Sounds like an excellent idea! If you ever get tired of swapping, an USB capture card is a lot cheaper than a new GPU with current prices. Good luck!

That’s tight fit. If you really need the two capture cards then that’s the room you’ve got to work with. If you can bring it down to just one card at the top that would be a lot better. I personally use a capture card that’s connected using USB3, which works very well and doesn’t require room inside the computer (even though I would have room).

Most videocards have two or three fans on the bottom these days, with such a tight fit one of the fans can easily collide with the capture card. And even when it doesn’t it will have a hard time sucking in air from there. You can try and find a blower type card, but that would be a bit harder to find.

And you wouldn’t happen to have a GPU inside the CPU? Because the iGPUs from AMD are very solid if you don’t need much in terms of performance (easily faster than the 1030 you mentioned). Otherwise two capture cards and a GPU is a big ask in such a small case.

The A series is pretty limited, I would go for a B series if you can find it. But keep in mind drivers for Intel on Windows are terrible and on Linux even worse. So that would not be a good time.

There’s plenty of cards that would fit in 40mm, but your airflow would be pretty bad. Do you have a pic of the inside of your system? There has to be a way to make something more fit.

But most 6600, 7600 and 9060 cards should be able to fit in 40mm. And with AMD the drivers on Linux are very good.

Look for second hand stuff and something like a 6000 series from AMD. Don’t buy cheap videocards new, they always suck.

Oof, that’s a terrible card. The GP108 chip is a rebatched mobile card Geforce MX 150, it’s a pretty ancient chip at this point. There are no drivers for it, so you’ll need to use older drivers or limited open source drivers. Unless you got it very very cheap that’s not going to work very well for you. It’s newer than a GT 220, but not by much.

I think the one I have is 41mm, so that would be a tight fit. Can’t you rearrange the cards a bit to make some room? With only 40mm that would be hard to fit in any modern card.

Yeah I have a RX 7600. It wasn’t brand new, but lightly used, bought it off a friend who upgraded. Paid around $200 for it, so I think a pretty good deal with current pricing.

Yeah get a simple but recent AMD card, older cards, especially nVidia are a nightmare. I had an old 980 TI which was a really good card still, but I couldn’t use it anymore because everything kept breaking. So I got me a recent AMD card and it has been running perfectly.

Nah I know exactly what that dude is referring to.

It’s a property these LLMs have where a large complex network can sometimes do weird things. For example OpenAI had an issue where their model would just not stop talking about goblins. They were having trouble fixing it and figuring out why the model was doing that. They described how with a big network like that it isn’t as simple as opening up the network and seeing the node where the offramp meme to goblinmode was stuck. And often it isn’t even just one single thing, it’s many different places which come together in a complex interaction to get to that end result.

They wrote some blogs and papers about how they tackled this, what the challenges were and some tools they made to help them figure it out. In the end they gave up and retrained the model to try and fix it. The model was simply too big, too much data to analyse and the interactions too complex to just flip a switch and fix it. The media then read that stuff and didn’t understand it and ran with it. They wrote stories about how OpenAI has no idea how their mystic models work. These dumb dumbs are just creating super smart AI gods and they don’t even have a single idea about how it works or how to fix it when it goes wrong.

This is obviously not true and very dumb, but that pretty much sums up the LLM based AI industry at the moment.

Also I don’t really agree with your description in general. Just because the average person doesn’t know how something works, most of us know somebody who does know. We understand that just because we only have a high level idea about how something works, we realize there are people who know exactly to the nitty gritty details of how stuff works. And with complex systems that might not be a single person, but a group of people. But we understand this is not fundamentally unknowable. So when somebody says “nobody knows”, we would understand that to mean most people don’t know but there are still plenty of people out there who do.

The exact same applies to these LLM systems. The general person might not understand these systems or only from a high level point of view, but there are people out there who do. I for one understand a lot about these LLM systems, at least the maths side of things. There’s plenty of things I don’t know, but like I said I know people who do know and it’s not like it’s unknowable, I could just go and find out.

These days it seems like people don’t have functioning brains anymore. You know we can just go learn things right? There is so much information out there to know and available for learning. For LLMs there are so many articles, videos, papers, books and free open source tools. You can just go and play around, go read up on how these things work. You can go as deep as you like.

In the end an LLM is just a math function, a large and complex one, but still a math function. You put numbers in and numbers come out. Put in the same numbers and the same numbers come out. Although there is a part that can apply randomization using a parameter called temperature, but if you disable the randomization the exact same answer pops out for a given input.

Wtf are you talking about “nobody knows”. You know we built this shit right? There’s so many papers about this, mathematical models, open source software you can play around with yourself.

Don’t fall for the AI company marketing, we know exactly how these things work. As for not having a theory about intelligence or thinking? You might not have any, but there’s whole fields of research out there about these subjects. Just because there isn’t a 30 sec explanation layman can understand, doesn’t mean we know a heck of a lot about the subject.

You sound like the tides come in tides go out, nobody can explain that dude right now.

Stop drinking the kool-aid my man, just because the AI companies label things as “thinking” and “reasoning” doesn’t it’s the same thing as those terms when applied to humans. There is an argument to be made the analogy works well enough with some terms, but especially with reasoning it’s pure marketing BS.

Internally reasoning is called “internal recursion” and it’s more of a scratch pad. It allows the model to split up tasks into smaller bits, where it would choke if it tried to tackle the whole thing at once. It goes through the question, tries to come up with results and writes those down in its scratch pad. That whole scratch pad including everything that came before (system prompt, prompt, context etc) gets put into the model again to produce the final result.

Not to be confused with Chain-of-Thought, which is similar but different. That’s where multiple passes get made and the tasks are actively broken up with different system prompts. This especially helps in tasks where the context would run out, but doing the same thing in smaller steps does work.

It’s very annoying how AI companies heavily anthropomorphize everything related to LLMs, right down to calling it AI instead of just an LLM and leave the intelligence out of it. This easily dupes and confuses people into thinking the tool is something it’s not. People think because they use terms like reasoning and thinking that these models have motivations, internal dialog, actual thoughts and even do things like planning, anticipating and predicting. This simply isn’t the case, it’s a mathematical model, you input numbers and it spits out numbers. When you input the same numbers, the same numbers come out (although parts can apply randomization, usually controlled through a parameter called temperature). It isn’t alive, it isn’t thinking or reasoning or smart. It doesn’t have motivations or evil intentions. It’s just numbers.

Was he working at the marketing department? This is such bullshit. LLMs ain’t a path to superintelligence, it isn’t even intelligence at all.

The reason the companies are focused on hacking is because there’s a lot of open security vulnerability databases they can use to train on. They can also very easily setup automated test scenarios and have a very binary indication of the result of the hack. This made it easy for them to focus on, to have something new to report. Their models have been stagnant for a while now and investors were starting to wonder why they should keep putting money into it. The focus on security and hacking was a quick win to report something new and interesting sounding, so they can keep the grift going. They have played that card tho, so for the new models they will need to think of something new.

All the narratives around LLMs are so annoying. They call it “reasoning” when all they do is loop through the question and answer twice. It isn’t reasoning at all, that’s just the label they put on it. They say the AI has “breached containment”, like it had some kind of evil master plan and used its cunning to circumvent top notch security. In reality they have an LLM running in loops, trying random shit. Trained on Reddit users discussing how to get around the corporate firewall to keep shitposting and watching Hentai at work. With thousands of these agents, burning millions upon millions of tokens by random chance some interesting shit happened. Which is cool from a technical standpoint, but not quite the narrative they are putting on.

They have already figured out just dumping more compute and memory into the thing is running into diminishing returns hard (except Altman it seems). And customers have figured out the new models are much more expensive and don’t really offer any real improvements. They’ve raided all of the data accessible to them, so that doesn’t help. They try to aggressively scrape more from the internet, but people are blocking their bots and the internet is flooded by slop so the quality goes way down. Nobody has figured out how to actually get these models to improve in real significant and sustainable way. The public has soured on them and companies are figuring out there is no ROI and just burning massive amounts of money on AI doesn’t help them at all.

But let’s all be afraid for some kind of super AI taking over the world, because that’s really what’s happening right now…

The idea of a dog whistle is as you say only dogs hear it. So in the use case of Nazis letting each other know they are Nazis it’s usually a subtle thing. A phrase or gesture that’s innocent on the face of it, but signals to other Nazis they are Nazis. Like what they tried to do with the OK hand signal.

Putting up your hand and shouting Heil Hitler isn’t really a dog whistle, as everyone knows at that point you are a Nazi.

The only reason I heard about that movie is because it was so terrible and specifically Gal Gadot was called out for not being able to act at all and having sung a song whilst not being able to sing as well.

Yeah agreed. I hate it how all these AI companies just flood users with marketing and have their tools obfuscate what it is actually doing. Unless you ask each time: “Did you just make that up?” you don’t know how much the result can be trusted. The filenames are a big mistake, they should have been neutral.

But for a regular user even if it showed all the Python code and what it did, what use would that be? Most people can’t understand that at all. And even tho I have 40 years of professional experience in the software development stage, I don’t know anything about sound processing. The script might load in the wave file and pass it onto some kind of processing library. But are the parameters correct? Is that even the right function to call? What caveats do those results have? How should one interpret those results? I would have no idea really and either spend a lot of time diving into it, ask a friend that does have that kind of knowledge or just give up.

It’s so easy with AI to think people can do stuff they otherwise couldn’t. But even if the chatbot correctly understood the request, generated the right code, fed the data correctly into that code and then produced a result, you’d still need an expert to understand if any of that was correct and how to draw conclusions from that. That’s the part that LLMs can’t do and probably can never do. They are trained on the internet, full of confidently incorrect people. People who have knowledge and help out someone on a forum somewhere, knowing what to apply and what not, guiding a person for that one exact usecase. The LLM then just takes that and applies it to all similar cases, correct or incorrect.

It’s hard to say what AI he actually used and if the result was fine or not. I know for a fact something like Microsoft Copilot does write python scripts in the background to do data crunching. That’s a pretty common business thing people use Copilot for, so that’s what it does. The issue I have with that is that it doesn’t share that code, but just confidently shows the results. So checking it is kinda hard, but at least I know that’s a feature of the system.

With the regular ChatGPT online interface, I’m not sure. It does have tool calling capabilities, but if it would write a script for data processing or happens to have a sound analysis tool available? I’m not sure. But he might have used a different service or a paid subscription or something. For a lot of people “ChatGPT” is just synonymous with any AI tool.

You are right, this is a terrible way to use AI and I personally wouldn’t trust any output like this. However I feel like this is the way it’s marketed to people and the way most people use it. It’s so confident in the answers, people just trust it. And when they are out of their depth, they don’t have any way to really check it.

Even though I hate this, I don’t blame Techmoan. He wanted to do something, but lacked expertise. I’m going to give the benefit of the doubt and say he didn’t reach for AI right away, but actually tried to do the analysis himself. Then when that didn’t work, he threw the data into this tool which says on the tin it can do it. The tool then confidently gives him exactly what he needed. We know that result might be total bullshit, but how is someone without any knowledge to know? It does look exactly like the results one would expect (which is what AI does, it generates an answer that looks right regardless of truth).

He’s now at risk of AI psychosis as well. As he thinks this went well, he might reach for it more in the future and believe everything it says. If he starts using it more and more, that’s a bad path to go down, especially for a YouTuber.

They should fight fire with fire. Do double of all the tariffs the US does and don’t ever give them up ever. Don’t take any concession, as any time anyone does, Trump does the Goofy meme and says: “I’ll fucking do it again”. There’s no talking to these people, they will only respond to pressure. Tariffs can be renegotiated after the next elections and my suggestion would be for those elections to be held sooner than planned. Trump needs to go, that’s the only way for anything to ever be fixed.

It’s always the ones you most expect…

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Yeah but what if Germany were to be hit by a 9.1 earthquake followed by a huge tsunami and have the disaster cause the need for emergency generators that were flooded by the tsunami? We can’t have that risk! Shutting down all nuclear reactors was the only option.