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.

My guess was that they knew gaming was niche and were willing to invest less in this headset and more in spreading the widespread idea that “Spatial Computing” is the next paradigm for work.

I VR a decent amount, and I really do like it a lot for watching TV and YouTube, and am toying with using it a bit for work-from-home where the shift in environment is surprisingly helpful.

It’s just limited. Streaming apps aren’t very good, there’s no great source for 3D movies (which are great, when Bigscreen had them anyways), they’re still a bit too hot and heavy for long-term use, the game library isn’t very broad and there haven’t been many killer app games/products that distinct it from other modalities, and it’s going to need a critical amount of adoption to get used in remote meetings.

I really do think it’s huge for given a sense of remote presence, and I’d love to research how VR presence affects remote collaboration, but there are so many factors keeping it tough to buy into.

They did try, though, and I think they’re on the right track. Facial capture for remote presence and hybrid meetings, extending the monitors to give more privacy and flexibility to laptops, strong AR to reduce the need to take the headset off - but they’re first selling the idea, and then maybe there will be a break. I’ll admit the industry is moving much slower than I’d anticipated back in 2012 when I was starting VR research.

I really want to see if worker owned cooperatives plus AI could do help democratize running companies (where appropriate). Not just LLMs, but a mix of techniques for different purposes (e.g., hierarchial task networks to help with operations and pipelining, LLM for assembling/disseminating information to workers).

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 get both sides of the argument here. I think we need to have this big reaction because companies have held so much power over employees for so long - I’ll avoid ranting about worker-owned cooperatives here - but the past few years I’ve surprised myself by moving into a bit of a “slippery slope” camp with these things. Not to say it shouldn’t happen, but that we need to be prepared for the follow-up.

Hopefully related example, in education: There were some really big push backs recently where I am over bad treatment of the students in highschool, all legit. The school board ignored it for a long time, it got bad, they finally took it seriously. Then they overcorrected and stopped believing teachers at all and started jumping straight to firing at almost any complaint. Then students started weaponizing complaints, and now teachers are getting fired for trying to enforce deadlines and for giving low marks because students are complaining about how deadlines, grades, and meeting grading requirements are detrimental to mental health and well-being, and now there are a bunch of these students from this board in my university classes failing hard and filing complaints about courses being too difficult and other things despite them having glowing reviews just a few years prior.

I guess what I’m getting at: I think it’s fair for someone to choose not to hire people like this because it’s possible that the people willing to stand up and make an important fuss over these things might not know where the line stands between a worthwhile complaint and a non-worthwhile one, and might make a company look badexternally even though it’s doing good internally, just not to someone new to the workforce’s expectations.

I also think it’s fair to go the opposite direction, because ultimately we need major change in the way companies/everything are structured that lead to these nasty layoffs and poor conditions and if someone does raise issues where there aren’t, hopefully we are prepared enough and in the right enough to take it seriously, but weather it and act in everyone’s best interests.

That too, and I can really efficiently manage the items going into bags given I backpack my groceries and want pretty specific configurations…

Totally agree. I forgot about those, as I’ve only encountered the weighing ones once in the past very long time and it was a mess, I can totally get hate if weighing ones are the only experience with them.

I almost exclusivity self-checkout for groceries, and it had drastically sped up my checkout time as most people in my area opt to use traditional checkout and the stores are still keeping lots of lanes open (just closing the express lanes). The last 3 times I’ve used a non-self checkout, each time I was double charged for items or didn’t have reduced prices applied and didn’t notice because I was bagging.

I researched creative AI and how AI can help people be creative, people thought it was a ridiculous and pointless topic. I’m biased.

Firstly, I think it’s important to see the non-chat applications. Goblin Tools is a great example of code we just couldn’t have written before. Purely from an NLP perspective, these tools are outstanding, if imperfect.

I’m excited to see new paradigms of applications come up when talented new developers are able to locally run LLMs and integrate them into their everyday programming, and too see what they can cook up in a world where that’s normal.

I’m interested in LLMs not to generate data on the fly, but to pre-generate and validate massive amounts of content or data than we’d otherwise be able to for things like games.

From a chat perspective, I like that it can support fleshing out ideas, parsing lots of data in a usable way.

And finally I’m excited for how lightweight LLMs could affect user interface design. I could imagine a future where OSs have swappable LLMs like they have shells that can allow for natural language interfacing with programs.

I don’t know, it’s just really accessible NLP, and that’s great.

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.

This is the start of the use cases I wanted to see take off with Mastodon/Lemmy/Kbin. Much like the previous era of distributed content with user-hosted voice servers and forums, having larger communities/organizations run their own instances and avoid trying to treat the space as one big pool of content is the real use case here. The fact that you can cross-instance subscribe and post makes it viable long-term.

It also gives “free” verification of information’s sources based on the domain, the same way that (modern) email gives you an extra layer of confidence when you see a verified domain. I would love the see the Government of Canada, CBC, Universities, all starting their own instances and utilizing them in unique and interesting ways. With enough adoption, official provincial/municipality instances could pop up to make organized communities easier.

It feels to me like a starting move away from the autocracy that the platform economy has created. It’s not universal, but I absolutely push back against too many instances trying to be “general purpose Reddit replacements” because that seems like a fleeting use case for what it can eventually become, and it just confuses the whole abstraction of what these decentralized socials afford.

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.