I know a company that burned $100k in tokens after they they let like 50 worker bees using general AI for OCR, simply converting images and PDFs to text.
They didn’t bother to create a skill, or teach the AI how to reuse a shared script so every request resulted in it writing a new python project, pulling libraries, using a frontier model rather than offloading a dumb one etc.
Basically find a business process that happens often and let em at it inefficiently, it’ll happily chew through the budget.
There has been some serious leaps in terms of quality. It couldn’t read human writing or half the fonts for that matter like 5 years ago, let alone 20.
OCR libraries have undoubtedly improved but LLMs are using the same open source libraries and tools available to anyone… there’s few cases where sending the work through general models is worth it for text conversion. Employees just needed a front end to upload, run something like tesseract behind the scenes, and spit out the result. It’s an egregiously stupid use of resources.
have undoubtedly improved but LLMs are using the same open source libraries and tools available to anyone…
I read a surprising article on Lemmy just a week ago that explained that that is not how LLM’s do OCR. LLM’s convert images into tokens and then treat them like text input. I can’t see how it works but it does. It’s why they are better than classic OCR neural nets but at the trade off of enormously larger computation cost.
Okay, that last one took 17 minutes to upscale on my GPU, so I’m not going further. But I’m using SD Ultimate Upscale, which is tile-based, so in theory that could be farmed out over a collection of GPUs and parallelized. Just need more compute hardware.
Burn your AI tokens and get promoted for using more AI!
I sadly don’t find the GitHub repo with the bash script anymore, but that thing seems to do the same thing
(Maybe I’ve misread though, I’m just going to sleep)
those companies arent profitable either and they have same problems in which it costs them more to run their products than they are currently charging people to use it.
Did you read the title? It says to spend Walmart and Amazon’s money on AI. And you said “those companies aren’t profitable either” which would mean, using normal rules of English grammar, that “Walmart and Amazon aren’t profitable and OpenAI and Anthropic aren’t profitable either”. So what are you talking about? What does “either” mean?
More companies with less money is better than a few companies with all the money.
Ultimately distributed power has to be more democratic, and centralized power has to be more fascistic.
That’s part of why governments having large distributed bureaucracies each with their own authority and independent ability to intervene is better than say; a single executive office/president controlling everything directly.
Distribution also leads to stability though (making it harder to challenge the status quo), so it’s a double edged sword.
What’s the most expensive thing one can do with AI and how do we do it on a mass scale to one company at at time?
I know a company that burned $100k in tokens after they they let like 50 worker bees using general AI for OCR, simply converting images and PDFs to text.
They didn’t bother to create a skill, or teach the AI how to reuse a shared script so every request resulted in it writing a new python project, pulling libraries, using a frontier model rather than offloading a dumb one etc.
Basically find a business process that happens often and let em at it inefficiently, it’ll happily chew through the budget.
Thats pretty much what people freaked out about llms doing at my work and all they use it for. I’m here like…we have had OCR for over 20 years.
People are duuumb.
There has been some serious leaps in terms of quality. It couldn’t read human writing or half the fonts for that matter like 5 years ago, let alone 20.
OCR libraries have undoubtedly improved but LLMs are using the same open source libraries and tools available to anyone… there’s few cases where sending the work through general models is worth it for text conversion. Employees just needed a front end to upload, run something like tesseract behind the scenes, and spit out the result. It’s an egregiously stupid use of resources.
I read a surprising article on Lemmy just a week ago that explained that that is not how LLM’s do OCR. LLM’s convert images into tokens and then treat them like text input. I can’t see how it works but it does. It’s why they are better than classic OCR neural nets but at the trade off of enormously larger computation cost.
deleted by creator
Maybe 16k image/video upscaling of super low quality shitposts?
File sizes are going to be huge! 2K is already a lot to upload, couldn’t imagine 16K right now.
Just expend some more compute time on doing compression and we’ll get those filesize numbers to a workable level.
zoom and enhance
zoom and enhance
Okay, that last one took 17 minutes to upscale on my GPU, so I’m not going further. But I’m using SD Ultimate Upscale, which is tile-based, so in theory that could be farmed out over a collection of GPUs and parallelized. Just need more compute hardware.
But as to filesize, that’s under 50kiB.
Whoah! Nice effort! Yeah a cluster could cut the time down a lot.
https://token-burner.tech/
Burn your AI tokens and get promoted for using more AI!
I sadly don’t find the GitHub repo with the bash script anymore, but that thing seems to do the same thing
(Maybe I’ve misread though, I’m just going to sleep)
Edit: ha! Found it!
https://github.com/dtnewman/burn-baby-burn
have it end to end build a fully featured web browser that works on Windows, MacOS, and Linux from scratch.
Thus transferring their money to openAI, Anthropic etc? How does that help?
They actually lose money as well, because they’re proving those tokens at below cost price.
It’s all going to Nvidia
those companies arent profitable either and they have same problems in which it costs them more to run their products than they are currently charging people to use it.
What do you mean by either? Walmart and Amazon make tens of billions in profit a year if not a quarter.
i thought we were talking about OpenAI, antrhropic, etc, not Walmart?
Did you read the title? It says to spend Walmart and Amazon’s money on AI. And you said “those companies aren’t profitable either” which would mean, using normal rules of English grammar, that “Walmart and Amazon aren’t profitable and OpenAI and Anthropic aren’t profitable either”. So what are you talking about? What does “either” mean?
ooh I see. my bad. erase the word either from my comment, it wasn’t necessary.
More companies with less money is better than a few companies with all the money.
Ultimately distributed power has to be more democratic, and centralized power has to be more fascistic.
That’s part of why governments having large distributed bureaucracies each with their own authority and independent ability to intervene is better than say; a single executive office/president controlling everything directly.
Distribution also leads to stability though (making it harder to challenge the status quo), so it’s a double edged sword.