to be more like VSCode.

    Jetbrains used to have a good docker integration, but that's either unmaintained or undergoing work to support dev-containers better - something that jetbrains practically had nearly a decade ago, which allowed you to code on another server (remote development).

    AI Assistant and Junie (their coding agents which I have to use for work) have consistently been behind the curve and just keep getting worse. They have 2 separate products for agentic development and can't decide which to keep, so they duplicate efforts and fail at both. Now I'm asking my employer to just give me Cursor like the rest of the team, so I don't have to put up with this Jetbrains crap.

    Privately, AI it's non-starter anyway, but my guess is that Jetbrains is also focussing more development efforts on that than actually fixing existing bugs.

     

    cross-posted from: https://lemdro.id/post/43991444

    I've found this very good website. Many torrents are unofficial and created by the website staff. Some torrents are official from the project maintainers.

    I have been seeding torrents on me VPS for some time. Since I don't use most of the bandwidth. Now I have purchased an HDD IPv6-only VPS in the same datacenter to use as NAS and seed more torrents.

    If you've got some spare bandwidth and disk space, you can seed the softwares you like.

     

    I've been testing VPN services and somehow every one with a client has written their own. Proton, Windscribe, IVPN, Mullvad, AirVPN, NordVPN, and the list goes on.

    Don't these services all do the same thing? Connect to a server to get a list of servers, then use WireGuard or OpenVPN to make a connection, and have some fancy stuff like split tunneling or a killswitch. It should just take configuration by adding profiles per service and it should be possible to use the same client.

    What am I missing?

     

    Goal: Take a recording of someone speaking, pass it through a CLI or GUI, output a recording with the speaker's voice replaced by an AI voice.

    I know there are closed-source or hosted services that do this, but they are a privacy risk and most surely also continue training their models on what users submit. The opensource solutions I have seen (last I checked 6 months ago or so) were all extremely outdated (around the beginning of the ChatGPT release) and required training using thousands of voice samples.

    Surely somebody has done this before and didn't need to be an expert in the field to do so...

    Non-solutions:

    • hosted services (elevenlabs, chatGPT, Gemini, ...)
    • "train your own AI"
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