Or just do it like reddit did, where you can delete your post content and remove your username from it, but the thread and comments remain.

Though with how the fediverse works, it’s possible to spin up a custom instance that highlights deleted content instead of deleting it, meaning the attempt to get rid of it can be what brings it more attention if anyone has decided to do it. Just like with vote identities, they aren’t anonymous and there are instances/sites that just show who voted for what.

I’m still undecided about their own text (deleting comments or post text disrupts the conversation but there’s valid reasons), but I don’t think the OP of a thread should have any control over the existence of that full comment thread.

Frankly, I don’t think users should have that power.

It’s more about the nature of a purge. If you were trying to put the best people for the roles in those roles prior to a purge, then a purge is going to leave you with worse people for the jobs, or at the very least, less experienced people doing those jobs.

It might have been sexism behind the reason women dominated those roles before the purge, but it’s normal for quality to drop after a political purge.

Same thing happened with the Nazis after Hitler did some rounds of purges in the Wermacht upper echelon after that assassination attempt with the bomb that injured him and discovering the plot for a military coup in its background. The purges really didn’t help their war efforts when they were already struggling.

Somewhere between 50 and 60, I think.

So it only counts if the British agree to it?

Though changing content goes both ways. Just this past week I was looking at the new releases on Netflix and saw they had added a new season of Harley Quinn and checked the info to see if it was the 3rd like I remembered and it seems that they simultaneously removed the earlier seasons.

And in general, I think there have been more good things lost than added, not to mention the way Netflix writes shows for people who aren’t paying attention is annoying as someone who does. Fuck off with repeating all the relevant information every single time it comes up.

Might be because hotels are saying there aren’t bookings, making it easier to see that demand isn’t involved in setting the prices.

Yeah, reinventing the wheel can be fun.

You can train it on all the source code, meta data for that source code, and documentation you want but it will never understand programming. It’s a text predictor that was trained on both sides of a bunch of debates. Contradictions mean nothing to it, but it usually only predicts what one side of the debate will say to champion its side, which means it will use confident and absolute language to “sell” whatever side of the debate it looks like the previous tokens are headed towards.

It is impressive what it can output sometimes and it makes a decent debate/exploration partner, but it will always have a chance at predicting a useless series of tokens or contradicting the previous thing it just said because a) its training data only trains it to predict tokens from statistics, and b) its training data includes some of those contradictions directly.

I have lost count of the times I’ve been “thinking out loud” about something with an LLM and realize something about what I’m thinking about that contradicts what it is currently saying, then I’ll add my new perspective and it agrees entirely, despite the contradiction. Sometimes it tries to resolve the contradiction, sometimes it just abandons what it said previously entirely, sometimes it adds more to the perspective that I hadn’t considered.

That’s fine for just shooting the shit about some random topic but horrible for a tool intended to provide expertise and reliability, when the response matters because it feeds into something else and you want to automate it. Should a tool just inject “are you sure?” after each response? What if it makes it second guess something that was correct? What if it’s one of those debates and it will endlessly switch sides when it faces any opposition? That’s a waste of resources and time.

Funny thing is I’m expecting this to eventually go back to scripting for automation. An LLM has a higher chance of outputting a script that does what you want (depending on the task) while you hold its hand than it does of consistently giving the correct output when it is thrown into an automated system directly. But you get “goodish” results much quicker just trying putting the LLMs everywhere, even if there’s some selection bias on the results (“didn’t work, didn’t work, oh it worked, great!”).

For someone fluent in all involved languages, sure.

But from the sounds of it, OP’s company outsources the translation but doesn’t fully trust the output they get back. They’re back to square one for verifying it, because if they knew both languages enough to verify, they could do the translations themselves.

The problem AI is trying to solve is “how do I access a skill I don’t have cheaply?” It’s only because it’s bad at that problem that it has shifted to “how can we use AI to get more production out of the skilled workers we still need to babysit the AI that is unreliable at everything?”

I’m sure you could involve a torch in there somehow to raise the stakes even more. Grilled Cheese Flamble has a certain ring to it.

It’s kinda like the push to return to office. It was driven by corps having invested in the “can’t fail (ignoring the last previous crash)” real estate market and buying their offices. If everyone suddenly works from home instead of in the office, then those investments go bad because demand for office space is way down. So they tell people to go back to the office, hoping to return to that “every business needs offices!” status quo and save their investments. Though the demand is false (especially combined with layoffs), so it won’t necessarily cause any new corp to want that office space. If they don’t have the sunk cost, then they don’t need to accept the rest of the fallacy.

With AI, it’s the same but just replace building investment with R&D as well as data centre investment. A lot of the companies really pushing AI are the ones that will profit from people going along with that. They really want to build a dependence amongst users as well as a good reputation for execs so they can get a return on the investment. Then there’s also the True Believers (who think LLMs are brilliant AIs that can solve anything if given the right prompts) and the FOMOs (who don’t know much about it but see the world moving towards it and don’t want to miss it because if it was a real AI, missing it could be a massive mistake). There’s also some people who just don’t have various skills and want the AI agents to fill those gaps (and probably don’t have a very good idea about what the LLMs are actually doing in those gaps).

At this point, I think it’s a mistake to go all in on this tech. LLMs aren’t reliable, and their ability to “perform” is more about their flexibility than being well-suited for any task. They’ll go directly from saying things that seem “insightful” (they have no insight) to making the dumbest “mistake” (a mistake requires intent, which they lack, they just predict tokens). But there’s all kinds of false and true (albeit misguided IMO) demand right now and it’s still in early pricing mode (remember the intent is to make that investment money back).

Oh and there’s also China which has been making more efficient models and open sourcing some of them. If they continue to do this, there’s a decent chance those investments will never give the desired returns, at least not to those who are trying to sell tokens. Or those who depend on those selling tokens, like any hardware companies selling hardware under the assumption that it will then make the money to pay for itself (which I believe both nVidia and AMD have done).

It’s mirroring the dotcom bubble with that last bit because network cable companies started loaning the money to pay for their cables to ISPs, expecting returns that never came.

Only if it’s been trained on discussions about it.

I would love if this attitude caught on with enough people to make the marketing industry implode.

I am aware of when I have a vague familiarity with a product or brand and know that that familiarity doesn’t equal good. These days it could mean anything from best in class to absolute shit. The only thing they have in common is that a lot of money was spent on marketing.

Other than that, I need to either take a gamble or do deep research into the thing I want to do (though I’m learning that the real thing I want to look into is the result I want because I might be starting with the wrong process to get there, but after that will still be research on the process and tools to do it, followed by what materials and features are good for that).

Funny thing is that in the end, I do want advertising. Only difference is I want advertising that can be trusted when marketing is often either pushing outright lies when it thinks it can get away with it or has flipped around their message so much so that they can talk their product up without outright lying. I want a reviewer that will call garbage garbage (or even better, go into detail about why they think it is garbage) and not have to worry about whether that means some producers won’t want to send them free shit to review.

He could end up with a fancy bunker door that leads to an unfinished tunnel.

How does being grumpy about something help? It’s just an emotional state, not a solution.

Nintendo? Like the playing card company?

It doesn’t spread person to person so it won’t have the explosive growth and spread to all corners of the globe like covid.

Edit: this one might, though it isn’t clear yet and if it does, it seems to require close contact or at least isn’t as explosive as covid.

And AI is going to put that into overdrive.

For a little while, I helped with some intern and recent grad interviews and holy shit some people didn’t have a clue. Had one guy on a remote interview that had a friend there helping him answer questions. It was obvious because he didn’t even mute his mic and we could hear them. And it was extra pathetic because his friend wasn’t even feeding him anything useful, like Bevis was helping Butthead with a software engineering interview.

We had a short break and when we resumed, he had at least figured out to mute his mic between questions (not that that helped, as muting yourself frequently when you’re one of the main speakers in the meeting alone is a red flag without some reason that should be obvious when it isn’t muted). Only resumed because I was fairly new to interviewing, if I got one of those today (and still did interviews), I would have ended it early.