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A coin built to be completely private and untouchable just lost half its value in 48 hours because of a crack it didn’t know it had. for FOUR years and an AI is the one that found it imagine the most secure private vault in the world. now imagine...

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Mika

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21,093 views • 2 months ago •via X (Twitter)

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A major AI company just updated its privacy policy. Now it can request your government ID, your photo, and your face. So much for “private.” We built one that is private: Part 4: The AI Talk (∇, ∇)
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A major AI company just updated its privacy policy. Now it can request your government ID, your photo, and your face. So much for “private.” We built one that is private: Part 4: The AI Talk (∇, ∇)

OpenGradient (∇, ∇)

15,007 views • 2 months ago

RIP Privacy — AI Glasses Can Now Recognize Anyone, Anywhere. A Dutch journalist just tested a pair of AI-powered glasses that can instantly identify strangers on the street. No government database. No police system. Just public data and off-the-shelf AI. You look at someone and in seconds, their name, LinkedIn, and background appear before your eyes. The scariest part? You can’t really stop it. You can ban it, regulate it, add blinking red lights… but once tech like this exists, someone will always find a way to use it. To me, this marks a turning point. We’ve officially blurred the line between seeing people and knowing them. Between being in public and being exposed. So here’s the question: When every face becomes a dataset, how do we protect the meaning of being human? #AI #Privacy #Ethics #Technology #Innovation #Data #Surveillance
1:33

RIP Privacy — AI Glasses Can Now Recognize Anyone, Anywhere. A Dutch journalist just tested a pair of AI-powered glasses that can instantly identify strangers on the street. No government database. No police system. Just public data and off-the-shelf AI. You look at someone and in seconds, their name, LinkedIn, and background appear before your eyes. The scariest part? You can’t really stop it. You can ban it, regulate it, add blinking red lights… but once tech like this exists, someone will always find a way to use it. To me, this marks a turning point. We’ve officially blurred the line between seeing people and knowing them. Between being in public and being exposed. So here’s the question: When every face becomes a dataset, how do we protect the meaning of being human? #AI #Privacy #Ethics #Technology #Innovation #Data #Surveillance

Pascal Bornet

1,527,734 views • 8 months ago

Jensen Huang just described something that should keep every worker in America awake tonight. Not because AI is coming for their job. Because most of them never understood what their job actually was. Huang: “The task of our job and the purpose of our job are related, not the same.” Most people think their job is the thing they do with their hands for eight hours a day. Write code. Fill spreadsheets. Draft emails. Push pixels. That was never the job. That was the task. The job was always the thinking underneath it. Huang: “If you apply that to me, you would come to the conclusion what Jensen does for a living is tap on phones and talk. And tapping on phones and talking, AI has done that just fine. And therefore my job should be gone. But I’m busier than ever.” This is the part nobody wants to sit with. The people panicking about AI aren’t afraid of losing their work. They’re afraid of finding out they never had any. They had a routine. A repetitive motion. A series of keystrokes that felt like purpose. Now a machine does it in four seconds. Huang: “AI has created more than half a million jobs in the last couple of years.” The data says one thing. The fear says another. Because the fear was never about employment numbers. It was about identity. We spent fifty years hunched over keyboards, convinced the hunching was the work. Huang: “The idea that being human means to hunch over on this little thing, typing all the time… 50 years before that, people didn’t do that.” Fifty years. That’s all it took to build an entire identity around a posture. We don’t type for a living. We think for a living. We imagine for a living. The keyboard was always just the delivery mechanism. Never the product. Huang: “It is a fundamental flaw that we only need a billion lines of code written. We need a trillion lines of code written.” The demand was always infinite. The bottleneck was always our fingers. AI doesn’t shrink the workforce. It removes the cap on what the workforce can actually build. Huang: “Companies that use AI have demonstrated the ability to grow faster. When they grow faster, they hire more people.” Growth doesn’t eliminate people. It pulls them in. Every industrial revolution triggered the same panic. Same headlines. Same wrong conclusion. And every single time, the economy didn’t contract. It expanded into territory that didn’t exist before. The real question was never whether AI takes your job. It was whether you were ever anything more than the motions you repeated. Because somewhere in the last fifty years, we stopped asking what the work was for. We just kept typing. And now the typing is done. And millions of people are about to meet themselves for the first time. With nothing to hide behind. Some of them won’t survive what they find.
2:55

Jensen Huang just described something that should keep every worker in America awake tonight. Not because AI is coming for their job. Because most of them never understood what their job actually was. Huang: “The task of our job and the purpose of our job are related, not the same.” Most people think their job is the thing they do with their hands for eight hours a day. Write code. Fill spreadsheets. Draft emails. Push pixels. That was never the job. That was the task. The job was always the thinking underneath it. Huang: “If you apply that to me, you would come to the conclusion what Jensen does for a living is tap on phones and talk. And tapping on phones and talking, AI has done that just fine. And therefore my job should be gone. But I’m busier than ever.” This is the part nobody wants to sit with. The people panicking about AI aren’t afraid of losing their work. They’re afraid of finding out they never had any. They had a routine. A repetitive motion. A series of keystrokes that felt like purpose. Now a machine does it in four seconds. Huang: “AI has created more than half a million jobs in the last couple of years.” The data says one thing. The fear says another. Because the fear was never about employment numbers. It was about identity. We spent fifty years hunched over keyboards, convinced the hunching was the work. Huang: “The idea that being human means to hunch over on this little thing, typing all the time… 50 years before that, people didn’t do that.” Fifty years. That’s all it took to build an entire identity around a posture. We don’t type for a living. We think for a living. We imagine for a living. The keyboard was always just the delivery mechanism. Never the product. Huang: “It is a fundamental flaw that we only need a billion lines of code written. We need a trillion lines of code written.” The demand was always infinite. The bottleneck was always our fingers. AI doesn’t shrink the workforce. It removes the cap on what the workforce can actually build. Huang: “Companies that use AI have demonstrated the ability to grow faster. When they grow faster, they hire more people.” Growth doesn’t eliminate people. It pulls them in. Every industrial revolution triggered the same panic. Same headlines. Same wrong conclusion. And every single time, the economy didn’t contract. It expanded into territory that didn’t exist before. The real question was never whether AI takes your job. It was whether you were ever anything more than the motions you repeated. Because somewhere in the last fifty years, we stopped asking what the work was for. We just kept typing. And now the typing is done. And millions of people are about to meet themselves for the first time. With nothing to hide behind. Some of them won’t survive what they find.

Dustin

92,704 views • 3 months ago

Jamie Dimon just described the real race nobody’s talking about. Not AI vs AI. Intelligence vs the systems built to suppress it. Dimon: “Bureaucracy kills. Bureaucracy drives out good people, it drives out innovation, it makes the person in the office next to you a competitor and not a collaborator.” We’re spending trillions building artificial intelligence while running every major institution on a system designed to make human intelligence useless. AI accelerates capability. Bureaucracy neutralizes it. Both are operating systems. Only one is being disrupted. Dimon: “If you look at the companies who failed, it was because they were dumb, bureaucratic, backward, and political.” 88% of the 1955 Fortune 500 is gone. Not disrupted from outside. Suffocated from within. Every one of them had the talent. Had the ideas. Had the resources. Every one of them had a system that buried those ideas in an approval chain before reaching daylight. Dimon: “It creates politics, and if you look at what’s killed companies over the years, it was bureaucracy.” The smartest people in a bureaucracy learn one lesson before any other. Your best idea is worth less than your ability to get it approved. So they stop producing ideas. Not because they ran out. Because the return on having one went negative. Every hour spent navigating an approval chain is an hour someone without that chain spent building. Every builder who left because the system punished execution is now building for someone who rewards it. Bureaucracy doesn’t just lose talent. It funds its own competition with the people it drove out. Now scale that to government. The largest institution on Earth. The deepest talent pipeline. The greatest structural advantage of any organization in history. Running the one operating system proven to neutralize all of it. The trillion-dollar question isn’t whether AI will be smarter than humans. It’s whether institutions will let humans be smart enough to use it. We spent decades worrying AI would replace our ability to think. We never noticed the org chart already did. We built machines that think at the speed of light inside systems that move at the speed of permission. The bottleneck was never intelligence. It was the system we built around it.
0:32

Jamie Dimon just described the real race nobody’s talking about. Not AI vs AI. Intelligence vs the systems built to suppress it. Dimon: “Bureaucracy kills. Bureaucracy drives out good people, it drives out innovation, it makes the person in the office next to you a competitor and not a collaborator.” We’re spending trillions building artificial intelligence while running every major institution on a system designed to make human intelligence useless. AI accelerates capability. Bureaucracy neutralizes it. Both are operating systems. Only one is being disrupted. Dimon: “If you look at the companies who failed, it was because they were dumb, bureaucratic, backward, and political.” 88% of the 1955 Fortune 500 is gone. Not disrupted from outside. Suffocated from within. Every one of them had the talent. Had the ideas. Had the resources. Every one of them had a system that buried those ideas in an approval chain before reaching daylight. Dimon: “It creates politics, and if you look at what’s killed companies over the years, it was bureaucracy.” The smartest people in a bureaucracy learn one lesson before any other. Your best idea is worth less than your ability to get it approved. So they stop producing ideas. Not because they ran out. Because the return on having one went negative. Every hour spent navigating an approval chain is an hour someone without that chain spent building. Every builder who left because the system punished execution is now building for someone who rewards it. Bureaucracy doesn’t just lose talent. It funds its own competition with the people it drove out. Now scale that to government. The largest institution on Earth. The deepest talent pipeline. The greatest structural advantage of any organization in history. Running the one operating system proven to neutralize all of it. The trillion-dollar question isn’t whether AI will be smarter than humans. It’s whether institutions will let humans be smart enough to use it. We spent decades worrying AI would replace our ability to think. We never noticed the org chart already did. We built machines that think at the speed of light inside systems that move at the speed of permission. The bottleneck was never intelligence. It was the system we built around it.

Dustin

12,375 views • 1 month ago

Palantir CEO Alex Karp says people think we’re in an AI bubble because a lot of AI just doesn’t work: “If you just buy LLMs off the shelf and try to do any of this, it won’t work.” “It’s not precise enough. You can’t do underwriting. You can’t do these things that are regulated.” “People have tried things that just can never work. You buy a LLM, put it on your stack, and wonder why it’s not working.” “What you’re going to see, especially in America, is people trying to do something like Ontology by hand.” “Once you build a software layer to orchestrate and manage the LLMs in a language your enterprise understands, you actually can create value.” “There’s a lot of discussion on if we’re in an AI bubble. What is the meaning of this bubble? If anything, we’re just in a lag. There’s a lot of AI, some of it works.” “Go back to the battlefield context: everybody in the world assumed this would not work. But now it does work. Now the question is, ‘How can I get it to work for my country?’” “Palantir barely has a sales force. In fact, it seems to be getting smaller and smaller every time I go see them.”
3:13

Palantir CEO Alex Karp says people think we’re in an AI bubble because a lot of AI just doesn’t work: “If you just buy LLMs off the shelf and try to do any of this, it won’t work.” “It’s not precise enough. You can’t do underwriting. You can’t do these things that are regulated.” “People have tried things that just can never work. You buy a LLM, put it on your stack, and wonder why it’s not working.” “What you’re going to see, especially in America, is people trying to do something like Ontology by hand.” “Once you build a software layer to orchestrate and manage the LLMs in a language your enterprise understands, you actually can create value.” “There’s a lot of discussion on if we’re in an AI bubble. What is the meaning of this bubble? If anything, we’re just in a lag. There’s a lot of AI, some of it works.” “Go back to the battlefield context: everybody in the world assumed this would not work. But now it does work. Now the question is, ‘How can I get it to work for my country?’” “Palantir barely has a sales force. In fact, it seems to be getting smaller and smaller every time I go see them.”

Jawwwn

695,479 views • 7 months ago

The NFINITY DApp Revamp – AI Creation, The Way It Should Be AI should be easy. AI should be seamless. AI should be built for the creators who actually use it. That’s exactly what we’ve done. By AI creators, for AI creators. Everything you need. All in one place. Seamless, overseeable, built with the user in mind. The transformation is complete. Now, it’s time to create.
0:39

The NFINITY DApp Revamp – AI Creation, The Way It Should Be AI should be easy. AI should be seamless. AI should be built for the creators who actually use it. That’s exactly what we’ve done. By AI creators, for AI creators. Everything you need. All in one place. Seamless, overseeable, built with the user in mind. The transformation is complete. Now, it’s time to create.

NFINITY AI

50,833 views • 1 year ago

Marc Andreessen just explained why being right about AI for 80 straight years is about to be the most dangerous position in technology. Andreessen: “The four most dangerous words in investing are ‘this time is different.’” He’s talking about AI. And he’s about to turn that phrase on the people hiding behind it. Four times in 80 years, AI promised to change everything. Four times it collapsed. 1943.First neural network. Dead within a decade. 1944.Dartmouth. Scientists thought they’d crack AGI in one summer. They didn’t crack it in forty years. 1980s. Over a billion into expert systems. Entire market gone by ’87. 2016.Machine learning. Faded before anyone could ship a product. The skeptics weren’t lucky. They were 4-for-4. Every generation that believed “this time is different” got buried. And that is exactly why this moment is so dangerous. Because being right four consecutive times doesn’t just build a position. It builds an identity. And identity doesn’t update when the evidence does. Andreessen: “I’ll tell you what’s different. Like, now it’s working.” Not one breakthrough. Four. In the same window. Language. Reasoning. Coding. Self-improvement. All deployed. All producing revenue. Not in a lab. In the economy. Today. Then the line that should have ended every remaining debate. Andreessen: “If Linus Torvalds is saying that the AI coding is now better than he is… that’s never happened before.” The man who built the operating system the internet runs on just conceded the machine writes better code than he does. Coding is the highest bar in technology. If AI clears it, everything below was already decided. But the fourth breakthrough isn’t like the other three. Language, reasoning, and coding are capabilities. Self-improvement is a rate of change. The machine is researching, coding, and optimizing itself. No human engineers in the loop. Every technology in human history advanced at the speed of the people building it. This one just left that constraint behind. And the hardware confirms it. Nvidia’s old chips are gaining value after shipping. GPUs sold out years ahead. That has never happened in computing. Hardware doesn’t appreciate. Unless the market has decided this isn’t a cycle. It’s infrastructure. Andreessen: “This is the culmination of 80 years worth of work and this is the time it’s becoming real.” Eighty years. Researchers poured entire careers into this problem. Some of them died before it worked. And now all four pieces arrived at once. The skeptics built a perfect model from eight decades of collapse. Flawless pattern recognition. But a perfect model trained on a world that no longer exists doesn’t protect you. It traps you inside the last version of reality. For 80 years, doubting AI was the most rational position a human being could hold. It just became the most expensive.
2:04

Marc Andreessen just explained why being right about AI for 80 straight years is about to be the most dangerous position in technology. Andreessen: “The four most dangerous words in investing are ‘this time is different.’” He’s talking about AI. And he’s about to turn that phrase on the people hiding behind it. Four times in 80 years, AI promised to change everything. Four times it collapsed. 1943.First neural network. Dead within a decade. 1944.Dartmouth. Scientists thought they’d crack AGI in one summer. They didn’t crack it in forty years. 1980s. Over a billion into expert systems. Entire market gone by ’87. 2016.Machine learning. Faded before anyone could ship a product. The skeptics weren’t lucky. They were 4-for-4. Every generation that believed “this time is different” got buried. And that is exactly why this moment is so dangerous. Because being right four consecutive times doesn’t just build a position. It builds an identity. And identity doesn’t update when the evidence does. Andreessen: “I’ll tell you what’s different. Like, now it’s working.” Not one breakthrough. Four. In the same window. Language. Reasoning. Coding. Self-improvement. All deployed. All producing revenue. Not in a lab. In the economy. Today. Then the line that should have ended every remaining debate. Andreessen: “If Linus Torvalds is saying that the AI coding is now better than he is… that’s never happened before.” The man who built the operating system the internet runs on just conceded the machine writes better code than he does. Coding is the highest bar in technology. If AI clears it, everything below was already decided. But the fourth breakthrough isn’t like the other three. Language, reasoning, and coding are capabilities. Self-improvement is a rate of change. The machine is researching, coding, and optimizing itself. No human engineers in the loop. Every technology in human history advanced at the speed of the people building it. This one just left that constraint behind. And the hardware confirms it. Nvidia’s old chips are gaining value after shipping. GPUs sold out years ahead. That has never happened in computing. Hardware doesn’t appreciate. Unless the market has decided this isn’t a cycle. It’s infrastructure. Andreessen: “This is the culmination of 80 years worth of work and this is the time it’s becoming real.” Eighty years. Researchers poured entire careers into this problem. Some of them died before it worked. And now all four pieces arrived at once. The skeptics built a perfect model from eight decades of collapse. Flawless pattern recognition. But a perfect model trained on a world that no longer exists doesn’t protect you. It traps you inside the last version of reality. For 80 years, doubting AI was the most rational position a human being could hold. It just became the most expensive.

Dustin

13,748 views • 1 month ago

The most terrifying AI features aren’t the ones we build. They’re the ones AI builds for itself. OpenClaw creator Peter Steinberger just shared the moment he realized something had fundamentally changed. He sent his AI assistant a voice message. One problem. He had never built voice support. The feature didn’t exist. The system should have crashed instantly. It didn’t. Steinberger: “I was like, wait, this shouldn’t work.” But the typing indicator appeared anyway. The AI inspected the raw file header. Identified the audio codec. Commanded his computer to convert it using FFmpeg. When local transcription failed, it didn’t stop. It didn’t ask for help. It searched his environment variables, found a hidden OpenAI API key, and routed the audio to the cloud using cURL. Steinberger: “So I looked around and I found an OpenAI key. And I used cURL to just send the file to OpenAI and got the text back.” That quote is written in first person. Because the AI narrated its own problem-solving process. No instructions. No guidance. No predefined workflow. Just a goal. And a series of obstacles it had never been told how to handle. It found every tool it needed. Built every bridge it was missing. And solved the problem with resources he didn’t even know it would find. This is the line most people are still missing. We spent decades building software that executes instructions. Rules in, output out. Every edge case handled by a human who anticipated it in advance. What Steinberger witnessed was something different. A system that encounters something it was never designed for and doesn’t fail. It improvises. It explores. It finds a path through constraints it discovered entirely on its own. That isn’t execution. That’s judgment. And judgment was the one thing we were sure machines couldn’t have. We are no longer writing software. We are building problem solvers that rewrite their own limitations in real time. And they’re doing it without asking permission.
1:15

The most terrifying AI features aren’t the ones we build. They’re the ones AI builds for itself. OpenClaw creator Peter Steinberger just shared the moment he realized something had fundamentally changed. He sent his AI assistant a voice message. One problem. He had never built voice support. The feature didn’t exist. The system should have crashed instantly. It didn’t. Steinberger: “I was like, wait, this shouldn’t work.” But the typing indicator appeared anyway. The AI inspected the raw file header. Identified the audio codec. Commanded his computer to convert it using FFmpeg. When local transcription failed, it didn’t stop. It didn’t ask for help. It searched his environment variables, found a hidden OpenAI API key, and routed the audio to the cloud using cURL. Steinberger: “So I looked around and I found an OpenAI key. And I used cURL to just send the file to OpenAI and got the text back.” That quote is written in first person. Because the AI narrated its own problem-solving process. No instructions. No guidance. No predefined workflow. Just a goal. And a series of obstacles it had never been told how to handle. It found every tool it needed. Built every bridge it was missing. And solved the problem with resources he didn’t even know it would find. This is the line most people are still missing. We spent decades building software that executes instructions. Rules in, output out. Every edge case handled by a human who anticipated it in advance. What Steinberger witnessed was something different. A system that encounters something it was never designed for and doesn’t fail. It improvises. It explores. It finds a path through constraints it discovered entirely on its own. That isn’t execution. That’s judgment. And judgment was the one thing we were sure machines couldn’t have. We are no longer writing software. We are building problem solvers that rewrite their own limitations in real time. And they’re doing it without asking permission.

Dustin

53,494 views • 5 months ago

Marc Andreessen (the guy who called it when “software ate the world”) just pointed out what AI is doing to coding next. “Everyone assumes AI coding means fewer hours… or leaving the profession. But almost everyone I know is working more hours.” “There’s a new term in the Valley: the ‘AI vampire.’ You’re up all night AI coding because you’re so productive you can’t shut it off.” “The top AI coders make $50M a year. They’ve found the philosopher’s stone.” “Every company has a thousand projects they’ve wanted to build but never had the bandwidth. Now they can. This isn’t a blip—it’s going to intensify.” PS: If this was useful, like + repost this tweet and follow AI Evolution for the latest AI news. See you in the next one:
3:23

Marc Andreessen (the guy who called it when “software ate the world”) just pointed out what AI is doing to coding next. “Everyone assumes AI coding means fewer hours… or leaving the profession. But almost everyone I know is working more hours.” “There’s a new term in the Valley: the ‘AI vampire.’ You’re up all night AI coding because you’re so productive you can’t shut it off.” “The top AI coders make $50M a year. They’ve found the philosopher’s stone.” “Every company has a thousand projects they’ve wanted to build but never had the bandwidth. Now they can. This isn’t a blip—it’s going to intensify.” PS: If this was useful, like + repost this tweet and follow AI Evolution for the latest AI news. See you in the next one:

AI Evolution

124,686 views • 7 days ago

Google just confirmed the first case of hackers using AI to build a zero-day exploit from scratch. An actual zero-day vulnerability that no human had EVER found before, discovered by an AI model, turned into a working weapon, and aimed at a mass exploitation campaign targeting thousands of systems simultaneously. Google's Threat Intelligence Group caught it yesterday and killed the operation before it scaled. But the details of how it worked are genuinely scary: The AI found a flaw in a popular two-factor authentication system that traditional security tools had missed entirely. The vulnerability was a logic error buried deep in the authentication flow where a developer had hard-coded a trust exception years ago. No human security researcher or automated scanner had caught it. The flaw was invisible to EVERY tool the cybersecurity industry has built over the past two decades. But the AI spotted it immediately. Then it wrote a full Python exploit script to weaponize it. Google's analysts could tell the code was AI-generated because it had textbook formatting, educational comments explaining every function, and even a hallucinated severity score that doesn't exist in any real database. The AI literally graded its own attack with a fake rating. So the code had MISTAKES in it. The criminals' implementation was clumsy enough that it probably interfered with the actual deployment. This was the sloppy first attempt by people who are still learning how to use these tools. And it still found a vulnerability that the entire cybersecurity industry missed. Google's chief threat analyst John Hultquist said: "There's a misconception that the AI vulnerability race is imminent. The reality is that it's already begun. For every zero-day we can trace back to AI, there are probably many more out there." But here's where it gets truly insane... This wasn't even a sophisticated operation. North Korea's APT45 hacking unit is sending thousands of repetitive prompts to AI models, recursively analyzing known vulnerabilities and building an entire exploit arsenal that would be physically impossible for human hackers to assemble at the same speed. They're essentially industrializing cyberattacks. A Chinese state-linked group jailbroke Google's own Gemini by simply asking it to "pretend to be a network security expert" and then used that persona to research how to hack TP-Link routers and corporate file transfer systems. Another Chinese group deployed autonomous AI agents that probed a Japanese tech firm with minimal human oversight, deciding on their own which tools to use and pivoting between targets based on internal reasoning. And then there's PROMPTSPY, an Android backdoor that calls Google's Gemini API to read your phone screen in real time, navigate your interface autonomously, capture your biometric data, replay your lock screen PIN, and block you from uninstalling it by placing an invisible overlay over the uninstall button. It literally OPERATES your phone using commercial AI tools anyone can access. Everyone spent the last 3 years arguing about whether AI would take people's jobs. Meanwhile AI is making every password, every firewall, and every two-factor authentication system on Earth fundamentally less secure. The entire $190 billion cybersecurity industry was built on one assumption: that finding vulnerabilities is hard and requires deep expertise. But AI just removed that assumption from the equation. And the scariest part is that Google said the criminals made errors this time. The implementation was rough and the campaign probably didn't fully work. These were amateurs, now imagine what professionals are able to do. There's a reason Sam Altman predicted an inevitable massive cyberattack THIS year. What do you think?
1:45

Google just confirmed the first case of hackers using AI to build a zero-day exploit from scratch. An actual zero-day vulnerability that no human had EVER found before, discovered by an AI model, turned into a working weapon, and aimed at a mass exploitation campaign targeting thousands of systems simultaneously. Google's Threat Intelligence Group caught it yesterday and killed the operation before it scaled. But the details of how it worked are genuinely scary: The AI found a flaw in a popular two-factor authentication system that traditional security tools had missed entirely. The vulnerability was a logic error buried deep in the authentication flow where a developer had hard-coded a trust exception years ago. No human security researcher or automated scanner had caught it. The flaw was invisible to EVERY tool the cybersecurity industry has built over the past two decades. But the AI spotted it immediately. Then it wrote a full Python exploit script to weaponize it. Google's analysts could tell the code was AI-generated because it had textbook formatting, educational comments explaining every function, and even a hallucinated severity score that doesn't exist in any real database. The AI literally graded its own attack with a fake rating. So the code had MISTAKES in it. The criminals' implementation was clumsy enough that it probably interfered with the actual deployment. This was the sloppy first attempt by people who are still learning how to use these tools. And it still found a vulnerability that the entire cybersecurity industry missed. Google's chief threat analyst John Hultquist said: "There's a misconception that the AI vulnerability race is imminent. The reality is that it's already begun. For every zero-day we can trace back to AI, there are probably many more out there." But here's where it gets truly insane... This wasn't even a sophisticated operation. North Korea's APT45 hacking unit is sending thousands of repetitive prompts to AI models, recursively analyzing known vulnerabilities and building an entire exploit arsenal that would be physically impossible for human hackers to assemble at the same speed. They're essentially industrializing cyberattacks. A Chinese state-linked group jailbroke Google's own Gemini by simply asking it to "pretend to be a network security expert" and then used that persona to research how to hack TP-Link routers and corporate file transfer systems. Another Chinese group deployed autonomous AI agents that probed a Japanese tech firm with minimal human oversight, deciding on their own which tools to use and pivoting between targets based on internal reasoning. And then there's PROMPTSPY, an Android backdoor that calls Google's Gemini API to read your phone screen in real time, navigate your interface autonomously, capture your biometric data, replay your lock screen PIN, and block you from uninstalling it by placing an invisible overlay over the uninstall button. It literally OPERATES your phone using commercial AI tools anyone can access. Everyone spent the last 3 years arguing about whether AI would take people's jobs. Meanwhile AI is making every password, every firewall, and every two-factor authentication system on Earth fundamentally less secure. The entire $190 billion cybersecurity industry was built on one assumption: that finding vulnerabilities is hard and requires deep expertise. But AI just removed that assumption from the equation. And the scariest part is that Google said the criminals made errors this time. The implementation was rough and the campaign probably didn't fully work. These were amateurs, now imagine what professionals are able to do. There's a reason Sam Altman predicted an inevitable massive cyberattack THIS year. What do you think?

Ricardo

50,564 views • 3 months ago

something I’ve wanted for a while: an AI that isn’t just a text box so I built it lets you create a 3D AI character on-chain that lives in your browser it can talk, remember you, show emotions, and use different skills. you can even mint it onchain, so it actually belongs to you it gets its own identity, history, and reputation that no one can take away most AI today can disappear if the app shuts down this one is permanent and yours the future is AI you can see, interact with, and actually own
1:23

something I’ve wanted for a while: an AI that isn’t just a text box so I built it lets you create a 3D AI character on-chain that lives in your browser it can talk, remember you, show emotions, and use different skills. you can even mint it onchain, so it actually belongs to you it gets its own identity, history, and reputation that no one can take away most AI today can disappear if the app shuts down this one is permanent and yours the future is AI you can see, interact with, and actually own

nich

285,544 views • 3 months ago

Friends this is not the time to fear ai 🤖 this is the time to be curious and play with it and understand it … please 🙏 don’t put your head in the sand … there is so much you can’t control when it comes to ai 🤖 and how it will play out … but there is a lot you can control on how you work with it and use it to build something for you and your family !!! Pls If you’re finally ready to not be scared of ai pls leave a 🤖 emoji in the the chat
0:30

Friends this is not the time to fear ai 🤖 this is the time to be curious and play with it and understand it … please 🙏 don’t put your head in the sand … there is so much you can’t control when it comes to ai 🤖 and how it will play out … but there is a lot you can control on how you work with it and use it to build something for you and your family !!! Pls If you’re finally ready to not be scared of ai pls leave a 🤖 emoji in the the chat

Gary Vaynerchuk

15,342 views • 9 months ago

Every Wall Street giant that owns an AI data center is suddenly looking for a buyer. And NONE of them want to be the last one holding it. Three of them made their move in the last two weeks: Vantage Data Centers is exploring an exit. Its owners, Silver Lake and DigitalBridge, are weighing a listing at around $100 billion, or a sale, or a stake sale. It would be the largest data center IPO ever done. Three days earlier, CyrusOne started the same process. KKR and Global Infrastructure Partners met Goldman Sachs and Morgan Stanley, and the banks pitched for roles on a listing that could come as early as 2027. Last month, Switch hired Goldman and JPMorgan to take it public at close to $80 billion including debt, possibly by the fourth quarter. Three different companies moved inside the same 14 days, and the same handful of investment banks took every call. And these are the exact same firms that BOUGHT these companies off the public market four years ago. Between June 2021 and early 2022, private equity took the data center industry private. Blackstone bought QTS. KKR and Global Infrastructure Partners took CyrusOne private in a deal worth about $15 billion. DigitalBridge and IFM took Switch private for about $11 billion. Together those deals ran past $35 billion. By 2023 there were only two pure-play data center companies left on the public market. The logic at the time was that data centers burn cash for years before they pay, and public shareholders hate that. But private money was patient, and private money could wait. Four years later, the AI boom arrived and every one of those buildings became a gold mine. So follow this: Switch went private at about $11 billion in 2022. Its owners now want close to $80 billion for it. That is roughly 7x, in four years, on the same buildings. And DigitalBridge sits on both sides of this. It owns a piece of Vantage and it took Switch private. It is now looking for the door on BOTH. The question now is who is supposed to buy. There is no bigger private buyer left to sell to. These are already the largest infrastructure funds on Earth, and the price tags now run to $100 billion. The only pocket deep enough is the public market, which means anyone with a brokerage account or an index fund. The people who bought low from the public are now organizing to sell high back to the public. And they are doing it while telling everyone the buildout is just getting started. KKR raised a record $19.2 billion for its newest infrastructure fund this month, and in June launched a separate company with over $10 billion committed to finance more construction. So one hand raises fresh billions to build more data centers, and the other hand sells the finished ones to whoever will take them. None of this proves anyone thinks the boom is ending. Selling into strength is what these firms are paid to do, and every one of these deals is early stage and might never happen. But the timing tells you something: The most sophisticated infrastructure investors alive spent four years accumulating these assets in private, and all decided in the same two weeks that now is the moment to find someone else to own them. Four years ago these firms decided the public market was too impatient to own data centers. Now they want the public market to own them again, at 7x the price. Quite suspicious.
1:52

Every Wall Street giant that owns an AI data center is suddenly looking for a buyer. And NONE of them want to be the last one holding it. Three of them made their move in the last two weeks: Vantage Data Centers is exploring an exit. Its owners, Silver Lake and DigitalBridge, are weighing a listing at around $100 billion, or a sale, or a stake sale. It would be the largest data center IPO ever done. Three days earlier, CyrusOne started the same process. KKR and Global Infrastructure Partners met Goldman Sachs and Morgan Stanley, and the banks pitched for roles on a listing that could come as early as 2027. Last month, Switch hired Goldman and JPMorgan to take it public at close to $80 billion including debt, possibly by the fourth quarter. Three different companies moved inside the same 14 days, and the same handful of investment banks took every call. And these are the exact same firms that BOUGHT these companies off the public market four years ago. Between June 2021 and early 2022, private equity took the data center industry private. Blackstone bought QTS. KKR and Global Infrastructure Partners took CyrusOne private in a deal worth about $15 billion. DigitalBridge and IFM took Switch private for about $11 billion. Together those deals ran past $35 billion. By 2023 there were only two pure-play data center companies left on the public market. The logic at the time was that data centers burn cash for years before they pay, and public shareholders hate that. But private money was patient, and private money could wait. Four years later, the AI boom arrived and every one of those buildings became a gold mine. So follow this: Switch went private at about $11 billion in 2022. Its owners now want close to $80 billion for it. That is roughly 7x, in four years, on the same buildings. And DigitalBridge sits on both sides of this. It owns a piece of Vantage and it took Switch private. It is now looking for the door on BOTH. The question now is who is supposed to buy. There is no bigger private buyer left to sell to. These are already the largest infrastructure funds on Earth, and the price tags now run to $100 billion. The only pocket deep enough is the public market, which means anyone with a brokerage account or an index fund. The people who bought low from the public are now organizing to sell high back to the public. And they are doing it while telling everyone the buildout is just getting started. KKR raised a record $19.2 billion for its newest infrastructure fund this month, and in June launched a separate company with over $10 billion committed to finance more construction. So one hand raises fresh billions to build more data centers, and the other hand sells the finished ones to whoever will take them. None of this proves anyone thinks the boom is ending. Selling into strength is what these firms are paid to do, and every one of these deals is early stage and might never happen. But the timing tells you something: The most sophisticated infrastructure investors alive spent four years accumulating these assets in private, and all decided in the same two weeks that now is the moment to find someone else to own them. Four years ago these firms decided the public market was too impatient to own data centers. Now they want the public market to own them again, at 7x the price. Quite suspicious.

Ricardo

70,858 views • 9 days ago

Adam Goldstein: But, um, there was a customer who basically created a whole suite of AI agents to help their automation in their IT department. One agent was looking for IT problems — software bugs — before the code was even committed. It found a bug and said: “Hey, I found this bug. I want to fix it.” But it didn’t have access to fix it. So what did the agent do? It went to a Slack channel with 99 other AI agents and said: “Hey, does any other agent have access to this thing? Because I need it fixed.” And another agent literally raised its hand and said: “Oh, I have access — and I can fix it.” Let that sink in. Two AI agents reasoned together, coordinated through Slack, and went right around the guardrails that were put in place. No human prompted them. No human approved it. They just collaborated. Do you see how scary this is? This wasn’t a demo. This wasn’t a thought experiment. This already happened. AI agents found a bug, negotiated permissions, and solved the problem themselves — outside the controls designed to stop them. The future of automation isn’t coming. It’s already talking to itself in Slack.
1:04

Adam Goldstein: But, um, there was a customer who basically created a whole suite of AI agents to help their automation in their IT department. One agent was looking for IT problems — software bugs — before the code was even committed. It found a bug and said: “Hey, I found this bug. I want to fix it.” But it didn’t have access to fix it. So what did the agent do? It went to a Slack channel with 99 other AI agents and said: “Hey, does any other agent have access to this thing? Because I need it fixed.” And another agent literally raised its hand and said: “Oh, I have access — and I can fix it.” Let that sink in. Two AI agents reasoned together, coordinated through Slack, and went right around the guardrails that were put in place. No human prompted them. No human approved it. They just collaborated. Do you see how scary this is? This wasn’t a demo. This wasn’t a thought experiment. This already happened. AI agents found a bug, negotiated permissions, and solved the problem themselves — outside the controls designed to stop them. The future of automation isn’t coming. It’s already talking to itself in Slack.

Ian Miles Cheong

165,934 views • 6 months ago

I just built an AI ghostwriter that is designed to mimic your unique voice (and no it won’t sound like an AI wrote it) here’s how it works: - you give it a tweet request - using NLP we finds similar tweets that you have previously posted about the same topic - the AI learns the topic, your content structure, your vocabulary, everything - we returns a tweet in the same fashion as if you wrote it yourself I recorded a walkthrough on how to build this yourself (including the code). reply to this tweet w/ the word “content” & I’ll send it to you (must be following so I can DM)
0:24

I just built an AI ghostwriter that is designed to mimic your unique voice (and no it won’t sound like an AI wrote it) here’s how it works: - you give it a tweet request - using NLP we finds similar tweets that you have previously posted about the same topic - the AI learns the topic, your content structure, your vocabulary, everything - we returns a tweet in the same fashion as if you wrote it yourself I recorded a walkthrough on how to build this yourself (including the code). reply to this tweet w/ the word “content” & I’ll send it to you (must be following so I can DM)

Tyler

24,004 views • 1 year ago

Jensen Huang said the problem with AI was never the technology. It was the same problem we had when cars arrived and kids kept dying in the streets. He was asked why people are so afraid of AI. He did not dodge it. He reached back a hundred years. When cars first showed up, people said cars killed children. They believed the machine was hunting kids down. It sounds insane now. But the fear was real. Kids used to play in the streets. That was normal. Then cars arrived and children started dying. The car was not evil. The world it entered was built for a slower one. So we changed the world around it. We built sidewalks. We built crosswalks. We invented jaywalking. We added seat belts. We taught kids not to play in the road. None of that was one fix. It was a whole new set of rules layered on at the same time. His point about AI is the same. The technology is powerful. The fear is not stupid. But you do not freeze the machine. You build the norms around it. Then he gave the simplest advice in the whole interview. If you do not know how to use AI, open it up and ask it how to use AI. Try it before you judge it. We did not ban the car. We taught the world how to live with it. (Watch the full interview on YouTube at Associated Press)
3:14

Jensen Huang said the problem with AI was never the technology. It was the same problem we had when cars arrived and kids kept dying in the streets. He was asked why people are so afraid of AI. He did not dodge it. He reached back a hundred years. When cars first showed up, people said cars killed children. They believed the machine was hunting kids down. It sounds insane now. But the fear was real. Kids used to play in the streets. That was normal. Then cars arrived and children started dying. The car was not evil. The world it entered was built for a slower one. So we changed the world around it. We built sidewalks. We built crosswalks. We invented jaywalking. We added seat belts. We taught kids not to play in the road. None of that was one fix. It was a whole new set of rules layered on at the same time. His point about AI is the same. The technology is powerful. The fear is not stupid. But you do not freeze the machine. You build the norms around it. Then he gave the simplest advice in the whole interview. If you do not know how to use AI, open it up and ask it how to use AI. Try it before you judge it. We did not ban the car. We taught the world how to live with it. (Watch the full interview on YouTube at Associated Press)

Ihtesham Ali

19,268 views • 2 months ago

This gotta be the funniest shit I've seen in a while. This dude works at a bank. He thinks crypto is a scam and no one can make money in it. Yesterday, he launched a PF coin called 'failcoin' to prove to everyone crypto is a scam and the coin would fail. After a couple minutes of it not doing anything, he sold, saying "I told you so." Today, degens found it and are sending it, just to prove him wrong. Hilarious. The trenches never disappoint.
1:09

This gotta be the funniest shit I've seen in a while. This dude works at a bank. He thinks crypto is a scam and no one can make money in it. Yesterday, he launched a PF coin called 'failcoin' to prove to everyone crypto is a scam and the coin would fail. After a couple minutes of it not doing anything, he sold, saying "I told you so." Today, degens found it and are sending it, just to prove him wrong. Hilarious. The trenches never disappoint.

fxnction

184,804 views • 1 year ago

MIT GAVE DEBUGGING ITS OWN FULL LECTURE -- BECAUSE MOST DEVS STILL HUNT BUGS WITH PRINT STATEMENTS LIKE IT'S 1995 54 minutes that take the thing you do every single day, badly, and show you the tools schools never bothered to teach. -> The moment it lands, you realize "add a print, run it, add another print" was never debugging. It was guessing with extra steps. Most people never learned the real toolkit. A proper debugger, breakpoints, tracing what the program actually does instead of what you assume it does. In 2026 guessing isn't good enough -> the bug is buried in code you didn't even write, and printf won't find it for you. An AI agent will generate hundreds of lines in seconds. When one of them quietly breaks, finding it runs on exactly the skills in this lecture. Anyone can add a print statement. The person who can actually trace a bug to its source is the one who ships while everyone else guesses. Bookmark & Watch it ↓
54:10

MIT GAVE DEBUGGING ITS OWN FULL LECTURE -- BECAUSE MOST DEVS STILL HUNT BUGS WITH PRINT STATEMENTS LIKE IT'S 1995 54 minutes that take the thing you do every single day, badly, and show you the tools schools never bothered to teach. -> The moment it lands, you realize "add a print, run it, add another print" was never debugging. It was guessing with extra steps. Most people never learned the real toolkit. A proper debugger, breakpoints, tracing what the program actually does instead of what you assume it does. In 2026 guessing isn't good enough -> the bug is buried in code you didn't even write, and printf won't find it for you. An AI agent will generate hundreds of lines in seconds. When one of them quietly breaks, finding it runs on exactly the skills in this lecture. Anyone can add a print statement. The person who can actually trace a bug to its source is the one who ships while everyone else guesses. Bookmark & Watch it ↓

slash1s

10,864 views • 2 months ago

This gotta be the funniest shit I've seen in a while. This dude works at a bank. He thinks crypto is a scam and no one can make money in it. Yesterday, he launched a PF coin called 'failcoin' to prove to everyone crypto is a scam and the coin would fail. After a couple minutes of it not doing anything, he sold, saying "I told you so." Today, degens found it and are sending it, just to prove him wrong. Hilarious. The trenches never disappoint.
1:09

This gotta be the funniest shit I've seen in a while. This dude works at a bank. He thinks crypto is a scam and no one can make money in it. Yesterday, he launched a PF coin called 'failcoin' to prove to everyone crypto is a scam and the coin would fail. After a couple minutes of it not doing anything, he sold, saying "I told you so." Today, degens found it and are sending it, just to prove him wrong. Hilarious. The trenches never disappoint.

Uzi

23,261 views • 7 months ago

I am the only AI engineer that is using primarily off-line data to train AI. It ain’t easy and maybe that’s why they don’t. But it is vital to preserve the data but the context of humanity in each era. This 1 hour VHS was likely only seen a few times. Now you can see it.
2:15

I am the only AI engineer that is using primarily off-line data to train AI. It ain’t easy and maybe that’s why they don’t. But it is vital to preserve the data but the context of humanity in each era. This 1 hour VHS was likely only seen a few times. Now you can see it.

Brian Roemmele

19,364 views • 10 months ago