It’s good to hear, but it’s not only about emulator core itself, it’s also about UI/UX of the shell. Duckstation’s interface and options are quite intuitive and easy to use. I remember Retroarch being a bit confusing/unfriendly last time I tried it, but it was so long ago, that it might not be the case anymore.
Sad news. This is the only PSX emu I’ve ever used because I always considered it the best.
It’s not so easy to ban VPNs. They need to setup DPI everywhere. And then people will start using DPI circumvention software.
Yeah, even though I hate the whole thing, I can’t deny it brings me joy to hear about “VPN use surge”, centralized sites dying in favor of shady clones, etc. I’d take total wild west any day over somewhat-free-but-very-polite-mild-and-centralized status quo of yesterday’s internet. The only problem is that there’s no guarantee people actually go that wild. They already did with VPNs, but regarding big site alternatives - I’m not so sure.
If you’ve ever had a rough night and ended up vomiting in the sink (hey, it happens), you may have found yourself with a gross, clogged mess. But before you reach for the plunger—or worse, call a plumber—consider this weird but effective trick: pee in the sink.
Yup, you read that right. According to fluidic chemistry enthusiasts and some Reddit plumbing veterans, urine can actually help break down and dislodge vomit clogs.
I’ve seen some article recently that the patterns of Wi-Fi/Bluetooth (don’t remember which one) interference with brainwaves can be scanned to reconstruct brainwave signature remotely, meaning that it might be possible to scan anyone’s EEG from Wi-Fi/Bluetooth distance. And there are some AI advancements for reconstructing inner monologue from EEG. So maybe we’re not so far from actual remote mind-reading.
Top 1 things you should never use AI for.
Hmm, I have kinda opposite opinion, hardware is pretty good, build quality is great, but the OS itself is meh. File manager is bad and clunky, desktop customization is very limited, network manager is buggy, especially with VPNs, no built-in functionality to import VPN config files like in Linux. Also, I used it for years and still couldn’t get used to all the shortcuts and "Mac-way"s of doing things. Just not for me perhaps. Not bad, but in terms of UX worse than both Windows and Linux for me.
I understand what you’re having in mind, I’ve had similar intuitions about AI in early 2000s. What exactly is “truly new” is an interesting topic ofc, but it’s a separate topic. Nowadays I’m trying to look at things more empyrically, without projecting my internal intuitions on everything. In practice it does generalize knowledge, use many forms of abstract reasoning and transfer knowledge across different domains. And it can do coding way beyond the level of complexity of what average software developer does at everyday work.
An LLM has “zero context” about your project’s specific stack and style guidelines. In other words, an AI might produce a generic <Modal> component, but integrating it into your app’s unique architecture is still a human task.
This is very old. Nowadays, in Copilot for example, you add files to context and tell “hey look how I did that thing there, do this new thing following the same structure, with the same naming conventions” and it’s enough. And tools like Cursor just throw your whole project into context by default.
They don’t really transfer solutions to new problems
Lets say there is a binary format some old game uses (Doom), and in it some of its lumps it can store indexed images, each pixel is an index of color in palette which is stored in another lump, there’s also a programming language called Rust, and a little known/used library that can look into binary data of that format, there’s also a GUI library in Rust that not many people used either. Would you consider it an “ability to transfer solutions to new problems” that it was able to implement extracting image data from that binary format using the library, extracting palette data from that binary format, converting that indexed image using extracted palette into regular rgba image data, and then render that as window background using that GUI library, the only reference for which is a file with names and type signatures of functions. There’s no similar Rust code in the wild at all for any of those scenarios. Most of this it was able to do from a few little prompts, maybe even from the first one. There sure were few little issues along the way that required repromting and figuring things together with it. Stuff like this with AI can take like half an hour while doing the whole thing fully manually could easily take multiple days just for the sake of figuring out APIs of libraries involved and intricacies of recoding indexed image to rgba. For me this is overpowered enough even right now, and it’s likely going to improve even more in future.
I wonder if author were following JS-sphere for the past five years. There’s SSR everywhere, stuff like NextJS is very popular. Some might say it’s overused even. Like, “please consider not using SSR if you do admin panel because it’s all cool and everyone does it nowadays but we can do SPA faster and it’s internal-only product so we don’t really benefit from SSR that much”.
This only proves some of them can’t solve all complex problems. I’m only claiming some of them can solve some complex problems. Not only by remembering exact solutions, but by remembering steps and actions used in building those solutions, generalizing, and transferring them to new problems. Anyone who tries using it for programming, will discover this very fast.
PS: Some of them were already used to solve problems and find patterns in data humans weren’t able to get other ways before (particle research in CERN, bioinformatics, etc).
Yeah, this is correct analogy, but much more complex problems than calculator. How much it is similar or not to humans way of thinking is completely irrelevant. And how much exact human type of thinking is necessary for any kind of problem solving or work is not something that we can really calculate. Considering that scientific breakthroughs, engineering innovations, medical stuff, complex math problems, programming, etc, do necessarily need human thinking or benefit from it as opposed to super advanced statistical meta-patterning calculator is wishful thinking. It is not based on any real knowledge we have. If you think it is wrong to give it our problems to solve, to give it our work, then it’s a very understandable argument, but you should say exactly that. Instead this AI-hate hivemind tries to downplay it using dismissive braindead generic phrases like “NoPe ItS nOt ReAlLy UnDeRsTaNdInG aNyThInG”. Okay, who tf asked? It solves the problem. People keep using it and become overpowered because of it. What is the benefit of trying to downplay its power like that? You’re not really fighting it this way if you wanted to fight it.
Coming up with even more vague terms to try to downplay it is missing the point. The point is simple: it’s able to solve complex problems and do very impressive things that even human struggle to, in very short time. It doesn’t really matter what we consider true abstract thought of true inference. If that is something humans do, then what it does might very well be more powerful than true abstract thought, because it’s able to solve more complex problems and perform more complex pattern matching.
Are you aware of generalization and it being able to infer things and work with facts in highly abstract way? Might not necessarily be judgement, but definitely more than just completion. If a model is capable of only completion (ie suggesting only the exact text strings present in its training set), it means it suffers from heavy underfitting in AI terms.
Imo it’s best when you prompt it to do things step by step, micromanage and always QC the result after every prompt. Either manually, or by reprompting until it gets thing done exactly how you want it. If you don’t have preference or don’t care, the problems will stockpile. If you didn’t understand what it did and moved on, it might not end well.
I think the point this article is trying to make is that while individualistic consumer-level ways to sidestep enshittification do work for those who follow them, they don’t fight the bad actors back efficiently and to achieve that it needs to be done collectively.