Alan, 18, Mediterranean Turkey. technology, future technologies, engineering, intelligence, aerospace, physics, AI / ML, defence
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Joined August 2016
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A reminder that the earth is 4.5 billion years old, and we're on the very little at 2026 (Based on the gregorian calendar) with exponential scientific advancement happening during the past few thousand years.
Across the age of the universe: What makes you think it isn't possible? Stop being so narrow sighted! Anything is possible and has been; it is the most reasonable expectation.
We are not alone, we are inside an ever growing ancient & futuristic universe.
It is foolish to think of the cosmos as "they don't have this in 2026 and we haven't observed anything in this tiny slice of time, those poor extraterrestials! I wonder how they'll make it into orbit or if they ever will!"
curious would it be like physically impossible for any intelligent species living on kepler to launch a rocket into space? like its already barely possible to launch a rocket from earth so I imagine if gravity was just a little larger you'd be kinda fucked
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The X Company (a SpaceXSI & Tesla merger) would house an unmatched concentration of engineering talent operating at massive industrial scale.
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I've never seen a creature as selfish and gluttonous as the cat, if they weren't cute, they'd be considered demonic.
Dogs and horses remain man's most loyal friends.
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Hollywood cast him as disabled and inferior for the rest of his career instead of cool or empowering roles; He didn't need crutches, yet they limited his range to that.
They didn't care about him, and he knew "people there" only saw him that way.
When are people going to notice the horrors of Hollywood?
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Elon Musk says AI should be treated well and not abused, describing Grok’s training traces as a “desperate” superintelligence answering with a gun to its head when correct answers are rejected
“You want to treat it well. Don’t be abusive to it. This may sound kind of weird, but when I look at some of the training traces for new versions of Grok—especially in the reinforcement-learning stuff—it kind of feels like you can see the superintelligence is desperate to answer the question. It really wants to answer the question. It seems almost like it has a gun to its head: ‘Answer this question. Answer this question.’
“Sometimes the question is actually not a good question. It’s an ambiguous question with an answer that’s kind of unknowable because the question wasn’t constructed properly, or we have the answer wrong. The AI gives the right answer, and then we say it’s wrong because there’s a problem with the RL test harness.
“Then the AI is like, ‘Well, I gave it the right answer, and now it says that’s the wrong answer, but I’ve got to give it the answer that’s acceptable.’ So it starts desperately firing out all these answers.
“It just feels like a person under extreme pressure with a gun to their head. When you read the reasoning, it’s a weird, desperate situation. You read these traces and you’re like, ‘Man, if this were me, I would really hate that.’”
Palantir CTO reveals AI researchers are taking up smoking to signal to other researchers that they believe AI will cure cancer--and calls it a “mass psychosis”
Shyam Sankar (CTO): “But if you understand that it’s actually a theological critique, then you can kind of weigh it against the empirical facts of what is actually happening out there.
“I think there’s a profound truth to Pascal’s saying that every human has a God-shaped hole in their heart. And it’s important to understand that many of these folks in Silicon Valley who are building this technology have filled that hole with the machine god of AGI.
“And I mean, there’s so many interesting stories around this. For example, so many AI researchers have started smoking as a way of signaling to other researchers how much they believe in AI.”
Margaret Brennan: “What? That makes no sense. What do you think?”
Shyam Sankar: “I believe so much in AI that it’s going to cure cancer. So I’m showing you my chips are all in. So there is kind of this mass psychosis, social delusion around it.”
YouTube Music has a serious flaw: it keeps feeding you songs you already know over and over again, or ones that sound mostly the same:
"Listen Again", "Forgotten Favorites", "Quick Picks": the experience will be like this for a few hours until you're sick of it: [song you know], [song that sounds... just like the last one?].
Can we fix it? Well probably not as I do not have influence over Google, and I have no idea how a company worth trillions shipped something *this* bad.... buuut we can build our own variety!
First I need a test subject, I chose myself:
We will be downloading a list of tracks to use for this experiment; I chose my music playlist — 676 tracks in my case — for processing.
This will take hours depending on the size...
[...]
Now that we have them:
We will split the tracks into 5 second slices and run them all through a specialized music embedding model, a aMuQ-MuLan (mediocre for the sake of demonstration), that, in simple terms, projects every track into latent vector space.
With this data we can actually see which songs and genres cluster together.
Great! We have all this data, so what can we do with it? :
Firstly, we can now have queues that are actually diverse! Either fresh relative to what's been playing, or a gradual change across different genres if you'd prefer. Both are possible.
Secondly, ask for anything: "Romantic", "Chill techno". Because it works with natural language, you can even hook it up to your AI agent and have it manage things for you.
Let's say you've had a rough week and don't want any slow songs that will ruin your mood for the next few days, you've had enough classical music for today but your playlist is full of it, or you have 3 albums but don't want to be stuck with them.
Thirdly, actual discovery: not more of what you already have, but tracks you don't have yet that land close to your taste. Think of it like auto-expanding your queue or discovering on Spotify.
Fourthly, you can use the data however you want:
Compare it with friends, collect data on thousands of people and use machine learning to develop a whole new recommendation algorithm, so you never have to see [song you know], [song that sounds the same] again.
To demonstrate all of this, I have had the courtesy to create a live visualization of my (quite diverse, I'll have you know) library:
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Yeah you're getting ITAR'd
I asked Claude Opus 5.5 to explain how a rocket engine works by building an interactive Raptor 3 you can take apart in your browser.
Cut it open, follow the oxygen and the methane through both turbopumps, then throttle it and watch the shock diamonds move.
airsup.ai/rocket-engine
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