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?
ok tough guy so how do I get fat T-rexes hm?
Jurassic Pork
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.
I’m…but…no…wait….
We’re not going to make it.
No, we aren’t.
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.
Ai agents use tools like this tho?
I hope this is sarcasm >_<
Foes it works on scanned image PDF?
.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 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.
Wow I’m starting to feel bad about that time I asked AI to make a joke about scatology & eschatology sounding similar.
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
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.
Yeah this article is already outdated and poorly researched
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…
All for software that’ll be out of date and fashion next year!
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.
Pre roasted coffee took off at this time.
It’s different in so far as we didn’t invest a trillion dollars in giant strip mines that destroyed these states.
I feel like literally everyone knows this now but theres so much invested in it theres no backing out.
Too many billionaires have a need to invest and a need for future gains. It’s a mental compulsion.
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.
“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 me 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.
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.
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.
Are you responding to something specific from this article? I feel like we might have read completely different articles.
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?
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’m not sure about the script or voice acting as I don’t understand Mandarin, but visually the ENTIRE thing is AI rendered.
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.
The author seems to be confusing user scalability with performance scaling:
The problem with generative AI, in the industry’s own jargon, is that it does not scale. The cost of growing from, say, a thousand users to a million is a key factor that venture capitalists examine when they evaluate start-ups.
This is a question of whether openai can handle 1 million users asking chatgpt to write a basic html website. That can be scaled horizontally and is just a matter of building more data centers.
The author then goes on to conflate this user scaling with performance scaling:
Yet the returns are diminishing. The bigger an AI model is, the less it improves with each added parameter, and so it must be made bigger at a faster rate just to sustain steady progress. I asked a few AI researchers whether they could name any other real-world software that scales so poorly. None of them could think of any. Even outside the world of software, it’s hard to find a comparable example, given that economy of scale is the principle that has made light bulbs, cars, and clothing so affordable. By economic and engineering measures, generative AI might be the worst technology ever deployed.
This is a question of whether chatgpt can generate a full complex web app. For this there may be a limit to this bigger model approach but this is common to most technologies, performance sometimes has hard limits. You aren’t going to get a car to go 300 mph by making the engine bigger and adding more cylinders, there’s diminishing returns, that doesn’t make cars the worst technology ever deployed… maybe they are but for other reasons.
Economies of scale also isn’t about performance scaling, it’s about capacity scaling. Capacity scaling for AI does reflect economies of scale, that’s why you have these large AI companies building large data centers.
That can be scaled horizontally and is just a matter of building more data centers.
That was a spectacular way of outlining the fact that you don’t at all understand the problems and limits with “AI” without having any awareness or understanding that you don’t at all understand the problems and limits with “AI.”
Ok, explain to me why you can’t scale out existing small and mid tier models horizontally? yes there are current resource limits on chips and energy but we know how to build those out and those types of limits are common to nearly every other industry, it’s just that no other industry has generated such rapid demand/investment for infrastructure.
There’s no O(n^2) problem on number of requests that would make handling large scale rollout impossible like the article is suggesting.
That can be scaled horizontally and is just a matter of building more data centers.
At one of my old jobs “just” was considered a bad word
Ok, remove the just then, the point still stands that it is a solvable problem. We know how to make data centers, it may not be easy or cheap but it’s possible just like we know how to build car factories.
Bubble sort is also a good algorithm if we “solve” its inefficiency by using more powerful hardware. It may not be easy or cheap, but…
I wouldn’t separate performance scalability and user scalability as they ultimately go hand in hand together. LLMs are inefficient by design.
I wouldn’t separate performance scalability and user scalability as they ultimately go hand in hand together.
Ok think of them as different scaling factors then, maybe n for number of requests and s for size of requests and c for complexity of requests. Scaling for n can be done horizontally by building more data centers which is possible. Scaling for s or c requires building bigger models which has diminishing returns.
Scaling for n is required to make the software business model work, like the article says. Scaling for s or c though isn’t required as long as your average user keeps those constant, which is possible.
LLMs are inefficient by design.
They are less efficient when compared to what traditional computing can already do, eg. Arithmetic, structured data analysis etc. There are things that traditional computing can’t do, eg. writing an essay, that can only be compared to the human brain which is hard to do. So you can say AI is inefficient at calculating 2 + 2 , but it’s a hard case to make that it’s inefficient at writing an essay.
Yeah and the point is that model improvements so far have meant making huge increases in size, which offsets the datacenters scale out.
The whole point is that this is futile because we will always be playing catch-up to model sizes, to our ultimate downfall. The tech needs to be smarter not larger. That’s why the whole cloud AI business is shit and not going to work. as anyone with a brain has been saying from the beginning.
Jesus Christ man, people’s homes are being sized with eminent domain for this shit. It ain’t worth it.
which offsets the data center scale out
This is only true if everyone is always using the top line model, which most people don’t. Both because most people just use the default, which is a low or mid tier model, and because it’s expensive. The top line models are becoming increasingly niche.
The tech needs to be smarter not larger.
I agree, that’s why more focus is being put on the harness and agent orchestration these days. You can achieve better results by having a large model orchestrate a bunch of smaller model agents to do simpler tasks then trying to have the large model one shot it. This doesn’t mean the whole cloud AI business is bullshit, they’re still going to need to build out a lot of capacity for these smaller models and still going to need large models to handle the planning and orchestration, it just means the call count for these larger models are going to be lower.
So it’s probably not going to be 1 million calls to a small model turns into 1 million calls to a larger model and the capacity never catches up, it’s going to be 1 million calls to a small model and 1,000 to a large one which is more feasible to build out.
people’s homes are being sized with eminent domain for this shit. It ain’t worth it.
I don’t agree with how the data centers are being rolled out, they can and should be built out with renewable energy and consent from the community which isn’t happening. I disagree that data centers shouldn’t be built at all or that it will be an unachievable Sisyphusian task to build them out.
The performance per parameter has been improving steadily though. Gemma 4 is ~4o level at a fraction of the parameters.
One does not simply walk into Mordor.
if only AI companies optimized their AI to run on less compute (in the data centers)
Efficiency is relative, if there is a solution that uses less resources then the other solution is more efficient, but if there is no other solution then the solution is the most efficient.
Is using fable 5 to do 2+2 efficient? No, because a calculator can do that with less resources
Is using fable 5 to rewrite a code base from zig to rust efficient? Maybe since the only other solution is a human it depends on how you compare human resources to compute resources. Time wise it’ll probably take the human longer since they require breaks.
Yea why train a human to be competent when we can rely on AI.
I’m sure in present day America that couldn’t possibly go wrong lol.
Currently it is way more cost efficient to use a human
Time wise it’ll probably take the human longer since they require breaks.
This is only true if your only metric for ‘success’ is lines of code written.
LLMs will output code much faster than a human can type, there’s no doubt about that. But when you consider how terrible even the frontier models still are at the architecture and maintenance side of the process, humans are still way, way more efficient.
There’s a lot of variation in quality with humans though.
I don’t doubt that there are some engineers better then the frontier models at coding considering architecture, maintenance performance etc. Those engineers tend to be more expensive though. I
don’t think an average engineer is better then the frontier models though, and say an entry level engineer fresh out of a boot camp would be significantly worse then even a tier 2 model.
Having worked closely with junior engineers and with high-end models, I strongly disagree. In almost all cases the output of the juniors is on par or better (albeit slower) than the LLMs ,and unlike an LLM, a junior actually learns and becomes much more consistent than the AI over time.
Are you talking about a one shot from the model or using a harness? I agree a junior dev can do better then a one shot, but with a proper harness with adversarial review cycles I don’t think a junior dev could
junior actually learns and becomes much more consistent than the AI over time.
A proper harness will have memory and will get more consistent the more you use it. You can “teach” it by adding skills and having it write it’s learnings either locally or to repo context files.
A proper harness will have memory and will get more consistent the more you use it.
Yeah, by creating a bunch of .md files of questionable quality that get fed into an already-limited context window, on top of a pile of all sorts of other context cruft from the harness, the model provider, and whatever else is propping up the system to give the illusion of intelligence…
I’ve experimented with harnesses quite a bit and I don’t understand the hype. The difference in quality has been middling in my experience, but the token burn has been significantly more — which makes sense when you understand that the more of the context window you use, the worse most models perform.
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.
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?
Okay. I’ll give it a whirl then.
AFAIK, their open models are distributed as weights, not executables and are therefore not able to start network connections / run code. There 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.
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.
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.
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.
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.
As per the 1890s, it probably also had to be lubricated with orphaned child blood.
Don’t give the AI MAGAs any ideas.
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?
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 🤡
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.
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.
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.
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.
Tokens aren’t going to get cheaper. Tokens must get more expensive, and soon. AI companies are making big losses even if you ignore the stratospheric debt for the immemse quantity of hardware.
Yeah you’re right they just appear cheaper due to circular financing and some memory improvements. I was trying to steel man my argument into the strongest possible point that still fails to make sense.
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.
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!
To be or not to be, that is…
ehhh I dunno… it’s an undeniable success for a few… and I’d wager that was the intent.
Some folks were dreaming of the Dark Enlightenment for some time, and along came the McGuffin they could use for this.
