Joined Apr 2026
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"I don't know what everyone else is seeing. I'm hearing they are very good for certain functions. Are you going to have them write all your production code? No. Are they great for prototyping? Yes. Can they help an experienced coder do more with less? Yes. It's a mixed bag. Are they going to replace all medical professionals with medical advice? No."
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📣 More Dutch companies are making their Android apps inaccessible on alternative mobile devices, by using the strictest Google Play Integrity API settings. 📣
After Funda and Rabobank, now a public collective for emergency controlrooms has made their app inactive on open source OS's. Caught by @rikviergever from @murena .
We can't keep being dependent on two US big tech companies. If we want more digital sovereignty, we'll need to invest in mobile alternatives.
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Shit like this is why we can't afford to stop the migrant boats 🤬
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Ukraine
I Crane
You crane
His Crane
Her Crane
It's Crane
My Crane
Your Crane
Their Crane
He Crane
She Crane
Them Crane
h4890
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Like, seriously, WTF. It's supposed to be burning Battlefield over there - that's why we accept all the "refugees" and keep sending them fuckin' money!
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Oh and look at that. Apparently that free speech absolutism Musk was preaching was somewhat overrated - the post got me locked out. Let's see what the appeal process does 🤔
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Publishers Are Losing Google Traffic As AI Answers Replace Links https://news.slashdot.org/story/26/07/31/1639202/publishers-are-losing-google-traffic-as-ai-answers-replace-links?utm_source=rss1.0mainlinkanon
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Would you like to see ICE deport NYC mayor Zohran Mamdani, a Muslim who was born in Uganda?
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So this is apparently needed.
Dear Mastodon,
While we all say "AI" regardless of whether we're talking about text, audio, images or video these are not all using the same underlying technology*.
Large Language Models, LLMs, use a technology known as Transformers. They "got smart" beginning in 2017 when a paper named "Attention is all you need" was published by Google. One way of describing LLMs is that by caring for everything that comes before, you can perform the next step.
Audio, Images and Video mostly use a technology called Diffusion which began in 2015 but became noticable better in quality with CLIP from 2021. That technology can be said to work by starting with noise and then seeing whether any changes you make cause an evaluation to be more or less what you want in the end.
LLMs do not want to store training data in the models because that means they're less smart. Knowing the answer to a question is useless - knowing how to get to the answer is valuable. Diffusion models however don't have a problem with this. Knowing what a rose looks like is knowing what a rose looks like. Knowing what a Dali painting looks like ... yeah you get it.
What has been a bit shocking to everyone is that LLMs turned out to be generally useful and not just language engines. While the intention was for them to understand text, produce text and reliably translate between languages, it seems that by using text to encode knowledge about the world we also got engines that could "think" and "reason" about the world - and that has over the last year or so caused an explosion in LLM usage in, for example, software development.
This was not something first dreamed up by commercial entities wanting to sell products - but by curious programmers wanting the Next Great Tool. If there's one thing us programmers are it's _lazy_. If we can automate a task we will.
*) Yes I'm taking a shortcut. There are overlapping areas, but the point still holds.
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Love the feedback I'm getting on the ST:TNG voice interface clone :) As an FYI, I've taken up the old IRC-practice of idling in a channel for anyone who'd want to discuss that or other local LLM projects.
Although nowadays I do prefer #Matrix to IRC: https://matrix.to/#/#LocalLLaMa:argot.se
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"Hidden debt at U.S. tech giants swelled eightfold in roughly four years to an estimated $1.65 trillion as artificial intelligence investments ballooned, a Nikkei study shows, exceeding actual debt and making it tougher for investors to assess risk.
Nikkei examined recent financial statements and other materials from Google owner Alphabet, Microsoft, Amazon, Meta and Oracle. The four companies aside from Oracle are scheduled to announce their second quarter earnings from Wednesday, meaning the figures may increase further.
The five companies' hidden debt, which does not appear on balance sheets, totaled $1.65 trillion in the most recent quarter, exceeding the roughly $1.35 trillion in debt reflected on their balance sheets. The data includes some estimates.
Meta's off-balance-sheet debt is particularly high at about $420 billion, nearly triple its recorded debt.
These companies are rapidly bolstering their data centers and other computing resources to power AI development, and are entering into long-term purchase agreements for graphics processing units (GPUs) and servers.
Constructing data centers requires investment in the billions or even tens of billions of dollars."
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Senator Bernie Moreno (R-OH):
"Here I am sitting in front of you six years later, saying: Who the FUCK do you think you were to do that?"
"It is a total disgrace what you did to this country."
About time someone told Fauci off like this.
@kylenabecker https://www.minds.com/newsfeed/1929323534375460864
"Here I am sitting in front of you six years later, saying: Who the FUCK do you think you were to do that?"
"It is a total disgrace what you did to this country."
About time someone told Fauci off like this.
@kylenabecker https://www.minds.com/newsfeed/1929323534375460864
Joined Apr 2026
