Well if you have a better way to generate Jurassic Park “but everyone’s fat” videos I’d love to hear it.
Hiring fat people?
It is no more different with the gold rushes to California, Oregon, and Alaska, where the real winners are the merchants selling pickaxes and work overalls.
It’s different in so far as we didn’t invest a trillion dollars in giant strip mines that destroyed these states.
Pre roasted coffee took off at this time.
Whoever thought that this machine that can predict next word in a sentence, next sentence in a conversation etc. should be used in place of all human intellectual work… should have his elderly care taken over by LLM.
What you have to understand is that the CEOs and upper management of these companies genuinely believe that human intelligence is just a predictive model. They are so divorced from humanity, from actual people, that they only trust each other, and even then, barely. Some psychologist posited that consciousness is in part predictive, then Curtis Yarvin latched onto that half-baked headline and wrote on it, and then these sick fucks all had the same ideas: That they each are always the smartest man in the room, that their genuinely shallow creativity is deeper than anyone else’s, and that if the next guy who they consider almost as smart as themselves says it’s because humans are just complex llm’s then it must be true; that if they are barely smarter than this shittu technology, then all of humanity must be at or below it.
I don’t need to know any CEO to find a person who thinks they are smarter or better than everyone else. Why americans gave so much power to those idiots is beyond me though.
Also - post title is disingenuous. Current wave of AI is an engineering marvel, marketed dishonestly and used haphazardly.
That’s what is wild about it. At any given point in time, the model is wholly consumed only with the very next token. Maybe that token is a running narrative of ‘reasoning’ or directly in the output, either way, the AI does not have anything to model anything beyond the very next token. It doesn’t have a destination in mind and is just finding the words to get there, it’s building it up word by word. The overall ‘meaning’ is an emergent property of just picking the very next token and seeing what happens.
Honestly, it’s shocking it works as well as it does. More shockingly, there are AI enthusiasts that argue that’s how the human brain works, which I can’t imagine someone going through life with every thought rooted in building it up word by word.
My masters degree in intelligent systems wept in the corner after seeing your explanation.
But I guess cars are just four wheels and a fancy basket people sit in.
Well that description of a car is actually fairly close to the fundamentals, add an engine or motor and a steering wheel, and you’ve got it. Yes, a lot of engineering goes into the best possible realization of those basics, efficiency, suspension, safety, maintenance, and just a ton of more stuff, and it is a very valued execution above and beyond what, say, the Model T delivered. Automotive engineers have done hard and valuable work and complicated work, but no one is surprised that Model T led to faster, more comfortable, safer, more convenient vehicles that move around. It is a bit more surprising that LLM architecture works as well as it does while always focusing on the next token without ability to go further, best case running through and messing up and regenerating until you have operator appropriate output.
The ‘seahorse emoji’ was a pretty fun example of this at work, as it didn’t have a seahorse emoji, but since it wasn’t trying to generate the emoji, it started by building up the words to confirm and introduce the emoji since obviously there will be one, then putting up a wrong thing, then the words that would go after the wrong thing, but the weight still suggested there should be a correct answer and to start generating words for another try, and so on. “Reasoning” does the job of incurring this hit out of sight a lot of the time. Looking at the reasoning chains you’ll see this behavior a fair amount, that the model suggests words that build toward an answer but fails on the key word and retries until something tests right or it models that it tried enough and it can’t find the key word that would have been expected. It can of course digest it’s own output and summarize the result without showing the operator spinning out, but it at all times is operating on the fundamental principle of model+very cleverly managed context influencing an answer one token at a time and ideally discarding the first run.
its not that simple. whatever opinion you might on llms have you have to agree this is oversimplifying at best.
I assumed that but everything I have seen as I dug deeper has been that at some level that is what is happening. If it is ‘reasoning’, it’s generating a ‘reasoning chain’ next token by next token and using that to influence the final output tokens. The reasoning chain is discarded and since the actual output is a continuation of the reasoning chain it may conceptually be described as allowing the model to ‘rethink’ things, but even as the generation of a ‘reasoning chain’ has results that more closely resemble reasoning, it is still a scenario where it’s building it one token at a time and we get to see meaning as an emergent property, rather than trying to find words to build to a more abstract concept like humans do. It just gets to throw away the intermediate work and the extra tokens manage to improve the ‘accuracy’ of the preserved final output.
The interesting bits are when it derives the likely hood of something being correct and does more passes, or splits the data apart in the first pass and opens up new context processes with specialized instructions to handle it. The code stuff goes full on ororborus on some models, writes out the code on one pass, checks for issues on another pass, runs the code looking for errors on a third pass and goes back to step 1 if it fails.
They’re getting a lot out of it for it primarily just being a weighted token generator wrapped in an orchestrator.
Particularly software development with very good and very quick tests allow rerolling and that is very appealing in those scenarios. Problems being that very good tests are rarer than people like to think and sometimes it just gets stuck in a loop. At work the other week someone set it at the task of fixing a bunch of build warnings that had accumulated over the years. It succeeded after burning through tokens to take 30 tries at it. It’s solution after all that hard thinking? It put // @ts-nocheck at the top of every file and called it a day.
But superficially, someone handed it a chore and didn’t have to think about it, and if no one looked deeper, then it was able to get to the desired behavior simply by rerunning the given task over and over without human intervention until it worked. Which is also broadly relatable as there’s a lot of humans in the industry acting broadly the same, but I’ve always been frustrated by those folks anyway.
Wide scoping like that rarely ends well. Give it the list, tell it to spin a new agent for every single issue and operate on them separately. If any of those agents run out of context memory, it will just shit the bed. Clarify that you need the test to actually work and not be commented out, and that the tests themselves still need to be viable and indicate a healthy working app, not just report that it’s healthy. Trying to get tests happy in AI is like making wishes on a monkey’s paw. Then assign someone to look after it because that bastard will chew through 10’s of thousands of dollars to fix a laundry list of tests, which in my dev experience will be all broken and ignored in 2-3 minor releases.
You can generally get it to do things, even somewhat complicated things, but it’s not easy, and the shit it gives you back while you’re trying to convince it to do the job is the same stuff you’d fire an employee over.
My favorite thing is to make it make ansible tools, They’re easy to read, easy to lock down, keys and passwords go in a vault that it’s forbidden to read. Then I have it write runbooks on how to operate the playbooks. I then make an index of runbooks, and ask it to do them. It’s an ansible job, so I can read it easily and make sure it’s not able to delete my drive or my production database.
If I lose access to cheap tokens, everything I’ve making is still usable, and for now, I can spend my time working on stuff that’s not simple enough that it can handle it well.
Its just a ball rolling down a hill trying to find the lowest point, but its like a super fancy ball with a ton of rules, and the hills are really detailed with like little walls and bridges and stuff.
Well yes and no. it is steered by a buffer of context as well that sorts/ranks/informs what the next word should be. That context differentiates if you are talking about apple the fruit or apple the company or apple the device. Heres a great overview if anyone is interested. And no, its not my video. Its a youtube intro to how AI works. Best watched with duckduckgo browser which trims out youtubes overly frequent ad interruptions. https://www.youtube.com/watch?v=OYvlznJ4IZQ
But that context is a mix of model output and other sources. The model output portion was generated token by token, and is combined in interesting ways with things like human response, search results, software output. It’s still a backward looking mechanism, rather than having established a concept as a goal and then trying to build the words to reach that concept like we do.
Size and strategy for managing the context has been critical for improved subjective results, but it still doesn’t exhibit the behavior of the words as a tool to address some concept, everything about the model is about the words themselves. So we end up with something very good at generating what seems right and there’s a super high chance of what seems to be right actually being right. Especially when the software can automatically execute commands and the good or bad results reach into the context window, enabling it to effectively get automatically second guessed. The potential for automatic verification in some scenarios automatically feeding the context window is what makes it particularly appealing for software folks, though not universal.
In 1897, they built the first music synthesizer. It worked, but it took up the basement of an entire city-block sized building, so it was essentially useless. After a few decades of development, it could fit in a suitcase, and be carried around.
Data Centers are like that 1897 synthesizer. Sure, it works, but at what cost? It clearly isn’t ready for prime time. Go back to the drawing board, tweak the problems, including regulations, and maybe in a couple of decades, we take another run at the new and improved version.
Data Centers are like that 1897 synthesizer. Sure, it works, but at what cost? It clearly isn’t ready for prime time. Go back to the drawing board, tweak the problems, including regulations, and maybe in a couple of decades, we take another run at the new and improved version.
The issue with AI is not a technical or development problem. It’s not even a regulation problem. It’s a capitalism problem. Infinite growth will still be as unscalable in a hundred years as it is now no matter how good and mature the tech is.
There are also hard mathematical limits stalling AI growth. Frontier models haven’t improved in like a year despite being fed money by basically the entire global economy. Diminishing returns on steroids basically. They’re already at the limit of what they can make, and going further gives a much smaller improvement in the model, and now I hear there might not be enough human written material on the internet to train them.
It also looks like hallucinations are inherent to LLMs and you can’t get rid of them. It’s a side effect of the model. What commercial applications are there then, if you can’t guarantee the output? It’s worse than a human for most things since it doesn’t know truth from lie and will confidently say both as if they’re fact. It also looks like prompt injection isn’t something you can fully guard against either.
What’s the value proposition when you can’t trust the output and the model might give a massive refund or discount to a customer and the courts rule the AI speaks on behalf of your company?
I feel like calling „hallucinations“ a side effect isn’t really describing the issue properly. They’re not a side effect, nor are they hallucinations. It implied that there’s somehow something that distinguishes „correct“ output from „incorrect“. There isn’t, it’s all just output. The output resembling actual factual reality is statistical chance.
It’s worse than a human for most things since it doesn’t know truth from lie and will confidently say both as if they’re fact
It works for most executives and sales folks.
Baseless confidence is the recipe for business success, which is why they love these AI chatbots.
Bigger problem for the business leaders is how sycophantic they want to be to the user. If an insurance company used it for claims, it might actually approve a claim, and that would be unforgivable for them.
Diminishing returns on steroids? No, clearly we just need to pump EVEN MORE MONEY AND DATA into this
If we just vaporize the future of everyone under 60 we can make our auto correct engine 3% less likely to lie out of its ass 🤡
Haven’t been improved in a year? By what metric are you basing that assertion?
IP theft is probably the main one I’ll concede the point on - that damage was done long ago so they haven’t “improved” on it.
But whether it’s reasoning or generation or building things… that’s a crazy take. Unless you consider them like a chatbot companion? I wouldn’t really know much on that front, I’ll concede.
It’s been marginal improvements for like 18 months now. I don’t know if you remember what they promised that long ago but current frontier models just ain’t it.
If you don’t believe me, then why? Put your argument in numbers.
Anthropic and openAI have both spent nation-state levels of money training these models and they only seem marginally better compared to the last ones? Maybe larger context and better reasoning but they still hallucinate, they still make the same mistakes and pitfalls.
Even with tokens getting dramatically cheaper inference on mythos or other frontier models is so expensive they need to start replacing skilled professionals and right now they just can’t. Productivity doesn’t seem to go up from AI use, if anything net productivity for an org goes down from people outsourcing human cognition onto their colleagues.
“How about instead of me summarizing this report i use Claude and then my colleague spends the cognitive effort deciding if Claude lied or not”
The guy using AI for everything looks super productive and the people stuck dealing with the work he’s “getting the AI to do for him” look like under performers when they’re actually load bearing in this new setup.
It’s worse than a human for most things since it doesn’t know truth from lie and will confidently say both as if they’re fact.
I think that’s where you’re wrong. It’s really not worse than a human. It’s smarter than like 90% of the population already. No it’s not perfect but it doesn’t have to be. Humans literally hallucinate and lie, all the time. AI hallucinations is an athropomorphism, AI metaphorically hallucinates, humans actually literally do.
The 10+% of the time it’s wrong you can’t blame it, sue it, imprison it, or apply leins against it if it causes real world damages to people or the company that operates it
“For entertainment purposes only” is still the level of liability AI companies operate at. Air Canada just had a ruling that anything their bot says is the speech of Air Canada and they’re bound by it. So far there’s always ways to prompt inject, and you sometimes don’t even need to do that. Just guiding the conversation in ways that are difficult to pin malice on to is enough.
But must make number go up by any means
Chanting:
Number go up! Number go up! #️⃣💨⬆️❗
It’s not even that it isn’t ‘ready for prime time’, largely to the extent it works, it works with not so crazy requirements.
The problem is that they don’t settle for what it is, they try to overextend it. In software development for example, in the cases where it works at all, you are 95% of the possible successes within 3 or 4 iterations. Problem is they demand that extra 5% which takes an order of magnitude more. Note that ‘100%’ here is the max success possible with AI, not 100% success in general, that number varies greatly with context. So it might be in a certain scenario more like going from 19% AI curated to 20% AI curated, at huge incremental expense.
In 1897, they built the first music synthesizer. It worked, but it took up the basement of an entire city-block sized building, so it was essentially useless. After a few decades of development, it could fit in a suitcase, and be carried around.
As per the 1890s, it probably also had to be lubricated with orphaned child blood.
Don’t give the AI MAGAs any ideas.
And back then nobody asked why do we need a mountain sized synthesizer if the children itself make already all the funny noises.
Here here! well said!
Many dystopian science fiction books have already become reality. Look at “Enemy of the State” for example.
Heck, back in like what 1971? Enders game predicted the whole internet Influencer concept.
fwiw Ender’s Game was published in 1985. (Based on a short story from 1977, but that story didn’t have that subplot with Peter and Valentine.)
Voice to text strikes again!
Ah, got it! I can remove the link or leave it up for others’ reference. Let me know if you have a preference.
He’s sadly right… My wife is Chinese, she watches AI Chinese dramas and she has told me that most of China watch AI dramas. And Gen Z watch AI, Everyone younger than 30 relies on AI.
Now before you downvote me, I’m 37 and I’ve worked in IT since I was 19… AI gives me nothing I can’t find for myself. The only time I’ve used AI was when chat GPT was fresh and new and I told it to be more sarcastic and snarky in it’s answers and I argued with it for 2-3 mins before I got bored.
Humans are much better at arguing with me and being sarcastic to me.
I don’t care for AI, Turn off all the air conditioners and AI burns out.
My wife is Chinese, she watches AI Chinese dramas and she has told me that most of China watch AI dramas. And Gen Z watch AI, Everyone younger than 30 relies on AI.
If it’s not that, I’m seeing people almost everywhere hunched on their phones, watching AI-generated summaries of… what? soap opera episodes and whole movies. And those people complaining about the low quality of education when they’re so fucking focused much on getting a piece of paper just to be hired even for a department store attendant.
What are AI Chinese dramas? How much is AI? Just the script, or is the entire thing AI animated? Where do you see these programs?
Okay update, It’s 100% AI scripted, voice acted and animated.
I have no idea what AI China has but apparently most of the videos are 2 mins, but there’s entire series.
Seems like most of it is just about marital problems between a husband and wife in different times and settings.
There’s an entire genre of these weird “fruit Love Island” AI dramas that pop up on my TikTok. It’s terribly animated anthropomorphic fruit, and the plot is usually that the wife is cheating on the husband with the boss and gets pregnant.
I have a family member addicted to these (well, not Chinese…). It’s pretty much all 100% slop, the voices, the animation, the script. My head hurts just seeing how it cuts every 5 seconds when I casually see it. The voices are so horribly soulless. The writing is inane and tortuously paced.
But people eat it up. Similar to (and large audience overlap) with people who toss massive money at shitty mobile games.
I’m not sure about the script or voice acting as I don’t understand Mandarin, but visually the ENTIRE thing is AI rendered.
Are you responding to something specific from this article? I feel like we might have read completely different articles.
“AI gives me nothing I can’t find for myself.” True, but that’s the point.
From a worker standpoint, while you’re researching that thing you can find for yourself, someone of equal skill to you that is leveraging AI has solved 3 of those issues. And someone with more skill has solved 10. And someone with lesser has either crashed the network, or solved that same 1.
From a personal use standpoint: is the value of creation in the journey and the learning? For some it is. For others it being able to remove barriers and obstacles to create a new website you’ve been wanting to, or planning out a garden, or brainstorming an idea.
You’ve used it once 2+ years ago and wrote it off completely. Do you also still do your laundry with a wasboard and clothesline? Is the Internet just a fad also?
It’s has value. It also has large risk. But pandoras box is open. Those that refuse to leverage and learn it will be at a disadvantage.
From a worker standpoint, while you’re researching that thing you can find for yourself, someone of equal skill to you that is leveraging AI has solved 3 of those issues. And someone with more skill has solved 10. And someone with lesser has either crashed the network, or solved that same 1.
This is complete fantasy. I’ve personally watched like 10 SEs decide LLMs are the best things since sliced bread and talk up how much more productive they are and without exception they’re all to varying degrees just worse at their job. Same for every other role I’ve observed as well, though I have fewer data points.
You’ve used it once 2+ years ago and wrote it off completely. Do you also still do your laundry with a wasboard and clothesline? Is the Internet just a fad also?
I just don’t get why people like you exist. You guys have the stupidest fucking arguments for why not wanting to use the regurgitation machine that murders the environment and causes people to kill themselves is bad.
But, like, do you still use NFTs? That was the future. It was so the future. Same fucking arguments from the same fucking mouth-breathing dipshits.
And using AI does not even make you faster or better at what you do. It can increase output, and it can cause you to do a shit job, and it can falate your ego so you think you are soooo much better and bigger than everyone else, you big strong muscly man, you. You’re basically just saying that workers can never and should never have any fucking downtime, ever. They cannot stop. They must always, always produce, more and more. And, like, fuck all the way off. You don’t need to output more. You do not need to be faster. You don’t need AI, even if it was as good as people pretend it is, which it is not!
Because AI sucks. It isn’t good at anything other than replicating an amalgamation of the data that is fed into it. Anyone that says it is good at any other thing is just shit at that specific thing and doesn’t know any better. LLMs are just huge giant matrices full of coefficients matching language segments. They don’t know jackshit. They have relatively small context windows compared to even the dumbest motherfucker on this planet and people working on making LLMs better are running out of shortcuts to make them better. The money they have been lent needs to start making it back. And they can’t make money because they are subsidizing everyone using AI with that venture capital because it still hasn’t made a fucking dime yet.
Don’t worry, guys. We just need to keep feeding more data into it! It’s so much better, guys, I swear! This one won’t hallucinate all the time, bro, I swear! Just one more model, it’s so close!
I feel like your projecting. My comment didn’t approach anything close to the unmedicated fantasy in which you seemed to have made up and responded to.
Nfts, no downtime, and anti-worker? Really? Not even gemini would hallucinate that out of my comment.
The world is accelerating faster than most of us are comfortable with. The future is bleak and we absorb utter chaos daily in the 24hr news cycle. It’s exhausting
I’m sorry that pandoras box is open. But it’s not going away.
I’m not naive enough to think we’re going back to a world without a machine that statically produces regurgitated characters that happen to match an exceedingly large class of common problems that people are looking for help with, whether it’s an over burdened worker at soulless corporation, an 18yo with an idea but not the background, a 21year assessing if the lease for their first apartment has red flags, or a mom making a video game based on her son’s stuffed animal named slappy.
Unfortunately, even with their small context windows, useless matrices, and lowly statistical representions of language segments, they are better at debugging, brainstorming, “coding”, and answering questions than a lot of people, let alone “the dumbest mother fucker on the planet”.
Unfortunately, even with their small context windows, useless matrices, and lowly statistical representions of language segments, they are better at debugging, brainstorming, “coding”, and answering questions than a lot of people, let alone “the dumbest mother fucker on the planet”.
That’s why those people do not code or do those other things, and, surprisingly, most of those people don’t suddenly think they are now better than a person who has done… Just… The smallest amount of actual training to become good at that thing.
You got that AI psychosis in you, buddy. Ask your AI friend for the nearest person to get you the hell away from it.
If only telling you how brainwashed you are would actually fox things. Let me know when you have enough tokens to reply to me.
There you go projecting again. Pushing a narrative about how I must feel about people that you push as being dumb.
It is hilarious how a comment where I say AI is pandoras box, aka the mechanism that spawned all of the world’s evil, and conceeding it has risks and flaws, all of a sudden makes me the world’s biggest AI sycophant because I say it has value.
As you grow older, or if you watch the movie Inside Out to completion, you will learn people can have multiple complex feelings about things. Only on reddit, and this place it seems, must someone have the most extreme all good or all bad else they’re the worst thing that ever existed. I truely hope you find the help you most surely need.
From a worker standpoint, while you’re researching that thing you can find for yourself, someone of equal skill to you that is leveraging AI has solved 3 of those issues.
And I’ve rejected their code for unnecessary complexity and issues they overlooked all three times so actually nothing was solved. AI does not increase productivity. At all. Study after study after study has confirmed this. AI can spew out what sounds reasonable to people of low skill. That’s it.
I don’t use AI, but I’ve watched enough other people who’s intelligence I used to respect devolve into token monkeys who understand nothing anymore.
That’s why was was careful to say engineers of equal skill with and without. I think you hate read and ignored that part. I did not say jreng+claude code is now a principal architect…
No I read that. My experience is that relative skill doesn’t matter. Introduce AI and you’ll churn out slop, not solve problems
I agree its pandoras box but I disagree about the quality of life it brings. What it is creating is larger electric bills that is strangling the lower class, killing consumer low end computers which also harms the lower class, creating a dependency which will likely increase in price, lowering childrens critical thinking (according to studies private schools are not being as affected), killing jobs, oh and the fuck ton of effect it has on climate change which also affects the lower class more.
Yeah but you see, everyone who’s gun-ho on AI doesn’t seem to be bother to give two fucks. It’s bat-shit people are chasing this like their lives depend on it. They don’t give a fuck about the lower class, they never have, it’s a new shiny toy, and until something catastrophic happens, nothing is going to change.
Eat the rich.
Yes. I agree those are all terrible. And I agree with your assessment of almost all of them. But I’m not naive enough to think that we can put the evils back in and switch back to a non-ai world. We live, we grow, we adapt. This is the future, no matter how bleak it is.
I didn’t say it improves quality of life. I said it can lower barriers of entry to some things, and it can improve a workers productivity when you compare two workers of equal skill.
improve a workers productivity when you compare two workers of equal skill.
It makes you seem more productive in most metrics that corporate america seems to measure, but that doesn’t really mean anything. Even if most of what the current AIs produce would be good quality (and it’s not, its mediocre at best), one hallucination costs you more time than everything it saved you.
Hard disagree. Random utility scripts, most, terraform, and ansible, brainstorming architecture decisions, throwing an error log at it and spitting back the plausible solution. All faster. But the engineer needs to be capable of knowing when it’s plausible or not.
I don’t think replacing sr engs with interns+claude is the solution. But many sr+principal engineers can be accelerated on a majority of tasks. That’s why was careful to say equally skilled engineer with/without.
For some it is. For others it being able to remove barriers and obstacles to create a new website you’ve been wanting to.
You’ve used it once 2+ years ago and wrote it off completely. Do you also still do your laundry with a wasboard and clothesline?
Okay but one of those is an artform and the other is physical labor.
Programming doesn’t really equate to physical labor like that.
No, but it helps design. How big of a box. What vegetables thrive together. What ones should be kept apart. What spacing. How much root depth. Can ground hogs get at it. Here look at this picture. Looks like a tomator disease, what do you think?
Programming equates to architecture. It equates to system design. You can have good (non-sycophantic) conversations about what, why, push back when it’s wrong, agree when it’s right.
And if it’s for a personal project, then for some people the labor and the learning is the value. For others maybe it’s showing the end product to their 6year old that dreamt of a game where a dolphin was flying in space with ninjas trying to attack it.
Efficiency deserves more attention than hype.
AI is the idea of putting a million monkeys in a room with a typewriter and waiting for Shakespeare.
The smart people already knew the monkeys would just starve to death. The business majors are just now figuring that out.
It was the best of times, it was the blurst of times.
To be or not to be, that is…
The ten thousandth monkey typed out the word “the” , fund them everything they’ll type out Shakespeare!
Dude, the math says it would be cheaper to fund research into necromancy to revive the actual Shakespeare than this.
Is there a petition?
Maybe a kickstarter or something?
I’ll buy that for a dollar!
Yeah no shit Sherlock. AI chat bots are just a way to market and sell a dystopian surveillance apparatus to the masses wrapped in the guise of it will right your bullshit corporate emails and messages for you all the while the government and the corporations are fucking you in the ass.
It was a barely functional technology that provides convenience and laziness well hiding it’s true purpose, Machine learning algorithms for facial recognition license prints tracking making it efficient and relatively economical to spy on and control an entire population.
what makes you think advances in LLMs have anything to do with ML for Computer vision? If you wanted the latter, you would’ve gotten that way cheaper than by training on reddit text.
12 upvotes for this thin conspiracy theory, congrats Lemmy.
If you think those datacenters being built are for LLMs only, I have a bridge to sell you.
ML and LLMs both need massive compute to work with the data sets involved.
Look, if you know a way to convert a PDF to text with less than 500GB of VRAM and 2000W of power used for twenty seconds, I’m all ears.
Runs on anything that runs Linux:
NAME pdftotext - Portable Document Format (PDF) to text converter (version 3.03) SYNOPSIS pdftotext [options] PDF-file [text-file] DESCRIPTION Pdftotext converts Portable Document Format (PDF) files to plain text. Pdftotext reads the PDF file, PDF-file, and writes a text file, text-file. If text-file is not speci‐ fied, pdftotext converts file.pdf to file.txt. If text-file is ´-', the text is sent to stdout. If PDF-file is ´-', it reads the PDF file from stdin.Upvoted for the sheer mic drop moment. Well done.
I hope this is sarcasm >_<
Foes it works on scanned image PDF?
I’m…but…no…wait….
We’re not going to make it.
No, we aren’t.
.djvu
Is that a new fringe edge compute mixture of experts from Antro-GPTx? Sounds gemini-flipiti-rad!
But it’s so good at programming if you already know how to program! Surely that’s worth burning the planet and crashing the world economy??
Actually, it’s better at programming if you don’t know how to program.
For example, someone who didn’t know how to program sent me a patch to my code to ‘fix’ a problem they encountered. It was changed to silently swallow the error that really really would have needed to be fixed, but they were so enthusiastic that the problem looked to go away that they wouldn’t let me know actually actionable debug information and just whined that I wouldn’t take Claude’s perfectly good fix for it. It is much better at satisfying folks that don’t know how to look for better.
To satisfy my standards, I would have had to extract more debugging info, probably construct a test case tickling that exact situation making an expected error to ensure it won’t just throw it away, and add it to the suite and then ask it to spin until the test case passed.
But alas, I’m neck deep in merge requests from folks that were formerly intimidated by coding and are now complaining that I’m not accepting more of their changes and more quickly because “all” I have to do is review the code which is obviously so much easier than writing it… It’s like the “I have an app idea, all I need is for you to do it”, but on steriods…
Actually still no
https://github.com/JustVugg/colibri
Everyone was desperate to be first because capitalism. But we are getting good models without the insane build out requirement. Which will be hilarious to leave the cunts holding the bag. Not that the planet is better for it in the end.
The engine is a single C file (c/glm.c, ~2,400 lines)
That file is almost 6k lines. The style also makes my eyes bleed. Why do people pretend stuffing 6k lines of code with almost no whitespace and meaningless variable names into a single file is a good thing? I’ve seen this a lot recently
Assuming the vars are all really short, it sounds like the same idea behind Webpack’s (et al) minification & mangling to achieve tiny performance gains everywhere it can. Which might mean there’s a dev version that isn’t squashed and ugly, but doesn’t make it to us…?
C is compiled; minification won’t make it faster.
~1 token per second (storage bound gen4 nvme)… Some of us have places to be.
Don’t get me wrong. Its impressive that it can run at all, but honestly the usecase is exceedingly narrow. You’d have better results with a structured quantized gpu-only gemma or qwen workflow. Quality over quantity, rely on validation and a structured process: lots of cross-model review and iteration loops with spec and test driven dev. You could probably get a working alpha by the time colibri set up the environment.
Yeah I’m just beginning my local AI journey on a 5080, tried Qwen3.6 27b Q4 and was getting like 1tps because of the vram overflow. Ran it over night at it was still chewing on generating a prompt for a sub agent when I got up in the middle of the night until it simply ended in some kind of “fetch failure” lol. I think I gave it something too large to tackle, but either way 1tps is kinda garbage.
You could use the 35B MoE model, tune it a little bit and get much better results. I have a 5060 ti and 70-80 tok/s are the norm
That’s what I generally use. I wanted to see if I could use the 27b to “review” what the 35b put out. The 35b has been working pretty well, but it’s not very thorough. I asked it to make a program and then 27b was like “this is a skeleton, there are folders but no contents.” Lol
These models generally are not capable enough to do one-shot vibe coding. They are pretty good as coding assistants if you tell them exactly what you want and let them focus on a specific aspect/part of code, not the whole thing at once.
Using an agent framework (I like Kilo on VSCode but there are many others) you can start with a planning session to let the model find out what you want to build. Then you let it write that gist into AGENTS.md and double check if that is what you want. AGENTS.md will be loaded into the context automatically so the model has a solid base of understanding for everything you do afterwards. Once you have that, building in vertical slices on top of that skeleton is much easier. Another neat trick is to ask the model a few questions about the current code base (if there is any…) at the beginning of a session, e.g. "How does feature x work in the current code? ". This primes the model for what you are about to do. All this is obviously a bit more work than just vibe coding away, but it lets you keep in control of the code and helps in being alert for errors these models (and all LLMs in general) will inevitably produce.
Thanks for the tips! What I had started doing the other day was having one session where it reviewed the code and created a document explaining what the program did in technical detail and then in another session I asked it to review that document before attempting anything with the program. Agent programs and harnesses are the next thing I need to start learning for sure.
Is that quantized? 4 bit Qwen 3.6 can get 22tps on a 1060.
It’s the q4 quantization, but it requires 20+GB vram and my 5080 only has 16
My framework 13 with shared RAM runs qwen quite well
Wow I’m starting to feel bad about that time I asked AI to make a joke about scatology & eschatology sounding similar.
Yeah this article is already outdated and poorly researched
All for software that’ll be out of date and fashion next year!
If only the mainstream media would have said this since 2023. We maybe even have dodged the bullet called the Trump second term, but now we’re heading towards a global financial collapse.
We are repeating an old pattern in computing: throw more hardware at the problem until efficiency becomes impossible to ignore. Bigger models have delivered remarkable gains, but they’re increasingly expensive. The next breakthroughs may come less from adding parameters and more from smarter architectures, better algorithms and more efficient inference.
Except there likely won’t be a lot of further breakthroughs if we burn down our planet faster than we already do.
This is all an expenditure of vast amounts of energy for literally no gain for anybody except a handful of billionaires and their corporations.
That’s literally exactly what Chinese researchers are doing at DeepSeek and they’ve built frontier models with that philosophy
DeepSeek has really led the way here, especially as they are a bit more hardware constrained. Plus they openly publish their findings and release open source models, so high hopes there.
It’s probably China’s play to pop the AI bubble, but I’m all for it (:
I wonder what all is in the deepseek code that is malicious. I’d like to try it but don’t want a million Mb/s of tracker shit across my network and can’t run it myself.
The beauty of it is that it doesn’t need tracker shit. It works to destroy the US AI bubble even without anything malicious within.
And considering how hard the US researchers have been trying to control the output of their general LLMs, with little success, why would the Chinese have found a way before everyone else even thinks it’s possible?
AFAIK, their open models are distributed as weights, not executables and are therefore not able to start network connections / run code. There is if of course tool-calling functionality but that just works by having the model output a special pattern and having something external run predetermined commands based on that.
They are open source models, nothing malicious about them. I’d be much more careful about where you run your agents on. The wrong prompt can even make a non-malicious model misbehave.
It isn’t about content generation at all. It’s about pattern recognition and prediction, which, in the hands of those with the most power to change the world, offers insights into our collective behavior that rulers from every age would have committed genocide to get. AI will tell them how to better build the prison the poor are being impoverished into.
It has the same flaw as every other overreaching evil. We outnumber them. A significant number on our side is willing to kill the other side.
Ima die in the crossfire for sure. But the evildoers always assume they are going to win and they literally never do. They always lose. Expensively.
It’s a dying echo. Nothing more.
Yes but we are effectively disconnected from our mutual self interest. We should already have risen up together and our complacency is a testament to their existing ability to pacify us.
The losing that you’re talking about happens on the scale of centuries, not years. History shows that empires built on malice can last for centuries. Look at the Romans, for example. Even the Soviet Union lasted 70 years.
If this echo dies out in a hundred years, what good is that going to do my son and myself?
Stop spreading criti-hype! Zuck didn’t invent a mind-control ray with targeted advertising, and Sam Altman doesn’t run a terminator factory with GenAI.
Wtf are you talking about? Do you even know the nature of data usage today? You can’t identify reality from science fiction?
AI enables them to better do what they have already been doing with analytics and user data. They are already doing it and have been for decades.
I’ve seen it first hand on smaller software products during analytics reviews and telemetry design discussions during preproduction through product launch and post launch. I know the questions that gets asked, the purpose of a telemetry hook for a user action, heard what they wished they could track and why. I know how they can cohort a user base, how they extrapolate and predict user behavior and user characteristics from that data to target content. There’s laws already written to prevent some data collection because of what is known can be done. That’s a small software product with a few millions users, not Amazon or Google who have billions of users, many of whom give them access to their entire phone telemetry at all times, cross device access and service wide account tracking, across decades of their lives. Location, region, timezone, battery usage, app usage, age, phone numbers, address, gender, mac ids, wifi connections and data usage etc etc etc.
With just my gender and age, you can make predictions, of some accuracy, using existing research data, about my life, who I am, what I think and how I behave. Every single piece of data more allows further clarity and breadth. That knowledge is what gives them more and more accurate predictions about me, you and all of us. Now they want cameras everywhere, microphones everywhere, OS real ID and VPNs to be banned so there is no anonymity. They want as much data as possible because they now have a data pattern recognition system(LLMs) that can effectively make use of that amount of data.
FFS, this isn’t science fiction anymore, it’s here, now. And those companies have never had your, or my, interests or well-being in mind. They will use it for power, as they always have.
“Generative” “AI” is about generation yes
“Generative” is a misnomer. It will never generate anything new, it can only regurgitate existing ideas based on patterns that already exist. It’s very good at pattern recognition and summarizing, but lacks the ability to form a distinct new idea.
lacks the ability to form a distinct new idea.
Yeah, but it’s got that in common with a frighteningly large number of people…
See management, marketing, streaming, social media, etc…
It’s only good at summarizing things which have coherence to its training set. Any ability to summarize input outside of its training is accidental.
It will never generate something novel. Whether it will generate something “new” depends on your definition of “new,” which is a little more ambiguous than “novel.”
Sorry if I’m being too pedantic.
Nope, pedant away. That’s a better way to convey what I was trying to say. Thanks.
Sure, but then neither will most people.
Shhh… Let the haters hate. Hate is all they have. It’s the only thing that makes them feel superior.
If that’s your reasoning then genes and genetics fall into the same bucket.
not even close.
Well, no.
That’s an interesting thought. Genetics is largely a mixing and copying process with occasional “hallucinations” in the form of transcription errors. Most of these errors result in the termination of the hallucinated code. Hallucinations that damage the termination process result in cancer. In the larger sense of evolution, there’s a robust external “review” process. Environmental pressures, predation, and resource availability weed out most of the mistakes and selects the results most likely to succeed.
It’s generating a prediction of our behavior for them to use to better control us.
<takes another hit from the bong>
paywall removed: https://archive.ph/J3VLi
Now I can read the Atlantic for free while also DDOSing people. The future is now.
I think ive missed a reference, how does that link contribute to ddosing?
Wild stuff, thanks
For sure, you’re welcome.