Lots of good comments here. I think there’s many reasons, but AI in general is being quite hated on. It’s sad to me - pre-GPT I literally researched how AI can be used to help people be more creative and support human workflows, but our pipelines around the AI are lacking right now. As for the hate, here’s a few perspectives:

• Training data is questionable/debatable ethics,
• Amateur programmers don’t build up the same “code muscle memory”,
• It’s being treated as a sole author (generate all of this code for me) instead of like a ping-pong pair programmer,
• The time saved writing code isn’t being used to review and test the code more carefully than it was before,
• The AI is being used for problem solving, where it’s not ideal, as opposed to code-from-spec where it’s much better,
• Non-Local AI is scraping your (often confidential) data,
• Environmental impact of the use of massive remote LLMs,
• Can be used (according to execs, anyways) to replace entry level developers,
• Devs can have too much faith in the output because they have weak code review skills compared to their code writing skills,
• New programmers can bypass their learning and get an unrealistic perspective of their understanding; this one is most egregious to me as a CS professor, where students and new programmers often think the final answer is what’s important and don’t see the skills they strengthen along the way to the answer.

I like coding with local LLMs and asking occasional questions to larger ones, but the code on larger code bases (with these small, local models) is often pretty non-sensical, but improves with the right approach. Provide it documented functions, examples of a strong and consistent code style, write your test cases in advance so you can verify the outputs, use it as an extension of IDE capabilities (like generating repetitive lines) rather than replacing your problem solving.

I think there is a lot of reasons to hate on it, but I think it’s because the reasons to use it effectively are still being figured out.

Some of my academic colleagues still hate IDEs because tab completion, fast compilers, in-line documentation, and automated code linting (to them) means you don’t really need to know anything or follow any good practices, your editor will do it all for you, so you should just use vim or notepad. It’ll take time to adopt and adapt.

As someone who researched AI pre-GPT to enhance human creativity and aid in creative workflows, it’s sad for me to see the direction it’s been marketed, but not surprised. I’m personally excited by the tech because I personally see a really positive place for it where the data usage is arguably justified, but we either need to break through the current applications of it which seems more aimed at stock prices and wow-factoring the public instead of using them for what they’re best at.

The whole exciting part of these was that it could convert unstructured inputs into natural language and structured outputs. Translation tasks (broad definition of translation), extracting key data points in unstructured data, language tasks. It’s outstanding for the NLP tasks we struggled with previously, and these tasks are highly transformative or any inputs, it purely relies on structural patterns. I think few people would argue NLP tasks are infringing on the copyright owner.

But I can at least see how moving the direction toward (particularly with MoE approaches) using Q&A data to support generating Q&A outputs, media data to support generating media outputs, using code data to support generating code, this moves toward the territory of affecting sales and using someone’s IP to compete against them. From a technical perspective, I understand how LLMs are not really copying, but the way they are marketed and tuned seems to be more and more intended to use people’s data to compete against them, which is dubious at best.

Not to fully argue against your point, but I do want to push back on the citations bit. Given the way an LLM is trained, it’s not really close to equivalent to me citing papers researched for a paper. That would be more akin to asking me to cite every piece of written or verbal media I’ve ever encountered as they all contributed in some small way to way that the words were formulated here.

Now, if specific data were injected into the prompt, or maybe if it was fine-tuned on a small subset of highly specific data, I would agree those should be cited as they are being accessed more verbatim. The whole “magic” of LLMs was that it needed to cross a threshold of data, combined with the attentional mechanism, and then the network was pretty suddenly able to maintain coherent sentences structure. It was only with loads of varied data from many different sources that this really emerged.

C could just be a blank and you have to bit blit the arrow on yourself.

I don’t know why, but I still can’t open a core file without going I’m in. I don’t do QA, though, and so tinkering with final breath of my program frozen in time maintains some novelty.

My two cents, after years of Markdown (and md to PDF solutions) and LaTeX and a full two years of trying to commit to bashing my head against Word for work purposes, I’m really enjoying Typst. It didn’t take long to convert my themes, having docs I can import which are basically just variables to share across documents in a folder has been really helpful. Haven’t gone too deep into it but I’m excited to give it a deeper test run over the next little bit.

Lots of immediate hate for AI, but I’m all for local AI if they keep that direction. Small models are getting really impressive, and if they have smaller, fine-tuned, specific-purpose AI over the “general purpose” LLMs, they’d be much more efficient at their jobs. I’ve been rocking local LLMs for a while and they’ve been great as a small compliment to language processing tasks in my coding.

Good text-to-speech, page summarization, contextual content blocking, translation, bias/sentiment detection, click bait detection, article re-titling, I’m sure there’s many great use cases. And purely speculation,but many traditional non-llm techniques might be able to included here that were overlooked because nobody cared about AI features, that could be super lightweight and still helpful.

If it goes fully remote AI, it loses a lot of privacy cred, and positions itself really similarly to where everyone else is. From a financial perspective, bandwagoning on AI in the browser but “we won’t send your data anywhere” seems like a trendy, but potentially helpful and effective way to bring in a demographic interested in it without sacrificing principles.

But there’s a lot of speculation in this comment. Mozilla’s done a lot for FOSS, and I get they need monetization outside of Google, but hopefully it doesn’t lead things astray too hard.

I sit somewhere tangential on this - I think Bret Victor’s thoughts are valid here, or my interpretation of them - that we need to start revisiting our tooling. Our IDEs should be doing a lot more heavy lifting to suit our needs and reduce the amount of cognitive load that’s better suited for the computer anyways. I get it’s not as valid here as other use cases, but there’s some room for improvements.

Having it in separate functions is more testable and maintainable and more readable when we’re thinking about control flow. Sometimes we want to look at a function and understand the nuts and bolts and sometimes we just want to know the overall flow. Why can’t we swap between views and inline the functions in our IDE when we want to see the full flow? In fact, why can’t we see the function inline but with the parameter variables replaced by passed values to get a feel for how the function will flow and compute what can be easily computed (assuming no global state)?

I could be completely off base, but more and more recently - especially after years of teaching introductory programming - I’m leaning more toward the idea that our IDEs should be doubling down on taking advantage of language features, live computation, and co-operating with our coding style… and not just OOP. I’d love to hear some places that I might be overlooking. Maybe this is all a moot point, but I think code design and tooling should go hand in hand.

Yeah, I knew freelance folks who provided long term support with such complicated setups. The base daily rate plus hourly with a monthly retainer and weekly on call fees. Wild.

Or, hourly = extremely high paid contract work.

Yeah I probably should have added the /s to that one.

Windows 11 has tabbed file explorer, a package manager, it’s quick, the interface looks nice and feels nice, and it’s been really stable for me. I don’t know where the complaints are at, it’s been great. All they need to do is regress all of the ads-in-your-OS stuff from 10. Bring back the start menu that doesn’t hang for 30 seconds looking something up online before showing you your installed programs.

Soap. 100%.

I know this post and comment might sound shilly but switching to more expensive microfibre underwear actually made a big impact on my life and motivated me to start buying better fitting and better material clothes.

I’d always bought cheap and thought anything else was silly. I was wrong. So much more comfortable, I haven’t had a single pair even begin to wear down a little bit, less sweating and feel cleaner, fit better, and haven’t been scrunchy or uncomfortable once compared to the daily issues of that cheap FotL life. This led to more expensive and longer lasting socks with textures I like better, better fitting shoes that survive more than one season.

It was spawned by some severe weight loss and a need to restock my wardrobe. My old underwear stuck around as backups to tell me I needed to do laundry, but going back to the old ones was bad enough that I stopped postponing laundry.

Basically, I really didn’t appreciate how much I absolutely hated so many textures I was constantly in contact with until I tried alternative underwear and realized you don’t have to just deal with that all the time.

It depends what “From Scratch” means to you, as I don’t know your level of programming or interests, because you could be talking about making a game from beginning to end, and you could be talking about…

• Using a general purpose game engine (Unity, Godot, Unreal) and pre-made assets (e.g., Unity Asset Store, Epic Marketplace)?
• Using a general purpose game engine almost purely as a rendering+input engine with a nice user interface and building your own engine overtop of that
• Using frameworks for user input and rendering images, but not necessarily ones built for games, so they’re more general purpose and you’ll need to write a lot of game code to put it all together into your own engine before you even starting “Making the game”, but offer extreme control over every piece so that you can make something very strange and experimental, but lots of technical overhead before you get started
• Writing your own frameworks for handling user input and rendering images… that same as previous, but you’ll spend 99% of your time trying to rewrite the wheel and get it to go as fast as any off the shelf replacement

If you’re new to programming and just want to make a game, consider Godot with GDScript - here’s a guide created in Godot to learn GDScript interactively with no programming experience. GDScript is like Python, a very widely used language outside of games, but it is exclusive to Godot so you’ll need to transfer it. You can also use C# in Godot, but it’s a bigger learning curve, though it is very general and used in a lot of games.

I’m a big Godot fan, but Unity and Unreal Engine are solid. Unreal might have a steeper learning curve, Godot is a free and open-source project with a nice community but it doesn’t have the extensive userbase and forum repository of Unity and Unreal, Unity is so widely used there’s lots of info out there.

If you did want to go really from scratch, you can try using something like Pygame in Python or Processing in Java, which are entirely code-created (no user interface) but offer lots of helpful functionality for making games purely from code. Very flexible. That said, they’ll often run slow, they’ll take more time to get started on a project, and you’ll very quickly hit a ceiling for how much you can realistically do in them before anything practical.

If you want to go a bit lower, C++ with SDL2, learning OpenGL, and learning about how games are rendered and all that is great - it will be fast, and you’ll learn the skills to modify Godot, Unreal, etc. to do anything you’d like, but similar caveats to previous; there’s likely a low ceiling for the quality you’ll be able to put out and high overhead to get started on a project.

Yeah, I really think it’s important to not see Lemmy as one singular community, or a lot of important use cases will go ignored.

Not the OP, but in Canada at least, I think you would legally be expected to because common law is (as far as I’m aware) very nearly marriage and is entirely implied by time living together in a conjugal relationship. It might be provincial to determine the actual property laws, though.

I don’t have a firm opinion here, but I think the key difference in your case is that a conjugal relationship has some expectation of… Oh I don’t know, mutuality? A landlord tenant relationship is a lease agreement. If your roommate didn’t sign any kind of lease agreement, they might have a legal case to just not pay you and suffer no consequences (I don’t know), but they’re not in a conjugal relationship, so there’s also no implication of shared ownership.

Without signing lease agreement and being in a conjugal relationship, I think there is a pretty fair case that expecting shared ownership is a fair assumption.

That all said, it’s also really up to the individuals to figure that out early, and the deception in the meme suggests that the agency to have that discussion wasn’t available, and that’s really the part I find problematic here.

That and expropriation/eminent domain, etc. Even if you pay your taxes, if the government needs it, they have processes to take it.

I’m not saying it’s an inherently bad thing, but it’s another one of those important things to realize is already present if anyone wants to argue for/against certain government reforms.

I certainly used to, and used to think it was essentially gender neutral, but again - in certain contexts like a male dominated classroom, the women/nb students could easily feel excluded by it. Outside of that, I also recognized my trans friends had a lot of thoughtless people intentionally misgendering them on the regular just to be mean, and finding small ways to reduce that reinforcement felt better than not. It was also surprisingly not that tough for me to adopt the more neutral language, so if it’s a subtle help with no skin off my back it just seems very win-win.

I know it’s controversial, but moving away from “guys” when I address a group and more or less defaulting to “they” when referring to people I don’t know.

They was practical, because I deal with so many students exclusively via email, and the majority of them have foreign names where I’d never be able to place a gender anyways if they didn’t state pronouns.

Switching away from guys was natural, but I’m in a very male dominated field and I’d heard from women students in my undergrad that they did feel just a bit excluded in a class setting (not as much social settings) when the professor addresses a room of 120 men and 5 women with “Guys”, so it just more or less fell to the side in favour of folks/everyone.