1. Be quick to adapt and learn. Whatever field you enter is going to look completely different in 30 years (if it even remains), so if you can adapt quickly and learn whatever skills are newly relevant, you’ll be in a decent place. In most employers there’s a large barrier to hiring someone new, so if your field is becoming irrelevant / automated / transformed, but you’re able to start doing some of the new work that your employer needs doing, then your role will grow to encompass that work and you’ll remain relevant and valuable to your employer. By adapting and picking these tasks up, you essentially can rotate out of irrelevant skills into relevant ones while keeping your job and seniority.
2. I’m not sure how AI affects this, but going with the “adapt and learn” of the previous point, focus more on having a lot of general skills rather than getting really investing in a few specific ones. Most jobs aren’t going to be pure <field>. In my career so far (I’m about a decade older than you for what it’s worth), I’ve differentiated myself from other statisticians & data scientists by also being strong at software development. My classmates from where I did my master’s are better statisticians than me, but they’re limited in what they can do because while they can develop a great model, they struggle to get it running well in a production environment. That limits their employability. I, on the other hand, had no problem with that and was able to have great success at my previous employer. I was then able to leverage that particular experience / skill combination to get my current job, where my employer was explicitly seeking only people with this combination of skills (and struggles quite a bit to find them!)
   • I would suggest focusing on developing skills that are complementary to whatever field you enter.
3. It’s hard to say what AI will look several years from now given how fast it’s changing, but so far I’ve noticed that its outputs can be either okay or very awful, and only someone who understands the field of that output can tell which of those outcomes it is. That’s why experienced programmers who use AI for coding are able to get some value (they filter out the awful output), while people who don’t understand coding ship slop. Ditto for other fields. Going with the “be okay at lots of skills” of my previous point, I suspect that this would help you get more out of AI than most people as you could more effectively filter out bad AI output in a variety of areas, which could give you a leg up.

Tl;DR always be learning new skills; focus on quantity over quality.

Nah Nvidia’s all for local models - people need GPUs to run them! OpenAI is trying to develop their own chips, so it isn’t in Nvidia’s interest for OpenAI to win. Probably ditto for Anthropic. Nvidia wins in a world where there is no AI provider big enough they can invest enough to bypass Nvidia. This is probably why Nvidia has been releasing their own open models, training sets, etc.

Also because browsers support OfflineAudioContext, which lets you do the same things but doesn’t try to output to speakers.

I like their razor but I use other blades. I’ve had previous safety razors that had hinges to open the top part to put the blade in. Those were fine until I dropped them. My Henson razor though is just 3 aluminium pieces that screw into each other, so virtually indestructible.

Basically don’t use drugs or you’ll destroy your life.

Sex was avoided as a topic, but we were taught that premarital sex is a sin. I was also teased as a kid about having crushes on girls (I’m male), despite my adament (and truthful) denials, and othertimes they’d joke that I’d change my mind once I was older. I won that though - turns out I was gay and never did change my mind.

As for LGBT issues, it was so taboo / not talked about that I didn’t even connect my confusing feelings for male classmates with me being gay until I was 18. I thought everyone felt like that and was frustrated when they wouldn’t reciprocate those emotions back.

I think OP is asking “In the worst case scenario of a malicious instance owner that is able to fork the Lemmy codebase (so could disable hashing) and intercept and record all communications going to and from their instance, what risk do I have as a user of that instance?”

The answer of course is yes, in theory a malicious instance owner could see the password you use and can see all your communications, votes, what you look at, etc. So use a unique password for that instance, and don’t use the instance for private communucations whose interception could seriously harm you.

On the topic of indie films, can people in-the-known share some suggestions for what’s worth watching?

This is unfortunately only true when a landlord fixes things beyond the absolute bare minimum (massive globs of caulk on caulk everywhere is my experience).

It’s “200” (200th episode). S10E06. Basically the characters are imagining different TV shows that could be based on their experiences. The puppets are only a short part of that episode.

Switching to something rolling release makes sense. My Arch setup (btw) has always felt much more stable than when I was using Ubuntu, because with Ubuntu I’d inevitatebly run into a bug, find out it was fixed months ago but won’t be backported, and then either live with it or try custom-installing the newer version of that thing. Or I’d install something manually that expected dependency X be the latest version, etc.

My friend is an indie game developer and he once told me the typical refund rate, which I think was only a little lower than this. I don’t think the game length is playing that large a role here, rather people use that refund policy as a way to try out a game (like a demo). Probably most of the people who refunded wouldn’t have bought it in the first place without that refund policy, so he shouldn’t really view this as “because of this policy I lost 55K sales”.

Short answer is yes, it requires hardware that to my knowledge isn’t available on normal consumer motherboards.

Of course, but a generation of homeowners grew up learning that, barring short disruptions, home prices always go up. Nevermind that this was largely due to interest rates steadily dropping since the 80s, reaching basically 0% during Covid.

Since there isn’t really room for interest rates to drop anymore people shouldn’t expect home prices to rise faster than incomes rise, but it’s going to be hard to undo 30 years of observation.

Unless it becomes cheaper than having a datacenter on earth per quanity of compute, it won’t happen in any meaningful scale even if these issues are solved.

I’ve been using AntennaPod for years now to listen to podcasts. It works great and feels like a premium app.

• I make a great vegan ice cream that is loved by many non-vegans.
• I beat depression and anxiety.
• I have a fun job that pays well and I’m good at it.

I bet they would be (against Valve) if a competitor donated to Republicans. I mean, they’re keeping a finished bridge they paid $0 for closed because the owner of a competing, tolled bridge donated to them. Of course they’ll open an investigation into a smallish leftish leaning company if a large donor asked them to.

I don’t think DDR4 is significantly cheaper. Plus, they would have had to go a CPU generation back too then and I think the AMD CPUs of that generation had way worse integrated graphics, so now you’d need a dedicated GPU as well.

What does it mean for a project to deserve the [AI] tag? This matters, because you may have a lot of projects where a developer may think “no” and someone else thinks “yes”. Some examples from my day job:

• Developer used AI to understand part of the codebase and suggest ways to accomplish goal. Developer incorporated that suggestion, though using their own knowledge deviated from AI’s suggestion in parts. Developer wrote the code themselves. Is this project [AI] or [NOT AI]?
• Developer used AI to review existing (human-written) code for quality and security purposes. AI noticed some issues and proposed fixes. Developer reviewed and accepted them. Is this project [AI]?
• Developer knew they wanted to implement a feature, and while implementing it there was a boilerplate function. Developer asked AI to write this function, manually reviewed it, confirmed it worked, and added it to the codebase. Is this project [AI]?

In these examples the developer carefully reviews the AI’s output, which I think distinguishes it from vibe-coded slop, which at least is what I want to ignore.

It’s also worth noting that an open-source project may receive and incorporate a well-written contribution where the human developer used AI carefully like this. Unless they disclosed that they used AI, it may be unknowable to the project maintainers whether their project is [AI] or not, depending on how you define it. What tag should these projects use?

I don’t know about the background updates, but the HDMI CEC allows for the device and the TV to sync with each. Think like adjusting the volume on the TV adjusting the volume on the soundbar, or your Steam machine to turn on when you turned on your TV. To my knowledge this required specific hardware normal consumer motherboards don’t support.