Do you think there’s any reason to believe that these tools are going to continue their breakneck progress? It seems like we’ve reached a point where throwing more GPUs and text at these things is not yielding more results, and they still don’t have the problem solving skills to work out tasks outside of their training set. It’s closer to a StackOverflow that magically has the answers to most questions you ask than a replacement for proper software engineering. I know you never know if a breakthrough is around the corner, but it feels like we’ve hit a plateau for the foreseeable future.

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.

Love a language that doesn’t care if you’re using inputs to get outputs or using outputs to get inputs

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/

I see this as an accessibility problem, computers have incredible power but taking advantage of it requires a very specific way of thinking and the drive to push through adversity (the computer constantly and correctly telling you “you’re doing it wrong”) that a lot of people can’t or don’t want to do. I don’t think they’re wrong or lazy to feel that way, and it’s a barrier to entry just like a set of stairs is to a wheelchair user.

The question is what to do about it, and there’s so much we as an industry should be doing before we even start to think about getting “normies” writing code or automating their phones. Using a computer sucks ass in so many ways for regular people, you buy something cheap and it’s slow as hell, it’s crapped up with adware and spyware out of the box, scammers are everywhere ready to cheat you out of your money… anyone here is likely immune to all that or knows how to navigate it but most people are just muddling by.

If we got past all that, I think it’d be a question of meeting users where they are. I have a car but I couldn’t replace the brakes, nor do I want to learn or try to learn, but that’s okay. My car is as accessible as I want it to be, and the parts that aren’t accessible, I go another route (bring it to a mechanic who can do the things I can’t). We can do this with computers too, make things easy for regular people but don’t try to make them all master programmers or tell them they aren’t “really” using it unless they’re coding. Bring the barrier down as low is it can go but don’t expect everyone to be trying to jump over it all the time, because they likely care about other things more.

I’m so confused that the same people can say “why does everyone get their undies in a bunch that we happily accept putting arbitrary data in columns regardless of type, that’s good, it’s flexible, but fine, we’ll put in a ‘strict’ keyword if you really want column types to mean something” and also “every other SQL says 1==‘1’ but this is madness, strings aren’t integers, what is everyone else thinking?!”

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.

See for yourself! This is someone solving day 1 of this year:

https://youtu.be/ym1ae-vBy6g

If you’re really going for it, you can automate some of the busy work like getting the input file and a template that’s ready to read it in and split it into lines, then it’s mostly a matter of skimming for what to do and knowing how to zip/map/fold/reduce/etc the data quickly. I was a bit surprised to see him having to add imports to his file, I’d think he’d have those ready to go, and of course quitting out of Vim to run the script isn’t necessary either, but I can’t judge because I definitely do that too…

A VPN is just a way to say “wrap up my normal internet packets and ship them somewhere specific before they continue the normal way.” The normal way is you want to get a message to some other server, and as a part of setting up the network you’re on, your machine should already have a list of other devices it’s physically connected to (“physically” could be “via radio waves” so not just wired) and they should have already advertised “hey, I’ve got access to these places too” for your information. Your router is likely the only one in your home network advertising anything that is on the larger internet, so all your outgoing messages will have to go that way to get to their destination. For example, I’ve got a phone, a wifi access point, a router, and my ISP’s box; my phone knows the WiFi access point is two hops away from internet because the access point said so, that’s the best one it can see, so it sends it that way and hopes it makes it. Each machine in between does the same thing until hopefully it gets where it is supposed to.

With a VPN, the same messages are wrapped in a second message that is addressed to the other end of the VPN. When it gets to the VPN provider, it’s unwrapped, then the inside message is sent off to wherever it’s supposed to go. If a message comes back to the VPN provider addressed to you (ish, this is simplifying a bit), it’s wrapped up the same way and sent back to you.

Big companies often put resources “behind” the VPN, so you can’t send messages from the outside addresses to the office printer, they’ll get blocked, but you can request a connection to the VPN, and messages that come in through that path do get allowed. The VPN can be one central place where you make sure everything coming in is allowed, then on the other side the security can be a little less tight.

VPNs also encrypt the internal message as a part of wrapping them up, which means that if you’re torrenting via a VPN, all anyone else can see is a message addressed to your VPN provider and then an encrypted message inside. And anyone you were exchanging messages with only ever saw traffic to and from the VPN provider, they never saw where it was going after your VPN provider got it. Only you and the VPN provider know what was happening on both ends, and hopefully they don’t look too closely or keep records.

Hopefully now it’s clear that Mullvad and similar won’t help you access your own things from outside, they’re only good for routing your stuff through them and then out into the rest of the internet. However, this isn’t secret magic tech: you can run your own VPN that goes in the other direction, allowing you into your own home network and then able to connect to things as if you were physically there. Tailscale is probably the easiest thing for things like that nowadays, it’ll set up a whole system where your devices can find each other and set up a mesh of secure, direct connections no matter where they are physically located. By default, just the direct device-to-device connections are re-routed, but you can also make a device an “exit node” that can route all your traffic like a traditional VPN.

Of course, that will be the exact opposite of what you want for privacy while torrenting, as it’s all devices that you clearly own and not hiding their identities whatsoever. But it’s very cool for home networking and self-hosting stuff.

“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.

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.

The .bin and .cue file are the parts of the actual game disc that you want. The .bin file contains almost all of the data and the .cue file contains some extra information about the structure of the CD. All the rest is Internet Archive stuff (and an image of the game cover of course).

To open it, you can convert it to a .iso disk image instead, which any Linux distribution can open as if it were a real CD. This blog post talks about how to do that. The last paragraph about mount you can probably replace with double-clicking the .iso file in the GUI I would guess.

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.

If you run the Node.js version, that’s all handled for you. It’s only if you want to do the party trick of keeping it all in a single HTML file that you need to worry about a plugin or anything like that. And even then, the server version exports to a standalone HTML file with one or two clicks.

Edit to add: it’s the only substantial Node package I’ve ever seen with zero dependencies. Very lightweight and simple to run.

No. The headsets are disabled when the play starts or when the play clock goes below 15 seconds.

These models aren’t great at tasks that require precision and analytical thinking. They’re trained on a fairly simple task, “if I give you some text, guess what the next bit of text is.” Sounds simple, but it’s incredibly powerful. Imagine if you could correctly guess the next bit of text for the sentence “The answer to the ultimate question of life, the universe, and everything is” or “The solution to the problems in the Middle East is”.

Recently, we’ve been seeing shockingly good results from models that do this task. They can synthesize unrelated subjects, and hold coherent conversations that sound very human. However, despite doing some things that up until recently only humans could do, they still aren’t at human-level intelligence. Humans read and write by taking in words, converting them into rich mental concepts, applying thoughts, feelings, and reasoning to them, and then converting the resulting concepts back into words to communicate with others. LLMs arguably might be doing some of this too, but they’re evaluated solely on words and therefore much more of their “thought process” is based on “what words are likely to come next” and not “is this concept being applied correctly” or “is this factual information”. Humans have much, much greater capacity than these models, and we live complex lives that act as an incredibly comprehensive training process. These models are small and trained very narrowly in comparison. Their excellent mimicry gives the illusion of a similarly rich inner life, but it’s mostly imitation.

All that comes down to the fact that these models aren’t great at complex reasoning and precise details. They’re just not trained for it. They got through “life” by picking plausible words and that’s mostly what they’ll continue to do. For writing a novel or poem, that’s good enough, but math and physics are more rigorous than that. They do seem to be able to handle code snippets now, mostly, which is progress, but in general this isn’t something that you can be completely confident in them doing correctly. They make silly mistakes because they aren’t really thinking it through. To them, there isn’t really much difference between answers like “that date is 7 days after Christmas” and “that date is 12 days after Christmas.” Which one it thinks is more correct is based on things it has seen, not necessarily an explicit counting process. You can also see this in things like that case where someone tried to use it to write a legal brief, where it came up with citations that seemed plausible but were in fact completely made up. It wasn’t trained on accurate citations, it was trained on words.

They also have a bad habit of sounding confident no matter what they’re saying, which makes it hard to use them for things you can’t check yourself. Anything they say could be right/accurate/good/not plagiarized, but the model won’t have a good sense of that, and if you don’t know either, you’re opening yourself up to risk of being misled.

Protect me from knowing what I don’t need to know. Protect me from even knowing that there are things to know that I don’t know. Protect me from knowing that I decided not to know about the things that I decided not to know about. Amen.

Lord, lord, lord. Protect me from the consequences of the above prayer. Amen.

That’s part of the point, you aren’t necessarily supposed to have an empty mind the whole time. I mean, if you can do that, great, but you aren’t failing if that’s not the case.

Imagine that your thoughts are buses, and your job is to sit at the bus stop and not get on any of them. Just notice them and let them go by. Like a bus stop, you don’t really control what comes by, but you do control which ones you get on board and follow. If you notice that you’ve gotten on a bus, that’s fine, just get off of it and go back to watching. Interesting things can happen if you just watch and notice which thoughts go by, and it’s good practice for noticing what you’re thinking and where you’re going and taking control of it yourself when it’s somewhere you don’t want to go.

I use TiddlyWiki for, well, a bunch of my projects, but primarily for my task management. You can use it as a single HTML file, which contains the entire wiki, your data, its own code, all of it, and of course use it in any browser you like. Saving changes is a bit of a pain until you find a browser extension or some other way of enabling more seamless editing than re-saving the edited wiki as another single HTML file, but there are many solutions to that as described on their site above.

The way I use it, which is more technical but also logistically simpler, is by running their very minimal Node.JS server which you can just visit and use in any browser which takes care of saving and syncing entirely.

The thing I like about TiddlyWiki is that although on its surface it’s a quirky little wiki with a fun party trick of fitting into an HTML file, what it actually is is a self-contained lightweight object database with a simple yet powerful query language and miniature front-end web development environment which they have used to implement a quirky little wiki. Each “article” is an object that is taggable and has key/value data, and “widgets” can be used in the text to edit and display that data, pulling from the “database” using filters. You can use it to make simple web apps for yourself and they come together very quickly once you know what you’re doing, and the entire thing is a demonstration of a complex web app that is also possible. The wiki’s implemented entirely using those same tools, and everything is open for you to tweak and edit to your liking.

I moved a Super Bowl guessing/fake gambling game that I run from a form and spreadsheet to a TiddlyWiki and now I can share an online dashboard that live updates for everyone and it was decently easy to make and works really well. With my task manager, I recently decided to add a feature where I can set an “agenda” value on any task, and they all show up in one place, so I could set it as “Boss” and then quickly see everything I wanted to bring up in our next 1 on 1 meeting. It took just a few minutes to add the text box to anything that gets tagged “Task” and then make another page that collected them all and displayed them in sections.