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.Google Cloud killed its own opening video 3 weeks before Google Cloud Next. In rehearsals, VP of Marketing Sarah Kennedy Ellis looked at the opener her team had built and called it. It was using AI, but not enough of it to actually showcase what the product could do....

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Jason ✨👾SaaStr.Ai✨ Lemkin

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18,773 次观看 • 16 天前 •via X (Twitter)

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Google wrote the paper that made ChatGPT possible. Then decided not to build ChatGPT. Sergey Brin just explained why on stage at Stanford and the reason is more embarrassing than anyone expected. In 2017, Google published the transformer paper. The architecture that powers every major AI model today. It came from their own researchers. Their own labs. Their own compute. Then they sat on it. Sergey was blunt about why. They underinvested in scaling the compute. They did not take it seriously enough. And when they finally had something worth shipping, they got scared. Chatbots say dumb things. Google had a reputation to protect. So they protected it instead of shipping. OpenAI was not scared. Ilya Sutskever, trained at Google, left and went there. Other Google researchers followed. They took the transformer architecture, scaled it, shipped it anyway, and captured the entire generative AI wave while Google watched. Sergey called it a mistake at Stanford in front of hundreds of students. The company that invented the technology did not build the product. The company that built the product did not invent the technology. That is the most expensive case of corporate hesitation in the history of the industry. --- Watch the full interview YT. Search: "Big ideas begin here: Sergey Brin at Stanford"
2:26

Google wrote the paper that made ChatGPT possible. Then decided not to build ChatGPT. Sergey Brin just explained why on stage at Stanford and the reason is more embarrassing than anyone expected. In 2017, Google published the transformer paper. The architecture that powers every major AI model today. It came from their own researchers. Their own labs. Their own compute. Then they sat on it. Sergey was blunt about why. They underinvested in scaling the compute. They did not take it seriously enough. And when they finally had something worth shipping, they got scared. Chatbots say dumb things. Google had a reputation to protect. So they protected it instead of shipping. OpenAI was not scared. Ilya Sutskever, trained at Google, left and went there. Other Google researchers followed. They took the transformer architecture, scaled it, shipped it anyway, and captured the entire generative AI wave while Google watched. Sergey called it a mistake at Stanford in front of hundreds of students. The company that invented the technology did not build the product. The company that built the product did not invent the technology. That is the most expensive case of corporate hesitation in the history of the industry. --- Watch the full interview YT. Search: "Big ideas begin here: Sergey Brin at Stanford"

Ihtesham Ali

244,348 次观看 • 1 个月前

A grandmother of a Russian soldier called a Russian propaganda show and complained that the body of her grandson had been rotting on the battlefield for six months and no one was collecting it because it was unclear where it was. In response, they started to interrupt and ridicule her, then simply disconnected her from the call.
3:29

A grandmother of a Russian soldier called a Russian propaganda show and complained that the body of her grandson had been rotting on the battlefield for six months and no one was collecting it because it was unclear where it was. In response, they started to interrupt and ridicule her, then simply disconnected her from the call.

Anton Gerashchenko

97,643 次观看 • 1 年前

“A Google engineer took all the IP of how Google designed their AI chips, moved to China, and then started a company. DeepSeek came out of nowhere. Everyone was so surprised at how capable their model was—or they managed to have a high-end espionage operation to steal all of our trade secrets from the United States and then re-implement them back in China… The way he stole the data out of Google's corporate cloud was so stupid. He just copy-pasted all the code into Apple Notes, exported it to PDF, printed it, and then just walked out with it. That’s it.” Alexandr Wang
10:45

“A Google engineer took all the IP of how Google designed their AI chips, moved to China, and then started a company. DeepSeek came out of nowhere. Everyone was so surprised at how capable their model was—or they managed to have a high-end espionage operation to steal all of our trade secrets from the United States and then re-implement them back in China… The way he stole the data out of Google's corporate cloud was so stupid. He just copy-pasted all the code into Apple Notes, exported it to PDF, printed it, and then just walked out with it. That’s it.” Alexandr Wang

Shawn Ryan

558,424 次观看 • 1 年前

Google just pulled off the biggest theft in the history of AI. And their OWN documents exposed it... Three of the largest publishers on Earth, Hachette, Cengage, and Elsevier, just sued Google in federal court, alongside best-selling author Scott Turow. Their claim is that Google built Gemini, its flagship AI, on millions of copyrighted works it never paid for or licensed. This is one of the biggest copyright cases ever aimed at an AI company: Before they trained Gemini, an internal Google document allegedly spelled out the risk directly. Using this material could expose the company to "$10Bs-$100Bs in potential fines." Google's own people put a number on the theft, in the tens of billions, but the company moved ahead anyway. Then it allegedly tried to DELETE the evidence... The complaint says Google stripped the copyright information off the works before feeding them into Gemini, so nobody could trace what the model had actually been trained on. Pull the fingerprints off first, and the theft gets much harder to prove later. And there's a second betrayal underneath the first: Most of this material was not scraped from some random corner of the internet. Publishers had handed it to Google years earlier for a narrow purpose, to make their catalogs searchable inside Google's own services. The lawsuit says Google took that trust and repurposed the content to build a machine that now competes directly with the people who supplied it. And that machine is the entire point. The complaint describes Gemini producing a full-length substitute for a copyrighted work in about 20 minutes. Something an author spent years writing can now be cloned in an afternoon by the company that trained on the original. No writer or publisher survives that. So why does this case matter more than the dozen other AI copyright fights? Because most of them turn on a fuzzy fair-use question argued years after the fact. This one arrives with an internal document that allegedly SHOWS Google weighed the cost of getting caught and trained on the material anyway. A jury does not need a law degree to read that. The fight now moves toward discovery, where Google's internal emails and training records get dragged into the open. If those files back up what the complaint claims, that $100 billion will turn into a real liability. Google spent years telling the world it was organizing information for everyone. Its own documents show it knew exactly whose information it was taking, and what the price of getting caught would be.
1:15

Google just pulled off the biggest theft in the history of AI. And their OWN documents exposed it... Three of the largest publishers on Earth, Hachette, Cengage, and Elsevier, just sued Google in federal court, alongside best-selling author Scott Turow. Their claim is that Google built Gemini, its flagship AI, on millions of copyrighted works it never paid for or licensed. This is one of the biggest copyright cases ever aimed at an AI company: Before they trained Gemini, an internal Google document allegedly spelled out the risk directly. Using this material could expose the company to "$10Bs-$100Bs in potential fines." Google's own people put a number on the theft, in the tens of billions, but the company moved ahead anyway. Then it allegedly tried to DELETE the evidence... The complaint says Google stripped the copyright information off the works before feeding them into Gemini, so nobody could trace what the model had actually been trained on. Pull the fingerprints off first, and the theft gets much harder to prove later. And there's a second betrayal underneath the first: Most of this material was not scraped from some random corner of the internet. Publishers had handed it to Google years earlier for a narrow purpose, to make their catalogs searchable inside Google's own services. The lawsuit says Google took that trust and repurposed the content to build a machine that now competes directly with the people who supplied it. And that machine is the entire point. The complaint describes Gemini producing a full-length substitute for a copyrighted work in about 20 minutes. Something an author spent years writing can now be cloned in an afternoon by the company that trained on the original. No writer or publisher survives that. So why does this case matter more than the dozen other AI copyright fights? Because most of them turn on a fuzzy fair-use question argued years after the fact. This one arrives with an internal document that allegedly SHOWS Google weighed the cost of getting caught and trained on the material anyway. A jury does not need a law degree to read that. The fight now moves toward discovery, where Google's internal emails and training records get dragged into the open. If those files back up what the complaint claims, that $100 billion will turn into a real liability. Google spent years telling the world it was organizing information for everyone. Its own documents show it knew exactly whose information it was taking, and what the price of getting caught would be.

Ricardo

24,403 次观看 • 2 天前

From being forced to breed with its own sibling before it was even a year old, to giving birth to puppies and then losing every single one of them on the very same day, and being abandoned by its owner the next day. At that point, it was terrified and heartbroken. Then they rescued her 💖 People often see the happy moments they share here, but this is the other side of dog rescue work — the heartbreaking stories, the trauma, and the fear these dogs have endured before finally finding a better life.
0:21

From being forced to breed with its own sibling before it was even a year old, to giving birth to puppies and then losing every single one of them on the very same day, and being abandoned by its owner the next day. At that point, it was terrified and heartbroken. Then they rescued her 💖 People often see the happy moments they share here, but this is the other side of dog rescue work — the heartbreaking stories, the trauma, and the fear these dogs have endured before finally finding a better life.

Beauty of music and nature 🌺🌺

175,027 次观看 • 1 个月前

watch this anon. i gave NVIDIA's biggest model ever a single task. 100 minutes and 440,000 tokens later, it had rendered nothing. not one important thing on the screen. this is Nemotron 3 Ultra. 550 billion parameters, a hybrid Mamba Transformer MoE, the largest model NVIDIA has ever shipped, and they built it specifically for long-running agentic coding. so i handed it exactly that: build a 3D scene from a spec, multiple files, iterate until the tests pass. the same task a frontier model one shotted in minutes. i genuinely wanted to be impressed. it ran for an hour and forty. burned through 440,000 tokens. wrote every file, passed its own tests, and proudly printed "task complete."the browser was blank. the 3D scene never rendered. not once. and the long horizon agentic behavior was genuinely good. it stayed on task the whole hour and forty, wrote real multi-file code, drove its own tools without derailing. it just couldn't turn any of that into something that actually runs. here's the part that gets me. it's a text model, it cannot see its own output. so it sat there looping on a broken vision tool, trying to "look" at the page, hitting error after error, never once reasoning its way out. it declared victory on an empty screen because it had no way to know the screen was empty. to be fair, i genuinely don't know what quant the NIM was serving, so maybe some of that's on the serving, not the model. but the biggest model NVIDIA has ever made, on the exact task it was designed for, couldn't tell it had built nothing in 100 minutes. same task on a local model, below thread👇.
4:07

watch this anon. i gave NVIDIA's biggest model ever a single task. 100 minutes and 440,000 tokens later, it had rendered nothing. not one important thing on the screen. this is Nemotron 3 Ultra. 550 billion parameters, a hybrid Mamba Transformer MoE, the largest model NVIDIA has ever shipped, and they built it specifically for long-running agentic coding. so i handed it exactly that: build a 3D scene from a spec, multiple files, iterate until the tests pass. the same task a frontier model one shotted in minutes. i genuinely wanted to be impressed. it ran for an hour and forty. burned through 440,000 tokens. wrote every file, passed its own tests, and proudly printed "task complete."the browser was blank. the 3D scene never rendered. not once. and the long horizon agentic behavior was genuinely good. it stayed on task the whole hour and forty, wrote real multi-file code, drove its own tools without derailing. it just couldn't turn any of that into something that actually runs. here's the part that gets me. it's a text model, it cannot see its own output. so it sat there looping on a broken vision tool, trying to "look" at the page, hitting error after error, never once reasoning its way out. it declared victory on an empty screen because it had no way to know the screen was empty. to be fair, i genuinely don't know what quant the NIM was serving, so maybe some of that's on the serving, not the model. but the biggest model NVIDIA has ever made, on the exact task it was designed for, couldn't tell it had built nothing in 100 minutes. same task on a local model, below thread👇.

Sudo su

32,589 次观看 • 20 天前

IIT Bombay had built their own version of "The Walking Truck" way back in 2002. This monster was called "Nataraj - The Walking Robot". It weighed at around 250kgs and had 6 legs. But the best part was it was built indigenously and was more of a proof of concept.
6:00

IIT Bombay had built their own version of "The Walking Truck" way back in 2002. This monster was called "Nataraj - The Walking Robot". It weighed at around 250kgs and had 6 legs. But the best part was it was built indigenously and was more of a proof of concept.

Varun Guru

20,838 次观看 • 3 个月前

IN 1994 SUN MICROSYSTEMS FILMED A WOMAN SWIPING THROUGH A TOUCHSCREEN TABLET AND VIDEO CALLING A COLLEAGUE MID TASK. THE iPAD SHIPPED 16 YEARS LATER shot on 35mm film, more than a hundred engineers and designers on it, set in the year 2004, and here's the thing almost nobody mentions it wasn't allowed to be science fiction, the team had one hard rule, only show things they actually believed could be built, so this wasn't a fantasy reel, it was a spec sheet pretending to be a movie she drags documents with her fingers, talks to the machine, pulls a file from the other side of the world, drops into a live video call without thinking about it, this is a boring afternoon for you now, it was an impossible budget in 1994 the strange part is they weren't dreaming, they were trying to build it, then the company that filmed the future quietly moved on and a different company shipped it a decade and a half later and got called visionary for it nobody stole it, they just forgot it was already designed, down to the gestures your thumbs make every morning you think the touchscreen era started when you first held a phone that felt alive, it was fully storyboarded before some of you were born watch it once and you can't unsee how little we actually invented ↓
12:18

IN 1994 SUN MICROSYSTEMS FILMED A WOMAN SWIPING THROUGH A TOUCHSCREEN TABLET AND VIDEO CALLING A COLLEAGUE MID TASK. THE iPAD SHIPPED 16 YEARS LATER shot on 35mm film, more than a hundred engineers and designers on it, set in the year 2004, and here's the thing almost nobody mentions it wasn't allowed to be science fiction, the team had one hard rule, only show things they actually believed could be built, so this wasn't a fantasy reel, it was a spec sheet pretending to be a movie she drags documents with her fingers, talks to the machine, pulls a file from the other side of the world, drops into a live video call without thinking about it, this is a boring afternoon for you now, it was an impossible budget in 1994 the strange part is they weren't dreaming, they were trying to build it, then the company that filmed the future quietly moved on and a different company shipped it a decade and a half later and got called visionary for it nobody stole it, they just forgot it was already designed, down to the gestures your thumbs make every morning you think the touchscreen era started when you first held a phone that felt alive, it was fully storyboarded before some of you were born watch it once and you can't unsee how little we actually invented ↓

0xbobaa

923,892 次观看 • 13 天前

RFK Jr: "In 1989... all these autoimmune diseases suddenly appeared." "The CDC thought it may be the vaccine schedule... so they looked at one vaccine, which is the hepatitis B vaccine. They looked at kids who had gotten it in the first 30 days, and then they looked at... kids who got it later or didn't get it at all, and they compared these two groups." "Among the kids who got it in the first 30 days, there was... a relative risk of 11.35 [for a subsequent autism diagnosis]. If you have a relative risk of two, causation is presumed... The link between smoking a pack of cigarettes a day for 20 years and getting lung cancer is a relative risk of 10. This was 11.35." "So they knew what it was, and then they had an emergency meeting that they called... and they had all of the big panjandrums from the vaccine industry, from the pharmaceutical industry... from NIH, CDC, FDA, the WHO, the European Medicines Agency, and they all got together for two days to talk about this study." "Somebody made a transcript of it, and that transcript was given to me... The second day they spent talking about how to hide it from the American public, and I published these things... I took a lot of heat at that point."
3:25

RFK Jr: "In 1989... all these autoimmune diseases suddenly appeared." "The CDC thought it may be the vaccine schedule... so they looked at one vaccine, which is the hepatitis B vaccine. They looked at kids who had gotten it in the first 30 days, and then they looked at... kids who got it later or didn't get it at all, and they compared these two groups." "Among the kids who got it in the first 30 days, there was... a relative risk of 11.35 [for a subsequent autism diagnosis]. If you have a relative risk of two, causation is presumed... The link between smoking a pack of cigarettes a day for 20 years and getting lung cancer is a relative risk of 10. This was 11.35." "So they knew what it was, and then they had an emergency meeting that they called... and they had all of the big panjandrums from the vaccine industry, from the pharmaceutical industry... from NIH, CDC, FDA, the WHO, the European Medicines Agency, and they all got together for two days to talk about this study." "Somebody made a transcript of it, and that transcript was given to me... The second day they spent talking about how to hide it from the American public, and I published these things... I took a lot of heat at that point."

Wide Awake Media

755,812 次观看 • 1 年前

According to Roy Scheider, "Filming 'Sorcerer' (1977) made 'Jaws' (1975) look like a picnic". He considered the Bridge scene to be the most dangerous he has ever acted in. The scene was so real that he said, "What the audience will see on that screen is what really happened." Building the bridge cost about a million dollars. It took 3 months to complete. Friedkin called it "A mad enterprise and definitely life threatening". The bridge was built on what they considered to be a "perfect river" in Dominican Republic. It was 12 feet deep. However, there were weeks without rainfall and the level of the river started dropping. The river has never went dry before according to the locals. When the bridge was completed, the river had only 1 feet of water making it impossible to film the scene there. With only 2 scenes left to film, the crew had to leave for Mexico to shoot the bridge scene. So they decided to shoot the scene in a river in an Aztec village in a remote location in Mexico. The Bridge had to be reassembled and anchored in the new location. The shutdown of production had lasted a month which was a huge expense to the management. When the crew arrived at the village, there was a huge exodus of the local population. When Friedkin enquired, he was informed by one of the authorities that it was because of word of William Friedkin's arrival. They were a deeply religious people, and when they heard the man who made "The Exorcist" (1973) was coming to their village: they felt it was "bad karma". But few of the locals stayed and helped with the filming. Before arriving, Friedkin was informed that it rained often in the area, but didn't know that it was only in the summer season. Since it was the fall, there was no rain and the river began to drop at the rate of six inches or more a day. The water was diverted to the location of the bridge in the river using large pipes and pumping equipment. Since Friedkin wanted to shoot the scene in the rain, they brought in half a dozen large sprinklers that drew water from upriver. The scene runs twelve minutes, roughly 10 percent of the final cut, but it took months to complete and cost more than $3 million, most of it not budgeted. ("The Friedkin Connection: A Memoir", William Friedkin, 2013 & Roy Scheider's interview to the The New York Times, 1977) P.S: On this day, 49 years ago, "Sorcerer" (1977) was released in the USA.
2:14

According to Roy Scheider, "Filming 'Sorcerer' (1977) made 'Jaws' (1975) look like a picnic". He considered the Bridge scene to be the most dangerous he has ever acted in. The scene was so real that he said, "What the audience will see on that screen is what really happened." Building the bridge cost about a million dollars. It took 3 months to complete. Friedkin called it "A mad enterprise and definitely life threatening". The bridge was built on what they considered to be a "perfect river" in Dominican Republic. It was 12 feet deep. However, there were weeks without rainfall and the level of the river started dropping. The river has never went dry before according to the locals. When the bridge was completed, the river had only 1 feet of water making it impossible to film the scene there. With only 2 scenes left to film, the crew had to leave for Mexico to shoot the bridge scene. So they decided to shoot the scene in a river in an Aztec village in a remote location in Mexico. The Bridge had to be reassembled and anchored in the new location. The shutdown of production had lasted a month which was a huge expense to the management. When the crew arrived at the village, there was a huge exodus of the local population. When Friedkin enquired, he was informed by one of the authorities that it was because of word of William Friedkin's arrival. They were a deeply religious people, and when they heard the man who made "The Exorcist" (1973) was coming to their village: they felt it was "bad karma". But few of the locals stayed and helped with the filming. Before arriving, Friedkin was informed that it rained often in the area, but didn't know that it was only in the summer season. Since it was the fall, there was no rain and the river began to drop at the rate of six inches or more a day. The water was diverted to the location of the bridge in the river using large pipes and pumping equipment. Since Friedkin wanted to shoot the scene in the rain, they brought in half a dozen large sprinklers that drew water from upriver. The scene runs twelve minutes, roughly 10 percent of the final cut, but it took months to complete and cost more than $3 million, most of it not budgeted. ("The Friedkin Connection: A Memoir", William Friedkin, 2013 & Roy Scheider's interview to the The New York Times, 1977) P.S: On this day, 49 years ago, "Sorcerer" (1977) was released in the USA.

DepressedBergman

76,198 次观看 • 26 天前

Marc Andreessen worked at IBM at the peak of their power. There were 12 layers of management between him as an intern and the CEO. He describes what that meant: if one layer lies to the layer above it, maybe that's okay. Two or three layers, the lies compound. Six layers, they really compound. Twelve layers... The CEO has absolutely no idea what's happening inside his own company. IBM even had a name for it. They called it the Big Gray Cloud, the cloud of men in gray suits that followed the CEO everywhere and made it physically impossible for him to ever talk to someone actually doing the work. "It was like a visit from the king. The king and the traveling court. A completely impervious bubble." That company controlled 80% of tech. Then it didn't. Elon looked at that model and built the opposite. The most dangerous thing in a large organization isn't incompetence. It's the distance between the truth and the top.
4:56

Marc Andreessen worked at IBM at the peak of their power. There were 12 layers of management between him as an intern and the CEO. He describes what that meant: if one layer lies to the layer above it, maybe that's okay. Two or three layers, the lies compound. Six layers, they really compound. Twelve layers... The CEO has absolutely no idea what's happening inside his own company. IBM even had a name for it. They called it the Big Gray Cloud, the cloud of men in gray suits that followed the CEO everywhere and made it physically impossible for him to ever talk to someone actually doing the work. "It was like a visit from the king. The king and the traveling court. A completely impervious bubble." That company controlled 80% of tech. Then it didn't. Elon looked at that model and built the opposite. The most dangerous thing in a large organization isn't incompetence. It's the distance between the truth and the top.

Founder Mode

220,034 次观看 • 3 个月前

There is a massive advantage to hiring young people, and Shopify was the first company to figure it out, says Pragmatic Engineer's Gergely Orosz. "Shopify's Head of Engineering Farhan Thawar told me that he saw something interesting years back. Shopify was so early to AI. They got Copilot licenses for everyone, with no cost limit. But they didn't see many teams using it." "But there was this one team that was using it a lot more. There was an intern on the team. They onboarded the intern and gave the intern his two-week task and the intern was done in a day. And they're like, 'How did you do that?' He's like, 'Oh well I just used AI? What's next?'" "And suddenly the people on the team felt stupid, right? So they started to learn from the intern and use AI." "The next thing Farhan did was hire an intern for every single Shopify team. This is why Shopify had a thousand interns."
1:27

There is a massive advantage to hiring young people, and Shopify was the first company to figure it out, says Pragmatic Engineer's Gergely Orosz. "Shopify's Head of Engineering Farhan Thawar told me that he saw something interesting years back. Shopify was so early to AI. They got Copilot licenses for everyone, with no cost limit. But they didn't see many teams using it." "But there was this one team that was using it a lot more. There was an intern on the team. They onboarded the intern and gave the intern his two-week task and the intern was done in a day. And they're like, 'How did you do that?' He's like, 'Oh well I just used AI? What's next?'" "And suddenly the people on the team felt stupid, right? So they started to learn from the intern and use AI." "The next thing Farhan did was hire an intern for every single Shopify team. This is why Shopify had a thousand interns."

TBPN

135,300 次观看 • 5 个月前

anthropic's head of product just revealed how they're able to ship faster than any other AI company. their secret: "side quest maxxing." here's how it works: instead of long-term roadmaps, anthropic runs on unplanned afternoon experiments. anyone on the team gets full freedom to spend an afternoon prototyping an idea and show it to the team. you get to skip the approval process entirely. then, employees at anthropic try it. if they keep using it the next day and the day after that, it gets polished into a real feature. if nobody touches it again, it dies. that's the whole process. claude code on desktop started as one engineer's afternoon project. he wanted it to work on desktop so he built a prototype. people on the team started using it immediately. so they shipped it. the todo list feature started the same way. someone built it, the team adopted it internally, and it became one of the most-used parts of the product. plugins started when one engineer shared a spec with claude code and the prototype that came back was close to production-ready. went from idea to working feature in a single session. they also killed standup meetings. instead of telling people what you're working on, you just show a working demo. all walk no talk basically the team structure makes this possible. > designers ship code. > engineers make product decisions. > product managers build prototypes. everyone can take an idea from concept to working demo without waiting on anyone else. the biggest features at a $380b company came from afternoon experiments that nobody asked for. honestly this matches my own experience cooking with ai. some of the best workflows i use every day came from just fucking around. opening a session with zero intention and asking claude what it can do, or jamming on a random idea to see where it goes. if you're only using ai for tasks you already have in mind, you're missing the best part. open a session with no agenda. ask it to surprise you. try building something stupid. half the time it goes nowhere. the other half it becomes the thing you use most. you need to be sidequestmaxxing.
0:30

anthropic's head of product just revealed how they're able to ship faster than any other AI company. their secret: "side quest maxxing." here's how it works: instead of long-term roadmaps, anthropic runs on unplanned afternoon experiments. anyone on the team gets full freedom to spend an afternoon prototyping an idea and show it to the team. you get to skip the approval process entirely. then, employees at anthropic try it. if they keep using it the next day and the day after that, it gets polished into a real feature. if nobody touches it again, it dies. that's the whole process. claude code on desktop started as one engineer's afternoon project. he wanted it to work on desktop so he built a prototype. people on the team started using it immediately. so they shipped it. the todo list feature started the same way. someone built it, the team adopted it internally, and it became one of the most-used parts of the product. plugins started when one engineer shared a spec with claude code and the prototype that came back was close to production-ready. went from idea to working feature in a single session. they also killed standup meetings. instead of telling people what you're working on, you just show a working demo. all walk no talk basically the team structure makes this possible. > designers ship code. > engineers make product decisions. > product managers build prototypes. everyone can take an idea from concept to working demo without waiting on anyone else. the biggest features at a $380b company came from afternoon experiments that nobody asked for. honestly this matches my own experience cooking with ai. some of the best workflows i use every day came from just fucking around. opening a session with zero intention and asking claude what it can do, or jamming on a random idea to see where it goes. if you're only using ai for tasks you already have in mind, you're missing the best part. open a session with no agenda. ask it to surprise you. try building something stupid. half the time it goes nowhere. the other half it becomes the thing you use most. you need to be sidequestmaxxing.

Ole Lehmann

106,034 次观看 • 2 个月前

$GOOGL CEO Sundar Pichai on why Google didn't ship ChatGPT first, and the signal they missed from OpenAI: We even conceived the product, it was Lambda. There was an engineer inside who thought it was sentient. Think of it as an early version of ChatGPT. In the multiverse, Google probably shipped that 9 months earlier... but the version I saw was a lot more toxic at a level we couldn't have possibly put it out. As a company with this search quality bias, we had a higher bar for what we thought was acceptable product quality. I think they were lucky to also see it on the coding side with GitHub. Maybe there was a signal we were missing. The jumps between GPT-2 and 3 and later 4 were more pronounced if you were using it for coding.
2:30

$GOOGL CEO Sundar Pichai on why Google didn't ship ChatGPT first, and the signal they missed from OpenAI: We even conceived the product, it was Lambda. There was an engineer inside who thought it was sentient. Think of it as an early version of ChatGPT. In the multiverse, Google probably shipped that 9 months earlier... but the version I saw was a lot more toxic at a level we couldn't have possibly put it out. As a company with this search quality bias, we had a higher bar for what we thought was acceptable product quality. I think they were lucky to also see it on the coding side with GitHub. Maybe there was a signal we were missing. The jumps between GPT-2 and 3 and later 4 were more pronounced if you were using it for coding.

Wall St Engine

150,559 次观看 • 3 个月前

i watched gemma 4 12b build something genuinely impressive today, and then loop itself to death right in front of me. the full run is in the video, sped up but completely uncut, watch it to the end and you will catch the exact moment it stops building and starts looping right in the middle of the work. the task was clean, build a single file gravity simulator, n-body physics, orbits, collisions, running locally on one 3090 through an agent. and for ten minutes it was a joy to watch. it reached for a symplectic integrator on its own, the correct one, the kind that keeps orbits stable instead of spiralling out. real gravity with softening, proper orbital velocities, momentum conserved on collision. the physics was right. the thing actually worked. then on the very last step, writing a few tests to prove its own code, it fell into a loop. not a crash, a loop. it started repeating itself and would not stop. ten more minutes, thirty four thousand tokens into a single answer, the same fragments over and over, until i killed it myself. so it's not that gemma can't code. it did the hard part beautifully. it cannot finish. it cannot hold a long task together without unravelling, and finishing is the entire job in agentic work. here's the part that stings. i run this exact task, same harness, same card, on the chinese open models, qwen especially, and i never see this. they build it, they test it, they stop. every single time. google has the raw capability, you can see it sitting right there in the code, and then the model loops itself to death on a task a 27b from alibaba finishes clean. open weights, apache 2.0, so much to love on paper. i just need it to know when to stop talking.
4:15

i watched gemma 4 12b build something genuinely impressive today, and then loop itself to death right in front of me. the full run is in the video, sped up but completely uncut, watch it to the end and you will catch the exact moment it stops building and starts looping right in the middle of the work. the task was clean, build a single file gravity simulator, n-body physics, orbits, collisions, running locally on one 3090 through an agent. and for ten minutes it was a joy to watch. it reached for a symplectic integrator on its own, the correct one, the kind that keeps orbits stable instead of spiralling out. real gravity with softening, proper orbital velocities, momentum conserved on collision. the physics was right. the thing actually worked. then on the very last step, writing a few tests to prove its own code, it fell into a loop. not a crash, a loop. it started repeating itself and would not stop. ten more minutes, thirty four thousand tokens into a single answer, the same fragments over and over, until i killed it myself. so it's not that gemma can't code. it did the hard part beautifully. it cannot finish. it cannot hold a long task together without unravelling, and finishing is the entire job in agentic work. here's the part that stings. i run this exact task, same harness, same card, on the chinese open models, qwen especially, and i never see this. they build it, they test it, they stop. every single time. google has the raw capability, you can see it sitting right there in the code, and then the model loops itself to death on a task a 27b from alibaba finishes clean. open weights, apache 2.0, so much to love on paper. i just need it to know when to stop talking.

Sudo su

39,574 次观看 • 1 个月前

A few weeks ago the US government forced Claude Fable 5 offline. It's back. And before they switch it off again, this is your shot to point the most powerful AI on earth at something of your own. Take the 26-year-old in Toronto. His bot went dark the day Fable 5 got pulled. The day it came back, it woke up and started printing again - $568,000 profit and counting. His wallet: Here's the story. When the ban hit, Anthropic had to switch Fable 5 off for everyone. They called it a misunderstanding with the government. Whatever it was, every bot running on that model stopped cold. His was one of them. He didn't rebuild a thing. The loop just sat there, paused, for weeks. Here's what that loop actually does. It's not one prompt to an AI. It's an agentic loop: a team of agents running on repeat. One reads the market, one places the trade, one tries to prove it wrong before a cent moves. Then it goes again, around the clock, keeping what works and dropping what doesn't. Fable 5 is the brain running that team. The exact model the government decided was too powerful to leave on. Point it at Bitcoin's 5-minute markets and it barely misses. Most of its trades close green, and it runs itself. He checks it from his phone. The government could switch off the model. It couldn't switch off the idea. Now the model's back, and so is the money. Save this before next headline. You'll want it the day Fable 5 hits offline again.
0:39

A few weeks ago the US government forced Claude Fable 5 offline. It's back. And before they switch it off again, this is your shot to point the most powerful AI on earth at something of your own. Take the 26-year-old in Toronto. His bot went dark the day Fable 5 got pulled. The day it came back, it woke up and started printing again - $568,000 profit and counting. His wallet: Here's the story. When the ban hit, Anthropic had to switch Fable 5 off for everyone. They called it a misunderstanding with the government. Whatever it was, every bot running on that model stopped cold. His was one of them. He didn't rebuild a thing. The loop just sat there, paused, for weeks. Here's what that loop actually does. It's not one prompt to an AI. It's an agentic loop: a team of agents running on repeat. One reads the market, one places the trade, one tries to prove it wrong before a cent moves. Then it goes again, around the clock, keeping what works and dropping what doesn't. Fable 5 is the brain running that team. The exact model the government decided was too powerful to leave on. Point it at Bitcoin's 5-minute markets and it barely misses. Most of its trades close green, and it runs itself. He checks it from his phone. The government could switch off the model. It couldn't switch off the idea. Now the model's back, and so is the money. Save this before next headline. You'll want it the day Fable 5 hits offline again.

cvxv666

24,055 次观看 • 18 天前

I’ve never shared this before. But I wanted to share a small story about the biggest project I had taken once. The project was one of the largest we’ve ever worked on. A product built from scratch, valued close to a billion dollars. The whole team didn’t just work on it; we lived it. The energy, the pressure, the excitement… it was unlike anything we had experienced before. We were fully booked for the next 6 months on this one project. Enjoyed every moment of it though, through and through.
1:23

I’ve never shared this before. But I wanted to share a small story about the biggest project I had taken once. The project was one of the largest we’ve ever worked on. A product built from scratch, valued close to a billion dollars. The whole team didn’t just work on it; we lived it. The energy, the pressure, the excitement… it was unlike anything we had experienced before. We were fully booked for the next 6 months on this one project. Enjoyed every moment of it though, through and through.

Arafat

47,009 次观看 • 7 个月前

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 次观看 • 2 个月前

NEW LONG FORM VIDEO: Fireball built a billion-dollar brand, then lost it Back in 2024, a woman walked into a gas station and bought a small bottle that looked just like Fireball whiskey. She took it home, realized something was off, and called her lawyer. Together, they filed a lawsuit. A woman sued Fireball for $5 million, claiming that some of the mini bottles were misleading customers. And it turned out she had a point. Some bottles labeled Fireball sold in liquor stores are real whiskey and clearly say so on the label. But the version she bought at the gas station was different. It wasn’t whiskey at all. It was a malt based beverage, similar to products like BuzzBallz, designed to get around liquor regulations so it can be sold in convenience stores. The problem was that the packaging looked nearly identical. It had the same branding, the same appearance, and the same reputation as the whiskey version, but it wasn’t actually whiskey. That raised serious questions about whether customers were being misled. In this video, we’ll cover how that lawsuit turned out and what it reveals about the bigger story behind Fireball. It went from a tiny brand doing just over $1 million a year in sales to a multibillion dollar phenomenon, and now it finds itself at the center of major shifts in the alcohol industry. This is the rise and fall of Fireball.
19:14

NEW LONG FORM VIDEO: Fireball built a billion-dollar brand, then lost it Back in 2024, a woman walked into a gas station and bought a small bottle that looked just like Fireball whiskey. She took it home, realized something was off, and called her lawyer. Together, they filed a lawsuit. A woman sued Fireball for $5 million, claiming that some of the mini bottles were misleading customers. And it turned out she had a point. Some bottles labeled Fireball sold in liquor stores are real whiskey and clearly say so on the label. But the version she bought at the gas station was different. It wasn’t whiskey at all. It was a malt based beverage, similar to products like BuzzBallz, designed to get around liquor regulations so it can be sold in convenience stores. The problem was that the packaging looked nearly identical. It had the same branding, the same appearance, and the same reputation as the whiskey version, but it wasn’t actually whiskey. That raised serious questions about whether customers were being misled. In this video, we’ll cover how that lawsuit turned out and what it reveals about the bigger story behind Fireball. It went from a tiny brand doing just over $1 million a year in sales to a multibillion dollar phenomenon, and now it finds itself at the center of major shifts in the alcohol industry. This is the rise and fall of Fireball.

Michael Girdley

519,582 次观看 • 2 个月前

This situation has been building for the past year and a half. The Flight Duty Time Limitation (FDTL) regulations issued by the DGCA were first notified in January 2024 (or March 2024). Airlines had more than enough time to adapt to these guidelines, and we believe they are important. But what was the DGCA doing all this time, given that it was responsible for enforcing the rules and ensuring that airlines complied over the last two years? These are its own guidelines, and it knew that a major airline—practically a monopoly/duopoly in the country—was not prepared. What was the Ministry doing, fully aware that this crisis was inevitable? It is not as if they were unaware. Yet the entire country was pushed into chaos, and all of this happened at a time when the President of Russia was visiting India. On one hand, they showcase Viksit Bharat; on the other, this is the reality unfolding before the world. : Sasikanth Senthil ji
2:38

This situation has been building for the past year and a half. The Flight Duty Time Limitation (FDTL) regulations issued by the DGCA were first notified in January 2024 (or March 2024). Airlines had more than enough time to adapt to these guidelines, and we believe they are important. But what was the DGCA doing all this time, given that it was responsible for enforcing the rules and ensuring that airlines complied over the last two years? These are its own guidelines, and it knew that a major airline—practically a monopoly/duopoly in the country—was not prepared. What was the Ministry doing, fully aware that this crisis was inevitable? It is not as if they were unaware. Yet the entire country was pushed into chaos, and all of this happened at a time when the President of Russia was visiting India. On one hand, they showcase Viksit Bharat; on the other, this is the reality unfolding before the world. : Sasikanth Senthil ji

Congress

11,126 次观看 • 7 个月前