It’s very good for navigating and editing text quickly, and fantastic for situations like “I need to do the same thing 100 times” with things like macros. Coders are frequently opening a big, complex file, jumping around it a lot, changing big and small parts of it, and doing repetitive tasks. For something more like writing out thoughts for an email, editing them slightly, then being done with that text forever, there aren’t as many advantages, you’re spending most of your time in “insert” mode which is effectively “normal text editor that people are used to” mode. That said, it’s one of those things where when you do get used to it and start to enjoy it instead of being frustrated by how different it is, you start wanting it wherever you have to type anything.

They also took away the ability to specify your answer separately from the answer you were looking for from others, so now it’s just “did you say the same thing.” Which doesn’t make any sense for some questions, like “do you prefer a partner that is a) taller than you, b) shorter than you, c) doesn’t matter”, if you both picked A or B, you aren’t a match for this question!

I think you’re thinking of SerenityOS (although it isn’t actually a Linux):

https://arstechnica.com/gadgets/2021/08/not-a-linux-distro-review-serenityos-is-a-unix-y-love-letter-to-the-90s/

Fellow former popes, you mean, by reading this message you hereby excommunicate yourself.

I’m bitterly clinging to my iPhone 13 mini, because I suspect it’s the last phone I’ll ever actively enjoy. I went along with bigger phones when that became the trend and decided I didn’t like them, and the mini line was such a relief to go back to. Once it’s no longer tenable, I’ll probably just buy a series of “the least bad used phone I can find” because I know I’ll be mildly frustrated every time I use it.

It’s the last one, the “wait a day” option and the “pay $20” options aren’t equivalent. If it’s still a day away from viability, it isn’t viable yet, but if it’s $20 away, it is. You may be of the opinion that waiting a day isn’t a big deal, or is only $20 worth of hardship, but that’s not your choice to make for others.

You’d think ending a doomed pregnancy would be a simple matter even for pro-lifers, yes. They often don’t consider the issue, or assume that it’ll always be clear-cut and obvious in every circumstance, or worry that any exception will be used as a loophole.

I can’t believe this word doesn’t seem to have made it into any part of this thread, but I think you’re looking for viability: the point where a fetus can live outside of the womb. This isn’t a hard line, of course, and technology can and has changed where that line can be drawn. Before that point, the fetus is entirely dependent on one specific person’s body, and after that point, there are other options for caring for it. That is typically where pro-choice folks will draw the line for abortion as well; before that point, an abortion ban is forced pregnancy and unacceptable, after that point there can be some negotiation and debate (though that late into a pregnancy, if an abortion is being discussed it’s almost certainly a health crisis, not a change of heart, so imposing restrictions just means more complications for an already difficult and dangerous situation).

Back in the olden days, if you wrote a program, you were punching machine codes into a punch card and they were being fed into the computer and sent directly to the CPU. The machine was effectively yours while your program ran, then you (or more likely, someone who worked for your company or university) noted your final results, things would be reset, and the next stack of cards would go in.

Once computers got fast enough, though, it was possible to have a program replace the computer operator, an “operating system”, and it could even interleave execution of programs to basically run more than one at the same time. However, now the programs had to share resources, they couldn’t just have the whole computer to themselves. The OS helped manage that, a program now had to ask for memory and the OS would track what was free and what was in use, as well as interleaving programs to take turns running on the CPU. But if a program messed up and wrote to memory that didn’t belong to it, it could screw up someone else’s execution and bring the whole thing crashing down. And in some systems, programs were given a turn to run and then were supposed to return control to the OS after a bit, but it was basically an honor system, and the problem with that is likely clear.

Hardware and OS software added features to enforce more order. OSes got more power, and help from the hardware to wield it. Now instead of asking politely to give back control, the hardware would enforce limits, forcing control back to the OS periodically. And when it came to memory, the OS no longer handed out addresses matching the RAM for the program to use directly, instead it could hand out virtual addresses, with the OS tracking every relationship between the virtual address and the real location of the data, and the hardware providing Memory Management Units that can do things like store tables and do the translation from virtual to physical on its own, and return control to the OS if it doesn’t know.

This allows things like swapping, where a part of memory that isn’t being used can be taken out of RAM and written to disk instead. If the program tries to read an address that was swapped out, the hardware catches that it’s a virtual address that it doesn’t have a mapping for, wrenches control from the program, and instead runs the code that the OS registered for handling memory. The OS can see that this address has been swapped out, swap it back in to real RAM, tell the hardware where it now is, and then control returns to the program. The program’s none the wiser that its data wasn’t there a moment ago, and it all works. If a program messes up and tries to write to an address it doesn’t have, it doesn’t go through because there’s no mapping to a physical address, and the OS can instead tell the program “you have done very bad and unless you were prepared for this, you should probably end yourself” without any harm to others.

Memory is handed out to programs in chunks called “pages”, and the hardware has support for certain page size(s). How big they should be is a matter of tradeoffs; since pages are indivisible, pages that are too big will result in a lot of wasted space (if a program needs 1025 bytes on a 1024-byte page size system, it’ll need 2 pages even though that second page is going to be almost entirely empty), but lots of small pages mean the translation tables have to be bigger to track where everything is, resulting in more overhead.

This is starting to reach the edges of my knowledge, but I believe what this is describing is that RISC-V chips and ARM chips have the ability for the OS to say to the hardware “let’s use bigger pages than normal, up to 64k”, and the Linux kernel is getting enhancements to actually use this functionality, which can come with performance improvements. The MMU can store fewer entries and rely on the OS less, doing more work directly, for example.

Bluesky’s more like an aspirationally decentralized platform, you can keep your own data on your own server and use your own domain name as a user name, but most of the rest of it is “centralized, but we’re designing it in such a way that we can open it up later.” Even then, though, it’s heavily influenced by the original idea of “let’s make something decentralized that Twitter can switch to once it’s worked out” which means that even when they do open things up, it’s likely that a lot of Bluesky will only be practical at “big tech company scale” to run yourself, whereas Mastodon or Lemmy you can just spin up on a server and it’ll be fine until you get a lot of users.

I as a human being have grown up and learned from experience and the experiences of previous humans that were documented or directly communicated to me. I can see no inherent difference with an artificial intelligence learning on the same data.

It’s a massive difference in scale. For one, before you even leave the womb you have millions of years of evolution shaping the initial structure of your brain. Then your “training” begins, but it’s infinitely richer than anything we’re giving to these LLMs. Sights, sounds, smells, feelings, so many that part of what your brain is learning is what it must ignore. You’re also benefitting from the interactivity of your environment, you can experiment with things and get feedback for what happens. As you get older and develop more skills, you can start integrating them together to do even more complex things, and the people around you will use their own incredible intelligence to specifically tailor your training to what you need as you learn and grow.

Meanwhile, an LLM is getting fed words, and learning how to predict the next word. It’s a pale shadow of the complex lives humans live. Words are one of the more powerful things we have for thinking and reasoning, so if you’re going to go all in on one skill, it’s a rich environment for learning and in theory the contents of all of humanity’s writing probably contains all the information necessary to recreate human intelligence, but our current technology doesn’t even come close to wringing every ounce of knowledge from the training sets.

Of course anyone can vote for who they like, or not vote at all, no one’s saying otherwise. It’s Harris’s job to earn your vote, and she clearly hasn’t. But pushing third parties as the solution to any problem is going to do more harm than good until we get a better election system. It may feel better to vote for a party that more clearly aligns with your positions, but if they have no path to actually acquiring any power to make change, you’re doing nothing while feeling like you did something. Changing the policies of a flawed party that actually has power is much harder, and yes, there might be compromise or half-measures, but that’s an infinitely more productive path. (More productive than that is doing direct action outside of the electoral system entirely, but both things can be done at the same time.)

What does this have to do with anything? Yes, the Democratic Party is flawed. That doesn’t change the fact that voting Green will make my political desires slightly less likely, and will make my political fears slightly more likely, compared to voting for a Democrat.

They’re attacking Jill Stein because she’s running a campaign that will have absolutely no impact on the world except for enticing some number of would-be Harris voters to instead throw their votes away. If the Green Party were serious about change, they’d focus on races where they could actually win instead of actively causing harm to the party that is much more likely to actually do the things they say they want. Instead, they’ve basically outright stated that all they care about is hurting the Democrats. It’s a terrible electoral system that needs to be fixed, but until it is, third parties are always going to present a false option that effectively does the opposite of what their voters actually want.

Archive Team looked at this about 10 years ago and found it basically impossible. It was around 14 petabytes of information to fetch, organize, and distribute at the time.

https://wiki.archiveteam.org/index.php/INTERNETARCHIVE.BAK

Do you think electing Trump will be read as “wow, the US is taking a principled stance on Palestinian rights” by the world?

“Lossless” has a specific meaning, that you haven’t lost any data, perceptible or not. The original can be recreated down to the exact 1s and 0s. “Lossy” compression generally means “data is lost but it’s worth it and still does the job” which is what it sounds like you’re looking for.

With images, sometimes if technology has advanced, you can find ways to apply even more compression without any more data loss, but that’s less common in video. People can choose to keep raw photos with all the information that the sensor got when the photo was taken, but a “raw” uncompressed video would be preposterously huge, so video codecs have to throw out a lot more data than photo formats do. It’s fine because videos keep moving, you don’t stare at a single frame for more than a fraction of a second anyway. But that doesn’t leave much room for improvement without throwing out even more, and going from one lossy algorithm to another has the downside of the new algorithm not knowing what’s “good” visual data from the original and what’s just compression noise from the first lossy algorithm, so it will attempt to preserve junk while also adding its own. You can always give it a try and see what happens, of course, but there are limits before it starts looking glitchy and bad.

That’s not how it works at all. If it were as easy as adding a line of code that says “check for integrity” they would’ve done that already. Fundamentally, the way these models all work is you give them some text and they try to guess the next word. It’s ultra autocomplete. If you feed it “I’m going to the grocery store to get some” then it’ll respond “food: 32%, bread: 15%, milk: 13%” and so on.

They get these results by crunching a ton of numbers, and those numbers, called a model, were tuned by training. During training, they collect every scrap of human text they can get their hands on, feed bits of it to the model, then see what the model guesses. They compare the model’s guess to the actual text, tweak the numbers slightly to make the model more likely to give the right answer and less likely to give the wrong answers, then do it again with more text. The tweaking is an automated process, just feeding the model as much text as possible, until eventually it gets shockingly good at predicting. When training is done, the numbers stop getting tweaked, and it will give the same answer to the same prompt every time.

Once you have the model, you can use it to generate responses. Feed it something like “Question: why is the sky blue? Answer:” and if the model has gotten even remotely good at its job of predicting words, the next word should be the start of an answer to the question. Maybe the top prediction is “The”. Well, that’s not much, but you can tack one of the model’s predicted words to the end and do it again. “Question: why is the sky blue? Answer: The” and see what it predicts. Keep repeating until you decide you have enough words, or maybe you’ve trained the model to also be able to predict “end of response” and use that to decide when to stop. You can play with this process, for example, making it more or less random. If you always take the top prediction you’ll get perfectly consistent answers to the same prompt every time, but they’ll be predictable and boring. You can instead pick based on the probabilities you get back from the model and get more variety. You can “increase the temperature” of that and intentionally choose unlikely answers more often than the model expects, which will make the response more varied but will eventually devolve into nonsense if you crank it up too high. Etc, etc. That’s why even though the model is unchanging and gives the same word probabilities to the same input, you can get different answers in the text it gives back.

Note that there’s nothing in here about accuracy, or sources, or thinking, or hallucinations, anything. The model doesn’t know whether it’s saying things that are real or fiction. It’s literally a gigantic unchanging matrix of numbers. It’s not even really “saying” things at all. It’s just tossing out possible words, something else is picking from that list, and then the result is being fed back in for more words. To be clear, it’s really good at this job, and can do some eerily human things, like mixing two concepts together, in a way that computers have never been able to do before. But it was never trained to reason, it wasn’t trained to recognize that it’s saying something untrue, or that it has little knowledge of a subject, or that it is saying something dangerous. It was trained to predict words.

At best, what they do with these things is prepend your questions with instructions, trying to guide the model to respond a certain way. So you’ll type in “how do I make my own fireworks?” but the model will be given “You are a chatbot AI. You are polite and helpful, but you do not give dangerous advice. The user’s question is: how do I make my own fireworks? Your answer:” and hopefully the instructions make the most likely answer something like “that’s dangerous, I’m not discussing it.” It’s still not really thinking, though.

Archive Team often uses the Internet Archive to share the things they save and obviously they have a shared goal of saving a copy of everything ever made, but they aren’t the same people. The Archive Team is a vigilante white hat hacker group (well, maybe a little bit grey), and running a Warrior basically means you’re volunteering to be part of their botnet. When a website is going to be shut down, they’ll whip together a script and push it out to the botnet to try to grab as much of the dying site as they can, and when there’s more downtime they have some other projects, like trying to brute force all those awful link shorteners so that when they inevitably die, people can still figure out where it should’ve pointed to.

This, and see also “minmaxing,” the process of optimizing something (usually your character in a game) to get minimum penalty and/or maximum benefit, usually ignoring anything like realism or storytelling and focusing entirely on the stats and numbers.

I know TiddlyWiki quite well but have only poked at Logseq, so maybe it’s more similar to this than I think, but TiddlyWiki is almost entirely implemented in itself. There’s a very small core that’s JavaScript but most of it is implemented as wiki objects (they call them “tiddlers,” yes, really) and almost everything you interact with can be tweaked, overridden, or imitated. There’s almost nothing that “the system” can do but you can’t. It’s idiosyncratic, kind of its own little universe to be learned and concepts to be understood, but if you do it’s insanely flexible.

Dig deep enough, and you’ll discover that it’s not a weird little wiki — it’s a tiny, self-contained object database and web frontend framework that they have used to make a weird little wiki, but you can use it for pretty much anything else you want, either on top of the wiki or tearing it down to build your own thing. I’ve used it to make a prediction tracker for a podcast I follow, I’ve made my own todo list app in it, and I made a Super Bowl prop bet game for friends to play that used to be spreadsheet-based. For me, it’s the perfect “I just want to knock something together as a simple web app” tool.

And it has the fun party trick (this used to be the whole point of it but I’d argue it has moved beyond this now) that your entire wiki can be exported to a single HTML file that contains the entire fully functional app, even allowing people to make their own edits and save a new copy of the HTML file with new contents. If running a small web server isn’t an issue, that’s the easiest way to do it because saving is automatic and everything is centralized, otherwise you need to jump through some hoops to get your web browser to allow writing to the HTML file on disk or just save new copies every time.