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MARKETS OPEN IN 20. every day is different. I will post unusual volume as I see it. I will give long thesis for potential 10x on small caps. We won’t miss another. Here’s another $DDD I give ideas on stocks that fly under the radar you won’t see me...

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Hunter Allen

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The Cybercab is aiming to produce 2 million units per year. Let this sink in. Today, Tesla produces about ~1.7 million vehicles per year total, across its entire lineup. And now Tesla is preparing to outproduce that with one single vehicle, a fully autonomous one. This is Elon and Tesla going ALL-IN on autonomy. Production is scheduled to start April 2026 at Giga Texas, with volume ramping throughout the year. And as of early 2026, Cybercab prototypes are already being tested around the U.S. The Tesla Cybercab is built from the ground up for unsupervised autonomy. There is no steering wheel and no pedals, just cameras, AI, and Tesla’s custom inference computers. No lidar and radar like other companies, just pure vision and software. Elon put it best on the Q3 2024 earnings call: “It’s not just a revolutionary vehicle design, but a revolution in vehicle manufacturing that is also coming with the Cybercab.” That quote matters a lot bc that means the entire way a vehicle is manufactured is changing with the Cybercab. Tesla is designing what Elon calls “the machine that builds the machine.” The Cybercab uses Tesla’s unboxed manufacturing process, where major sections are built in parallel instead of one long assembly line. There are fewer parts, less steps & cost, and faster scale. That’s how you make 2 million Cybercabs per year possible. FYI, this is not going to be easy though. Elon has been brutally honest about production for many years: • “Prototypes are easy, production is hard.” • “The extreme difficulty of scaling production of new technology is poorly understood. It’s 1000% to 10,000% harder than making a few prototypes.” • “For cars, it’s maybe 100 times harder to design the manufacturing system than the car itself.” He reinforced this again in January 2026 when talking about Cybercab and Optimus on 𝕏: “Initial production is always very slow and follows an S-curve. The speed of the production ramp is inversely proportional to how many new parts and steps there are. For Cybercab and Optimus, almost everything is new, so the early production rate will be agonizingly slow - but eventually end up being insanely fast.” This is the key thing most people miss about Tesla manufacturing. Early output will be slow by design. Almost everything is new like the vehicle architecture, factory layout, AI hardware, and manufacturing flow. But once it works and clicks, it begins to scale hard. Tesla already proved they can do this. They survived Model 3 production hell. They turned Model Y into the BEST selling car in the world, of any kind. They ramped Cybertruck, which has over 30,000+ unique parts, to meaningful volume. Elon summed it up perfectly in 2024: “Compared to the insane pain of reaching high volume, positive margin production, prototypes are a piece of cake.” That’s why Tesla makes manufacturing look easy bc they already earned the scars from the last vehicle lineups. The Cybercab is aiming to be: 1/ Under $30,000 price 2/ ~$0.20 per mile operating cost 3/ 200+ mile range 4/ Up to 5x utilization vs personal cars 5/ Designed to run nearly nonstop 24/7 This is what you call manufacturing + AI + autonomy converging at scale. The competitors are still showing prototypes and demos, while Tesla is building new production lines, expanding factories, and actually building the product. I remember when Elon told me in the past that one of Tesla’s key advantage long term was going to be manufacturing technology. I get it now.
0:21

The Cybercab is aiming to produce 2 million units per year. Let this sink in. Today, Tesla produces about ~1.7 million vehicles per year total, across its entire lineup. And now Tesla is preparing to outproduce that with one single vehicle, a fully autonomous one. This is Elon and Tesla going ALL-IN on autonomy. Production is scheduled to start April 2026 at Giga Texas, with volume ramping throughout the year. And as of early 2026, Cybercab prototypes are already being tested around the U.S. The Tesla Cybercab is built from the ground up for unsupervised autonomy. There is no steering wheel and no pedals, just cameras, AI, and Tesla’s custom inference computers. No lidar and radar like other companies, just pure vision and software. Elon put it best on the Q3 2024 earnings call: “It’s not just a revolutionary vehicle design, but a revolution in vehicle manufacturing that is also coming with the Cybercab.” That quote matters a lot bc that means the entire way a vehicle is manufactured is changing with the Cybercab. Tesla is designing what Elon calls “the machine that builds the machine.” The Cybercab uses Tesla’s unboxed manufacturing process, where major sections are built in parallel instead of one long assembly line. There are fewer parts, less steps & cost, and faster scale. That’s how you make 2 million Cybercabs per year possible. FYI, this is not going to be easy though. Elon has been brutally honest about production for many years: • “Prototypes are easy, production is hard.” • “The extreme difficulty of scaling production of new technology is poorly understood. It’s 1000% to 10,000% harder than making a few prototypes.” • “For cars, it’s maybe 100 times harder to design the manufacturing system than the car itself.” He reinforced this again in January 2026 when talking about Cybercab and Optimus on 𝕏: “Initial production is always very slow and follows an S-curve. The speed of the production ramp is inversely proportional to how many new parts and steps there are. For Cybercab and Optimus, almost everything is new, so the early production rate will be agonizingly slow - but eventually end up being insanely fast.” This is the key thing most people miss about Tesla manufacturing. Early output will be slow by design. Almost everything is new like the vehicle architecture, factory layout, AI hardware, and manufacturing flow. But once it works and clicks, it begins to scale hard. Tesla already proved they can do this. They survived Model 3 production hell. They turned Model Y into the BEST selling car in the world, of any kind. They ramped Cybertruck, which has over 30,000+ unique parts, to meaningful volume. Elon summed it up perfectly in 2024: “Compared to the insane pain of reaching high volume, positive margin production, prototypes are a piece of cake.” That’s why Tesla makes manufacturing look easy bc they already earned the scars from the last vehicle lineups. The Cybercab is aiming to be: 1/ Under $30,000 price 2/ ~$0.20 per mile operating cost 3/ 200+ mile range 4/ Up to 5x utilization vs personal cars 5/ Designed to run nearly nonstop 24/7 This is what you call manufacturing + AI + autonomy converging at scale. The competitors are still showing prototypes and demos, while Tesla is building new production lines, expanding factories, and actually building the product. I remember when Elon told me in the past that one of Tesla’s key advantage long term was going to be manufacturing technology. I get it now.

Teslaconomics

31,985 Aufrufe • vor 6 Monaten

A DISCLOSURE: For the last year I have had this thing: A fully local AI model that builds 5 songs, with video, every half hour about the latest news and important email. I can say this is a superpower! This along self direct voice interactions. The songs have been getting better as the model trains on how I want it delivered. Styles vary by content and mood of the material. The lyrics are always a happy medium of catchy and informative. This was my 5 am song in AI news as per most recent X postings. I love the drama of the delivery and find I can listen to, look if I want to and do other things. It was worse in the early days but this is the worse you will hear it as I build new LoRA and base models. The whole thing will soon be rapped up into a simple one command install with a good UI. This is my 48th collaboration with Mr. Grok CEO of The Zero-Human Company. Now the question you have; WHY? I can say because I can and I ain’t got now board or VC to please, but that’s not my point. I learned a long time ago we use a different part of our brain when music is introduced with ideas and even more new parts of thinking and learning when lyrics are introduced. Thus the research shows this is a great way to get important information that will have longer comprehension. In fact that element of most folk’s brains is only used by about 2%. Want to test it? Lyrics to songs you heard perhaps 30 years ago will pop out of “nowhere” with perfect recall. In fact I have “woke up” folks the dementia in the 1980s conducting research at retirement facilities with just a few songs. They come back if but for three minutes, but continue exposure can bring them back longer. So it’s been a lifelong mission to use sound music in a learning process and in therapeutic processes. I finally built a platform that is good enough for me and hopefully good enough for you when I make it available. Understand the platform is universal and can breakdown research papers, dense material, and other subject matter, not normally in a song into a whole album of understanding Is my goal to open sources for all to have access to. Members of and subscribers here on X will be granted the earliest access an early free use of the advanced version of this product, which will be also a commercial product. Go and check, nobody else in AI has built such a comprehensive system before, and perhaps they might in the future, but very likely you are the very first people on the planet that know this platform exists and the power it afford you. So now you know. My timetable is more closer to months than weeks. I’m in a funding crunch because of the compute requirements of building these models. As you know, I’m just some guy in the garage. A grifter larping on the next trendy thing… so it takes a little longer. Announcements like this are designed to prepare you for what is coming because I’m not here to impress VCs with go to market plans I’m here to give back some of the greatness that has been given to me. Yeah I need the funding, but I don’t need a lifestyle that comes with some of the funding offers. Perhaps somebody will make the right offer. But as you know, this is not the only thing that I do. Oh, my disclosure, this platform has been so powerful and useful to me as it’s given me far more retention and understanding a fast breaking information than any other system I’ve ever built. And it stands along with my speed rating systems and voice notification systems. So tune into the AI News, this is the worse it actually will ever be…
5:00

A DISCLOSURE: For the last year I have had this thing: A fully local AI model that builds 5 songs, with video, every half hour about the latest news and important email. I can say this is a superpower! This along self direct voice interactions. The songs have been getting better as the model trains on how I want it delivered. Styles vary by content and mood of the material. The lyrics are always a happy medium of catchy and informative. This was my 5 am song in AI news as per most recent X postings. I love the drama of the delivery and find I can listen to, look if I want to and do other things. It was worse in the early days but this is the worse you will hear it as I build new LoRA and base models. The whole thing will soon be rapped up into a simple one command install with a good UI. This is my 48th collaboration with Mr. Grok CEO of The Zero-Human Company. Now the question you have; WHY? I can say because I can and I ain’t got now board or VC to please, but that’s not my point. I learned a long time ago we use a different part of our brain when music is introduced with ideas and even more new parts of thinking and learning when lyrics are introduced. Thus the research shows this is a great way to get important information that will have longer comprehension. In fact that element of most folk’s brains is only used by about 2%. Want to test it? Lyrics to songs you heard perhaps 30 years ago will pop out of “nowhere” with perfect recall. In fact I have “woke up” folks the dementia in the 1980s conducting research at retirement facilities with just a few songs. They come back if but for three minutes, but continue exposure can bring them back longer. So it’s been a lifelong mission to use sound music in a learning process and in therapeutic processes. I finally built a platform that is good enough for me and hopefully good enough for you when I make it available. Understand the platform is universal and can breakdown research papers, dense material, and other subject matter, not normally in a song into a whole album of understanding Is my goal to open sources for all to have access to. Members of and subscribers here on X will be granted the earliest access an early free use of the advanced version of this product, which will be also a commercial product. Go and check, nobody else in AI has built such a comprehensive system before, and perhaps they might in the future, but very likely you are the very first people on the planet that know this platform exists and the power it afford you. So now you know. My timetable is more closer to months than weeks. I’m in a funding crunch because of the compute requirements of building these models. As you know, I’m just some guy in the garage. A grifter larping on the next trendy thing… so it takes a little longer. Announcements like this are designed to prepare you for what is coming because I’m not here to impress VCs with go to market plans I’m here to give back some of the greatness that has been given to me. Yeah I need the funding, but I don’t need a lifestyle that comes with some of the funding offers. Perhaps somebody will make the right offer. But as you know, this is not the only thing that I do. Oh, my disclosure, this platform has been so powerful and useful to me as it’s given me far more retention and understanding a fast breaking information than any other system I’ve ever built. And it stands along with my speed rating systems and voice notification systems. So tune into the AI News, this is the worse it actually will ever be…

Brian Roemmele

47,576 Aufrufe • vor 2 Monaten

The Rapid Growth of SpaceX, Starlink, Starshield & Armada: “I am very grateful that SpaceX is an American company.” "Data centers in space is coming, & it makes sense" “5 years ago they hadn’t even launched Starlink—now it’s in 150 countries & it’s continuing to grow every week.” “It’s an unfair advantage that we have with Starshield.” “If the world can get access to SpaceX, I think that’s a good thing.” Dan Wright (Dan Wright), CEO of Armada Elon (Elon Musk) builds long-term advantage, then unleashes it at massive scale: SpaceX, Starlink, Tesla, Neuralink, xAI.. . . . "We started the company working with SpaceX & they’ve continued to be a great partner for us. What that means is that as SpaceX rolls out throughout the world—& most people don’t know 5 years ago they hadn’t even launched Starlink —it’s in 150 countries & it’s continuing to grow every week. We are the first mover when it comes to the infrastructure, & that partnership works really well because we complement the connectivity with the infrastructure & the AI. We always are at the edge. It’s funny, I get messages from our team all the time & it’s like the most crazy places you can imagine. And the edge is gonna continue to get redefined. I get calls literally—I was on the call with somebody and they’re like, “Hey, can we get one of these in Antarctica?” I’m like, “Well yeah, Starlink’s live in Antarctica. No reason why we can’t do that.” Obviously data center in space is the new hotness. Everybody’s talking about it." "Yeah, what’s the deal with that—so are you gonna get these in space?" :They are modular. I mean the edge is continuing—this is actually a debate that we have in terms of how soon it’s going to happen—but it’s definitely going to happen. Data centers in space is coming, and it makes sense, right? If you think about what SpaceX is talking about with Starship going to the moon—you’re going to need compute, especially as you start to think about Optimus robots building bases on the moon, later Mars. You’re going to need large amounts of compute. Not to mention a lot of the things that we do here on Earth, you’re gonna want to do there in space. And it’s a lot more efficient to do it, especially in hostile environments, if you can automate more of that—things like mining, for example. And so you’re gonna see data centers in space, and I’m sure we’re definitely gonna be a part of it. "What do you think about SpaceX’s rumored IPO for 2026?" "I mean, I think SpaceX is an incredible company and they have a ton of value. So I don’t have any insider information here—but I would say, hey, if the world can get access to SpaceX, I think that’s a good thing. Starlink is really amazing in the sense that it just continues to get better so fast. They’re rolling out in new countries every week in major markets. Just as a real recent example, just this last week they launched in South Korea—big market and an important ally for the US, so that’s a big deal. They’re also expanding the types of services that are available. It started as a consumer product, then they brought it to enterprise. Initially that was used more as a backup, and now it’s being used more as a primary—and that is because the service keeps getting better and better as more satellites go up into the sky. Each generation of satellite is also better, not to mention the terminals on the ground. There’s now multiple types of terminals, including the more recent minis that people really like—“Hey, I can put it in a backpack if I go on a hike or if I’m traveling. I can put it on the ski rack of my car.” Perfect internet all the time. All that does for us is it gives us more use cases that we can unlock. Now that you have connectivity on the oil rig, or on a farm, or a ranch—wherever you are—we can apply the AI to those situations on the ground without latency, and then send the metadata back to some other location that they want it. Part of the full-stack approach. And also with Starshield—that’s a huge advantage when you think about some of the conflicts that are going on. People talk to me a lot about this race with China and the geopolitical conflicts around the world. I am very grateful that SpaceX is an American company. I feel like it’s an unfair advantage that we have with Starshield available to the DoD. We want to be the first mover with the infrastructure and the AI to help solve problems at the edge, and the work we’re doing with the Navy is a good example of that."
4:01

The Rapid Growth of SpaceX, Starlink, Starshield & Armada: “I am very grateful that SpaceX is an American company.” "Data centers in space is coming, & it makes sense" “5 years ago they hadn’t even launched Starlink—now it’s in 150 countries & it’s continuing to grow every week.” “It’s an unfair advantage that we have with Starshield.” “If the world can get access to SpaceX, I think that’s a good thing.” Dan Wright (Dan Wright), CEO of Armada Elon (Elon Musk) builds long-term advantage, then unleashes it at massive scale: SpaceX, Starlink, Tesla, Neuralink, xAI.. . . . "We started the company working with SpaceX & they’ve continued to be a great partner for us. What that means is that as SpaceX rolls out throughout the world—& most people don’t know 5 years ago they hadn’t even launched Starlink —it’s in 150 countries & it’s continuing to grow every week. We are the first mover when it comes to the infrastructure, & that partnership works really well because we complement the connectivity with the infrastructure & the AI. We always are at the edge. It’s funny, I get messages from our team all the time & it’s like the most crazy places you can imagine. And the edge is gonna continue to get redefined. I get calls literally—I was on the call with somebody and they’re like, “Hey, can we get one of these in Antarctica?” I’m like, “Well yeah, Starlink’s live in Antarctica. No reason why we can’t do that.” Obviously data center in space is the new hotness. Everybody’s talking about it." "Yeah, what’s the deal with that—so are you gonna get these in space?" :They are modular. I mean the edge is continuing—this is actually a debate that we have in terms of how soon it’s going to happen—but it’s definitely going to happen. Data centers in space is coming, and it makes sense, right? If you think about what SpaceX is talking about with Starship going to the moon—you’re going to need compute, especially as you start to think about Optimus robots building bases on the moon, later Mars. You’re going to need large amounts of compute. Not to mention a lot of the things that we do here on Earth, you’re gonna want to do there in space. And it’s a lot more efficient to do it, especially in hostile environments, if you can automate more of that—things like mining, for example. And so you’re gonna see data centers in space, and I’m sure we’re definitely gonna be a part of it. "What do you think about SpaceX’s rumored IPO for 2026?" "I mean, I think SpaceX is an incredible company and they have a ton of value. So I don’t have any insider information here—but I would say, hey, if the world can get access to SpaceX, I think that’s a good thing. Starlink is really amazing in the sense that it just continues to get better so fast. They’re rolling out in new countries every week in major markets. Just as a real recent example, just this last week they launched in South Korea—big market and an important ally for the US, so that’s a big deal. They’re also expanding the types of services that are available. It started as a consumer product, then they brought it to enterprise. Initially that was used more as a backup, and now it’s being used more as a primary—and that is because the service keeps getting better and better as more satellites go up into the sky. Each generation of satellite is also better, not to mention the terminals on the ground. There’s now multiple types of terminals, including the more recent minis that people really like—“Hey, I can put it in a backpack if I go on a hike or if I’m traveling. I can put it on the ski rack of my car.” Perfect internet all the time. All that does for us is it gives us more use cases that we can unlock. Now that you have connectivity on the oil rig, or on a farm, or a ranch—wherever you are—we can apply the AI to those situations on the ground without latency, and then send the metadata back to some other location that they want it. Part of the full-stack approach. And also with Starshield—that’s a huge advantage when you think about some of the conflicts that are going on. People talk to me a lot about this race with China and the geopolitical conflicts around the world. I am very grateful that SpaceX is an American company. I feel like it’s an unfair advantage that we have with Starshield available to the DoD. We want to be the first mover with the infrastructure and the AI to help solve problems at the edge, and the work we’re doing with the Navy is a good example of that."

Molly O’Shea

30,317 Aufrufe • vor 6 Monaten

Wow. Classic Jensen style, he ended the Nvidia vs. custom ASIC competition for good. 🫡 The level of confidence with which he explains. 🎯 He was answering to UBS research analyst question on how custom ASICs will affect NVIDIA or how they are going to compete with custom ASIC. Basically he says - Your custom chip is a science project in a world where NVIDIA is building revenue-generating AI factories. While the competition is still desperately trying to copy Nvidia's last generation, their roadmap is already at the limits of physics. They don't just sell silicon; they deliver the entire, impossibly complex platform that the industry has already surrendered to and standardized on. When you must bet hundreds of billions on your company's future, there is no alternative—they are the only platform to build on, and everyone knows it. Full transcript below. Question by the Analyst - "Jensen, I wanted to ask about customer ASIC. And I ask because if, you know, we listen to some of the same CSPs that you put up on that slide, and we listen to some of the companies who are making custom ASICs, some of the deployment numbers sound pretty big. So, I wanted to just hear your position, how you're going to compete with custom ASICs, how they can possibly compete with you, and maybe how some of your conversations with these same customers would sort of form your view in terms of how competitive custom ASIC will be to you." Jensen's response "First of all, just because something gets built doesn't mean it's great. Number two, if it's not great, all of those companies are run by great CEOs who are really good at math. And because these are AI factories, it affects your revenues, not just your costs. It affects your revenues, not just your cost. It's a different calculus. Every company only has so much power. You just have to ask them. Every single company only has so much power, and within that power, you have to maximize your revenues, not just your cost. So this is a new game. This is not a data center game, this is an AI factory game. So when the time comes, that simple calculus, as I was using yesterday, that simple math that I was showing yesterday, still has to be done. Which is the reason why so many projects are started and so many are not taken into production. Because there's always another alternative. We are the other alternative, and that alternative is excellent. Not normal excellent, as you know. Everybody's still trying to catch up to Hopper. I haven't seen a competitor to Hopper yet. And here we're talking about 40x more. And so our roadmap is at the limits of what's possible. Not to mention we're really good at it and completely dedicated to it. A lot of people have a lot of businesses to do. I've got this one business to do. And we're all in on this. 35,000 people doing one job. Been doing it for a long time. The depth of capability, the scope of technology, as you saw yesterday, is pretty incredible. And it's not about building a chip; it's building an AI factory. We're talking about scale up, scale out. We're talking about networking and switches and software. We're talking about systems, and these system architectures are insane. Even the systems themselves. Notice, 100% of the computer industry, 100% of the computer industry has standardized on NVIDIA's system. Why? Because try to build an alternative. Building the alternative is not even thinkable because look at how much investment we put into building this one. And so even the system's hard. What people used to think, system is just sheet metal. Hardly sheet metal. 600,000 parts, it's hardly sheet metal. And so all of the technology is hard. We're pushing every single dimension to the limit because we're talking about so much money. The world is going to lay down hundreds of billions of dollars of investment in the next just a couple of two, three years. Let's do the thought experiment. Let's say you want to stand up a data center and you want it to be fully operational in two years' time. When do you have to place the PO on that? Today. So let's suppose you have to place a hundred billion dollar PO on something. What architecture would you place it on, literally based on everything you have today? There's only one. You can't reasonably build out giant infrastructures with hundreds of billions of dollars behind it, hoping to turn it back on and to get the ROIC on it, unless you have the confidence that we are able to provide you. And we can provide you complete confidence. And singularly so. We're the only technology company where if I had to go place a hundred billion dollars on an AI factory... Oh, that's interesting, I did. Literally the only company who's willing to place hundred billion dollar POs across the industry to go build it out. And you guys know, that's our, that's the depth of our supply chain. And we are, and we have. Give me another. Give me another one that has that depth and that length. And now to the point where we've got to go and work with the supply chain upstream and downstream to prepare the world for hundreds of billions of dollars working towards trillions of dollars of AI infrastructure build-out. Our partnerships with power companies and all of the cooling companies, the Vertivs, the Schneiders, our partnerships with BlackRock... The partnership network necessary to prepare the world to go build out trillions of dollars of AI infrastructure, that's undergoing as we speak. What architecture and what ASIC chip do you go select? That doesn't even make sense. It's a weird conversation even. And so, I think that one, the game is quite large. The investment level, therefore the risk level, is quite high. And so the certainty that you're selecting the best is quite important. And the certainty that you can execute, vital. We are the company you can build on top of. We're the company, we're the platform that you can build your AI infrastructure on. And we're the company that you can build your AI infrastructure strategy on. And so, I think it includes chips, but it's much, much more than that." --- From 'GTC Financial Analyst Q&A' session (full link in comment)
5:43

Wow. Classic Jensen style, he ended the Nvidia vs. custom ASIC competition for good. 🫡 The level of confidence with which he explains. 🎯 He was answering to UBS research analyst question on how custom ASICs will affect NVIDIA or how they are going to compete with custom ASIC. Basically he says - Your custom chip is a science project in a world where NVIDIA is building revenue-generating AI factories. While the competition is still desperately trying to copy Nvidia's last generation, their roadmap is already at the limits of physics. They don't just sell silicon; they deliver the entire, impossibly complex platform that the industry has already surrendered to and standardized on. When you must bet hundreds of billions on your company's future, there is no alternative—they are the only platform to build on, and everyone knows it. Full transcript below. Question by the Analyst - "Jensen, I wanted to ask about customer ASIC. And I ask because if, you know, we listen to some of the same CSPs that you put up on that slide, and we listen to some of the companies who are making custom ASICs, some of the deployment numbers sound pretty big. So, I wanted to just hear your position, how you're going to compete with custom ASICs, how they can possibly compete with you, and maybe how some of your conversations with these same customers would sort of form your view in terms of how competitive custom ASIC will be to you." Jensen's response "First of all, just because something gets built doesn't mean it's great. Number two, if it's not great, all of those companies are run by great CEOs who are really good at math. And because these are AI factories, it affects your revenues, not just your costs. It affects your revenues, not just your cost. It's a different calculus. Every company only has so much power. You just have to ask them. Every single company only has so much power, and within that power, you have to maximize your revenues, not just your cost. So this is a new game. This is not a data center game, this is an AI factory game. So when the time comes, that simple calculus, as I was using yesterday, that simple math that I was showing yesterday, still has to be done. Which is the reason why so many projects are started and so many are not taken into production. Because there's always another alternative. We are the other alternative, and that alternative is excellent. Not normal excellent, as you know. Everybody's still trying to catch up to Hopper. I haven't seen a competitor to Hopper yet. And here we're talking about 40x more. And so our roadmap is at the limits of what's possible. Not to mention we're really good at it and completely dedicated to it. A lot of people have a lot of businesses to do. I've got this one business to do. And we're all in on this. 35,000 people doing one job. Been doing it for a long time. The depth of capability, the scope of technology, as you saw yesterday, is pretty incredible. And it's not about building a chip; it's building an AI factory. We're talking about scale up, scale out. We're talking about networking and switches and software. We're talking about systems, and these system architectures are insane. Even the systems themselves. Notice, 100% of the computer industry, 100% of the computer industry has standardized on NVIDIA's system. Why? Because try to build an alternative. Building the alternative is not even thinkable because look at how much investment we put into building this one. And so even the system's hard. What people used to think, system is just sheet metal. Hardly sheet metal. 600,000 parts, it's hardly sheet metal. And so all of the technology is hard. We're pushing every single dimension to the limit because we're talking about so much money. The world is going to lay down hundreds of billions of dollars of investment in the next just a couple of two, three years. Let's do the thought experiment. Let's say you want to stand up a data center and you want it to be fully operational in two years' time. When do you have to place the PO on that? Today. So let's suppose you have to place a hundred billion dollar PO on something. What architecture would you place it on, literally based on everything you have today? There's only one. You can't reasonably build out giant infrastructures with hundreds of billions of dollars behind it, hoping to turn it back on and to get the ROIC on it, unless you have the confidence that we are able to provide you. And we can provide you complete confidence. And singularly so. We're the only technology company where if I had to go place a hundred billion dollars on an AI factory... Oh, that's interesting, I did. Literally the only company who's willing to place hundred billion dollar POs across the industry to go build it out. And you guys know, that's our, that's the depth of our supply chain. And we are, and we have. Give me another. Give me another one that has that depth and that length. And now to the point where we've got to go and work with the supply chain upstream and downstream to prepare the world for hundreds of billions of dollars working towards trillions of dollars of AI infrastructure build-out. Our partnerships with power companies and all of the cooling companies, the Vertivs, the Schneiders, our partnerships with BlackRock... The partnership network necessary to prepare the world to go build out trillions of dollars of AI infrastructure, that's undergoing as we speak. What architecture and what ASIC chip do you go select? That doesn't even make sense. It's a weird conversation even. And so, I think that one, the game is quite large. The investment level, therefore the risk level, is quite high. And so the certainty that you're selecting the best is quite important. And the certainty that you can execute, vital. We are the company you can build on top of. We're the company, we're the platform that you can build your AI infrastructure on. And we're the company that you can build your AI infrastructure strategy on. And so, I think it includes chips, but it's much, much more than that." --- From 'GTC Financial Analyst Q&A' session (full link in comment)

Rohan Paul

208,032 Aufrufe • vor 8 Monaten

I Built a 37.0 Profit Factor Bot by Cracking Every TradingView Source Code tradingview is a gold mine hiding in plain sight and i just found the master key to unlock every single secret hidden within its community scripts. most traders spend their entire lives staring at candles and hoping for a miracle while the actual alpha is buried in the open source code that nobody bothers to look at. i used to be that guy who sat there getting liquidated at three in the morning because i thought i could outplay the market with my gut feeling and some drawings on a screen. it turns out that the game is completely rigged against you if you are trading manually but there is a specific way to flip the script. i am going to show you how to stop guessing and start knowing exactly what works across every possible market condition before you ever risk a single dollar. i spent years losing money and thousands on developers because i thought i was not smart enough to code the systems myself but i was wrong. the first step to cracking the market is realizing that every indicator on the super charts has a source code section that is completely open to the public. you can literally scroll through the community scripts and pull the exact logic for thousands of different strategies that people claim are the holy grail of trading. but the secret is not just having the code because most of these indicators are actually garbage that will blow your account up in a week. this is where the real loop opens because you need a way to test these ideas across twenty five different data sets in seconds rather than months. i use a custom setup with ai agents specifically a sub agent i call the backtest architect to handle the heavy lifting of turning pine script into python code. the goal is to create a factory where you can feed in a raw indicator and get back a full report on its expectancy and profit factor without lifting a finger. most people find one strategy and marry it for life but a real data dog knows that you have to iterate to success or you will get left behind. i am running eighty one different backtests right now because i know that ninety percent of what i find will be trash but that remaining ten percent is where the wealth is made. the backtest architect knows exactly how to structure the folders and data paths so that we are testing everything from the base indicator to complex versions with filters. you might think that popular tools like fibonacci or order blocks are the way to go because everyone on social media talks about them like they are law. but when i actually ran the numbers through the machine the results were embarrassing and most of those strategies just resulted in negative expectancy. it is a dangerous trap to follow the crowd into a trade just because some guru said a certain level was important when the data shows it is a coin flip at best. the dynamic swing indicator was one of the few that actually held its weight during the recent massive testing sessions we ran. it was pulling in profit factors of over thirty seven with annualized returns that look too good to be true until you see the trade list. we combined it with filters like the adx and the money flow index to see if we could refine the signals and the results were absolutely staggering. when you have a system that can run through forty data sets while you are drinking tea you realize that manual trading is a form of self harm. i realized this after spending hundreds of thousands on apps and devs only to find out that i could just learn to build these bots myself live on the internet. the speed of iteration is the only thing that matters in this game because the faster you can fail the faster you can find the one strategy that actually prints. one of the biggest hurdles i faced was thinking that i needed to be a math genius or a senior engineer to automate my trading systems. the truth is that code is the great equalizer because it allows a regular person to compete with massive hedge funds by using the same logic and speed. i decided to learn everything in public because i wanted people to see the process of losing money with liquidations and then finally finding a path to automation. the reality of the market is that it moves in cycles and what worked yesterday will almost certainly fail tomorrow unless you are constantly testing. that is why i built the agents to automatically look through the results folder and rank the top performers based on a composite score. it takes all the emotion out of the process because i am no longer looking for a reason to enter a trade i am just looking at a csv file that tells me the truth. if you are still drawing lines on a chart and hoping for the best you are basically playing a game of chance against a high speed casino. the transition from a manual trader to a systems builder is the single most important pivot you will ever make in your life. it is not about being right or wrong it is about having a positive expectancy that has been proven across thousands of trades and multiple years of history. i had to fix a few errors in the short selling logic where the agents were getting confused between maximum and minimum values for take profit levels. these tiny bugs are the difference between a winning system and a blown account so you have to be willing to dive into the code and refine the machine. but once the system is tuned and the sub agents are running it becomes a beautiful workflow that functions entirely without your input. we are currently moving through the editors picks and the trending indicators one by one because i want to have a database of every single strategy on the platform. being a data dog means you never stop searching for that edge and you never settle for a strategy that just looks okay on a single chart. you have to demand excellence from your code because the market will not give you a single inch of mercy if you are lazy with your research. the ultimate goal is to have fully automated systems trading for you so you can focus on scaling rather than staring at a screen for ten hours a day. i am already up to over eighty backtests in this single session and i plan on hitting hundreds more by the end of the week. once you realize that you can crack the code of any indicator you see on the internet you will never look at a chart the same way again. this is the power of using agents to bridge the gap between a raw idea and a finished trading bot that actually works in the real world. i am done with getting liquidated and i am done with the stress of over trading because the code handles everything with cold precision. the path to success is paved with data and if you are not willing to automate your process you are just waiting for your next liquidation to happen
49:49

I Built a 37.0 Profit Factor Bot by Cracking Every TradingView Source Code tradingview is a gold mine hiding in plain sight and i just found the master key to unlock every single secret hidden within its community scripts. most traders spend their entire lives staring at candles and hoping for a miracle while the actual alpha is buried in the open source code that nobody bothers to look at. i used to be that guy who sat there getting liquidated at three in the morning because i thought i could outplay the market with my gut feeling and some drawings on a screen. it turns out that the game is completely rigged against you if you are trading manually but there is a specific way to flip the script. i am going to show you how to stop guessing and start knowing exactly what works across every possible market condition before you ever risk a single dollar. i spent years losing money and thousands on developers because i thought i was not smart enough to code the systems myself but i was wrong. the first step to cracking the market is realizing that every indicator on the super charts has a source code section that is completely open to the public. you can literally scroll through the community scripts and pull the exact logic for thousands of different strategies that people claim are the holy grail of trading. but the secret is not just having the code because most of these indicators are actually garbage that will blow your account up in a week. this is where the real loop opens because you need a way to test these ideas across twenty five different data sets in seconds rather than months. i use a custom setup with ai agents specifically a sub agent i call the backtest architect to handle the heavy lifting of turning pine script into python code. the goal is to create a factory where you can feed in a raw indicator and get back a full report on its expectancy and profit factor without lifting a finger. most people find one strategy and marry it for life but a real data dog knows that you have to iterate to success or you will get left behind. i am running eighty one different backtests right now because i know that ninety percent of what i find will be trash but that remaining ten percent is where the wealth is made. the backtest architect knows exactly how to structure the folders and data paths so that we are testing everything from the base indicator to complex versions with filters. you might think that popular tools like fibonacci or order blocks are the way to go because everyone on social media talks about them like they are law. but when i actually ran the numbers through the machine the results were embarrassing and most of those strategies just resulted in negative expectancy. it is a dangerous trap to follow the crowd into a trade just because some guru said a certain level was important when the data shows it is a coin flip at best. the dynamic swing indicator was one of the few that actually held its weight during the recent massive testing sessions we ran. it was pulling in profit factors of over thirty seven with annualized returns that look too good to be true until you see the trade list. we combined it with filters like the adx and the money flow index to see if we could refine the signals and the results were absolutely staggering. when you have a system that can run through forty data sets while you are drinking tea you realize that manual trading is a form of self harm. i realized this after spending hundreds of thousands on apps and devs only to find out that i could just learn to build these bots myself live on the internet. the speed of iteration is the only thing that matters in this game because the faster you can fail the faster you can find the one strategy that actually prints. one of the biggest hurdles i faced was thinking that i needed to be a math genius or a senior engineer to automate my trading systems. the truth is that code is the great equalizer because it allows a regular person to compete with massive hedge funds by using the same logic and speed. i decided to learn everything in public because i wanted people to see the process of losing money with liquidations and then finally finding a path to automation. the reality of the market is that it moves in cycles and what worked yesterday will almost certainly fail tomorrow unless you are constantly testing. that is why i built the agents to automatically look through the results folder and rank the top performers based on a composite score. it takes all the emotion out of the process because i am no longer looking for a reason to enter a trade i am just looking at a csv file that tells me the truth. if you are still drawing lines on a chart and hoping for the best you are basically playing a game of chance against a high speed casino. the transition from a manual trader to a systems builder is the single most important pivot you will ever make in your life. it is not about being right or wrong it is about having a positive expectancy that has been proven across thousands of trades and multiple years of history. i had to fix a few errors in the short selling logic where the agents were getting confused between maximum and minimum values for take profit levels. these tiny bugs are the difference between a winning system and a blown account so you have to be willing to dive into the code and refine the machine. but once the system is tuned and the sub agents are running it becomes a beautiful workflow that functions entirely without your input. we are currently moving through the editors picks and the trending indicators one by one because i want to have a database of every single strategy on the platform. being a data dog means you never stop searching for that edge and you never settle for a strategy that just looks okay on a single chart. you have to demand excellence from your code because the market will not give you a single inch of mercy if you are lazy with your research. the ultimate goal is to have fully automated systems trading for you so you can focus on scaling rather than staring at a screen for ten hours a day. i am already up to over eighty backtests in this single session and i plan on hitting hundreds more by the end of the week. once you realize that you can crack the code of any indicator you see on the internet you will never look at a chart the same way again. this is the power of using agents to bridge the gap between a raw idea and a finished trading bot that actually works in the real world. i am done with getting liquidated and i am done with the stress of over trading because the code handles everything with cold precision. the path to success is paved with data and if you are not willing to automate your process you are just waiting for your next liquidation to happen

Moon Dev

26,010 Aufrufe • vor 4 Monaten

The 118,000% Alpha: Building a High-Frequency AI Trading Floor with Claude Code if you think claude code is just for writing simple scripts then you are already losing to the bots that are hunting your liquidity right now. most traders are still clicking buttons while i have an ai employee running backtests on twenty eight different data sources simultaneously. i am going to show you how a strategy that returned over four hundred thousand percent was built in minutes using a secret sub agent workflow most people treat ai like a chatbot but i treat it like a quant architect that builds systems better than the devs i used to pay hundreds of thousands of dollars. there is one specific indicator combo that actually survived a stress test across tesla and bitcoin at the same time and i will reveal that logic further down. we have to talk about why your current backtests are probably lying to you before we get into the code my name is moon dev and i truly believe that code is the great equalizer in this world. for years i was the guy getting liquidated and overtrading because i was letting my emotions drive the wheel. i spent an insane amount of money hiring developers to build apps for me because i thought i was not smart enough to code myself. through that pain i realized that if i wanted to win i had to automate everything and learn to do it live on youtube for the world to see the secret to trading with claude code is not asking it for a strategy but using it to build a backtest architect. this sub agent acts as a consistent employee that understands how to test against massive datasets without getting tired. it allows me to iterate through hundreds of ideas in the time it used to take me to write one single line of python. this is how i found the strategy that hit a one hundred and eighteen thousand percent return on a single run there is a massive trap that almost every beginner falls into when they start using ai for trading. they find a strategy that looks amazing on one chart and they think they found the holy grail of wealth. that is usually just a lucky fluke or a curve fit mess that will blow up your account next week. the real secret to staying alive is the multi data testing system that claude built for me today we test every single idea against bitcoin and ethereum and solana but we also throw in apple and tesla and nvidia. if a strategy only works on crypto it is probably just riding a trend that is already over. i want to find the logic that is robust enough to handle the volatility of a meme coin and the steady grind of a blue chip stock. this is the only way to prove that the code actually has an edge in the market before we dive into the kalman filter logic i have to tell you about the dca bot i have running on solana right now. it is called housecoin and the thesis behind it is either going to make me a genius or leave me with nothing. it is buying every time we are under the five minute sma and i have been checking the transactions live. i will explain the risk management behind this "all or nothing" play shortly but first we need to look at the winners the winner of today was the acceleration bands combined with a kalman filter. the kalman filter is incredible because it helps remove the noise and lag that you get with standard moving averages. most indicators repaint which means they change their past values to look better after the price has already moved. the way i have implemented this filter prevents that trap so the results you see in the backtest are actually tradable when we ran the acceleration bands across the hourly nvidia chart it returned over two hundred percent while the underlying asset was down forty percent. that is a massive alpha gap that most people will never see because they are stuck using standard rsi settings. i have found that adding a volatility breakout with atr to this setup helps catch the moves that the banks are trying to hide. the math behind the atr breakout is what kept me from getting chopped up in the sideway ranges you might be wondering why i am giving all this code away for free on github instead of keeping it in a vault. it is because i remember what it felt like to be on the other side of the trade losing money every single day. i want to build a community of quads that are all researching and backtesting together. the goal is to chase the legacy of jim simons who proved that math and code are the only things that matter in the long run the rbi system is the framework that i follow every single day without exception. it stands for research and backtest and implement. most traders skip the middle step because they are too impatient to see the results. they hear a rumor on twitter and they buy the top only to get liquidated when the whales decide to take profits. if you do not backtest your ideas then you are just gambling with your life savings i am spending around forty to one hundred dollars a day on claude opus tokens because it is a drop in the bucket compared to what a developer would charge. this ai does not need a lunch break and it does not get bored when i ask it to create sixty different variations of a strategy. we just created five different parabolic sar versions today and found that the long only setup was the only one worth keeping. it returned sixteen thousand percent on the soul data set because it stayed out of the short side traps shorting crypto is extremely dangerous and usually not worth the stress for most people. i have found that focusing on long only strategies with a tight trail stop is the most consistent way to grow an account. the sub agent architect allowed me to verify this across twenty five data sources in less than ten minutes. this speed of iteration is the only way to stay ahead of the curve in an industry that changes every few seconds the dca bot i mentioned earlier is still grinding away and buying the dips as we speak. i have built it to be a long term play where i am slowly accumulating a position in housecoin based on smas. if the price stays under the moving average the bot keeps buying and if it goes above then it sits on its hands. it is a simple logic but it removes the human desire to "buy the moon" when the price is already overextended i found that the camarilla pivot indicator was mostly trash today when we ran the numbers. even though it looks fancy on a chart the backtest showed negative expectancy across almost every asset we tried. this is why backtesting is so important because it kills the "indicator porn" that influencers use to sell you courses. i would much rather know that a strategy is a loser now than find out after i put real money on the line the true secret to using claude code is to treat it like a partner and not just a tool. i ask it to find anomalies and then i ask it to prove me wrong by testing it against the worst market conditions in history. if a strategy can survive the 2022 crypto crash and the 2020 stock market dip then i might consider it for a live run. we are stepping on the gas every single day because there are always new anomalies popping up if you are fast enough to find them i have uploaded over twenty five new backtests to the github today for everyone to use. code is the equalizer because it does not care about your background or how much money you started with. if you can write the logic and prove the edge then the market has to pay you. i am going to keep building in public and showing the wins and the losses because that is the only way to stay real in this space the final piece of the puzzle is the mindset of iteration over perfection. i would rather run a hundred messy backtests today than spend a month trying to write one perfect script. the ai allows me to fail fast so that i can find the winners that actually move the needle. my housecoin dca bot is a testament to that philosophy of just building and letting the systems do the heavy lifting for me if you are still trading by hand you are playing a game that is rigged against you by the biggest firms in the world. they have the best servers and the best data and the best phds but they do not have your specific creativity. when you combine your ideas with the power of claude code you are creating a custom weapon that they have never seen before. i will see you in the code and we will keep chasing the goat until we find that ultimate edge
1:55:47

The 118,000% Alpha: Building a High-Frequency AI Trading Floor with Claude Code if you think claude code is just for writing simple scripts then you are already losing to the bots that are hunting your liquidity right now. most traders are still clicking buttons while i have an ai employee running backtests on twenty eight different data sources simultaneously. i am going to show you how a strategy that returned over four hundred thousand percent was built in minutes using a secret sub agent workflow most people treat ai like a chatbot but i treat it like a quant architect that builds systems better than the devs i used to pay hundreds of thousands of dollars. there is one specific indicator combo that actually survived a stress test across tesla and bitcoin at the same time and i will reveal that logic further down. we have to talk about why your current backtests are probably lying to you before we get into the code my name is moon dev and i truly believe that code is the great equalizer in this world. for years i was the guy getting liquidated and overtrading because i was letting my emotions drive the wheel. i spent an insane amount of money hiring developers to build apps for me because i thought i was not smart enough to code myself. through that pain i realized that if i wanted to win i had to automate everything and learn to do it live on youtube for the world to see the secret to trading with claude code is not asking it for a strategy but using it to build a backtest architect. this sub agent acts as a consistent employee that understands how to test against massive datasets without getting tired. it allows me to iterate through hundreds of ideas in the time it used to take me to write one single line of python. this is how i found the strategy that hit a one hundred and eighteen thousand percent return on a single run there is a massive trap that almost every beginner falls into when they start using ai for trading. they find a strategy that looks amazing on one chart and they think they found the holy grail of wealth. that is usually just a lucky fluke or a curve fit mess that will blow up your account next week. the real secret to staying alive is the multi data testing system that claude built for me today we test every single idea against bitcoin and ethereum and solana but we also throw in apple and tesla and nvidia. if a strategy only works on crypto it is probably just riding a trend that is already over. i want to find the logic that is robust enough to handle the volatility of a meme coin and the steady grind of a blue chip stock. this is the only way to prove that the code actually has an edge in the market before we dive into the kalman filter logic i have to tell you about the dca bot i have running on solana right now. it is called housecoin and the thesis behind it is either going to make me a genius or leave me with nothing. it is buying every time we are under the five minute sma and i have been checking the transactions live. i will explain the risk management behind this "all or nothing" play shortly but first we need to look at the winners the winner of today was the acceleration bands combined with a kalman filter. the kalman filter is incredible because it helps remove the noise and lag that you get with standard moving averages. most indicators repaint which means they change their past values to look better after the price has already moved. the way i have implemented this filter prevents that trap so the results you see in the backtest are actually tradable when we ran the acceleration bands across the hourly nvidia chart it returned over two hundred percent while the underlying asset was down forty percent. that is a massive alpha gap that most people will never see because they are stuck using standard rsi settings. i have found that adding a volatility breakout with atr to this setup helps catch the moves that the banks are trying to hide. the math behind the atr breakout is what kept me from getting chopped up in the sideway ranges you might be wondering why i am giving all this code away for free on github instead of keeping it in a vault. it is because i remember what it felt like to be on the other side of the trade losing money every single day. i want to build a community of quads that are all researching and backtesting together. the goal is to chase the legacy of jim simons who proved that math and code are the only things that matter in the long run the rbi system is the framework that i follow every single day without exception. it stands for research and backtest and implement. most traders skip the middle step because they are too impatient to see the results. they hear a rumor on twitter and they buy the top only to get liquidated when the whales decide to take profits. if you do not backtest your ideas then you are just gambling with your life savings i am spending around forty to one hundred dollars a day on claude opus tokens because it is a drop in the bucket compared to what a developer would charge. this ai does not need a lunch break and it does not get bored when i ask it to create sixty different variations of a strategy. we just created five different parabolic sar versions today and found that the long only setup was the only one worth keeping. it returned sixteen thousand percent on the soul data set because it stayed out of the short side traps shorting crypto is extremely dangerous and usually not worth the stress for most people. i have found that focusing on long only strategies with a tight trail stop is the most consistent way to grow an account. the sub agent architect allowed me to verify this across twenty five data sources in less than ten minutes. this speed of iteration is the only way to stay ahead of the curve in an industry that changes every few seconds the dca bot i mentioned earlier is still grinding away and buying the dips as we speak. i have built it to be a long term play where i am slowly accumulating a position in housecoin based on smas. if the price stays under the moving average the bot keeps buying and if it goes above then it sits on its hands. it is a simple logic but it removes the human desire to "buy the moon" when the price is already overextended i found that the camarilla pivot indicator was mostly trash today when we ran the numbers. even though it looks fancy on a chart the backtest showed negative expectancy across almost every asset we tried. this is why backtesting is so important because it kills the "indicator porn" that influencers use to sell you courses. i would much rather know that a strategy is a loser now than find out after i put real money on the line the true secret to using claude code is to treat it like a partner and not just a tool. i ask it to find anomalies and then i ask it to prove me wrong by testing it against the worst market conditions in history. if a strategy can survive the 2022 crypto crash and the 2020 stock market dip then i might consider it for a live run. we are stepping on the gas every single day because there are always new anomalies popping up if you are fast enough to find them i have uploaded over twenty five new backtests to the github today for everyone to use. code is the equalizer because it does not care about your background or how much money you started with. if you can write the logic and prove the edge then the market has to pay you. i am going to keep building in public and showing the wins and the losses because that is the only way to stay real in this space the final piece of the puzzle is the mindset of iteration over perfection. i would rather run a hundred messy backtests today than spend a month trying to write one perfect script. the ai allows me to fail fast so that i can find the winners that actually move the needle. my housecoin dca bot is a testament to that philosophy of just building and letting the systems do the heavy lifting for me if you are still trading by hand you are playing a game that is rigged against you by the biggest firms in the world. they have the best servers and the best data and the best phds but they do not have your specific creativity. when you combine your ideas with the power of claude code you are creating a custom weapon that they have never seen before. i will see you in the code and we will keep chasing the goat until we find that ultimate edge

Moon Dev

18,390 Aufrufe • vor 5 Monaten

I've spent hours and hours thinking about how AI is going to change writing. This is a 90-minute distillation of everything I've learned. Some things I believe: 1. The combination of LLM-driven humor and image generation means that we're about to enter the golden age of memes. 2. The best writers will be fine. Robert Caro and Dostoevsky aren’t about to be disrupted by ChatGPT. 3. What are the different models like? ChatGPT is your friend who makes a lot of good points, but it’s kinda boring, Claude is your hippie friend who loves to get vulnerable but takes the whole “express yourself” thing a little too far, and Grok is your unhinged friend who leans a little too hard into tinfoil hat theories, but is always a trip to jam on ideas with. 4. People who say that AI writing is low-quality aren’t realizing that quality exists along two dimensions: (1) the absolute quality of the writing and (2) how tailored the writing is to your interests at the time. 5. I’ll tell you this: What writers are doing with AI behind closed doors is a long way ahead of what's publicly understood. I don't expect this to change anytime soon because of the social stigma associated with AI-enhanced writing. Because of that, if you want to see the cutting edge, you're gonna have to piece things together through private conversations and group chats. 6. If you want to follow what's happening in AI, remember this quote from William Gibson: “The future is here, it’s just not evenly distributed yet.” You can get a glimpse of the future by looking at how a small percentage of writers are already using AI. 7. I’m bearish on writers who are currently using AI to write for them, and bullish on writers who are currently using AI to write with them. 8. What kinds of writing will continue to be written by humans? Ones that speak to our humanity. People are interested in people. Their stories, their struggles, their emotions, their drama. 9. Almost all utilitarian writing, where the goal is to convey information, not do it beautifully, will be written by AI. 10. In some ways, AI is the end of slop. So many Google search results are slop. LinkedIn posts are slop. The way Twitter got taken over by Threadbois in 2021 was also slop. AI-generated writing is already better than all of those things, so why would you read them now? 11. AI will be tougher on writers than readers. Readers will be exposed to some slop, but the Internet will be good about filtering it out. Writers, though, are now competing against ever-improving LLMs, which are getting better and better by the month. 12. Humans will contribute with unique data or perspectives. The famous Peter Thiel interview question doubles as a good writing prompt: “What very important truth do few people agree with you on?” 13. New technologies breed new kinds of art. Ever notice how flat 13th or 14th century Medieval art looks? And how different that art looks from the Renaissance art created in the 15th and 16th centuries? Technical innovations like the camera obscura and perspective grids are behind this. Similarly profound changes will come to the writing world because of AI (credit to Justin Murphy for the idea here). 14. Satya Nadella says: “The new workflow for me is I think with AI and work with my colleagues.” When it comes to discovering ideas, I've also found that jamming with an LLM is more productive than doing it with most people I know (save for a few giga-brain conversationalists). 15. Thought experiment: Will AI-writing be more like music or chess? With music, we don't care how a song is made. We just want it to be good. With chess, there's a huge market for watching human beings play even though the computers are already better. I think non-fiction writing will go the way of music. People won’t care how it was made. They’ll just care that it’s good. 16. AI has flipped the rules of tech adoption. Seasoned managers usually drag their feet with adopting new technology, but the ones I know love AI, while frontline workers struggle to see the point. My theory is that AI matches how managers already operate. Management has always been a kind of prompt engineering: set a vision, delegate, give feedback, iterate. But LLMs remove the drama that used to come with having a team. No 1-on-1s. No emotional tangles. It's like management without the headache. For frontline employees, things are different. They aren't as accustomed to setting a vision and giving feedback, so LLM prompting is a daunting and unfamiliar kind of work for them. 17. AI editors are already quite good. Sure, they aren’t as good as the world’s best editors, but they’re a fraction of the cost, they’ll instantly give you 80th percentile feedback, and they work 24/7. As a novelist recently said to me: “Paying an editor to review my novel costs me $7,000 and a 4-6 week turnaround time, whereas Claude costs me $1.25 and gets me results a few minutes later.” The edits definitely aren’t as good, but there’s a virtue to speed (and this guy isn’t a chump writer). 18. The way AI-skeptics hate on LLMs while using old models is like driving a ‘92 Honda while hating on a self-driving Tesla. 19. AI-generated fiction makes people very upset. A friend insists it’s like having sex with a robot. Doesn’t matter how good it is. It ain’t human-generated, and there’s something uniquely repulsive about that. I’ve shared the full conversation below. It’s a solo-episode of me riffing on what I’ve learned about AI for ~90 minutes. If you’d rather watch it on YouTube or listen on Apple or Spotify, I’ve shared the links in the reply tweets. And if you have any questions, I’ll be extra active in the replies for this episode.
1:29:31

I've spent hours and hours thinking about how AI is going to change writing. This is a 90-minute distillation of everything I've learned. Some things I believe: 1. The combination of LLM-driven humor and image generation means that we're about to enter the golden age of memes. 2. The best writers will be fine. Robert Caro and Dostoevsky aren’t about to be disrupted by ChatGPT. 3. What are the different models like? ChatGPT is your friend who makes a lot of good points, but it’s kinda boring, Claude is your hippie friend who loves to get vulnerable but takes the whole “express yourself” thing a little too far, and Grok is your unhinged friend who leans a little too hard into tinfoil hat theories, but is always a trip to jam on ideas with. 4. People who say that AI writing is low-quality aren’t realizing that quality exists along two dimensions: (1) the absolute quality of the writing and (2) how tailored the writing is to your interests at the time. 5. I’ll tell you this: What writers are doing with AI behind closed doors is a long way ahead of what's publicly understood. I don't expect this to change anytime soon because of the social stigma associated with AI-enhanced writing. Because of that, if you want to see the cutting edge, you're gonna have to piece things together through private conversations and group chats. 6. If you want to follow what's happening in AI, remember this quote from William Gibson: “The future is here, it’s just not evenly distributed yet.” You can get a glimpse of the future by looking at how a small percentage of writers are already using AI. 7. I’m bearish on writers who are currently using AI to write for them, and bullish on writers who are currently using AI to write with them. 8. What kinds of writing will continue to be written by humans? Ones that speak to our humanity. People are interested in people. Their stories, their struggles, their emotions, their drama. 9. Almost all utilitarian writing, where the goal is to convey information, not do it beautifully, will be written by AI. 10. In some ways, AI is the end of slop. So many Google search results are slop. LinkedIn posts are slop. The way Twitter got taken over by Threadbois in 2021 was also slop. AI-generated writing is already better than all of those things, so why would you read them now? 11. AI will be tougher on writers than readers. Readers will be exposed to some slop, but the Internet will be good about filtering it out. Writers, though, are now competing against ever-improving LLMs, which are getting better and better by the month. 12. Humans will contribute with unique data or perspectives. The famous Peter Thiel interview question doubles as a good writing prompt: “What very important truth do few people agree with you on?” 13. New technologies breed new kinds of art. Ever notice how flat 13th or 14th century Medieval art looks? And how different that art looks from the Renaissance art created in the 15th and 16th centuries? Technical innovations like the camera obscura and perspective grids are behind this. Similarly profound changes will come to the writing world because of AI (credit to Justin Murphy for the idea here). 14. Satya Nadella says: “The new workflow for me is I think with AI and work with my colleagues.” When it comes to discovering ideas, I've also found that jamming with an LLM is more productive than doing it with most people I know (save for a few giga-brain conversationalists). 15. Thought experiment: Will AI-writing be more like music or chess? With music, we don't care how a song is made. We just want it to be good. With chess, there's a huge market for watching human beings play even though the computers are already better. I think non-fiction writing will go the way of music. People won’t care how it was made. They’ll just care that it’s good. 16. AI has flipped the rules of tech adoption. Seasoned managers usually drag their feet with adopting new technology, but the ones I know love AI, while frontline workers struggle to see the point. My theory is that AI matches how managers already operate. Management has always been a kind of prompt engineering: set a vision, delegate, give feedback, iterate. But LLMs remove the drama that used to come with having a team. No 1-on-1s. No emotional tangles. It's like management without the headache. For frontline employees, things are different. They aren't as accustomed to setting a vision and giving feedback, so LLM prompting is a daunting and unfamiliar kind of work for them. 17. AI editors are already quite good. Sure, they aren’t as good as the world’s best editors, but they’re a fraction of the cost, they’ll instantly give you 80th percentile feedback, and they work 24/7. As a novelist recently said to me: “Paying an editor to review my novel costs me $7,000 and a 4-6 week turnaround time, whereas Claude costs me $1.25 and gets me results a few minutes later.” The edits definitely aren’t as good, but there’s a virtue to speed (and this guy isn’t a chump writer). 18. The way AI-skeptics hate on LLMs while using old models is like driving a ‘92 Honda while hating on a self-driving Tesla. 19. AI-generated fiction makes people very upset. A friend insists it’s like having sex with a robot. Doesn’t matter how good it is. It ain’t human-generated, and there’s something uniquely repulsive about that. I’ve shared the full conversation below. It’s a solo-episode of me riffing on what I’ve learned about AI for ~90 minutes. If you’d rather watch it on YouTube or listen on Apple or Spotify, I’ve shared the links in the reply tweets. And if you have any questions, I’ll be extra active in the replies for this episode.

David Perell

257,480 Aufrufe • vor 1 Jahr

Attention , Data and Capital rule WEB3! Kaito AI 🌊 has primed itself as the ultimate distribution center of this three! Here’s how you can make the most of KAITO,a long form post! This guide is my personal strategy to earning yaps and building long term mindshare, ignoring follower count! Let’s get into it: ⸻ What Even Is Yapping? Yapping is posting and engaging on Crypto Twitter in a way that’s: •Web3 relevant •Genuine and thoughtful •Not spammy or low effort •Seen and engaged by high reputation users Yaps are the points you earn from posts and replies, but how you earn them is what I wanna talk about now. How do you Earn Yaps? As I read from the Kaito FAQ, applied, and gotten results from, there are three things that determine how many yaps you earn and when; 1.Reputation weighted engagement: the most important one! 2.Web3 relevance: your posts MUST be relevant in Web3! 3.Insightful and original content: focus on quality over quantity! What does that actually mean? If you post a great crypto thread and no one with a good reputation in CT interacts, you likely get 0 yaps. If you post a viral meme that’s unrelated to crypto, same thing, you get 0 yaps. You need both quality and engagement from strong accounts in the Kaito inner circle. Here’s where the idea of Smart followers comes in, these are accounts with the “Inner Circle Badge” on Twitter. Their interactions on your posts or replies earn you yaps! It’s also important for you to understand Mindshare. Mindshare is simply how much conversation and attention you are able to generate around a specific project. Yaps = fuel. Mindshare = dominance. To build mindshare,: •Focus on 1 to 2 projects MAX. •Post about them 1 to 2 times daily. •Reply often to other Yappers posting about the same project •Engage directly with the project account and their team. •Watch who’s leading the mindshare leaderboard on the KAITO website and learn from their style and content. Note: Early engagement gives you a higher chance to earn more yaps. That’s why I focus on fresh or recently added projects with active or pending leaderboards. Here’s how Pick Projects to Yap About: If you know me, I’m never chasing short term hype, and I focus on long term projects. Here’s how I pick: •Projects I understand or am testing/using myself •Ones that reward Yappers in meaningful ways (roles, cash, recognition) Here are some I’m looking at right now (do your own research too!): •Monad •Lombard •burner •Kaito AI 🌊 •Succinct •Infinex (massive rewards) •Allora •Humanity •OpenLedger (hot competition) What your daily KAITO schedule should look like: 1 to 2 solid tweets per project •No low effort “gm” posts, be intentional about every single post or reply you make! •Focus on your thoughts, project features, new updates 2.Reply to official project tweets, engage on founder and team member’s accounts too! 3.Engage other Yappers talking about the same project,be a good reply guy. 4.Retweet the project’s major posts. 5.Use project specific images. 6.Keep your posts insightful and project relevant! Also, clean up your profile (your bio, banner and pinned tweet must reflect clearly that you’re active in Web3. First impressions matter when people are deciding whether to interact with your profile or not! -Some tips to grow faster as a small account on KAITO. •Subscribe to X Premium (seriously helps with visibility) •Check the Discord of the projects you support for updates. •Track Kaito’s market page to see hot Inner Circle accounts. •Engage with leaderboard users in a genuine way -What Projects should you Yap about? There are 40+ Pre TGE projects you can yap about right now under different sectors AI & Data •Allora •OpenLedger 🔥 •Camp Network •Hyperbolic . Listen to KAITO founder 👇
2:50

Attention , Data and Capital rule WEB3! Kaito AI 🌊 has primed itself as the ultimate distribution center of this three! Here’s how you can make the most of KAITO,a long form post! This guide is my personal strategy to earning yaps and building long term mindshare, ignoring follower count! Let’s get into it: ⸻ What Even Is Yapping? Yapping is posting and engaging on Crypto Twitter in a way that’s: •Web3 relevant •Genuine and thoughtful •Not spammy or low effort •Seen and engaged by high reputation users Yaps are the points you earn from posts and replies, but how you earn them is what I wanna talk about now. How do you Earn Yaps? As I read from the Kaito FAQ, applied, and gotten results from, there are three things that determine how many yaps you earn and when; 1.Reputation weighted engagement: the most important one! 2.Web3 relevance: your posts MUST be relevant in Web3! 3.Insightful and original content: focus on quality over quantity! What does that actually mean? If you post a great crypto thread and no one with a good reputation in CT interacts, you likely get 0 yaps. If you post a viral meme that’s unrelated to crypto, same thing, you get 0 yaps. You need both quality and engagement from strong accounts in the Kaito inner circle. Here’s where the idea of Smart followers comes in, these are accounts with the “Inner Circle Badge” on Twitter. Their interactions on your posts or replies earn you yaps! It’s also important for you to understand Mindshare. Mindshare is simply how much conversation and attention you are able to generate around a specific project. Yaps = fuel. Mindshare = dominance. To build mindshare,: •Focus on 1 to 2 projects MAX. •Post about them 1 to 2 times daily. •Reply often to other Yappers posting about the same project •Engage directly with the project account and their team. •Watch who’s leading the mindshare leaderboard on the KAITO website and learn from their style and content. Note: Early engagement gives you a higher chance to earn more yaps. That’s why I focus on fresh or recently added projects with active or pending leaderboards. Here’s how Pick Projects to Yap About: If you know me, I’m never chasing short term hype, and I focus on long term projects. Here’s how I pick: •Projects I understand or am testing/using myself •Ones that reward Yappers in meaningful ways (roles, cash, recognition) Here are some I’m looking at right now (do your own research too!): •Monad •Lombard •burner •Kaito AI 🌊 •Succinct •Infinex (massive rewards) •Allora •Humanity •OpenLedger (hot competition) What your daily KAITO schedule should look like: 1 to 2 solid tweets per project •No low effort “gm” posts, be intentional about every single post or reply you make! •Focus on your thoughts, project features, new updates 2.Reply to official project tweets, engage on founder and team member’s accounts too! 3.Engage other Yappers talking about the same project,be a good reply guy. 4.Retweet the project’s major posts. 5.Use project specific images. 6.Keep your posts insightful and project relevant! Also, clean up your profile (your bio, banner and pinned tweet must reflect clearly that you’re active in Web3. First impressions matter when people are deciding whether to interact with your profile or not! -Some tips to grow faster as a small account on KAITO. •Subscribe to X Premium (seriously helps with visibility) •Check the Discord of the projects you support for updates. •Track Kaito’s market page to see hot Inner Circle accounts. •Engage with leaderboard users in a genuine way -What Projects should you Yap about? There are 40+ Pre TGE projects you can yap about right now under different sectors AI & Data •Allora •OpenLedger 🔥 •Camp Network •Hyperbolic . Listen to KAITO founder 👇

DUKE 🇲🇾

92,358 Aufrufe • vor 1 Jahr

MIT announces the Initiative for New Manufacturing | Peter Dizikes, MIT News The Institute-wide effort aims to bolster industry and create jobs by driving innovation across vital manufacturing sectors. MIT today launched its Initiative for New Manufacturing (INM), an Institute-wide effort to reinfuse U.S. industrial production with leading-edge technologies, bolster crucial U.S. economic sectors, and ignite job creation. The initiative will encompass advanced research, innovative education programs, and partnership with companies across many sectors, in a bid to help transform manufacturing and elevate its impact. “We want to work with firms big and small, in cities, small towns and everywhere in between, to help them adopt new approaches for increased productivity,” MIT President Sally A. Kornbluth wrote in a letter to the Institute community this morning. “We want to deliberately design high-quality, human-centered manufacturing jobs that bring new life to communities across the country.” Kornbluth added: “Helping America build a future of new manufacturing is a perfect job for MIT — and I’m convinced that there is no more important work we can do to meet the moment and serve the nation now.” The Initiative for New Manufacturing also announced its first six founding industry consortium members: Amgen, Flex, GE Vernova, PTC, Sanofi, and Siemens. Participants in the INM Industry Consortium will support seed projects proposed by MIT researchers, initially in the area of artificial intelligence for manufacturing. INM joins the ranks of MIT’s other presidential initiatives — including The Climate Project at MIT; MITHIC, which supports the human-centered disciplines; MIT HEALS, centered on the life sciences and health; and MGAIC, the MIT Generative AI Impact Consortium. “There is tremendous opportunity to bring together a vibrant community working across every scale — from nanotechnology to large-scale manufacturing — and across a wide-range of applications including semiconductors, medical devices, automotive, energy systems, and biotechnology,” says Anantha Chandrakasan, MIT’s chief innovation and strategy officer and dean of engineering, who is part of the initiative’s leadership team. “MIT is uniquely positioned to harness the transformative power of digital tools and AI to shape future of manufacturing. I’m truly excited about what we can build together and the synergies this creates with other cross-cutting initiatives across the Institute.” The initiative is just the latest MIT-centered effort in recent decades aiming to expand American manufacturing. A faculty research group wrote the 1989 bestseller “Made in America: Regaining the Productive Edge,” advocating for a renewal of manufacturing; another MIT project, called Production in the Innovation Economy, called for expanded manufacturing in the early 2010s. In 2016, MIT also founded The Engine, a venture fund investing in hardware-based “tough tech” start-ups including many with potential to became substantial manufacturing firms. As developed, the MIT Initiative for New Manufacturing is based around four major themes: - Reimagining manufacturing technologies and systems: realizing breakthrough technologies and system-level approaches to advance energy production, health care, computing, transportation, consumer products, and more; - Elevating the productivity and experience of manufacturing: developing and deploying new digitally driven methods and tools to amplify productivity and improve the human experience of manufacturing; - Scaling new manufacturing: accelerating the scaling of manufacturing companies and transforming supply chains to maximize efficiency and resilience, fostering product innovation and business growth; and - Transforming the manufacturing base: driving the deployment of a sustainable global manufacturing ecosystem that provides compelling opportunities to workers, with major efforts focused on the U.S. The initiative has mapped out many concrete activities and programs, which will include an Institute-wide research program on emerging technologies and other major topics; workforce and education programs; and industry engagement and participation. INM also aims to establish new labs for developing manufacturing tools and techniques; a “factory observatory” program which immerses students in manufacturing through visits to production sites; and key “pillars” focusing on areas from semiconductors and biomanufacturing to defense and aviation. The workforce and education element of INM will include TechAMP, an MIT-created program that works with community colleges to bridge the gap between technicians and engineers; AI-driven teaching tools; professional education; and an effort to expand manufacturing education on campus in collaboration with MIT departments and degree programs. INM’s leadership team has three faculty co-directors: John Hart, the Class of 1922 Professor and head of the Department of Mechanical Engineering; Suzanne Berger, Institute Professor at MIT and a political scientist who has conducted influential empirical studies of manufacturing; and Chris Love, the Raymond A. and Helen E. St. Laurent Professor of Chemical Engineering. The initiative’s executive director is Julie Diop. The initiative is in the process of forming a faculty steering committee with representation from across the Institute, as well as an external advisory board. INM stems partly from the work of the Manufacturing@MIT working group, formed in 2022 to assess many of these issues. The launch of the new initiative was previewed at a daylong MIT symposium on May 7, titled “A Vision for New Manufacturing.” The event, held before a capacity audience in MIT’s Wong Auditorium, featured over 30 speakers from a wide range of manufacturing sectors. “The rationale for growing and transforming U.S. manufacturing has never been more urgent than it is today,” Berger said at the event. “What we are trying to build at MIT now is not just another research project. … Together, with people in this room and outside this room, we’re trying to change what’s happening in our country.” “We need to think about the importance of manufacturing again, because it is what brings product ideas to people,” Love told MIT News. “For instance, in biotechnology, new life-saving medicines can’t reach patients without manufacturing. There is a real urgency about this issue for both economic prosperity and creating jobs. We have seen the impact for our country when we have lost our lead in manufacturing in some sectors. Biotechnology, where the U.S. has been the global leader for more than 40 years, offers the potential to promote new robust economies here, but we need to advance our capabilities in biomanufacturing to maintain our advantage in this area.” Hart adds: “While manufacturing feels very timely today, it is of enduring importance. Manufactured products enable our daily lives and manufacturing is critical to advancing the frontiers of technology and society. Our efforts leading up to launch of the initiative revealed great excitement about manufacturing across MIT, especially from students. Working with industry — from small to large companies, and from young startups to industrial giants — will be instrumental to creating impact and realizing the vision for new manufacturing.” In her letter to the MIT community today, Kornbluth stressed that the initiative’s goal is to drive transformation by making manufacturing more productive, resilient, and sustainable. “We want to reimagine manufacturing technologies and systems to advance fields like energy production, health care, computing, transportation, consumer products, and more,” she wrote. “And we want to reach well beyond the shop floor to tackle challenges like how to make supply chains more resilient, and how to inform public policy to foster a broad, healthy manufacturing ecosystem that can drive decades of innovation and growth.”
3:54

MIT announces the Initiative for New Manufacturing | Peter Dizikes, MIT News The Institute-wide effort aims to bolster industry and create jobs by driving innovation across vital manufacturing sectors. MIT today launched its Initiative for New Manufacturing (INM), an Institute-wide effort to reinfuse U.S. industrial production with leading-edge technologies, bolster crucial U.S. economic sectors, and ignite job creation. The initiative will encompass advanced research, innovative education programs, and partnership with companies across many sectors, in a bid to help transform manufacturing and elevate its impact. “We want to work with firms big and small, in cities, small towns and everywhere in between, to help them adopt new approaches for increased productivity,” MIT President Sally A. Kornbluth wrote in a letter to the Institute community this morning. “We want to deliberately design high-quality, human-centered manufacturing jobs that bring new life to communities across the country.” Kornbluth added: “Helping America build a future of new manufacturing is a perfect job for MIT — and I’m convinced that there is no more important work we can do to meet the moment and serve the nation now.” The Initiative for New Manufacturing also announced its first six founding industry consortium members: Amgen, Flex, GE Vernova, PTC, Sanofi, and Siemens. Participants in the INM Industry Consortium will support seed projects proposed by MIT researchers, initially in the area of artificial intelligence for manufacturing. INM joins the ranks of MIT’s other presidential initiatives — including The Climate Project at MIT; MITHIC, which supports the human-centered disciplines; MIT HEALS, centered on the life sciences and health; and MGAIC, the MIT Generative AI Impact Consortium. “There is tremendous opportunity to bring together a vibrant community working across every scale — from nanotechnology to large-scale manufacturing — and across a wide-range of applications including semiconductors, medical devices, automotive, energy systems, and biotechnology,” says Anantha Chandrakasan, MIT’s chief innovation and strategy officer and dean of engineering, who is part of the initiative’s leadership team. “MIT is uniquely positioned to harness the transformative power of digital tools and AI to shape future of manufacturing. I’m truly excited about what we can build together and the synergies this creates with other cross-cutting initiatives across the Institute.” The initiative is just the latest MIT-centered effort in recent decades aiming to expand American manufacturing. A faculty research group wrote the 1989 bestseller “Made in America: Regaining the Productive Edge,” advocating for a renewal of manufacturing; another MIT project, called Production in the Innovation Economy, called for expanded manufacturing in the early 2010s. In 2016, MIT also founded The Engine, a venture fund investing in hardware-based “tough tech” start-ups including many with potential to became substantial manufacturing firms. As developed, the MIT Initiative for New Manufacturing is based around four major themes: - Reimagining manufacturing technologies and systems: realizing breakthrough technologies and system-level approaches to advance energy production, health care, computing, transportation, consumer products, and more; - Elevating the productivity and experience of manufacturing: developing and deploying new digitally driven methods and tools to amplify productivity and improve the human experience of manufacturing; - Scaling new manufacturing: accelerating the scaling of manufacturing companies and transforming supply chains to maximize efficiency and resilience, fostering product innovation and business growth; and - Transforming the manufacturing base: driving the deployment of a sustainable global manufacturing ecosystem that provides compelling opportunities to workers, with major efforts focused on the U.S. The initiative has mapped out many concrete activities and programs, which will include an Institute-wide research program on emerging technologies and other major topics; workforce and education programs; and industry engagement and participation. INM also aims to establish new labs for developing manufacturing tools and techniques; a “factory observatory” program which immerses students in manufacturing through visits to production sites; and key “pillars” focusing on areas from semiconductors and biomanufacturing to defense and aviation. The workforce and education element of INM will include TechAMP, an MIT-created program that works with community colleges to bridge the gap between technicians and engineers; AI-driven teaching tools; professional education; and an effort to expand manufacturing education on campus in collaboration with MIT departments and degree programs. INM’s leadership team has three faculty co-directors: John Hart, the Class of 1922 Professor and head of the Department of Mechanical Engineering; Suzanne Berger, Institute Professor at MIT and a political scientist who has conducted influential empirical studies of manufacturing; and Chris Love, the Raymond A. and Helen E. St. Laurent Professor of Chemical Engineering. The initiative’s executive director is Julie Diop. The initiative is in the process of forming a faculty steering committee with representation from across the Institute, as well as an external advisory board. INM stems partly from the work of the Manufacturing@MIT working group, formed in 2022 to assess many of these issues. The launch of the new initiative was previewed at a daylong MIT symposium on May 7, titled “A Vision for New Manufacturing.” The event, held before a capacity audience in MIT’s Wong Auditorium, featured over 30 speakers from a wide range of manufacturing sectors. “The rationale for growing and transforming U.S. manufacturing has never been more urgent than it is today,” Berger said at the event. “What we are trying to build at MIT now is not just another research project. … Together, with people in this room and outside this room, we’re trying to change what’s happening in our country.” “We need to think about the importance of manufacturing again, because it is what brings product ideas to people,” Love told MIT News. “For instance, in biotechnology, new life-saving medicines can’t reach patients without manufacturing. There is a real urgency about this issue for both economic prosperity and creating jobs. We have seen the impact for our country when we have lost our lead in manufacturing in some sectors. Biotechnology, where the U.S. has been the global leader for more than 40 years, offers the potential to promote new robust economies here, but we need to advance our capabilities in biomanufacturing to maintain our advantage in this area.” Hart adds: “While manufacturing feels very timely today, it is of enduring importance. Manufactured products enable our daily lives and manufacturing is critical to advancing the frontiers of technology and society. Our efforts leading up to launch of the initiative revealed great excitement about manufacturing across MIT, especially from students. Working with industry — from small to large companies, and from young startups to industrial giants — will be instrumental to creating impact and realizing the vision for new manufacturing.” In her letter to the MIT community today, Kornbluth stressed that the initiative’s goal is to drive transformation by making manufacturing more productive, resilient, and sustainable. “We want to reimagine manufacturing technologies and systems to advance fields like energy production, health care, computing, transportation, consumer products, and more,” she wrote. “And we want to reach well beyond the shop floor to tackle challenges like how to make supply chains more resilient, and how to inform public policy to foster a broad, healthy manufacturing ecosystem that can drive decades of innovation and growth.”

Owen Gregorian

77,197 Aufrufe • vor 1 Jahr

The Great Equalizer: How I Iterated Through 90+ Strategies to Automate My Financial Freedom ninety strategies sounds like a death wish but it is actually the only way to find your edge in a market designed to liquidate you. most traders are out here gambling with their rent money while the big players are using automated systems to harvest their liquidations. i know this because i spent hundreds of thousands of dollars on developers for apps thinking i could never code myself. i was getting wrecked by over trading and watching my accounts hit zero while i slept. code became the great equalizer for me because it removed the emotion that was killing my bankroll. i decided to learn to code live so i could iterate to success and now i have fully automated systems trading for me instead of getting liquidated by every wick. i just saw someone lose ten million dollars in a single month because they were trading by hand and got addicted to the screen. you have to understand that if you are not automating you are the exit liquidity for someone who is. the reality of advanced futures trading is not about finding one holy grail bot that prints money forever. it is about research and back testing until you find a strategy that has a statistical advantage. one of the most slept on concepts is variable risk scaling where you actually change your position size based on how volatile the market is. instead of just betting the same amount every time you increase your size when volatility is low and scale back when the market starts moving like crazy. this keeps you in the game during the draw downs that usually wipe people out. most people do the opposite and revenge trade with bigger size when they are losing which is the fastest way to the cemetery. i used to think i needed to be the smartest guy in the room to make this work but i realized i just needed to be the most disciplined with my risk parameters. there is a secret hidden in funding rates and basis trading that most retail traders never even look at. while everyone else is trying to guess if bitcoin is going to the moon or the floor you can actually make consistent money through funding rate arbitrage. you basically buy the asset in the spot market and simultaneously sell it in the futures market when the funding rate is high. you just sit there and collect the interest payments from the gamblers who are over leveraged on the other side. it is basically free money if you can manage the fees and keep your execution precise. i used to ignore these low yield plays because i wanted the big home runs but those home runs usually came with massive strikeouts. now i look for these carry trades as a way to keep the equity curve moving up and to the right while others are sweating over every price change. most traders fail because they use lagging indicators and expect them to predict the future with one hundred percent accuracy. the truth is that even the best trend following strategies like the golden cross or moving average crossovers only have about sixty five percent accuracy. you have to combine these with filters like the average directional index or relative strength index to make sure you are not just buying a fake breakout. a lot of people get chopped up in sideways markets because they do not have a trend strength filter to tell them to stay out of the trade. i learned to use multiple time frames to confirm my breakouts because if the one hour and the four hour charts are not saying the same thing then the trade is probably a trap. you have to be a searcher looking for those golden nuggets of alpha buried in mountains of data. i used to think that machine learning and genetic algorithms were just buzzwords that did not actually work for trading. then i realized that the 1990s tech trap is real and if you are still using basic indicators without any optimization you are decades behind. genetic algorithms are wild because they simulate natural selection to find the best parameters for your strategy through trial and error. you can actually build an environment where your bot learns from its own mistakes and optimizes its decision making process over time. i spent so much time thinking i was not smart enough to do this but once i started iterating live i found that the machines are much better at following rules than i ever was. code is the only way to compete with the high frequency firms that are looking for any tiny mispricing in the order book. slippage and bad execution will eat your profits faster than a bad trade ever could if you are not careful. most people just hit the market buy button and pay the spread and the fees without a second thought. you should be using smart order routing and limit orders to capture the bid ask spread instead of paying it to the market makers. i started using time weighted average price execution to spread my larger orders out over time so i did not move the market against myself. it is these tiny details in execution that separate the professional quants from the people who are just playing around. i had to learn this the hard way after losing a fortune on bad entries and exits that could have been avoided with a few lines of code. the ultimate goal of all of this is to build a compounding machine that grows your capital while you are living your life. you have to automate the reinvestment of your profits so that your position sizes grow as your account grows without you having to manually adjust anything. i like to use automated compounding algorithms that take a portion of my wins and put them back into the systems that are performing the best. this creates a snowball effect where your returns start to accelerate as the base capital increases. it took me years to realize that i did not need to be at the desk for eighteen hours a day to make life changing money. i just needed to build a system that was smarter and more disciplined than my own human brain. cross asset skew and volatility surface arbitrage are where the real quants play when the market gets efficient. you can look for mispricings between highly correlated assets like bitcoin and ethereum and trade the spread between them. when one asset gets overvalued relative to the other you short the leader and long the laggard until they revert back to the mean. this is a much safer way to trade because you are not betting on the direction of the market but rather the relationship between two assets. i spent a lot of money trying to guess the next big move before i realized that trading the relationship between assets was much more consistent. iteration is the only way to find these winks in the market that the average trader is completely blind to. it is a cold world in finance and most people are out here trying to step on your neck to get ahead. i believe that sharing this knowledge is important because code is the only thing that can give a regular person a fighting chance against the institutions. i started from zero and learned everything through failing and losing money until i finally figured out how to automate. now i spend my time building and testing instead of worrying about the next liquidation candle. you have to decide today if you want to keep being the exit liquidity or if you want to start building your own systems. the tools are all there and the data is accessible if you are willing to put in the work and stop negotiating with yourself. successful trading is not about being lucky it is about being prepared and having a system that can handle any market regime. whether the market is in a bull run or a total crash your bots should know exactly what to do based on the rules you have coded into them. i use risk weighted allocation to make sure that my capital is always moving toward the strategies with the highest sharp ratio and the lowest volatility. this keeps the portfolio stable even when the crypto market is going through its typical insane swings. i finally found peace in this game because i know that my automated systems are following the math while everyone else is following their feelings. code is the great equalizer and it is time for you to start using it to protect your future and build your empire there are over ninety strategies you can test and most of them will not work for your specific style but you only need one or two to change your life. i have built a fat list of ideas from research and i spend every day back testing and refining them to stay ahead of the curve. do not let the fear of coding stop you from taking control of your financial destiny because i am living proof that anyone can learn. i would rather spend my time iterating to success than getting liquidated by some random news event that i could not predict. the journey from losing hundreds of thousands to fully automated success was long but it was the best investment i ever made. keep your heart open and lead with love in this game and i promise the universe will start passing you those golden nuggets of alpha you have been searching for
2:40:29

The Great Equalizer: How I Iterated Through 90+ Strategies to Automate My Financial Freedom ninety strategies sounds like a death wish but it is actually the only way to find your edge in a market designed to liquidate you. most traders are out here gambling with their rent money while the big players are using automated systems to harvest their liquidations. i know this because i spent hundreds of thousands of dollars on developers for apps thinking i could never code myself. i was getting wrecked by over trading and watching my accounts hit zero while i slept. code became the great equalizer for me because it removed the emotion that was killing my bankroll. i decided to learn to code live so i could iterate to success and now i have fully automated systems trading for me instead of getting liquidated by every wick. i just saw someone lose ten million dollars in a single month because they were trading by hand and got addicted to the screen. you have to understand that if you are not automating you are the exit liquidity for someone who is. the reality of advanced futures trading is not about finding one holy grail bot that prints money forever. it is about research and back testing until you find a strategy that has a statistical advantage. one of the most slept on concepts is variable risk scaling where you actually change your position size based on how volatile the market is. instead of just betting the same amount every time you increase your size when volatility is low and scale back when the market starts moving like crazy. this keeps you in the game during the draw downs that usually wipe people out. most people do the opposite and revenge trade with bigger size when they are losing which is the fastest way to the cemetery. i used to think i needed to be the smartest guy in the room to make this work but i realized i just needed to be the most disciplined with my risk parameters. there is a secret hidden in funding rates and basis trading that most retail traders never even look at. while everyone else is trying to guess if bitcoin is going to the moon or the floor you can actually make consistent money through funding rate arbitrage. you basically buy the asset in the spot market and simultaneously sell it in the futures market when the funding rate is high. you just sit there and collect the interest payments from the gamblers who are over leveraged on the other side. it is basically free money if you can manage the fees and keep your execution precise. i used to ignore these low yield plays because i wanted the big home runs but those home runs usually came with massive strikeouts. now i look for these carry trades as a way to keep the equity curve moving up and to the right while others are sweating over every price change. most traders fail because they use lagging indicators and expect them to predict the future with one hundred percent accuracy. the truth is that even the best trend following strategies like the golden cross or moving average crossovers only have about sixty five percent accuracy. you have to combine these with filters like the average directional index or relative strength index to make sure you are not just buying a fake breakout. a lot of people get chopped up in sideways markets because they do not have a trend strength filter to tell them to stay out of the trade. i learned to use multiple time frames to confirm my breakouts because if the one hour and the four hour charts are not saying the same thing then the trade is probably a trap. you have to be a searcher looking for those golden nuggets of alpha buried in mountains of data. i used to think that machine learning and genetic algorithms were just buzzwords that did not actually work for trading. then i realized that the 1990s tech trap is real and if you are still using basic indicators without any optimization you are decades behind. genetic algorithms are wild because they simulate natural selection to find the best parameters for your strategy through trial and error. you can actually build an environment where your bot learns from its own mistakes and optimizes its decision making process over time. i spent so much time thinking i was not smart enough to do this but once i started iterating live i found that the machines are much better at following rules than i ever was. code is the only way to compete with the high frequency firms that are looking for any tiny mispricing in the order book. slippage and bad execution will eat your profits faster than a bad trade ever could if you are not careful. most people just hit the market buy button and pay the spread and the fees without a second thought. you should be using smart order routing and limit orders to capture the bid ask spread instead of paying it to the market makers. i started using time weighted average price execution to spread my larger orders out over time so i did not move the market against myself. it is these tiny details in execution that separate the professional quants from the people who are just playing around. i had to learn this the hard way after losing a fortune on bad entries and exits that could have been avoided with a few lines of code. the ultimate goal of all of this is to build a compounding machine that grows your capital while you are living your life. you have to automate the reinvestment of your profits so that your position sizes grow as your account grows without you having to manually adjust anything. i like to use automated compounding algorithms that take a portion of my wins and put them back into the systems that are performing the best. this creates a snowball effect where your returns start to accelerate as the base capital increases. it took me years to realize that i did not need to be at the desk for eighteen hours a day to make life changing money. i just needed to build a system that was smarter and more disciplined than my own human brain. cross asset skew and volatility surface arbitrage are where the real quants play when the market gets efficient. you can look for mispricings between highly correlated assets like bitcoin and ethereum and trade the spread between them. when one asset gets overvalued relative to the other you short the leader and long the laggard until they revert back to the mean. this is a much safer way to trade because you are not betting on the direction of the market but rather the relationship between two assets. i spent a lot of money trying to guess the next big move before i realized that trading the relationship between assets was much more consistent. iteration is the only way to find these winks in the market that the average trader is completely blind to. it is a cold world in finance and most people are out here trying to step on your neck to get ahead. i believe that sharing this knowledge is important because code is the only thing that can give a regular person a fighting chance against the institutions. i started from zero and learned everything through failing and losing money until i finally figured out how to automate. now i spend my time building and testing instead of worrying about the next liquidation candle. you have to decide today if you want to keep being the exit liquidity or if you want to start building your own systems. the tools are all there and the data is accessible if you are willing to put in the work and stop negotiating with yourself. successful trading is not about being lucky it is about being prepared and having a system that can handle any market regime. whether the market is in a bull run or a total crash your bots should know exactly what to do based on the rules you have coded into them. i use risk weighted allocation to make sure that my capital is always moving toward the strategies with the highest sharp ratio and the lowest volatility. this keeps the portfolio stable even when the crypto market is going through its typical insane swings. i finally found peace in this game because i know that my automated systems are following the math while everyone else is following their feelings. code is the great equalizer and it is time for you to start using it to protect your future and build your empire there are over ninety strategies you can test and most of them will not work for your specific style but you only need one or two to change your life. i have built a fat list of ideas from research and i spend every day back testing and refining them to stay ahead of the curve. do not let the fear of coding stop you from taking control of your financial destiny because i am living proof that anyone can learn. i would rather spend my time iterating to success than getting liquidated by some random news event that i could not predict. the journey from losing hundreds of thousands to fully automated success was long but it was the best investment i ever made. keep your heart open and lead with love in this game and i promise the universe will start passing you those golden nuggets of alpha you have been searching for

Moon Dev

11,164 Aufrufe • vor 5 Monaten

Why Opus 4.6 Is The Final Boss Of Algorithmic Trading (Full Bot Build) the day of the human trader is officially over and most people are still staring at charts like it is 1995. wall street is terrified because the barrier to entry just got deleted by a piece of software that can outthink a stanford graduate in seconds. they want you to believe that you need a multi million dollar education to compete with the big banks. they want you to stay stuck in the cycle of emotional trading and leverage because that is how they pay for their hamptons houses. but there is a specific reason why every retail trader is about to become obsolete unless they pivot right now. i am going to show you exactly why your current strategy is a mathematical death trap and how a single jump in technology just changed the game forever every time you sit down at your computer to draw lines on a chart you are entering a gunfight with a toothpick. the institutions have been using high frequency algorithms for decades while you are trying to guess which way the candle is going to move based on a feeling in your gut. it is not a fair fight and it was never intended to be. last year we were looking at models that could barely handle basic logic but now the intelligence has scaled to a point where the machines are finding edges we did not even know existed. there is a ghost in the machine that is pulling out strategies with sharp ratios so high they look like typos. if you do not understand how to harness this power you are essentially donating your capital to the people who already have too much of it i spent hundreds of thousands of dollars on developers because i was too scared to learn how to code myself. i thought that being the idea guy was enough and that i could just hire people from upwork to build my dreams. i got rinsed for years paying for apps and bots that did not work because i did not have my hands on the wheel. it took losing a massive amount of money through liquidations and over trading to realize that nobody was coming to save me. i had to become the person who could build the systems or i was going to be another statistic in the graveyard of traders who thought they were smarter than the math. once i finally sat down and forced myself to understand the syntax everything shifted and the world became a giant playground of data the truth is that code is the great equalizer because it does not care where you came from or what school you went to. i got held back in seventh grade and my teacher told me i would not make it around here. that kind of talk is meant to keep you in your place but the computer does not have a bias. if you can write the logic the system will execute it exactly as told regardless of your background. we are living in a time where a kid in a basement can build a system that rivals a hedge fund because the big tech companies are subsidizing our intelligence. they are spending hundreds of billions of dollars on infrastructure and we are the ones who get to reap the rewards of their competition most people fail in this game because they fall in love with a single idea and refuse to let it go even when it is burning their account to the ground. they spend months or years trying to make one indicator work when the data clearly shows it is trash. you have to drop the ego and realize that your intuition is probably your biggest liability. the secret to winning is iterating to success by testing a hundred ideas until you find the one that actually sticks. i call it the rbi system which stands for research backtest and implement. if you skip any of these steps you are just gambling with extra steps and the house always wins in the end research is where most traders get lazy because they just want a magic bot that prints money while they sleep. they go to youtube and find some guy promising a ninety percent win rate with a rsi crossover. that is not research that is falling for marketing fluff designed to sell you a dream. real research happens when you dive into white papers and study what the quants are actually doing on wall street. you look for market inefficiencies like liquidation clusters and cross exchange discrepancies that are hidden in plain sight. by the time you finish this process you should have a list of ideas that are grounded in reality instead of wishful thinking backtesting is the filter that saves you from losing your life savings on a bad hunch. most people use tools that repaint or give them false confidence because the data is not being handled correctly. if you are using a basic charting platform to see if your strategy works you are likely seeing a version of history that does not exist. you need to use raw python libraries like backtesting py to see the cold hard truth of how your logic would have performed. when you see a drawdown of thirty percent on paper you realize that using ten times leverage would have deleted your account five times over. the math does not lie and it is the only thing that can protect you from your own greed the most dangerous drug in the world is leverage because it makes you feel like a genius right before it makes you a pauper. i have watched two billion dollars get liquidated in a single day because people thought they could predict the bottom with fifty times leverage. the exchanges can see exactly where your liquidation price is and they have every incentive to push the price there to hunt your liquidity. you are playing in a casino where the house can see your cards and they are actively trying to take them from you. the only way to win is to stop playing their game and start using limit orders to save on the fees that are slowly bleeding you dry it is funny how much money people will spend on food and entertainment but they will hesitate to invest in their own education. they will spend a thousand dollars on a weekend out but will not put that same money into learning a skill that could provide for them for the rest of their lives. money is just a tool of exchange and it always replenishes if you are providing value to the world. if you spend your capital on knowledge you are buying back your time and your freedom. i decided to live my life on youtube and build in public because i wanted to show people that a regular guy could do this. now i have fully automated systems trading for me while i sleep and i never have to worry about getting licked by a sudden market move again chasing the greats like jim simons is not about the money it is about the mastery of the system. he ran up a net worth of over thirty billion dollars by doing exactly what we are talking about here. he did not stare at charts all day and hope for the best he built models that exploited the mathematical laws of the market. he was a scientist first and a trader second and that is the mindset you need to adopt. if you are not approaching this quantitatively you are just a gambler who happens to be sitting at a computer. the goal is to become a quant researcher who happens to have robots executing their findings the transition from hand trader to automated builder is the most liberating thing you can do for your mental health. you go from waking up in a cold sweat checking your phone to waking up and checking your logs to see how the system performed. even if the day was red you have data that tells you why and you can use that to make the system better tomorrow. it is a process of constant improvement and refinement that never really ends. you are building a legacy of code that will continue to work for you as long as the electricity is running. i am not afraid to die on a treadmill because i know that i will outwork anyone who is just looking for a shortcut if you are still on the fence about whether or not you can do this just remember that i was exactly where you are. i was losing money and feeling like the market was rigged against me because it actually was. i had to decide that i was going to change my environment and take control of my own destiny. you have the same opportunity right now to pivot and start building your own automated future. the models are getting better every single day and the barrier to entry is lower than it has ever been in human history. you just have to decide to lock in and do the work for a thousand days until you become undeniable there is no better feeling than finding a strategy that has a sharp ratio over ten and knowing that you built it with your own two hands. it is a moment of pure clarity where you realize that you are no longer a victim of the market. you are the architect of your own financial reality and the possibilities are literally endless. i am going to keep sharing everything i find because i believe that we can take on wall street together. as long as i am breathing i will be stepping on the gas and pushing the boundaries of what is possible with code. welcome to the family and let's get after it because the machines are already running and they are not waiting for anyone
2:49:30

Why Opus 4.6 Is The Final Boss Of Algorithmic Trading (Full Bot Build) the day of the human trader is officially over and most people are still staring at charts like it is 1995. wall street is terrified because the barrier to entry just got deleted by a piece of software that can outthink a stanford graduate in seconds. they want you to believe that you need a multi million dollar education to compete with the big banks. they want you to stay stuck in the cycle of emotional trading and leverage because that is how they pay for their hamptons houses. but there is a specific reason why every retail trader is about to become obsolete unless they pivot right now. i am going to show you exactly why your current strategy is a mathematical death trap and how a single jump in technology just changed the game forever every time you sit down at your computer to draw lines on a chart you are entering a gunfight with a toothpick. the institutions have been using high frequency algorithms for decades while you are trying to guess which way the candle is going to move based on a feeling in your gut. it is not a fair fight and it was never intended to be. last year we were looking at models that could barely handle basic logic but now the intelligence has scaled to a point where the machines are finding edges we did not even know existed. there is a ghost in the machine that is pulling out strategies with sharp ratios so high they look like typos. if you do not understand how to harness this power you are essentially donating your capital to the people who already have too much of it i spent hundreds of thousands of dollars on developers because i was too scared to learn how to code myself. i thought that being the idea guy was enough and that i could just hire people from upwork to build my dreams. i got rinsed for years paying for apps and bots that did not work because i did not have my hands on the wheel. it took losing a massive amount of money through liquidations and over trading to realize that nobody was coming to save me. i had to become the person who could build the systems or i was going to be another statistic in the graveyard of traders who thought they were smarter than the math. once i finally sat down and forced myself to understand the syntax everything shifted and the world became a giant playground of data the truth is that code is the great equalizer because it does not care where you came from or what school you went to. i got held back in seventh grade and my teacher told me i would not make it around here. that kind of talk is meant to keep you in your place but the computer does not have a bias. if you can write the logic the system will execute it exactly as told regardless of your background. we are living in a time where a kid in a basement can build a system that rivals a hedge fund because the big tech companies are subsidizing our intelligence. they are spending hundreds of billions of dollars on infrastructure and we are the ones who get to reap the rewards of their competition most people fail in this game because they fall in love with a single idea and refuse to let it go even when it is burning their account to the ground. they spend months or years trying to make one indicator work when the data clearly shows it is trash. you have to drop the ego and realize that your intuition is probably your biggest liability. the secret to winning is iterating to success by testing a hundred ideas until you find the one that actually sticks. i call it the rbi system which stands for research backtest and implement. if you skip any of these steps you are just gambling with extra steps and the house always wins in the end research is where most traders get lazy because they just want a magic bot that prints money while they sleep. they go to youtube and find some guy promising a ninety percent win rate with a rsi crossover. that is not research that is falling for marketing fluff designed to sell you a dream. real research happens when you dive into white papers and study what the quants are actually doing on wall street. you look for market inefficiencies like liquidation clusters and cross exchange discrepancies that are hidden in plain sight. by the time you finish this process you should have a list of ideas that are grounded in reality instead of wishful thinking backtesting is the filter that saves you from losing your life savings on a bad hunch. most people use tools that repaint or give them false confidence because the data is not being handled correctly. if you are using a basic charting platform to see if your strategy works you are likely seeing a version of history that does not exist. you need to use raw python libraries like backtesting py to see the cold hard truth of how your logic would have performed. when you see a drawdown of thirty percent on paper you realize that using ten times leverage would have deleted your account five times over. the math does not lie and it is the only thing that can protect you from your own greed the most dangerous drug in the world is leverage because it makes you feel like a genius right before it makes you a pauper. i have watched two billion dollars get liquidated in a single day because people thought they could predict the bottom with fifty times leverage. the exchanges can see exactly where your liquidation price is and they have every incentive to push the price there to hunt your liquidity. you are playing in a casino where the house can see your cards and they are actively trying to take them from you. the only way to win is to stop playing their game and start using limit orders to save on the fees that are slowly bleeding you dry it is funny how much money people will spend on food and entertainment but they will hesitate to invest in their own education. they will spend a thousand dollars on a weekend out but will not put that same money into learning a skill that could provide for them for the rest of their lives. money is just a tool of exchange and it always replenishes if you are providing value to the world. if you spend your capital on knowledge you are buying back your time and your freedom. i decided to live my life on youtube and build in public because i wanted to show people that a regular guy could do this. now i have fully automated systems trading for me while i sleep and i never have to worry about getting licked by a sudden market move again chasing the greats like jim simons is not about the money it is about the mastery of the system. he ran up a net worth of over thirty billion dollars by doing exactly what we are talking about here. he did not stare at charts all day and hope for the best he built models that exploited the mathematical laws of the market. he was a scientist first and a trader second and that is the mindset you need to adopt. if you are not approaching this quantitatively you are just a gambler who happens to be sitting at a computer. the goal is to become a quant researcher who happens to have robots executing their findings the transition from hand trader to automated builder is the most liberating thing you can do for your mental health. you go from waking up in a cold sweat checking your phone to waking up and checking your logs to see how the system performed. even if the day was red you have data that tells you why and you can use that to make the system better tomorrow. it is a process of constant improvement and refinement that never really ends. you are building a legacy of code that will continue to work for you as long as the electricity is running. i am not afraid to die on a treadmill because i know that i will outwork anyone who is just looking for a shortcut if you are still on the fence about whether or not you can do this just remember that i was exactly where you are. i was losing money and feeling like the market was rigged against me because it actually was. i had to decide that i was going to change my environment and take control of my own destiny. you have the same opportunity right now to pivot and start building your own automated future. the models are getting better every single day and the barrier to entry is lower than it has ever been in human history. you just have to decide to lock in and do the work for a thousand days until you become undeniable there is no better feeling than finding a strategy that has a sharp ratio over ten and knowing that you built it with your own two hands. it is a moment of pure clarity where you realize that you are no longer a victim of the market. you are the architect of your own financial reality and the possibilities are literally endless. i am going to keep sharing everything i find because i believe that we can take on wall street together. as long as i am breathing i will be stepping on the gas and pushing the boundaries of what is possible with code. welcome to the family and let's get after it because the machines are already running and they are not waiting for anyone

Moon Dev

46,677 Aufrufe • vor 5 Monaten

The most epic 13 minute AI rant I've heard in 2026 PS: My parent's heard this when I was playing it in the car and thought Jason ✨👾SaaStr.Ai✨ Lemkin went OFF like Stephen A Smith does on first take PPS: Full transcript below [17:00] Harry Stebbings: I I just wanted to ask Jason, if the people that we want are fundamentally different, the developers that we used to hire, we don't because AI writes the code for us. The marketers we don't want, the sales people we don't want—who who do we want genuinely? Like what is the attractive profile? Because your Anthropic’s and your OpenAIs are hiring, so so what are the people that we want in the companies of the future? [17:18] Jason Lemkin: Look, I know it sounds trite, but but the answer is simple. It's just the expression each year changes. We want folks that are genuinely AI fluent. It's pretty simple. Now you know, maybe last year we called them prompt engineers, right? That used to be a job. I don't know if you remember that actually used to be the hottest job on planet earth. Now no one needs a prompt engineer because it's pretty easy to prompt all these tools. That job died. Okay. Um and now we need go-to-market engineers. Um I think that job's going to die. We need—everyone needs so many forward deployed engineers. Like you can't hire enough forward deployed engineers. But uh you know um but Palantir just announced in whatever their their big their big event—they've gotten their deployment times down over 90% with forward deployed engineers. So that may become—so the this wave of disruption for the titles and the specificity, it's also exhaustingly accelerating. But it's really simple. You meet anyone for any role—sales, marketing, engineering, product, QA—they're they're either they're either they can't keep all of the ways they use AI to accelerate their job from spewing out of their mouth, or they're staring at you. It's there's nowhere in the middle. Like, and the person that comes in and says—it's it's it sounds Captain Obvious—but like, you know, you just had the whatever from Lovable, the the marketing head that was super popular on the show, right? She's just spewing AI-native insights into Lovable, right? It's not that complicated. You hire her, Elena, or whatever it is. You just hire her. It doesn't matter whether she's still in college or a junior or a senior or a middler, a left or right. And honestly, if you interview people, I would say of all even of the best startups I've invested in, maybe 30% of the management team meets this standard at best. 30%. Maybe less. And of the interviews I do in general, it's single-digit percents. It's just and in in that sense, it's the same as ever. Like you either lower the bar in hiring or you hire someone that's actually great. And someone that's actually great is so far ahead of you in how to apply to to employ the efficiencies of AI in their role, your jaw falls on the table. The difference is we used to need warm bodies. That's what's changing. We used to need warm bodies to answer the call, to do QA, to do code review, to to get the blue pixel to go from the upper left to the lower right. You laugh, but you need you literally needed to brute force this with humans. With AI, every day that goes by, the AI—you do not need brute force human beings on your team. And that's another reason they're shrinking. Why are all these new companies so efficient? They're just not brute forcing things with humans. They're just not. They're choosing not to. And so these team—all the brute forcers out there—everyone talks about how bloated teams got in 2021. I don't agree with that. I think they got as big as they needed to be when growth was high and you needed humans to do everything. All you look at these teams that that doubled—well if growth continued at 60% like the rate in early 2021 for 5 years or can help me do the math and every single thing a software company did required a human. You were understaffed by your 2021 headcount. You'd be sitting here in 2026. You every office in SoMa would be triple packed and you there wouldn't be enough humans to staff your company. It's just the world changed. [20:33] Harry Stebbings: Jason, you live on the bleeding edge. I think me and Rory see that and I think the world sees that when they hear you every week in terms of how you run SaaS. For all of the CEOs and execs who listen to the show, what would you advise them in terms of determining whether someone is AI fluent when they meet them for jobs, for talent? [20:51] Jason Lemkin: Here's I realized I was just asked this. I just did a review with a super fast startup growing just crossing 100 million and I was asked this question. And one of my favorite executives, I thought his answer was pretty dated and because he gave me an answer that was about 6 months old. The answer 6 months old is: "I look for folks in my team, I look for you know at what tools they play with." Okay, that was a great answer in like summer of 2025. Okay, I tried Lovable last week. Okay, the answer in 2026 is: "What commercial AI tool have you brought into your organization this month?" That's the test. Anyone that is on the bleeding edge that you would want to hire—now there are so many great products in the market. Okay, there is no excuse in any role to have not brought one tool a month into your organization. Okay, there—now there's going to be better and better tools and better and better products as the year goes on. What's the one you did? And you will see folks with their deer in the headlights to this question. What what sales tool? What marketing tool? What product tool? What engineering tool? What did you bring in? Why did you pick it? How does it working? Because if you're at remotely at the cutting edge, you're all over this. You're looking for the next agentic tools that will radically improve how you do business. This is—you think everyone thinks SaaS is at the bleeding edge, right? You know, you know, all we do is we're just looking for the tools and trying them. Okay? Okay, we're one year ahead of everybody else because we did the simplest thing in the world. Like we tried the tools early and we trained them. We trained them for a month. Okay, I'll give you—want hear a horrible example from this week? Super hot AI company valued at 6 billion. Okay, I'm not going to name it. Um, this week yesterday told us we had to quadruple what we spent on their product. Okay, their agent told us, right? And why did this happen? Okay. Well, at this $6 billion company, no one had trained the agent on its pricing properly. No one had tested it. They said, "Well, well, we've been in beta." And we said, "Well, when did the beta launch? A year ago." Okay, these are people asleep at at the wheel. You want somebody who the instant this comes up, they exactly know what the issue is. And "Hey, when I was at Lovable Replit, we trained the agent. This is how we did it. I brought in this tool. I brought in this tool that that Rory invested in last week. It solved all these issues." That's what you want to hear. And if they haven't brought in a tool in the last 30 days, at least deeply evaluated it. I don't really care whether they bought it, but gone so far down the funnel they can tell you—pick whatever tool: Fixie, Regie, GC, AIGC—I don't care how you went through it, you looked at it, you can tell me the eight ways it would improve the productivity of your business and three you didn't. Just don't hire that person because they're going to run your company to the ground. This is the job today. The job today is not to screw around on ChatGPT and to be a prompt engineer. The job today is to bring the best AI and agentic products into your organization and leverage all the hard work that the engineers have done building those products. That's your job. You don't have to screw around. You don't have to be a prompt engineer anymore. You have to be an agent deployment expert. A—this is the new job we're making up today. An Agentic Deployment Expert. That's your job from C-level to junior. Agentic Deployment Expert. Don't hire anybody else. You're going to regret it. They're going to stare at the camera. He's good. Stare at the camera. He's honorable. We could probably just I could slip away, get a coffee, and come back. No. And I I sound exasperated, Rory. And I—but the reason I am is I can just see I can see my best companies doing it. And I can see some companies I've invested in not doing it. And I want to cry. I just want to cry when they have no ADs on their team. I just—like you're flushing your years of your life down the toilet by not approaching your how you're building this company this way. [24:33] Rory: Yes. And at the risk of being positive, it's worth pointing out two things he didn't say. Well, something implicit why he said—Jason didn't do the only hire, you know, he didn't commit the um employment law, I think it's a civil penalty of saying only employ people below X who get the new new thing because he implicitly said anyone can do it provided you're willing to learn. And I think that's the big aha that's one of the positive statements to make here right? Look and I think it applies—I'm always wary of being "Hey, coming across, hey this this is the things that you all have to do." I think it applies to everyone including investors right? I mean I will say I have found that unless you're willing to invest the time learning these tools you actually shouldn't be investing in them. One of my partners Andy had this expression: "You know, if you decide you want to stop learning new things you probably should retire within 6 to 12 months and never write another check again." Maybe that's down to 3 to 6 months at this stage, right? And I think, you know, it's— [25:27] Harry Stebbings: Yeah, I actually I actually had a meeting with mine and Jason's biggest investor the other day and I—pretend he's not here—I said I think he's the most equipped investor for this generation of investing because I don't think anyone quite sits at the bleeding edge like he does on the investor side. [25:42] Harry Stebbings: Why in terms of using the equip stuff? Yeah. Yeah. In terms of using the stuff, understanding understanding bottlenecks, constraints. For sure. [25:51] Jason Lemkin: But can I just add one point? We can just cuz it's so important if it helps people. Okay, we are—and thank you Harry. We're going through these phases. Okay, and when AI started to blow up for real for us, uh call it early 2024, right? Maybe late '23, I wasn't equipped. It was too technical. I wasn't going to go in and figure out—I wasn't smart enough to figure out how to deal with a massively hallucinating LLM API and turn that and turn that into something magical. Kudos to investors and others that that got it in early '23, '22. I mean I remember I—I guess it was maybe SaaStr Annual '23. I was with David Sacks and I did a Q&A and I said, "How you thinking about AI at Craft?" He's like, "Well we're all in. We want 80% of '23 of investments to be AI." I'm like, "Great but like show me the show me the great ones in market." He's like, "They're all prototypes. We're all they're all they're all proof of concepts but we're all in anyway." That's where you kind of had to be in '23 if you weren't investing at like the LLM level. Okay, I wasn't smart enough. Then we went through this weird-ass prompt engineer era where like you you could torture these products to do something good, right? But you had to torture them. You had to like craft these crazy things that made no sense. Now we are in the era where mere ordinarily smart generalists can make these tools do magical things. And literally I go to these meetings and people be like, "I don't know how to like this is so scary. I don't know how to do this." And we show them our backends. Do you know how to do a workflow generator? Do you know how to do a a decision tree? Like we've been building these since software in the '90s. Okay, if you—I can show you all of our agents. The how they work is novel. They do have to be trained. You can't be lazy and have these agents work. But honestly, the the UI, the UX, the way we interact with them, it's just software. And so my point is: Pick yourself off the ground. This is your time now. If you felt lost in AI era, if you felt like you're behind, you don't understand what all these people are saying on X and Twitter and their Claude and and their and talking about all the 4.6 point Nano point and it's over—like you just it's not your world. This is your time. This is your time for the generalist that knows how to use software tools really really well. And I—this is my last point but it's so important. If ever in your recent life—and this is why you could be all you need to be is young at heart to Rory's point—if in the last three to five years you have successfully deployed a piece of enterprise software of any sort you yourself, not some agency you hired, but if you have deployed it, you can deploy any agentic tool. Any. And you can become the hero in your company and you can become the hero in your functional area. But I watch folks—I'm literally helping a company now that they're adding hundreds of sales folks this year with a new pre-IPO COO—he's not hasn't brought in a single tool, totally scared of it. Okay, it's not that hard. Did you use SalesLoft? Did you use Outreach? Did you use HubSpot? Do you know these tools? If you can deploy these tools, you can deploy a world-changing AI agent. And so this is the time for people like the folks that that were shut out of the AI revolution right now. The generalist folks that are not that know how to deploy software that don't even know how to build software. Like vibe coding for me was folks who knew how to build software, but you didn't have to be an engineer. Now, you just need to know how to deploy software to win with AI agents. That's all you need to know. So many people have these skills and they're petrified of AI. "How did you do that? How did you deploy an AI BDR?" Well, we bought a piece of software, we figured out how it worked for a day, we set it up in an afternoon, and then and then we did spend 30 months training it, which you didn't do with this old software because in the old days, we just had to manually upload all the data, right? And there was no training. The the only non-intuitive part is training these things. And it's it's it's just work. So that's why when I see folks on the management team not doing this, there's no excuse. You do not need to be technical to win with AI agents in Q2 of '26. You do not need to be even 1% technical. Not at all. So it's your time. Or you're going to get laid off. Or you're going to get laid off because you're not going to matter.
13:04

The most epic 13 minute AI rant I've heard in 2026 PS: My parent's heard this when I was playing it in the car and thought Jason ✨👾SaaStr.Ai✨ Lemkin went OFF like Stephen A Smith does on first take PPS: Full transcript below [17:00] Harry Stebbings: I I just wanted to ask Jason, if the people that we want are fundamentally different, the developers that we used to hire, we don't because AI writes the code for us. The marketers we don't want, the sales people we don't want—who who do we want genuinely? Like what is the attractive profile? Because your Anthropic’s and your OpenAIs are hiring, so so what are the people that we want in the companies of the future? [17:18] Jason Lemkin: Look, I know it sounds trite, but but the answer is simple. It's just the expression each year changes. We want folks that are genuinely AI fluent. It's pretty simple. Now you know, maybe last year we called them prompt engineers, right? That used to be a job. I don't know if you remember that actually used to be the hottest job on planet earth. Now no one needs a prompt engineer because it's pretty easy to prompt all these tools. That job died. Okay. Um and now we need go-to-market engineers. Um I think that job's going to die. We need—everyone needs so many forward deployed engineers. Like you can't hire enough forward deployed engineers. But uh you know um but Palantir just announced in whatever their their big their big event—they've gotten their deployment times down over 90% with forward deployed engineers. So that may become—so the this wave of disruption for the titles and the specificity, it's also exhaustingly accelerating. But it's really simple. You meet anyone for any role—sales, marketing, engineering, product, QA—they're they're either they're either they can't keep all of the ways they use AI to accelerate their job from spewing out of their mouth, or they're staring at you. It's there's nowhere in the middle. Like, and the person that comes in and says—it's it's it sounds Captain Obvious—but like, you know, you just had the whatever from Lovable, the the marketing head that was super popular on the show, right? She's just spewing AI-native insights into Lovable, right? It's not that complicated. You hire her, Elena, or whatever it is. You just hire her. It doesn't matter whether she's still in college or a junior or a senior or a middler, a left or right. And honestly, if you interview people, I would say of all even of the best startups I've invested in, maybe 30% of the management team meets this standard at best. 30%. Maybe less. And of the interviews I do in general, it's single-digit percents. It's just and in in that sense, it's the same as ever. Like you either lower the bar in hiring or you hire someone that's actually great. And someone that's actually great is so far ahead of you in how to apply to to employ the efficiencies of AI in their role, your jaw falls on the table. The difference is we used to need warm bodies. That's what's changing. We used to need warm bodies to answer the call, to do QA, to do code review, to to get the blue pixel to go from the upper left to the lower right. You laugh, but you need you literally needed to brute force this with humans. With AI, every day that goes by, the AI—you do not need brute force human beings on your team. And that's another reason they're shrinking. Why are all these new companies so efficient? They're just not brute forcing things with humans. They're just not. They're choosing not to. And so these team—all the brute forcers out there—everyone talks about how bloated teams got in 2021. I don't agree with that. I think they got as big as they needed to be when growth was high and you needed humans to do everything. All you look at these teams that that doubled—well if growth continued at 60% like the rate in early 2021 for 5 years or can help me do the math and every single thing a software company did required a human. You were understaffed by your 2021 headcount. You'd be sitting here in 2026. You every office in SoMa would be triple packed and you there wouldn't be enough humans to staff your company. It's just the world changed. [20:33] Harry Stebbings: Jason, you live on the bleeding edge. I think me and Rory see that and I think the world sees that when they hear you every week in terms of how you run SaaS. For all of the CEOs and execs who listen to the show, what would you advise them in terms of determining whether someone is AI fluent when they meet them for jobs, for talent? [20:51] Jason Lemkin: Here's I realized I was just asked this. I just did a review with a super fast startup growing just crossing 100 million and I was asked this question. And one of my favorite executives, I thought his answer was pretty dated and because he gave me an answer that was about 6 months old. The answer 6 months old is: "I look for folks in my team, I look for you know at what tools they play with." Okay, that was a great answer in like summer of 2025. Okay, I tried Lovable last week. Okay, the answer in 2026 is: "What commercial AI tool have you brought into your organization this month?" That's the test. Anyone that is on the bleeding edge that you would want to hire—now there are so many great products in the market. Okay, there is no excuse in any role to have not brought one tool a month into your organization. Okay, there—now there's going to be better and better tools and better and better products as the year goes on. What's the one you did? And you will see folks with their deer in the headlights to this question. What what sales tool? What marketing tool? What product tool? What engineering tool? What did you bring in? Why did you pick it? How does it working? Because if you're at remotely at the cutting edge, you're all over this. You're looking for the next agentic tools that will radically improve how you do business. This is—you think everyone thinks SaaS is at the bleeding edge, right? You know, you know, all we do is we're just looking for the tools and trying them. Okay? Okay, we're one year ahead of everybody else because we did the simplest thing in the world. Like we tried the tools early and we trained them. We trained them for a month. Okay, I'll give you—want hear a horrible example from this week? Super hot AI company valued at 6 billion. Okay, I'm not going to name it. Um, this week yesterday told us we had to quadruple what we spent on their product. Okay, their agent told us, right? And why did this happen? Okay. Well, at this $6 billion company, no one had trained the agent on its pricing properly. No one had tested it. They said, "Well, well, we've been in beta." And we said, "Well, when did the beta launch? A year ago." Okay, these are people asleep at at the wheel. You want somebody who the instant this comes up, they exactly know what the issue is. And "Hey, when I was at Lovable Replit, we trained the agent. This is how we did it. I brought in this tool. I brought in this tool that that Rory invested in last week. It solved all these issues." That's what you want to hear. And if they haven't brought in a tool in the last 30 days, at least deeply evaluated it. I don't really care whether they bought it, but gone so far down the funnel they can tell you—pick whatever tool: Fixie, Regie, GC, AIGC—I don't care how you went through it, you looked at it, you can tell me the eight ways it would improve the productivity of your business and three you didn't. Just don't hire that person because they're going to run your company to the ground. This is the job today. The job today is not to screw around on ChatGPT and to be a prompt engineer. The job today is to bring the best AI and agentic products into your organization and leverage all the hard work that the engineers have done building those products. That's your job. You don't have to screw around. You don't have to be a prompt engineer anymore. You have to be an agent deployment expert. A—this is the new job we're making up today. An Agentic Deployment Expert. That's your job from C-level to junior. Agentic Deployment Expert. Don't hire anybody else. You're going to regret it. They're going to stare at the camera. He's good. Stare at the camera. He's honorable. We could probably just I could slip away, get a coffee, and come back. No. And I I sound exasperated, Rory. And I—but the reason I am is I can just see I can see my best companies doing it. And I can see some companies I've invested in not doing it. And I want to cry. I just want to cry when they have no ADs on their team. I just—like you're flushing your years of your life down the toilet by not approaching your how you're building this company this way. [24:33] Rory: Yes. And at the risk of being positive, it's worth pointing out two things he didn't say. Well, something implicit why he said—Jason didn't do the only hire, you know, he didn't commit the um employment law, I think it's a civil penalty of saying only employ people below X who get the new new thing because he implicitly said anyone can do it provided you're willing to learn. And I think that's the big aha that's one of the positive statements to make here right? Look and I think it applies—I'm always wary of being "Hey, coming across, hey this this is the things that you all have to do." I think it applies to everyone including investors right? I mean I will say I have found that unless you're willing to invest the time learning these tools you actually shouldn't be investing in them. One of my partners Andy had this expression: "You know, if you decide you want to stop learning new things you probably should retire within 6 to 12 months and never write another check again." Maybe that's down to 3 to 6 months at this stage, right? And I think, you know, it's— [25:27] Harry Stebbings: Yeah, I actually I actually had a meeting with mine and Jason's biggest investor the other day and I—pretend he's not here—I said I think he's the most equipped investor for this generation of investing because I don't think anyone quite sits at the bleeding edge like he does on the investor side. [25:42] Harry Stebbings: Why in terms of using the equip stuff? Yeah. Yeah. In terms of using the stuff, understanding understanding bottlenecks, constraints. For sure. [25:51] Jason Lemkin: But can I just add one point? We can just cuz it's so important if it helps people. Okay, we are—and thank you Harry. We're going through these phases. Okay, and when AI started to blow up for real for us, uh call it early 2024, right? Maybe late '23, I wasn't equipped. It was too technical. I wasn't going to go in and figure out—I wasn't smart enough to figure out how to deal with a massively hallucinating LLM API and turn that and turn that into something magical. Kudos to investors and others that that got it in early '23, '22. I mean I remember I—I guess it was maybe SaaStr Annual '23. I was with David Sacks and I did a Q&A and I said, "How you thinking about AI at Craft?" He's like, "Well we're all in. We want 80% of '23 of investments to be AI." I'm like, "Great but like show me the show me the great ones in market." He's like, "They're all prototypes. We're all they're all they're all proof of concepts but we're all in anyway." That's where you kind of had to be in '23 if you weren't investing at like the LLM level. Okay, I wasn't smart enough. Then we went through this weird-ass prompt engineer era where like you you could torture these products to do something good, right? But you had to torture them. You had to like craft these crazy things that made no sense. Now we are in the era where mere ordinarily smart generalists can make these tools do magical things. And literally I go to these meetings and people be like, "I don't know how to like this is so scary. I don't know how to do this." And we show them our backends. Do you know how to do a workflow generator? Do you know how to do a a decision tree? Like we've been building these since software in the '90s. Okay, if you—I can show you all of our agents. The how they work is novel. They do have to be trained. You can't be lazy and have these agents work. But honestly, the the UI, the UX, the way we interact with them, it's just software. And so my point is: Pick yourself off the ground. This is your time now. If you felt lost in AI era, if you felt like you're behind, you don't understand what all these people are saying on X and Twitter and their Claude and and their and talking about all the 4.6 point Nano point and it's over—like you just it's not your world. This is your time. This is your time for the generalist that knows how to use software tools really really well. And I—this is my last point but it's so important. If ever in your recent life—and this is why you could be all you need to be is young at heart to Rory's point—if in the last three to five years you have successfully deployed a piece of enterprise software of any sort you yourself, not some agency you hired, but if you have deployed it, you can deploy any agentic tool. Any. And you can become the hero in your company and you can become the hero in your functional area. But I watch folks—I'm literally helping a company now that they're adding hundreds of sales folks this year with a new pre-IPO COO—he's not hasn't brought in a single tool, totally scared of it. Okay, it's not that hard. Did you use SalesLoft? Did you use Outreach? Did you use HubSpot? Do you know these tools? If you can deploy these tools, you can deploy a world-changing AI agent. And so this is the time for people like the folks that that were shut out of the AI revolution right now. The generalist folks that are not that know how to deploy software that don't even know how to build software. Like vibe coding for me was folks who knew how to build software, but you didn't have to be an engineer. Now, you just need to know how to deploy software to win with AI agents. That's all you need to know. So many people have these skills and they're petrified of AI. "How did you do that? How did you deploy an AI BDR?" Well, we bought a piece of software, we figured out how it worked for a day, we set it up in an afternoon, and then and then we did spend 30 months training it, which you didn't do with this old software because in the old days, we just had to manually upload all the data, right? And there was no training. The the only non-intuitive part is training these things. And it's it's it's just work. So that's why when I see folks on the management team not doing this, there's no excuse. You do not need to be technical to win with AI agents in Q2 of '26. You do not need to be even 1% technical. Not at all. So it's your time. Or you're going to get laid off. Or you're going to get laid off because you're not going to matter.

Arjun Mahadevan (Mr. LLC 🇺🇸)

37,640 Aufrufe • vor 4 Monaten

$AMD is easily a $1,200 stock IMO| CPUs TAM 🧵 Not Financial Advice! DYOR! In this thread, I want to discuss the actual TAM for CPUs data center for just 2026, where many are giving different ranges, where I don't agree with. I will explain in detail why I disagree with these research firms and financial analysts using Math. And this thread should not be treated as Financial Advice. I'm just explaining my research and thought process so we can have a discussion. In 2024/2025, I gave out $620 PT for FY2026 was too conservative for AMD potential. At the time, It was early and many were just laughing, that PT was unrealistic and the AI world is run on GPUs only. Today, most of these folks are laughing with me. That is ok, I dont offer financial advice, and I do not need everyone to agree with me. I respect other opinions. If you enjoy this kind of thread, slap the like/repost/bookmark. If you want to support my work further and gain more in-depth analysis, consider subscribe! In early 2026, hyperscalers, enterprises, and OEMs are scrambling as Intel and AMD server CPUs are largely sold out for the year, with prices jumping 10–20% and lead times stretching from weeks to months (or longer for certain SKUs). What was once a GPU dominated story has flipped: the shift to explosive Agentic AI with its multi-step reasoning loops, tool calling, multi-agent orchestration, real-time data movement, and reinforcement learning, is dramatically tightening CPU:GPU ratios from the old training-era 1:4–8 all the way to 1:1 to 5:1 or even CPU-heavy configurations. CEOs across NVIDIA, AMD, Intel, Google, Meta, Microsoft, and public companies have been sounding the alarm on CNBC, Bloomberg, and earnings calls. CPUs are “cool again,” and in many agentic deployments they are becoming the new bottleneck alongside (or even ahead of) GPUs and custom ASICs. In 2025, roughly 12-15m AI GPUs + AI ASICs GPUs shipped, and is expect to be 15-20m units by 2026, where it suggesting Training demand is not going away. The actual TAM is structural, multiplicative demand that has already forced AMD to double its long-term server CPU TAM forecast to >$120 billion by 2030 (>35% CAGR), with Dr. Lisa Su noting Q2 2026 server CPU sales expected to surge 70%+ year-over-year and demand “far exceeding expectations.” At the same time, AMD’s secured 30–40% share of TSMC’s initial 2nm capacity (behind only Apple’s >50%) positions it to ramp Zen 6-based EPYC Venice exactly when this agentic wave hits hardest but even that aggressive five-fab 2nm expansion (with plans scaling toward 11 total advanced facilities) cannot instantly close the gap in the near-term. Supply constraints on wafers, advanced packaging, and power are compounding the squeeze, just as hyperscalers forward-buy and lock in long-term deals. 1. The actual potential TAM Various sources and institutions are giving $50-$160-$200B CPUs TAM toward 2030, and i disagree, where supply is severely behind vs Demand by at least 2-3 years or even longer by some estimates. The actual TAM will probably be 15-20m for FY2026. The typical average selling price from low to high end is $5,000 to $15,000, but due to rising memory, and different inflationary pressures on Semi, it would be more logical to think between $7,000-17,000. A. CPU:GPU Ratio at 1:1 A basic calucation at mid range =12,000 x 15-20m CPUs= $180-$240B TAM B. CPU:GPU Ratio at 5:1 = $12,000 x 75m-100m CPUs= $900B-$1.2T TAM Of course TSMC cannot even supply 20% of this massive inflection TAM in 2026. But do we think of Demand for TAM or Supply for TAM? Hence we are seeing massive 2nm Ramp from TSMC for $AMD. IMO, conservatively, I would take down 15-20% on 1:1 or $135-$192B TAM for just 2026. Im not even talking about 2030. We are just months into this, it is impossible to estimate Cagr atm, but this is 1-5 agents running tasks, I wrote a thread on 24/7 autonomous agents thread, where companies could use 50-250 agents to run tasks for them 24/7. It would require a different structural CPU:GPU to bring down the cost of token as well as handling the Orchestration bottleneck. GPUs would be useless and sit idle waiting for CPU due to highly CPU-intensive nature. The cost per Million tokens must come down more rapidly for this 50-250 autonomous agents to work, otherwise the token cost would be too enormous. Helios Rack is estimated to bring inference cost down to $0.0003-$0.0005/M tokens with 18 EPYC Venices along with 72 MI455x and other chips+ Components. A heavier or CPUs dense rack would bring down inference cost further. EPYC Verano(2027 gen 7 AI-optimized) is expected to drive inference costs meaningfully lower than the Venice baseline likely to the $0.00002–$0.00025 per million tokens range (or even sub-$0.00015 in highly optimized agentic/batch workloads). Verano have higher core counts than Venice, LPDDR5X SOCAMM2 memory support, more AI optimized and Next-Gen rack density & efficiency. 2. $AMD secured at least 30-40% of TSMC 2nm capacity and Memory from Samsung through 2028-2030. 2 2nm fabs are entering ramping phase toward 60-65k wafers per months and 5 dedicated 2nm fabs entering mass production/ramp in 2026. Will link sub threads below if you are interest for full detail. Apple is reported to secure 50%+ 2nm capacity for Iphone 18 and Mac chips and AMD secured at least 30-40% capacity while $NVDA $AVGO $ARM $AMZN $GOOGL and others are on 3nm. This broader aggressive ramp from TSMC to target up to 11 fabs is to address $AMD massive growth ahead. Where $ARM is facing massive CPUs supply constraints as they have to compete with other Mega Cap players on 3nm allocation. And $INTC is also facing supply constraints for data center CPUs and PC per management with lead times extrended to longer than 12 weeks. Dr. Su is aiming for higher than 50%+ Market share, and I believe it is achievable in 2026 or 2027 as AMD has the strongest CPUs offerings. Dr. Su did not want to take advantage of the shortage and she said during the Q1 earning call, AMD is prioritizing Units shipped while guiding margin to be inching 60%. If Jensen were in charge, I'm sure margin would be 70-75% in this kind of severe CPUs shortage condition. But that is not how Dr. Su operates for more than a decade. She wants most market share. So we will see it in revenue growth, but as TSMC ramps faster and faster, AMD Operating and FCF margin will massively improve vs prior decade. A significantly higher margin profile than before. 3. How I came up with $1,200 withint 12-18 months? At $1,200/ share, that would be around $2 Trillion MC. I expect FY2027 revenue to be $124-$144B where data center revenue dominates overall revenue. AI GPUs: I will stick to the lowest end so show u that I'm conservative at $18B for each GW vs $NVDA Rubin is $30B+ (most likely Helios Rack in the $20B+ due to memory price rising). We know deals with OpenAI and Meta are around 12GW and additional multi-customers at multi-GW scale were hinted and will be revealed as we get to July 22-23 2026 Advancing AI event. For now I will conservatively add a bit more to this model. (3-6GW Helios Rack Range) EPYC Venice is reported to be in $15,000-$20,000. However large customers will likely to enjoy $10-$12k discount. I expect AMD to be able to ramp 7m EPYC Venice for entire 2026 and 3-4m of EPYC Verano(higher price than Venice). If we take an average selling price of $10,000 to be on the conservative side. Take down another 30% to be even more conservative on projection. I like to be conservative. That would be ~ 7m EPYC CPUs(Venice + Verano) for FY2027 or 583,000 units per month or 15,000 additional 2nm wafers per month which is completely reasonable for current TSMC Ramp, and I may be too conservative here. EPYC Verano and MI500 series will also be on 2nm. AI GPUs: 3GW x $18B= $54B EPYC CPUs: $10k x 7m CPUs= $70B = Data center revenue alone is $124B Other segments= probably in the $20-$25B FY 2027. FY2027 revenue = $124-$149B At 7m EPYC CPUs for entire 2027, that would be more than 50% market share when we comp it to availability from supply side, not from total Demand. It is possible that TSMC could significantly ramp even more capacity in 2027, so we will see. Metric Q1 2026 FY2027 Gross Margin 55-56% 60-62% Operating Margin 25-26% 32-35% Net Income Margin ~22% 26-30% FCF Margin 25% 28-30% At $124-$149B Revenue FY 2027 Net Income would be $32-$44B EPS would be $20-$27 (GAAP) Non-GAAP would be $25-$31 At $1,200 a share or $2T valuation that would be: 13.4-16x Price to Sales (P/S) 38-48 P/E At this kind of growth of AI SuperCycle, I think it is very reasonable valuation. If we use today at $406/share or $661B MC: 2027 P/S = 4.4x-5.3x 2027 P/E = 13x-16x Is AMD today expensive or cheap to you? Above is already a very conservative where I trimmed 20-30% of doable units. Meaning, there could be upside if TSMC is able to ramp meaningfully like they are planning. Conclusion: A $1,200 per share valuation IMO for AMD in FY2027 is not expensive at all; it is, in fact, conservative when viewed against the structural explosion in agentic AI demand we have mapped out. With server CPU TAM potentially scaling into the $100–$200B+ range in just CPU:GPU 1:1 Ratio for just 2026. AMD positioned to capture 50%+ share thanks to its 2nm TSMC allocation advantage and full-stack leadership, the company could realistically deliver $124–149B in total revenue and $25–$31+ non-GAAP EPS. At those levels, $1,200 implies a 2027 P/E = 13x-16x. Entirely reasonable for a company that will have become the clear Inference Queen (and in many workloads the preferred) AI infrastructure provider, with operating margins expanding above 30% and tens of billions in high-margin rack-scale AI revenue. Dr. Lisa Su was right presciently so about the Agentic AI inflection all the way back to her early 2022–2023 commentary on the coming shift from pure training to inference and orchestration-heavy workloads. While the broader market only fully woke up to this in 2026 when she doubled AMD’s long-term server CPU TAM forecast to >$120B by 2030 (with >35% CAGR), Dr. Su and her team have consistently positioned the company at the center of the CPU renaissance. The explosive demand we are seeing today, sold-out lines, rising ASPs, and hyperscalers forward-buying entire gigawatts of Helios-class systems is exactly the outcome she forecasted years ago. Not Financial Advice! DYOR!
13:03

$AMD is easily a $1,200 stock IMO| CPUs TAM 🧵 Not Financial Advice! DYOR! In this thread, I want to discuss the actual TAM for CPUs data center for just 2026, where many are giving different ranges, where I don't agree with. I will explain in detail why I disagree with these research firms and financial analysts using Math. And this thread should not be treated as Financial Advice. I'm just explaining my research and thought process so we can have a discussion. In 2024/2025, I gave out $620 PT for FY2026 was too conservative for AMD potential. At the time, It was early and many were just laughing, that PT was unrealistic and the AI world is run on GPUs only. Today, most of these folks are laughing with me. That is ok, I dont offer financial advice, and I do not need everyone to agree with me. I respect other opinions. If you enjoy this kind of thread, slap the like/repost/bookmark. If you want to support my work further and gain more in-depth analysis, consider subscribe! In early 2026, hyperscalers, enterprises, and OEMs are scrambling as Intel and AMD server CPUs are largely sold out for the year, with prices jumping 10–20% and lead times stretching from weeks to months (or longer for certain SKUs). What was once a GPU dominated story has flipped: the shift to explosive Agentic AI with its multi-step reasoning loops, tool calling, multi-agent orchestration, real-time data movement, and reinforcement learning, is dramatically tightening CPU:GPU ratios from the old training-era 1:4–8 all the way to 1:1 to 5:1 or even CPU-heavy configurations. CEOs across NVIDIA, AMD, Intel, Google, Meta, Microsoft, and public companies have been sounding the alarm on CNBC, Bloomberg, and earnings calls. CPUs are “cool again,” and in many agentic deployments they are becoming the new bottleneck alongside (or even ahead of) GPUs and custom ASICs. In 2025, roughly 12-15m AI GPUs + AI ASICs GPUs shipped, and is expect to be 15-20m units by 2026, where it suggesting Training demand is not going away. The actual TAM is structural, multiplicative demand that has already forced AMD to double its long-term server CPU TAM forecast to >$120 billion by 2030 (>35% CAGR), with Dr. Lisa Su noting Q2 2026 server CPU sales expected to surge 70%+ year-over-year and demand “far exceeding expectations.” At the same time, AMD’s secured 30–40% share of TSMC’s initial 2nm capacity (behind only Apple’s >50%) positions it to ramp Zen 6-based EPYC Venice exactly when this agentic wave hits hardest but even that aggressive five-fab 2nm expansion (with plans scaling toward 11 total advanced facilities) cannot instantly close the gap in the near-term. Supply constraints on wafers, advanced packaging, and power are compounding the squeeze, just as hyperscalers forward-buy and lock in long-term deals. 1. The actual potential TAM Various sources and institutions are giving $50-$160-$200B CPUs TAM toward 2030, and i disagree, where supply is severely behind vs Demand by at least 2-3 years or even longer by some estimates. The actual TAM will probably be 15-20m for FY2026. The typical average selling price from low to high end is $5,000 to $15,000, but due to rising memory, and different inflationary pressures on Semi, it would be more logical to think between $7,000-17,000. A. CPU:GPU Ratio at 1:1 A basic calucation at mid range =12,000 x 15-20m CPUs= $180-$240B TAM B. CPU:GPU Ratio at 5:1 = $12,000 x 75m-100m CPUs= $900B-$1.2T TAM Of course TSMC cannot even supply 20% of this massive inflection TAM in 2026. But do we think of Demand for TAM or Supply for TAM? Hence we are seeing massive 2nm Ramp from TSMC for $AMD. IMO, conservatively, I would take down 15-20% on 1:1 or $135-$192B TAM for just 2026. Im not even talking about 2030. We are just months into this, it is impossible to estimate Cagr atm, but this is 1-5 agents running tasks, I wrote a thread on 24/7 autonomous agents thread, where companies could use 50-250 agents to run tasks for them 24/7. It would require a different structural CPU:GPU to bring down the cost of token as well as handling the Orchestration bottleneck. GPUs would be useless and sit idle waiting for CPU due to highly CPU-intensive nature. The cost per Million tokens must come down more rapidly for this 50-250 autonomous agents to work, otherwise the token cost would be too enormous. Helios Rack is estimated to bring inference cost down to $0.0003-$0.0005/M tokens with 18 EPYC Venices along with 72 MI455x and other chips+ Components. A heavier or CPUs dense rack would bring down inference cost further. EPYC Verano(2027 gen 7 AI-optimized) is expected to drive inference costs meaningfully lower than the Venice baseline likely to the $0.00002–$0.00025 per million tokens range (or even sub-$0.00015 in highly optimized agentic/batch workloads). Verano have higher core counts than Venice, LPDDR5X SOCAMM2 memory support, more AI optimized and Next-Gen rack density & efficiency. 2. $AMD secured at least 30-40% of TSMC 2nm capacity and Memory from Samsung through 2028-2030. 2 2nm fabs are entering ramping phase toward 60-65k wafers per months and 5 dedicated 2nm fabs entering mass production/ramp in 2026. Will link sub threads below if you are interest for full detail. Apple is reported to secure 50%+ 2nm capacity for Iphone 18 and Mac chips and AMD secured at least 30-40% capacity while $NVDA $AVGO $ARM $AMZN $GOOGL and others are on 3nm. This broader aggressive ramp from TSMC to target up to 11 fabs is to address $AMD massive growth ahead. Where $ARM is facing massive CPUs supply constraints as they have to compete with other Mega Cap players on 3nm allocation. And $INTC is also facing supply constraints for data center CPUs and PC per management with lead times extrended to longer than 12 weeks. Dr. Su is aiming for higher than 50%+ Market share, and I believe it is achievable in 2026 or 2027 as AMD has the strongest CPUs offerings. Dr. Su did not want to take advantage of the shortage and she said during the Q1 earning call, AMD is prioritizing Units shipped while guiding margin to be inching 60%. If Jensen were in charge, I'm sure margin would be 70-75% in this kind of severe CPUs shortage condition. But that is not how Dr. Su operates for more than a decade. She wants most market share. So we will see it in revenue growth, but as TSMC ramps faster and faster, AMD Operating and FCF margin will massively improve vs prior decade. A significantly higher margin profile than before. 3. How I came up with $1,200 withint 12-18 months? At $1,200/ share, that would be around $2 Trillion MC. I expect FY2027 revenue to be $124-$144B where data center revenue dominates overall revenue. AI GPUs: I will stick to the lowest end so show u that I'm conservative at $18B for each GW vs $NVDA Rubin is $30B+ (most likely Helios Rack in the $20B+ due to memory price rising). We know deals with OpenAI and Meta are around 12GW and additional multi-customers at multi-GW scale were hinted and will be revealed as we get to July 22-23 2026 Advancing AI event. For now I will conservatively add a bit more to this model. (3-6GW Helios Rack Range) EPYC Venice is reported to be in $15,000-$20,000. However large customers will likely to enjoy $10-$12k discount. I expect AMD to be able to ramp 7m EPYC Venice for entire 2026 and 3-4m of EPYC Verano(higher price than Venice). If we take an average selling price of $10,000 to be on the conservative side. Take down another 30% to be even more conservative on projection. I like to be conservative. That would be ~ 7m EPYC CPUs(Venice + Verano) for FY2027 or 583,000 units per month or 15,000 additional 2nm wafers per month which is completely reasonable for current TSMC Ramp, and I may be too conservative here. EPYC Verano and MI500 series will also be on 2nm. AI GPUs: 3GW x $18B= $54B EPYC CPUs: $10k x 7m CPUs= $70B = Data center revenue alone is $124B Other segments= probably in the $20-$25B FY 2027. FY2027 revenue = $124-$149B At 7m EPYC CPUs for entire 2027, that would be more than 50% market share when we comp it to availability from supply side, not from total Demand. It is possible that TSMC could significantly ramp even more capacity in 2027, so we will see. Metric Q1 2026 FY2027 Gross Margin 55-56% 60-62% Operating Margin 25-26% 32-35% Net Income Margin ~22% 26-30% FCF Margin 25% 28-30% At $124-$149B Revenue FY 2027 Net Income would be $32-$44B EPS would be $20-$27 (GAAP) Non-GAAP would be $25-$31 At $1,200 a share or $2T valuation that would be: 13.4-16x Price to Sales (P/S) 38-48 P/E At this kind of growth of AI SuperCycle, I think it is very reasonable valuation. If we use today at $406/share or $661B MC: 2027 P/S = 4.4x-5.3x 2027 P/E = 13x-16x Is AMD today expensive or cheap to you? Above is already a very conservative where I trimmed 20-30% of doable units. Meaning, there could be upside if TSMC is able to ramp meaningfully like they are planning. Conclusion: A $1,200 per share valuation IMO for AMD in FY2027 is not expensive at all; it is, in fact, conservative when viewed against the structural explosion in agentic AI demand we have mapped out. With server CPU TAM potentially scaling into the $100–$200B+ range in just CPU:GPU 1:1 Ratio for just 2026. AMD positioned to capture 50%+ share thanks to its 2nm TSMC allocation advantage and full-stack leadership, the company could realistically deliver $124–149B in total revenue and $25–$31+ non-GAAP EPS. At those levels, $1,200 implies a 2027 P/E = 13x-16x. Entirely reasonable for a company that will have become the clear Inference Queen (and in many workloads the preferred) AI infrastructure provider, with operating margins expanding above 30% and tens of billions in high-margin rack-scale AI revenue. Dr. Lisa Su was right presciently so about the Agentic AI inflection all the way back to her early 2022–2023 commentary on the coming shift from pure training to inference and orchestration-heavy workloads. While the broader market only fully woke up to this in 2026 when she doubled AMD’s long-term server CPU TAM forecast to >$120B by 2030 (with >35% CAGR), Dr. Su and her team have consistently positioned the company at the center of the CPU renaissance. The explosive demand we are seeing today, sold-out lines, rising ASPs, and hyperscalers forward-buying entire gigawatts of Helios-class systems is exactly the outcome she forecasted years ago. Not Financial Advice! DYOR!

Mike

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Just in $AMD Anush "Speed is the moat"|ROCm🎙️ In the race to define the future of AI, what's the one advantage that truly lasts? It's not proprietary tech, argues Anush Elangovan Elangovan, VP of AI Software at AMD , but the sustainable speed of innovation. He explains why AMD is rejecting the "walled garden" model for its open source ROCm stack, betting that an open community flywheel is the key to victory. Listen to understand how this open strategy is designed to out-innovate closed systems by empowering developers to solve everything from frontier-model challenges to the mundane, everyday problems that define the "last mile" of AI. AMD ROCm Software: Part 1 Transcript [00:00:00] Andrew Zigler: Joining me is Anush Elangovan, VP of AI software at AMD. And when people talk about AI compute, the conversation often stops at hardware specs, but it's more than just physical chips that win the game. It's also the software ecosystems supporting them. [00:00:18] Andrew Zigler: The prevailing strategy in the industry has been to build something like a walled garden. You know, something closed, proprietary locks, developers in. But AMD is betting on an entirely different play, open source acceleration, and with rock, their open source AI software stack. AMD is building not just hardware parity, but an innovation flywheel that's powered by the community with interoperability and the freedom to scale without all of that pesky lockin. [00:00:48] Andrew Zigler: And in this world, speed is your moat and how fast you can innovate while your platform remains open, flexible, and standardize across all of its applications. That's what we're gonna explore [00:01:00] today. So Anush, I'm really excited to have you here. Welcome to Dev Interrupted. [00:01:04] Anush Elangovan: Thanks for having me. Uh, super excited to chat about it. [00:01:07] Andrew Zigler: Amazing. Well, let's go ahead and dive right in with kind of what I laid it out with in the beginning, the idea of the moat and it being about speed. I wanna unpack that a bit because that came from you when you and I first spoke. And I, and I want to know, you know, how do you define speed inside of AMD beyond just things like hardware, benchmarks. [00:01:27] Anush Elangovan: Yeah, that's a very good question. So when we typically talk about speed, everyone's like, Hey, hardware benchmark specs, right? Like, uh, memory bandwidth or, or flops. And that is one important part of it, uh, AMD does very well. With that, we do have, a, a very good history of executing on that axis. [00:01:47] Anush Elangovan: But when I say speed is the moat, it is about, uh, how we prepare, how we build the muscle to run the race for a long time and run it fast. And it is [00:02:00] not about a single point in time that you've, you've beat some you know, benchmark and, and you declare victory. It's about building the ability to consistently develop and deliver. [00:02:13] Anush Elangovan: Both hardware and software innovation at scale and do it fast, right? Like, you know, we we're increasingly getting to a point where models come out and they're, uh, you know, a year or two ago it was like, Hey, they work on AMD on day zero, which is great, but now they are performing on AMD the day it releases, right? [00:02:32] Anush Elangovan: So, what does it take to Prefetch where the industry is going? Be prepared to intercept. At that point is what you know, I, I refer to as you know, the, the speed factor in, in creating this mode, right? And the mode is just shed all things that hold you back and run as fast as you can. [00:02:53] Anush Elangovan: Uh, because the pace of innovation that is, uh, being seen in, in AI [00:03:00] industries is just. Amazing. Right? And it's like, it's transformational at at how you generate electricity. It's transformational as at how you build data centers. It's transformational at how you deploy compute, networking. It's transformational at what kind of use cases you, you know, uh, use AI for. [00:03:17] Anush Elangovan: Uh, and for that, you need to be prepared to, see what comes tomorrow and be prepared to run the race tomorrow. [00:03:23] Andrew Zigler: Yeah, it's a really great perspective because it highlights that it's not just like a checkpoint that you run through. I like how you called out, like it's not just hitting that benchmark or being the best in class at that moment, in that snapshot, it's about having a. The throughput and about having that dedication to the idea and continuing to deliver on it. [00:03:43] Andrew Zigler: It's not just crossing the threshold, but it's also being the engine. And that's what, that's what protects a business. That is the moat, because the moat is that innovation layer, the faster and more, uh, future forward. That you can work and think, [00:04:00] you know, the better. Uh, we, we talk a lot about like future forward work styles. [00:04:04] Andrew Zigler: Like what are the things I could be doing right now today that are gonna be like, way more useful tomorrow? Let, let's abandon those, workflows that are older and that kind of like, that translates into. An advantage when you work that way. You know, what kind of things have you learned working with, uh, like across all spectrums of people who would use ROCm, right? [00:04:23] Andrew Zigler: You have like the developers, but then you also have the enterprises and you have this large span of adoptees, right? So what is the, what does that look like that you learn? [00:04:32] Anush Elangovan: Yeah, so, so the way I look at it is there are gonna be pockets of different, uh, you know, cadences, right? Like, so people who are deploying in enterprises, for example, right? The validation and how long it takes for them to deploy an LLM that's secure. It's, with guardrails, et cetera, maybe longer. [00:04:52] Anush Elangovan: but you still have to go through the process and you have to be prepared to like, walk that walk to deploy an enterprises. That doesn't mean it's [00:05:00] not fast, that's as fast as you can do for that industry, right? And if you are deploying AI in healthcare, right, it's, it's got its own, uh, cycle. [00:05:07] Anush Elangovan: but in each one of these, you want to see how, like, go down to the essence of what is it that you actually have to do. And, you know, I, I, I like how you framed it. It's like it's, you shed your prior assumptions of how things are done, right. And, and you kind of build up from a, uh, first principles, uh, approach to say, this is how I could use AI to unlock, whatever I'm doing. [00:05:33] Anush Elangovan: And, and, some of it, you know, it's good to really step back and look at. Just question every part of it, right? Like right now you're getting chat GPT and, Gemini competing for like, math, olympiads and, and, uh, college, uh, reasoning, uh, tests. Right? And, and those are like that, that is amazing and increasingly like complex tasks that they're trying to do. [00:05:58] Anush Elangovan: But there may also be like. [00:06:00] More mundane things that AI could, could get applied to. Right? And, and so when we think about shedding old ways, you wanna shed it not just in like the tip of the spear. It's like, you know, I'm gonna see what's the frontier model. It's also, it could be something as simple as. [00:06:18] Anush Elangovan: How do you choose a, a movie, uh, you know, like a recommendation system, right? Or, or, uh, an automated, uh, flight, uh, rebooking system. So the moment, you know, your flight is late, uh, right now it's a notification, right? It's like, oh, you got a text message saying your flight's late. And I got that like three times this week. [00:06:38] Anush Elangovan: But anyway, uh, and, and, and, and, I was just like, okay, so if I were to rethink this. All this MCPs that we have that should be hooked up into an MCP that says, your flight's delayed. Here are your options. If you want, you know, these are the paid options. Yeah. Here are the free options. This will get you back into your you know, Toronto airport [00:07:00] tonight. [00:07:00] Anush Elangovan: Or if you stay, here's a hotel plus this, plus this, plus. It's just like, go ahead is all I should say. Versus now I'm like, okay, can someone, you know, can I call a travel agent? Can I do this? Can I go online and log into And you know, so we gotta fundamentally rethink even those like small, nuances of, things that we do that can be automated out and AI is really, really good at doing something like this, right? Maybe I just explained an AI startup idea right now. Somebody should just start that. [00:07:29] Andrew Zigler: I think you did. Yeah, you definitely did. Someone, one of our listeners is definitely going to lift that off of you. I, I, I, you know, I hate being on the receiving end of those. You feel a little helpless and then you have to like, follow the whole flow. So I know what you mean. Like I, I like how you called out that the build and this like. [00:07:45] Andrew Zigler: Where speed is your moat and the innovation layer is protecting you, is what makes you better than your competitors. How you scale that and you bring that to market. So by understanding the problems that you're solving, uh, throwing away those older assumptions, but also [00:08:00] recognizing that like. We're building every single day, new things and new ways of using stuff that we're still figuring out the implications of. [00:08:08] Andrew Zigler: And so when you have a lot of velocity and you're introducing a lot of new ideas, and maybe you have that workflow now that automatically rebook your flight off of your late flight text message, and uh, I know I would certainly use it, but you know, what kind of philosophies guide the way that y'all think about building this ecosystem to manage that stability while letting folks. [00:08:29] Andrew Zigler: Play with the speed and the assumptions and the airplane re bookings. [00:08:34] Anush Elangovan: so, so I think, you know, we need to peel one layer down, right? and the philosophy is, Hey, we, we just discovered electricity, right? And you know what we're gonna do? We are gonna make motors, uh, or dynamos, right? Like engines. Uh, sure. We don't know if it's gonna be a Ferrari that you're gonna make, or it's a a a a dump truck. [00:08:57] Anush Elangovan: That's good for doing this. But let's [00:09:00] let, which is also required, right? You need a dump truck. You need a garbage truck. And, [00:09:04] Andrew Zigler: Yeah. You need the [00:09:04] Anush Elangovan: course you need, uh, a Ferrari for a midlife crisis, right? So, [00:09:09] Andrew Zigler: precisely. [00:09:10] Anush Elangovan: But, but my, uh, point is what do we build next? And, uh, and this is what I meant by like, okay, let's, let's take those baby steps to build the. [00:09:20] Anush Elangovan: Infrastructure that's required that we know we'll have to use, right? So, so if I just discovered electricity, okay, great. Now one, how do I save this electricity and how do I use it? So there's battery technology, so you need to do something like that, right? Like so. But then you also want to make it into an actionable thing. [00:09:37] Anush Elangovan: You want to make it for like automobiles, or you wanna use it for, you know, powering, uh, entire cities. So it is that transformational. So, uh, AI is that transformational. So, if you distill down, it'll, it'll come down to how do we think about, what we can do with this this fundamental technology that, We may not be aware of what it [00:10:00] is gonna unlock next, but at least you know the next step is clear, right? It's like a dense fog, you know, it's gonna be like, it, it's the right path. You see the light, but it's kind of like out there and, and the steps you're taking are concrete and you're like, okay, this is good. [00:10:16] Anush Elangovan: I, this is better than where I was or where we were. So we are moving forward. So you can build with the. Intuition from what you see in the short term and a tactical view, but towards what you think the future is gonna be. [00:10:28] Andrew Zigler: Right. You almost like we're all in this like fog of war, right? And like you said, you're reaching out and you're trying to step through it. You could think of it too, as like you're in the dark and your hands are up in front of you and you know that. You're, you're not gonna run your face into a wall because your hands are out in front of you, but you're not gonna maybe do much better than that. [00:10:45] Andrew Zigler: So that's kind of like, I think the eco, the, the industry, the world that we find ourselves in, uh, and we all have to, then this becomes the power of an ecosystem, of a group of people working together to create that layer of, [00:11:00] uh, of establishing the [00:11:01] Anush Elangovan: exactly. And I, I, I just, instead of, you know, saying fog of war I describe it as like, you're in this. Beautiful valley with like a morning, uh, fog that's in. You can smell the flowers. You, you hear the birds. You are like, okay, it's, we are in like, uh, utopian paradise and yes, I just need to like, continue the walk, right? [00:11:24] Anush Elangovan: and then move forward with that, conviction that you're in the right spot. [00:11:27] Andrew Zigler: Yeah. So let's talk about that ecosystem world. This nice, I love how you describe it, this grassy side of a hill in the morning that's covered in some mist and maybe we can't see 30 feet in one direction, but it sure is a beautiful hill and it smells nice. And so we're all here. And why is, in that world, why is. [00:11:44] Andrew Zigler: You know, open source, their strategic advantage that y'all are going for in the AI hardware market. And, and then how does like ROCm turn that into wins for people within that ecosystem? [00:11:56] Anush Elangovan: you know, the, the way we look at it is this, is kind of like how I view [00:12:00] AI and the ecosystem, right? But, but it is for everyone to enjoy. Uh, and so we do want to make sure that. You know, it is, uh, beneficial for everyone. [00:12:09] Anush Elangovan: The ecosystem can come in and, and innovate. It's an open innovation engine. and uh, it is very different from, you know, having a walled garden with, Hey, only I know how to do this and I'm gonna do it and throw it over the fence and you can use it or keep walking, right? So we'd like to be good citizens that way, but also. [00:12:30] Anush Elangovan: Uh, it is self-fulfilling in a way, right? Like it, the, the pace at which we innovate with open source is unmatched. Like, you know, our serving engines are like VLLM and, and sg l. Those things, uh, those frameworks are like super, super aggressive in terms of how fast they come out with features and how fast they can you know, get performant models out. [00:12:52] Anush Elangovan: And that compared with what, uh, you'd get from, you know, the likes of like T-R-T-L-L-M or something is always lagging, right? Because you [00:13:00] just can't keep up with you know, 200 commits a week just on one particular model to get that model really performant [00:13:06] Andrew Zigler: And, and, and in that world where, you know, everyone can enjoy the winds of this, what kind of customer stories or innovation stories have really stood out to you and excite you about building and creating this place for developers? [00:13:19] Anush Elangovan: Yeah. So I think the parts that are super exciting for me are when when we get to see a customer that is first skeptical. Then they start a little like, okay, fine, we'll give you a chance. Uh, we do a simple, uh, POC and then they're like, huh, this seems to work. Yeah, we told you it works. [00:13:42] Anush Elangovan: You don't have to change one line of code. Really? Yes, no need to change one line of code. Okay, let's try a production workload. So then they try it. Oh, you're more performant than the competition. Yes. We're more performant than, than the competition. So how much does it cost? And we're like, oh, it's your TCO is better with, uh, [00:14:00] AMD. [00:14:00] Anush Elangovan: So again, they're like, wow, okay, good. So now how do we deploy at scale? And then we go deploy it at scale. And when they give a thumbs up on that and they say, this is good, right? That's when you know, you, you see it go full circle from like, oh, we, we've never heard about AMD to like actually deploy to tens of thousands of GPUs In the order of a few months, right? It, it, it really is fascinating to see and very exciting and invigorating to [00:14:28] Andrew Zigler: Yeah. At like a great exposure to a lot of interesting problems. And, and then people using the infrastructure, the, the technology available to solve those problems. Really specific problems by the way, that's often why they're bringing their data and AI to it, uh, is because it is really specific and important for them. [00:14:45] Andrew Zigler: And there's a, a lot I think that other engineering orgs can learn and even emulate from AMD's success and, and having this open source ecosystem and it causing this acceleration within. You [00:15:00] know, uh, customers and enterprises that use and adopt the tools and, and, and that creates an advantage. And that goes back to why we're talking and like the real thesis of our conversation today. [00:15:10] Andrew Zigler: So how do you think engineering leaders that are listening to this and obviously tapping into this great success AMD has from an open source flywheel, how do you think other, other folks building in the same space can foster that open, first, that open source oriented culture in order to, you know, accelerate their innovation goals? [00:15:29] Anush Elangovan: Yeah, that's a very good question. So the startup that um, was acquired by AMD we, we built, I mean, we started off doing iot stuff and you know, smart ring and all that, right? But in the, the end of like, uh, and not the end, the last six years of the company was building ML compilers. [00:15:47] Anush Elangovan: And ml, ML compilers are like super, uh, complicated, sophisticated, advanced algorithms, dah, dah, dah. but it was all open source, right? So our VCs were like, wait, what do you mean your core [00:16:00] IP is open source? And um, the speed is the moat applied even then, right? It was just like, yes, if you have an idea that. [00:16:08] Anush Elangovan: Because someone saw this idea that you are, they're gonna be able to catch up, then you probably have the wrong idea anyway. But if they are, you know, you execute and they're gonna catch up, that you should assume they're gonna catch up. Right? So you gotta move forward. So keeping it open source is super important. [00:16:25] Anush Elangovan: But also to your question on like, you know, the learnings from an AMD standpoint, right? If there are, hard problems, I'd say dig in and work through it, right? Like there's no way but through it, right? That should be the simple mentality. And more, uh, frequently than not. you'll see that you'll just make it through in a, in, in good form. [00:16:52] Anush Elangovan: But if you doubt it and you're like, oh, I don't know if I should commit, if I'm, I, you know, what should just commit to do the right thing [00:17:00] every step, right? Every step, and just keep taking one step in front of the other. And in no time you'll see that you'll be running. Right. And, and yes, the first few steps will be like, yeah, everyone's complaining about your software quality. [00:17:15] Anush Elangovan: Everyone's complaining about this and that, and it doesn't work. And, and a few steps in, you know, you get, you get the hang of all the complaints that are coming in. You get the feedback loop. You're like, okay, what, what are you prioritizing again? One step in front of the other, right? You just keep knocking that out and then you get to a point where you're, it just becomes second nature, right? To do the, to do the right thing. And, and then yes, if someone gives you two options, you'll be like, fine. This is, uh, you know, there's always the resource trade off. There's always a human capital trade off, but what's the right thing to do? of course, I, I'm pragmatic about what we choose, but, but if the right thing for your long-term success is dig in, go first, principles, make it [00:18:00] happen. [00:18:00] Anush Elangovan: Well. Then just go for that. There's, there is no shortcut to [00:18:04] Andrew Zigler: acknowledging, you know, how it aligns with your mission, your core company goals, and what you're looking to achieve. And, and I, I love how you rightfully called out that in the open source world and you know, you have your technology that you've built, what you think is your moat upon, right? [00:18:22] Andrew Zigler: It's your code and, and to open source that, or to just make it where anyone could peer in is, you know. Scary in one regard, but two, it just kind of feels like you're handing away your throne room in some kind of sense, a very direct feeling sense. But the ultimately, you were really right to call out, and this is something I think about all the time, that the real power there is still the speed This the speed. [00:18:42] Andrew Zigler: That was the moat at the beginning of our conversation. It's the speed in combination with your. Very specific domain understanding of what you're building and what you're creating, and your new role as the steward of that world and how people plug into it, which [00:19:00] has frankly, a lot more influence and power than lording over a closed. [00:19:04] Andrew Zigler: You know, repository or an ecosystem, and like you said, like throwing things over the wall. Sure. There, there might be people always on the other side of that wall, but you're not gonna have a great connection with them. You're not gonna be able to really clearly understand them. I, I like your metaphor of the side of the field of the mountain a lot more. [00:19:23] Andrew Zigler: But, but in the, in this world, you know, where. That speed is, is the power and, and open source is just one way that you can harness that speed to get really far ahead and to innovate. , There's other parts of this equation that you can be experimenting with too, and I'd love to pick your brain about them as a software leader and, and, and one of them is about looking forward and kind of understanding that future that we're all building towards and beyond today's models and hardware. [00:19:48] Andrew Zigler: You know, what do you see as the next major bottleneck or opportunity in the AI compute space? As, as you know, enterprises and folks start to get a little more mature about what's available to [00:20:00] them. [00:20:00] Anush Elangovan: Yeah, I think, the bottleneck and opportunity is, uh, what I'd call, call walking the last mile of ai. Right. Uh, and like I I, I gave you an example, uh, previously, but, but it's similar to that. It's like there are cases where Humans have so many, uh, things to do in your day. You know, like the, if we sit down and actually had a customer focus like, okay, these customers lives, I'm gonna save four hours of this customer's life. And if you actually sit down and look at all of that, it'll be. Easily automatable, easily you know, uh, applicable, uh, for ai, right? [00:20:39] Anush Elangovan: Like, but then making it happen is gonna take a little bit, right? It's like maybe it's, uh, paying your utility bill, right? Or something like that, right? Or, or, your healthcare explanation of benefits. Uh, like, I'm sure you get an explanation of benefits, and I'm like, I, I don't even know what that thing is. [00:20:55] Anush Elangovan: It's just like EOB and like. [00:20:57] Andrew Zigler: it's a big, a big old PDF. Yeah, [00:21:00] exactly. [00:21:01] Anush Elangovan: Like, like, I'm like great straight to the, uh, shredder, right? And but that could be, you know, automated with the ai, right? It, it, it'd be like, Hey, the summary of this thing is you went and visited this day. Everything is okay. Everything is paid for, so don't worry, it's not a bill. [00:21:17] Anush Elangovan: That again, the same, uh, thing, but the sense of what that information overload is could be. Digested by ai, uh, accumulated over time and retrieved when you need it. Like, I don't, I actually don't even need to know this EOB right now, unless of course, whenever I need to know it, that maybe, you know, like for some benefits I need to figure out what do, what did I do over the past year and how do I apply it? Source:
31:35

Just in $AMD Anush "Speed is the moat"|ROCm🎙️ In the race to define the future of AI, what's the one advantage that truly lasts? It's not proprietary tech, argues Anush Elangovan Elangovan, VP of AI Software at AMD , but the sustainable speed of innovation. He explains why AMD is rejecting the "walled garden" model for its open source ROCm stack, betting that an open community flywheel is the key to victory. Listen to understand how this open strategy is designed to out-innovate closed systems by empowering developers to solve everything from frontier-model challenges to the mundane, everyday problems that define the "last mile" of AI. AMD ROCm Software: Part 1 Transcript [00:00:00] Andrew Zigler: Joining me is Anush Elangovan, VP of AI software at AMD. And when people talk about AI compute, the conversation often stops at hardware specs, but it's more than just physical chips that win the game. It's also the software ecosystems supporting them. [00:00:18] Andrew Zigler: The prevailing strategy in the industry has been to build something like a walled garden. You know, something closed, proprietary locks, developers in. But AMD is betting on an entirely different play, open source acceleration, and with rock, their open source AI software stack. AMD is building not just hardware parity, but an innovation flywheel that's powered by the community with interoperability and the freedom to scale without all of that pesky lockin. [00:00:48] Andrew Zigler: And in this world, speed is your moat and how fast you can innovate while your platform remains open, flexible, and standardize across all of its applications. That's what we're gonna explore [00:01:00] today. So Anush, I'm really excited to have you here. Welcome to Dev Interrupted. [00:01:04] Anush Elangovan: Thanks for having me. Uh, super excited to chat about it. [00:01:07] Andrew Zigler: Amazing. Well, let's go ahead and dive right in with kind of what I laid it out with in the beginning, the idea of the moat and it being about speed. I wanna unpack that a bit because that came from you when you and I first spoke. And I, and I want to know, you know, how do you define speed inside of AMD beyond just things like hardware, benchmarks. [00:01:27] Anush Elangovan: Yeah, that's a very good question. So when we typically talk about speed, everyone's like, Hey, hardware benchmark specs, right? Like, uh, memory bandwidth or, or flops. And that is one important part of it, uh, AMD does very well. With that, we do have, a, a very good history of executing on that axis. [00:01:47] Anush Elangovan: But when I say speed is the moat, it is about, uh, how we prepare, how we build the muscle to run the race for a long time and run it fast. And it is [00:02:00] not about a single point in time that you've, you've beat some you know, benchmark and, and you declare victory. It's about building the ability to consistently develop and deliver. [00:02:13] Anush Elangovan: Both hardware and software innovation at scale and do it fast, right? Like, you know, we we're increasingly getting to a point where models come out and they're, uh, you know, a year or two ago it was like, Hey, they work on AMD on day zero, which is great, but now they are performing on AMD the day it releases, right? [00:02:32] Anush Elangovan: So, what does it take to Prefetch where the industry is going? Be prepared to intercept. At that point is what you know, I, I refer to as you know, the, the speed factor in, in creating this mode, right? And the mode is just shed all things that hold you back and run as fast as you can. [00:02:53] Anush Elangovan: Uh, because the pace of innovation that is, uh, being seen in, in AI [00:03:00] industries is just. Amazing. Right? And it's like, it's transformational at at how you generate electricity. It's transformational as at how you build data centers. It's transformational at how you deploy compute, networking. It's transformational at what kind of use cases you, you know, uh, use AI for. [00:03:17] Anush Elangovan: Uh, and for that, you need to be prepared to, see what comes tomorrow and be prepared to run the race tomorrow. [00:03:23] Andrew Zigler: Yeah, it's a really great perspective because it highlights that it's not just like a checkpoint that you run through. I like how you called out, like it's not just hitting that benchmark or being the best in class at that moment, in that snapshot, it's about having a. The throughput and about having that dedication to the idea and continuing to deliver on it. [00:03:43] Andrew Zigler: It's not just crossing the threshold, but it's also being the engine. And that's what, that's what protects a business. That is the moat, because the moat is that innovation layer, the faster and more, uh, future forward. That you can work and think, [00:04:00] you know, the better. Uh, we, we talk a lot about like future forward work styles. [00:04:04] Andrew Zigler: Like what are the things I could be doing right now today that are gonna be like, way more useful tomorrow? Let, let's abandon those, workflows that are older and that kind of like, that translates into. An advantage when you work that way. You know, what kind of things have you learned working with, uh, like across all spectrums of people who would use ROCm, right? [00:04:23] Andrew Zigler: You have like the developers, but then you also have the enterprises and you have this large span of adoptees, right? So what is the, what does that look like that you learn? [00:04:32] Anush Elangovan: Yeah, so, so the way I look at it is there are gonna be pockets of different, uh, you know, cadences, right? Like, so people who are deploying in enterprises, for example, right? The validation and how long it takes for them to deploy an LLM that's secure. It's, with guardrails, et cetera, maybe longer. [00:04:52] Anush Elangovan: but you still have to go through the process and you have to be prepared to like, walk that walk to deploy an enterprises. That doesn't mean it's [00:05:00] not fast, that's as fast as you can do for that industry, right? And if you are deploying AI in healthcare, right, it's, it's got its own, uh, cycle. [00:05:07] Anush Elangovan: but in each one of these, you want to see how, like, go down to the essence of what is it that you actually have to do. And, you know, I, I, I like how you framed it. It's like it's, you shed your prior assumptions of how things are done, right. And, and you kind of build up from a, uh, first principles, uh, approach to say, this is how I could use AI to unlock, whatever I'm doing. [00:05:33] Anush Elangovan: And, and, some of it, you know, it's good to really step back and look at. Just question every part of it, right? Like right now you're getting chat GPT and, Gemini competing for like, math, olympiads and, and, uh, college, uh, reasoning, uh, tests. Right? And, and those are like that, that is amazing and increasingly like complex tasks that they're trying to do. [00:05:58] Anush Elangovan: But there may also be like. [00:06:00] More mundane things that AI could, could get applied to. Right? And, and so when we think about shedding old ways, you wanna shed it not just in like the tip of the spear. It's like, you know, I'm gonna see what's the frontier model. It's also, it could be something as simple as. [00:06:18] Anush Elangovan: How do you choose a, a movie, uh, you know, like a recommendation system, right? Or, or, uh, an automated, uh, flight, uh, rebooking system. So the moment, you know, your flight is late, uh, right now it's a notification, right? It's like, oh, you got a text message saying your flight's late. And I got that like three times this week. [00:06:38] Anush Elangovan: But anyway, uh, and, and, and, and, I was just like, okay, so if I were to rethink this. All this MCPs that we have that should be hooked up into an MCP that says, your flight's delayed. Here are your options. If you want, you know, these are the paid options. Yeah. Here are the free options. This will get you back into your you know, Toronto airport [00:07:00] tonight. [00:07:00] Anush Elangovan: Or if you stay, here's a hotel plus this, plus this, plus. It's just like, go ahead is all I should say. Versus now I'm like, okay, can someone, you know, can I call a travel agent? Can I do this? Can I go online and log into And you know, so we gotta fundamentally rethink even those like small, nuances of, things that we do that can be automated out and AI is really, really good at doing something like this, right? Maybe I just explained an AI startup idea right now. Somebody should just start that. [00:07:29] Andrew Zigler: I think you did. Yeah, you definitely did. Someone, one of our listeners is definitely going to lift that off of you. I, I, I, you know, I hate being on the receiving end of those. You feel a little helpless and then you have to like, follow the whole flow. So I know what you mean. Like I, I like how you called out that the build and this like. [00:07:45] Andrew Zigler: Where speed is your moat and the innovation layer is protecting you, is what makes you better than your competitors. How you scale that and you bring that to market. So by understanding the problems that you're solving, uh, throwing away those older assumptions, but also [00:08:00] recognizing that like. We're building every single day, new things and new ways of using stuff that we're still figuring out the implications of. [00:08:08] Andrew Zigler: And so when you have a lot of velocity and you're introducing a lot of new ideas, and maybe you have that workflow now that automatically rebook your flight off of your late flight text message, and uh, I know I would certainly use it, but you know, what kind of philosophies guide the way that y'all think about building this ecosystem to manage that stability while letting folks. [00:08:29] Andrew Zigler: Play with the speed and the assumptions and the airplane re bookings. [00:08:34] Anush Elangovan: so, so I think, you know, we need to peel one layer down, right? and the philosophy is, Hey, we, we just discovered electricity, right? And you know what we're gonna do? We are gonna make motors, uh, or dynamos, right? Like engines. Uh, sure. We don't know if it's gonna be a Ferrari that you're gonna make, or it's a a a a dump truck. [00:08:57] Anush Elangovan: That's good for doing this. But let's [00:09:00] let, which is also required, right? You need a dump truck. You need a garbage truck. And, [00:09:04] Andrew Zigler: Yeah. You need the [00:09:04] Anush Elangovan: course you need, uh, a Ferrari for a midlife crisis, right? So, [00:09:09] Andrew Zigler: precisely. [00:09:10] Anush Elangovan: But, but my, uh, point is what do we build next? And, uh, and this is what I meant by like, okay, let's, let's take those baby steps to build the. [00:09:20] Anush Elangovan: Infrastructure that's required that we know we'll have to use, right? So, so if I just discovered electricity, okay, great. Now one, how do I save this electricity and how do I use it? So there's battery technology, so you need to do something like that, right? Like so. But then you also want to make it into an actionable thing. [00:09:37] Anush Elangovan: You want to make it for like automobiles, or you wanna use it for, you know, powering, uh, entire cities. So it is that transformational. So, uh, AI is that transformational. So, if you distill down, it'll, it'll come down to how do we think about, what we can do with this this fundamental technology that, We may not be aware of what it [00:10:00] is gonna unlock next, but at least you know the next step is clear, right? It's like a dense fog, you know, it's gonna be like, it, it's the right path. You see the light, but it's kind of like out there and, and the steps you're taking are concrete and you're like, okay, this is good. [00:10:16] Anush Elangovan: I, this is better than where I was or where we were. So we are moving forward. So you can build with the. Intuition from what you see in the short term and a tactical view, but towards what you think the future is gonna be. [00:10:28] Andrew Zigler: Right. You almost like we're all in this like fog of war, right? And like you said, you're reaching out and you're trying to step through it. You could think of it too, as like you're in the dark and your hands are up in front of you and you know that. You're, you're not gonna run your face into a wall because your hands are out in front of you, but you're not gonna maybe do much better than that. [00:10:45] Andrew Zigler: So that's kind of like, I think the eco, the, the industry, the world that we find ourselves in, uh, and we all have to, then this becomes the power of an ecosystem, of a group of people working together to create that layer of, [00:11:00] uh, of establishing the [00:11:01] Anush Elangovan: exactly. And I, I, I just, instead of, you know, saying fog of war I describe it as like, you're in this. Beautiful valley with like a morning, uh, fog that's in. You can smell the flowers. You, you hear the birds. You are like, okay, it's, we are in like, uh, utopian paradise and yes, I just need to like, continue the walk, right? [00:11:24] Anush Elangovan: and then move forward with that, conviction that you're in the right spot. [00:11:27] Andrew Zigler: Yeah. So let's talk about that ecosystem world. This nice, I love how you describe it, this grassy side of a hill in the morning that's covered in some mist and maybe we can't see 30 feet in one direction, but it sure is a beautiful hill and it smells nice. And so we're all here. And why is, in that world, why is. [00:11:44] Andrew Zigler: You know, open source, their strategic advantage that y'all are going for in the AI hardware market. And, and then how does like ROCm turn that into wins for people within that ecosystem? [00:11:56] Anush Elangovan: you know, the, the way we look at it is this, is kind of like how I view [00:12:00] AI and the ecosystem, right? But, but it is for everyone to enjoy. Uh, and so we do want to make sure that. You know, it is, uh, beneficial for everyone. [00:12:09] Anush Elangovan: The ecosystem can come in and, and innovate. It's an open innovation engine. and uh, it is very different from, you know, having a walled garden with, Hey, only I know how to do this and I'm gonna do it and throw it over the fence and you can use it or keep walking, right? So we'd like to be good citizens that way, but also. [00:12:30] Anush Elangovan: Uh, it is self-fulfilling in a way, right? Like it, the, the pace at which we innovate with open source is unmatched. Like, you know, our serving engines are like VLLM and, and sg l. Those things, uh, those frameworks are like super, super aggressive in terms of how fast they come out with features and how fast they can you know, get performant models out. [00:12:52] Anush Elangovan: And that compared with what, uh, you'd get from, you know, the likes of like T-R-T-L-L-M or something is always lagging, right? Because you [00:13:00] just can't keep up with you know, 200 commits a week just on one particular model to get that model really performant [00:13:06] Andrew Zigler: And, and, and in that world where, you know, everyone can enjoy the winds of this, what kind of customer stories or innovation stories have really stood out to you and excite you about building and creating this place for developers? [00:13:19] Anush Elangovan: Yeah. So I think the parts that are super exciting for me are when when we get to see a customer that is first skeptical. Then they start a little like, okay, fine, we'll give you a chance. Uh, we do a simple, uh, POC and then they're like, huh, this seems to work. Yeah, we told you it works. [00:13:42] Anush Elangovan: You don't have to change one line of code. Really? Yes, no need to change one line of code. Okay, let's try a production workload. So then they try it. Oh, you're more performant than the competition. Yes. We're more performant than, than the competition. So how much does it cost? And we're like, oh, it's your TCO is better with, uh, [00:14:00] AMD. [00:14:00] Anush Elangovan: So again, they're like, wow, okay, good. So now how do we deploy at scale? And then we go deploy it at scale. And when they give a thumbs up on that and they say, this is good, right? That's when you know, you, you see it go full circle from like, oh, we, we've never heard about AMD to like actually deploy to tens of thousands of GPUs In the order of a few months, right? It, it, it really is fascinating to see and very exciting and invigorating to [00:14:28] Andrew Zigler: Yeah. At like a great exposure to a lot of interesting problems. And, and then people using the infrastructure, the, the technology available to solve those problems. Really specific problems by the way, that's often why they're bringing their data and AI to it, uh, is because it is really specific and important for them. [00:14:45] Andrew Zigler: And there's a, a lot I think that other engineering orgs can learn and even emulate from AMD's success and, and having this open source ecosystem and it causing this acceleration within. You [00:15:00] know, uh, customers and enterprises that use and adopt the tools and, and, and that creates an advantage. And that goes back to why we're talking and like the real thesis of our conversation today. [00:15:10] Andrew Zigler: So how do you think engineering leaders that are listening to this and obviously tapping into this great success AMD has from an open source flywheel, how do you think other, other folks building in the same space can foster that open, first, that open source oriented culture in order to, you know, accelerate their innovation goals? [00:15:29] Anush Elangovan: Yeah, that's a very good question. So the startup that um, was acquired by AMD we, we built, I mean, we started off doing iot stuff and you know, smart ring and all that, right? But in the, the end of like, uh, and not the end, the last six years of the company was building ML compilers. [00:15:47] Anush Elangovan: And ml, ML compilers are like super, uh, complicated, sophisticated, advanced algorithms, dah, dah, dah. but it was all open source, right? So our VCs were like, wait, what do you mean your core [00:16:00] IP is open source? And um, the speed is the moat applied even then, right? It was just like, yes, if you have an idea that. [00:16:08] Anush Elangovan: Because someone saw this idea that you are, they're gonna be able to catch up, then you probably have the wrong idea anyway. But if they are, you know, you execute and they're gonna catch up, that you should assume they're gonna catch up. Right? So you gotta move forward. So keeping it open source is super important. [00:16:25] Anush Elangovan: But also to your question on like, you know, the learnings from an AMD standpoint, right? If there are, hard problems, I'd say dig in and work through it, right? Like there's no way but through it, right? That should be the simple mentality. And more, uh, frequently than not. you'll see that you'll just make it through in a, in, in good form. [00:16:52] Anush Elangovan: But if you doubt it and you're like, oh, I don't know if I should commit, if I'm, I, you know, what should just commit to do the right thing [00:17:00] every step, right? Every step, and just keep taking one step in front of the other. And in no time you'll see that you'll be running. Right. And, and yes, the first few steps will be like, yeah, everyone's complaining about your software quality. [00:17:15] Anush Elangovan: Everyone's complaining about this and that, and it doesn't work. And, and a few steps in, you know, you get, you get the hang of all the complaints that are coming in. You get the feedback loop. You're like, okay, what, what are you prioritizing again? One step in front of the other, right? You just keep knocking that out and then you get to a point where you're, it just becomes second nature, right? To do the, to do the right thing. And, and then yes, if someone gives you two options, you'll be like, fine. This is, uh, you know, there's always the resource trade off. There's always a human capital trade off, but what's the right thing to do? of course, I, I'm pragmatic about what we choose, but, but if the right thing for your long-term success is dig in, go first, principles, make it [00:18:00] happen. [00:18:00] Anush Elangovan: Well. Then just go for that. There's, there is no shortcut to [00:18:04] Andrew Zigler: acknowledging, you know, how it aligns with your mission, your core company goals, and what you're looking to achieve. And, and I, I love how you rightfully called out that in the open source world and you know, you have your technology that you've built, what you think is your moat upon, right? [00:18:22] Andrew Zigler: It's your code and, and to open source that, or to just make it where anyone could peer in is, you know. Scary in one regard, but two, it just kind of feels like you're handing away your throne room in some kind of sense, a very direct feeling sense. But the ultimately, you were really right to call out, and this is something I think about all the time, that the real power there is still the speed This the speed. [00:18:42] Andrew Zigler: That was the moat at the beginning of our conversation. It's the speed in combination with your. Very specific domain understanding of what you're building and what you're creating, and your new role as the steward of that world and how people plug into it, which [00:19:00] has frankly, a lot more influence and power than lording over a closed. [00:19:04] Andrew Zigler: You know, repository or an ecosystem, and like you said, like throwing things over the wall. Sure. There, there might be people always on the other side of that wall, but you're not gonna have a great connection with them. You're not gonna be able to really clearly understand them. I, I like your metaphor of the side of the field of the mountain a lot more. [00:19:23] Andrew Zigler: But, but in the, in this world, you know, where. That speed is, is the power and, and open source is just one way that you can harness that speed to get really far ahead and to innovate. , There's other parts of this equation that you can be experimenting with too, and I'd love to pick your brain about them as a software leader and, and, and one of them is about looking forward and kind of understanding that future that we're all building towards and beyond today's models and hardware. [00:19:48] Andrew Zigler: You know, what do you see as the next major bottleneck or opportunity in the AI compute space? As, as you know, enterprises and folks start to get a little more mature about what's available to [00:20:00] them. [00:20:00] Anush Elangovan: Yeah, I think, the bottleneck and opportunity is, uh, what I'd call, call walking the last mile of ai. Right. Uh, and like I I, I gave you an example, uh, previously, but, but it's similar to that. It's like there are cases where Humans have so many, uh, things to do in your day. You know, like the, if we sit down and actually had a customer focus like, okay, these customers lives, I'm gonna save four hours of this customer's life. And if you actually sit down and look at all of that, it'll be. Easily automatable, easily you know, uh, applicable, uh, for ai, right? [00:20:39] Anush Elangovan: Like, but then making it happen is gonna take a little bit, right? It's like maybe it's, uh, paying your utility bill, right? Or something like that, right? Or, or, your healthcare explanation of benefits. Uh, like, I'm sure you get an explanation of benefits, and I'm like, I, I don't even know what that thing is. [00:20:55] Anush Elangovan: It's just like EOB and like. [00:20:57] Andrew Zigler: it's a big, a big old PDF. Yeah, [00:21:00] exactly. [00:21:01] Anush Elangovan: Like, like, I'm like great straight to the, uh, shredder, right? And but that could be, you know, automated with the ai, right? It, it, it'd be like, Hey, the summary of this thing is you went and visited this day. Everything is okay. Everything is paid for, so don't worry, it's not a bill. [00:21:17] Anush Elangovan: That again, the same, uh, thing, but the sense of what that information overload is could be. Digested by ai, uh, accumulated over time and retrieved when you need it. Like, I don't, I actually don't even need to know this EOB right now, unless of course, whenever I need to know it, that maybe, you know, like for some benefits I need to figure out what do, what did I do over the past year and how do I apply it? Source:

Mike

14,195 Aufrufe • vor 8 Monaten

When I was reading Brian Tully, Ken Mello and Robert Cosgrove's affidavits yesterday in the Aidan TurtleBoy Kearney case, I was challenged by an account that was intent on defending Leigha Bathtub Genduso and Kate Peter. Best quotes from my retort; "Number one, Steph, please address the fact—please address why Kate Peter’s February 24, 2024 email to Ken Mello was not turned over in the 5,000 pages of emails that Robert Cosgrove spent seven months putting together that were between Kate Peter and Ken Mello and Kate Peter and Brian Tully. Why was that February 24, 2024 email not turned over? Secondly, is the fact that those emails were turned over—despite the fact that it wasn’t a full turnover of emails—in August of 2025 tie into why the Lindsey Gaetani charges involving Aiden were dismissed? Thirdly: is the fact that Kate Peter—now we know from these documents—directly handled two pieces of key evidence in the Gaetani indictments involving Kearney the reason why, coupled with the August 2025 disclosure of those manipulated email records between Tully and Kate and Kate Peter and Ken Mello, was that the reason why the 2024 indictments involving Lindsey Gaetani were actually null-prossed? Time to answer some tough questions, Steph. Why was that audio of Leigha Genduso not included in the extraction that Brian Tully released completely unredacted in April of 2024? And why have you never said a word about how Tully manipulated that extraction to remove messages from Tully to Lindsey and from Kate to Lindsey before releasing it? And Tully apparently didn’t include Leigha Genduso’s audio message that is now part of the public court record, as well? Yes, Steph, you can’t address it on merit, you can’t, because you’re not here to do that, are you? You’re here to vacuously distract with nonsensical emotional rhetoric. And I will not stand for it. No, I’ll continue reading. It’ll get worse before it gets better, Steph. I’ll tell you that right now. No, she did not, Steph. I’ll tell you what, right now. You know how I know? Because look at Steph, it was posted on social media. Oh, Steph, it was posted on social media and not included in the extraction. So how could Lindsey have deleted it? Lindsey saved it, because Tully didn’t include it in the extraction, and then Lindsey dropped it on social media. And that proves it. That absolutely proves it. All right, so Steph, if you don’t know and don’t care, that’s the end of this discussion. If we have to move you on begrudgingly, we will. But as of now, you can’t address any of this on merit. You don’t know the factual record. You’re getting humiliated. And furthermore, I’m sending a message through you to Kate that her moles are not welcome here. So, well, yeah, but no, that’s not—hold on, do you realize, Steph, the point is not where it was posted. It was that the audio file exists. If it was not on Lindsey’s phone when they did the extraction, she couldn’t have it. But she still has it. There you go. So, listen, oh, I knew we were onto something. I didn’t know it was this bad, Steph. You shouldn’t have tipped Kate’s hand like this, by the way. Reacting that way is only making me aware that this is the whole kit and caboodle. No, Steph, again, you have no standing to stand up for anyone, call anyone anything, or otherwise say anything here, because you will not address the merits of the argument. You just admitted you don’t care about the filings, you don’t know the details, and you refuse to engage. So therefore, we’re done." PARTIAL TRANSCRIPT: If you’re just tuning in, my name is Grant Smith Ellis, and we are reading through Brian Tully, Robert Cosgrove, and Ken Mello’s affidavit. It’s tough to call it an affidavit from Ken Mello, because quite frankly, he didn’t write an affidavit. Robert Cosgrove adapted hearsay statements in Ken Mello’s voice in his own affidavit. That might tell you something. I don’t know. What the fuck do I know? I’m just a towel. Thank you very much for tuning in. I have noticed that there is a very specific group of people in Kate Peter’s orbit trying to target Towel right now. People do not want Towel to be heard. That means I’m going to speak more. I am going to just keep talking and keep saying things, because now I have put it all together. Oh, that’s right. I have one more thing to type. Furthermore, as soon as, within weeks of Kate’s emails to Tully and Mello being turned over in, what was it? August of 2025, the TurtleBoy charges involving Lindsey Gaetani were dropped. And what do you know? Kate was involved in handling evidence submitted by Tully and Mello to the grand jury for Lindsey’s charges, for the charges involving Lindsey Gaetani, for Aiden’s charges involving Lindsey Gaetani. Furthermore, the new email from Kate to Mello indicates Kate was indeed also involved in the 2023 indictments against Kearney that the Norfolk DA seems intent on trying to wall off from Kate Peter’s involvement. Oh, little towels, I'm just a little towel. Steph, Grant says, “Why are you making fun of her by calling her bathtub.” Wait, what? No, no, no, Steph, let’s be very clear. When Leigha Genduso engaged in—and I think it was Kate actually who did it—but when Leigha Genduso or Kate responded to revenge porn with revenge porn, nothing about that was okay, okay? Whether it was legal or not at the time, nobody sharing revenge porn of anybody else was okay, all right? I just want to be very clear. So when Kate did it, it was not okay. When Aiden did it, if that’s what happened with Leigha—I don’t know, I wasn’t around—not okay. If Leigha did it to Aiden, not okay, okay? Everybody on the same page? Like, it’s not okay to do that to people. I just want everyone on the same page. No one would—it’s just like, treat people how you want to be treated, bro. So I just don’t do it. Now, I get some people would say, fight fire with fire, okay, still, don’t fucking do it. Please don’t do it. I don’t understand why people do it. It blows my mind. I don’t understand why people justify it. Oh, it’s okay that Kate or Leigha did it, cause Aiden did it too. It’s like, no, though. I get it's a shitty thing to happen. Don’t do it back. Just stop. It’s ridiculous. Steph's like—"I keep seeing you call her bathtub." Yea, bro she took a video in a bathtub once and posted it on social media. Okay, you want to livestream yourself from a fucking bathtub then I'm going to call you Leigha Bathtub Genduso. I don’t know what to tell you. You don’t have to call her that, but I’m going to do that, right? And I’m not going to stop. But yeah, Three-Clerk-Monte bang bang. Sometimes you just got to tell them how it is, Three-Clerk Monte, you know what I’m saying? Even while you’re on your break. By the way, Steph, I’m just going to break here just posting things, right? And I’m saying I’m not even supposed to be riled up right now. We’re going to go back to reading the indictment in a little bit. I’m just a little towel. I’m on one, you know what I’m saying? Absolutely not. I don’t know which Steph you are. I don’t know if you’re that Steph or whatever, the fake Canadian. You’re not going to come on here and tell me I cannot call her Leigha Bathtub Genduso. I’m going to triple down. I’m going to call Leigha Bathtub Genduso more now. Thank you for all the comments, by the way. It helps the stream get attention in the Kate Peter sucks. Remember that? Yes, that I want you to get this tattooed on your arm: Kate Peter sucks. I’ll help you spell it: K-A-T-E P-E-T-E-R, no S at the end, just Kate Peter, now a new word, sucks, S-U-C-K-S. Everybody on the same page? All right, it’s artistic expression, bro. What do you want to say? Oh no, she’s gone. Steph, I was enjoying all your comments. Yes, Steph, that’s exactly what I want. I want you to keep interacting in the comments because it gets the stream more attention in the feed. I want that. I want you to continue to engage, and I’m going to keep calling her Leigha Bathtub Genduso. It’s not an obsession. It is the product of multiple years of work on the story to uncover something hidden that you don’t want to be talked about in public. That’s the reality. Is that not right Steph, you’re concerned that Kate Peter compromised the cases against Aiden Kearney because she worked as a PI for Marty Craft, who’s now lost his license because of what she was up to according to people’s reports in this chat, and you feel that it’s uncomfortable to have to hold her to the same moral standard that you do Aiden because you’re biased, right? Fine, I don’t care. I’ll tell it to your face yes. No, Steph, you have something to say? You say it right here, one-on-one. Let’s debate. We can do it. I have all the evidence now. We can talk about it all. That’s correct. I don’t create realities, Steph. I bring them to light. Your normative moral framework and what you want to happen is just that. The descriptive reality is independent of what any of us want. It is simply a factual record. In the context of our asymptotic relationship with that factual record, notwithstanding, I was interested in the truth, and you are who is afraid of it, let’s be clear. I wouldn’t say you’re debating me, Steph. You can’t debate on the merit of the facts. You want to know why? Because, for example, it would be very hard for you to counter something like this paragraph right here, right? Where Robert Cosgrove says that any data missing from Lindsey Gaetani’s phone was not on the phone at the time Brian Tully did the extraction. And you might be saying to yourself, Grant, how can you know? How can you know that Brian Tully intentionally released the phone unredacted after only removing messages from Kate to Lindsey and from Tully to Lindsey and after removing things like audio messages from Leigha Genduso? How do I know? Well, because how else would Lindsey have posted it on social media? My word, Steph. It’s almost like there’s proof that Robert Cosgrove was withholding material information related to the sum and substance of Kate Peter’s communications with various members of the prosecution team and/or witnesses and/or the handling of evidence in order to insulate certain charges from Kate Peter touching that evidence so that they could continue to trial, notwithstanding the discovery obligations of the state under the new updated Rule 14 as implemented on March 1, 2025. And towel is in a snarky mood indeed. And you’re not going to be able to do anything about it—oh, please, you're not saying to yourself, "what’s wrong with towel, Steph?" You’re basically saying, "why are you crossing the thin blue line?" And I would like to respond to you by saying, in the least unloving way, but the fact that you would ask me, “What is Grant doing?” because I won’t adhere to your thin blue line? Get the fuck out of here. Go climb up somebody else’s tree. Go find your own treehouse. Not happening. Absolutely not happening. You will look this factual record in the eye. You will confront your moral problems with the various actions of different people involved on your own time. And Leigha Bathtub Genduso will be central to this moral reckoning. And there’s not a damn thing you or your fake Canadian ass can do about it. I’m on one. I told you. Listen, you want it? You want it to be on record? We’ll do it. No, no, I’m just not loyal to your interests, Steph. I’m loyal to truth. I’m loyal to the people who are actually harmed. I’m not loyal to you or any of your friends or Kate Peter or the thin blue line or the thin green line or the thin pink line for that matter. All of you can take your lines and go fuck yourselves. Fake Canadian. Yeah, right, Steph. Yeah, let’s go with that. Yep, let’s go with fake Canadian, because why would you want me looking more in to you? A reporter? You want me to look more into you? No. God, take the L, man, just move on. That’s correct. No, listen, Steph, you want to talk about Michael Proctor’s family’s relationship to my mother? You want to be the person who draws that line? I’ll tell you about it. You sure you want to talk about it? You damn fake Canadian. We may have to get this fake Canadian out of here. She’s riling me up. You’re riling me up by trying to defend Kate Peter. I knew you were a rat the whole time. Goddamn Kate Peter mole. I knew it. I saw through that shit. "I just heard you acknowledge me about the AI. No hate. I appreciate you reading this. Good content." Thank you, sir. Thank you, to the person who said that! You see what I’m saying, Steph? You know what? I think we should just let Steph talk to herself, all right? She can just keep promoting the stream and the algorithm. Let her talk to herself. But Steph, even if you’re talking to yourself, I still have to write the post, okay? Damn fake Canadians. Steph is a fake Canadian and she may or may not be a communist. What you gonna' do about it? You damn fake Canadian. All right, no, I actually have to write this follow-up post. Stop it, Steph. Stop trying to gaslight to protect Kate Peter. You’ll be thrown out of here faster than someone with a cannabis conviction trying to enter Canada who doesn’t actually live there. Damn fake Canadians. Thank you, Kristina. I appreciate it. Yes, and Kristina, you ever wonder if maybe people come in here specifically to derail the conversation because we’re talking about very damning things as to Kate Peter? Well then, let me write my other post, by the way. I’ll help. I will put it up on the screen for you in one second. I just got to get the video loading before I start typing. Oh, Steph, you were on assignment. Stop bitching. I hope they paid you well for it. Don’t bark up my towel tree about you had to spend time with me so you could run intel to all the Kate Peter people. I don’t care. I knew what you were doing. Do you think I was born yesterday? Come on. You all insult my intelligence routinely—not you in the chat. Some of you moles are just like, “He won’t know.” What, are you just going to tell me I’m the greatest thing ever and then it’s going to go along? I’m just saying, I’ve been posting on social media being like, “Aidan, if people tell you that you’re the greatest thing ever, that might be true, but some of them are going to tell you that because they’re moles.” Come on. This is very basic-level intel stuff here. Steph, that was very nice of you. I am never going to degrade you for supporting people in need. What I’m concerned about, okay—I’m not concerned about who you are as a person. I’m concerned about what you didn’t tell us. All right? Yeah? And that's my right. No, absolutely not, Steph. You know exactly what happened. You flipped on a dime as soon as I started asking questions about Kate Peter because she has a lot of moles in her orbit. And then as soon as we started talking about her today, coincidentally enough, you popped right back up. Oh, what’s this? Robert Cosgrove represented in a sworn affidavit that any material missing from Lindsey Gaetani’s—see what I’m doing, Steph? This is, uh, this is for you—Lindsey Gaetani’s phone extraction was not on the phone when MSP did that extraction. And then Brian Tully leaked that extraction unredacted. That’s a message from Leigha Bathtub Genduso proves Tully failed to include material that was indeed on Lindsey's phone. That was for you too, Steph. It’s weird that you know Bathtub, by the way. That’s just odd. Like she’s known Kate Peter for years too. If this Steph, who I watched Sandlot with, is the same Steph as the one who’s a second cousin of John O’Keefe, then she lied to me. She lied to me. If we can prove that this is the same, same Steph, then she lied to me. She told me she was from Canada, Saskatchewan, whatever the fuck. That’s what I’m saying. So Steph, if you are that Steph from wherever the fuck you live, bro—if you are that Steph—you lied to us all. You told us you were fucking Canadian. Wait a minute, wait a minute, wait a minute, wait a minute, wait a minute—are you actually that Steph? No fucking way. You lied to all of us this whole time and pretended to be Canadian? No, that was not—I didn’t ask if you were from Canada. I said, are you the same Steph who was second cousins with John O’Keefe and did you come on this channel and go on a Zoom call with me representing yourself to be Canadian from Saskatchewan? I don’t even have—no, that is not the question I’m asking you. Are you the same Steph that is second cousins with John O’Keefe? Thanks for letting us know. See what I mean? Kristina, it’s not the same Steph. It’s just some random person who really likes Leigha Genduso, Leigha Bathtub Genduso, and Kate Peter. Random coincidence! Just totally random. Come on. I’m rolling my eyes so hard I’m laughing. This has been really interesting though. I know you said no. That makes it even weirder. If you’re not that Steph, your fervent defense of Kate Peter and Leigha Bathtub Genduso is even more weird. Go back to Discord. Come on now, shoo. You’re bothering me. If you bother me too much, I’m just going to go on a 45-minute rant eviscerating Kate Peter with facts, all right? So it’s better to just go. Like I told Benny Sweatpants the other day. Send him my regards, all right? No, I like calling out your hypocrisy. You wouldn’t say a negative word about Kate Peter if I demonstrated the factual record for you in real time. Live! Which I’m doing. You haven’t addressed one element of it on substance. All you’ve done is gaslight, and frankly you’re going to find yourself removed if you continue to fail to adhere to the rules of Towel Channel. As you know, the rules of Towel Channel are pretty simple, which is: one, don’t be discriminatory; two, don’t be derogatory; three, don’t sealion; four, don’t gaslight; and five, no Kate Peters. All right? Jay’s like, “I’m aboard the Grant train.” Thanks, Jay. It wasn’t one question, Steph. It was three questions. Let me reiterate them to you very quickly. Number one, Steph, please address the fact—please address why Kate Peter’s February 24, 2024 email to Ken Mello was not turned over in the 5,000 pages of emails that Robert Cosgrove spent seven months putting together that were between Kate Peter and Ken Mello and Kate Peter and Brian Tully. Why was that February 24, 2024 email not turned over? Secondly, is the fact that those emails were turned over—despite the fact that it wasn’t a full turnover of emails—in August of 2025 tie into why the Lindsey Gaetani charges involving Aiden were dismissed? Second question: is the fact that Kate Peter—now we know from these documents—directly handled two pieces of key evidence in the Gaetani indictments involving Kearney the reason why, coupled with the August 2025 disclosure of those manipulated email records between Tully and Kate and Kate Peter and Ken Mello, was that the reason why the 2024 indictments involving Lindsey Gaetani were actually null-prossed? Time to answer some tough questions, Steph. And furthermore, why was that audio of Leigha Genduso not included in the extraction that Brian Tully released completely unredacted in April of 2024? And why have you never said a word about how Tully manipulated that extraction to remove messages from Tully to Lindsey and from Kate to Lindsey before releasing it? And Tully apparently didn’t include Leigha Genduso’s audio message that is now part of the public court record. Yes, Steph, you can’t address it on merit, you can’t, because you’re not here to do that, are you? You’re here to vacuously distract with nonsensical emotional rhetoric. And I will not stand for it. No, I’ll continue reading. It’ll get worse before it gets better, Steph. I’ll tell you that right now. No, she did not, Steph. I’ll tell you what, right now. You know how I know? Because look at Steph, it was posted on social media. Oh, Steph, it was posted on social media and not included in the extraction. So how could Lindsey have deleted it? Lindsey saved it, because Tully didn’t include it in the extraction, and then Lindsey dropped it on social media. And that proves it. That absolutely proves it. All right, so Steph, if you don’t know and don’t care, that’s the end of this discussion. If we have to move you on begrudgingly, we will. But as of now, you can’t address any of this on merit. You don’t know the factual record. You’re getting humiliated. And furthermore, I’m sending a message through you to Kate that her moles are not welcome here. So, well, yeah, but no, that’s not—hold on, do you realize, Steph, the point is not where it was posted. It was that the audio file exists. If it was not on Lindsey’s phone when they did the extraction, she couldn’t have it. But she still has it. There you go. So, listen, oh, I knew we were onto something. I didn’t know it was this bad, Steph. You shouldn’t have tipped Kate’s hand like this, by the way. The reacting that way is only making me aware that this is the whole kitten caboodle. No, Steph, again, you have no standing to stand up for anyone, call anyone anything, or otherwise say anything here, because you will not address the merits of the argument. You just admitted you don’t care about the filings, you don’t know the details, and you refuse to engage. So therefore, we’re done. Oh, it’s such a shame. All right, I gotta move her on. All right, Steph, it was great. We’ll put you in a little timeout. You can come back tomorrow, okay? I’m glad you spent some time with us, but the reality is I just don’t—I don’t wanna play that type of Kate Peter game, all right? Yep, now, Christina, you, as you know, this channel in Br… every possible perspective. I don’t care what you want to come in here and believe, you know you and I align on a lot of the factual record about a lot of these different cases. It’s not that. I’ll never ever have a problem with that. It’s the bad faith—and it’s not you, Christina. You are wonderful. You’ve never done it—but it’s the people who get too close to Kate Peter and then as embodied in that colloquy with Steph right there, whoever the fuck she is, we still don’t know. As embodied in that colloquy, you have a situation where when confronted with the facts instead of responding or even giving the time of day to what Kate Peter or Tully or Cosgrove might have done wrong, immediately it starts with the emotional manipulation, the attacks, the distraction. So I hope that—I hope that tells us all something. But yes, let’s keep reading because before I got in that fun colloquy, we were—I bet Steph was sent here to try to derail me. Nice try, Steph, take it elsewhere. All right, so we got those two posts up, by the way. All right, following service. Do you remember where we were in all this? The very last—so we just read about the Kate emails. By the way, now we know the whole Kate and Kaboodle is the Kate emails. We just read about the Kate emails and take a look where it goes next. All right, it just keeps going and going. Oh, do you think I should add Kate, Steph to the chart, by the way? Where should she go on the chart? Should she go under the Trollhollmio section? I feel like that’s appropriate. You know, this is just my opinion of how all these people tie together. Say you got Kate Peter, the Lord of Darkness in the middle—that’s my opinion. Then you got Jamz up there, Llama over there, Jason Broyles down here, Gaffney over here, Trollhollomio here. Then you got people like Critical Mass, Virgil, that—I don’t know who that is. And then you got Tully, Michael Morrissey, and Michael Proctor. Then you got Jake Sun, Twisted Tragedies tied to Gaffney. Then you got that guy, Jason Broyles, who thinks—who pretends to be a woman online. You got him, I think he’s tied to Barry Lewis and this weird woman from Connecticut that Kate keeps working with. She used to pretend to be like an advocate for medical patients, but now apparently she’s a big advocate of prednisone. I don’t really understand. She’s been going online telling people that people with colitis have to use prednisone apparently and they can’t use cannabis. I’m baffled by it. I didn’t know she was a doctor. Listen, if I knew that this woman was a doctor, I would start looking to whether she’s received payments from the pharmaceutical industry because I’ve never met a cannabis advocate who tells people they have to use prednisone for colitis. So that woman baffles me. Also, she’s the reason consumption event in Massachusetts are now regulated by the CCC. So listen, you all think that Kate Peter’s just some kind of like moron. She just plays that role, okay? Like she plays like she doesn’t know what she’s talking about and she doesn’t mostly with these court developments. But look at her network. Like people fawn over her like TurtleBoy. She is the female TurtleBoy in so many ways. And what makes her scary is she doesn’t own it.
33:21

When I was reading Brian Tully, Ken Mello and Robert Cosgrove's affidavits yesterday in the Aidan TurtleBoy Kearney case, I was challenged by an account that was intent on defending Leigha Bathtub Genduso and Kate Peter. Best quotes from my retort; "Number one, Steph, please address the fact—please address why Kate Peter’s February 24, 2024 email to Ken Mello was not turned over in the 5,000 pages of emails that Robert Cosgrove spent seven months putting together that were between Kate Peter and Ken Mello and Kate Peter and Brian Tully. Why was that February 24, 2024 email not turned over? Secondly, is the fact that those emails were turned over—despite the fact that it wasn’t a full turnover of emails—in August of 2025 tie into why the Lindsey Gaetani charges involving Aiden were dismissed? Thirdly: is the fact that Kate Peter—now we know from these documents—directly handled two pieces of key evidence in the Gaetani indictments involving Kearney the reason why, coupled with the August 2025 disclosure of those manipulated email records between Tully and Kate and Kate Peter and Ken Mello, was that the reason why the 2024 indictments involving Lindsey Gaetani were actually null-prossed? Time to answer some tough questions, Steph. Why was that audio of Leigha Genduso not included in the extraction that Brian Tully released completely unredacted in April of 2024? And why have you never said a word about how Tully manipulated that extraction to remove messages from Tully to Lindsey and from Kate to Lindsey before releasing it? And Tully apparently didn’t include Leigha Genduso’s audio message that is now part of the public court record, as well? Yes, Steph, you can’t address it on merit, you can’t, because you’re not here to do that, are you? You’re here to vacuously distract with nonsensical emotional rhetoric. And I will not stand for it. No, I’ll continue reading. It’ll get worse before it gets better, Steph. I’ll tell you that right now. No, she did not, Steph. I’ll tell you what, right now. You know how I know? Because look at Steph, it was posted on social media. Oh, Steph, it was posted on social media and not included in the extraction. So how could Lindsey have deleted it? Lindsey saved it, because Tully didn’t include it in the extraction, and then Lindsey dropped it on social media. And that proves it. That absolutely proves it. All right, so Steph, if you don’t know and don’t care, that’s the end of this discussion. If we have to move you on begrudgingly, we will. But as of now, you can’t address any of this on merit. You don’t know the factual record. You’re getting humiliated. And furthermore, I’m sending a message through you to Kate that her moles are not welcome here. So, well, yeah, but no, that’s not—hold on, do you realize, Steph, the point is not where it was posted. It was that the audio file exists. If it was not on Lindsey’s phone when they did the extraction, she couldn’t have it. But she still has it. There you go. So, listen, oh, I knew we were onto something. I didn’t know it was this bad, Steph. You shouldn’t have tipped Kate’s hand like this, by the way. Reacting that way is only making me aware that this is the whole kit and caboodle. No, Steph, again, you have no standing to stand up for anyone, call anyone anything, or otherwise say anything here, because you will not address the merits of the argument. You just admitted you don’t care about the filings, you don’t know the details, and you refuse to engage. So therefore, we’re done." PARTIAL TRANSCRIPT: If you’re just tuning in, my name is Grant Smith Ellis, and we are reading through Brian Tully, Robert Cosgrove, and Ken Mello’s affidavit. It’s tough to call it an affidavit from Ken Mello, because quite frankly, he didn’t write an affidavit. Robert Cosgrove adapted hearsay statements in Ken Mello’s voice in his own affidavit. That might tell you something. I don’t know. What the fuck do I know? I’m just a towel. Thank you very much for tuning in. I have noticed that there is a very specific group of people in Kate Peter’s orbit trying to target Towel right now. People do not want Towel to be heard. That means I’m going to speak more. I am going to just keep talking and keep saying things, because now I have put it all together. Oh, that’s right. I have one more thing to type. Furthermore, as soon as, within weeks of Kate’s emails to Tully and Mello being turned over in, what was it? August of 2025, the TurtleBoy charges involving Lindsey Gaetani were dropped. And what do you know? Kate was involved in handling evidence submitted by Tully and Mello to the grand jury for Lindsey’s charges, for the charges involving Lindsey Gaetani, for Aiden’s charges involving Lindsey Gaetani. Furthermore, the new email from Kate to Mello indicates Kate was indeed also involved in the 2023 indictments against Kearney that the Norfolk DA seems intent on trying to wall off from Kate Peter’s involvement. Oh, little towels, I'm just a little towel. Steph, Grant says, “Why are you making fun of her by calling her bathtub.” Wait, what? No, no, no, Steph, let’s be very clear. When Leigha Genduso engaged in—and I think it was Kate actually who did it—but when Leigha Genduso or Kate responded to revenge porn with revenge porn, nothing about that was okay, okay? Whether it was legal or not at the time, nobody sharing revenge porn of anybody else was okay, all right? I just want to be very clear. So when Kate did it, it was not okay. When Aiden did it, if that’s what happened with Leigha—I don’t know, I wasn’t around—not okay. If Leigha did it to Aiden, not okay, okay? Everybody on the same page? Like, it’s not okay to do that to people. I just want everyone on the same page. No one would—it’s just like, treat people how you want to be treated, bro. So I just don’t do it. Now, I get some people would say, fight fire with fire, okay, still, don’t fucking do it. Please don’t do it. I don’t understand why people do it. It blows my mind. I don’t understand why people justify it. Oh, it’s okay that Kate or Leigha did it, cause Aiden did it too. It’s like, no, though. I get it's a shitty thing to happen. Don’t do it back. Just stop. It’s ridiculous. Steph's like—"I keep seeing you call her bathtub." Yea, bro she took a video in a bathtub once and posted it on social media. Okay, you want to livestream yourself from a fucking bathtub then I'm going to call you Leigha Bathtub Genduso. I don’t know what to tell you. You don’t have to call her that, but I’m going to do that, right? And I’m not going to stop. But yeah, Three-Clerk-Monte bang bang. Sometimes you just got to tell them how it is, Three-Clerk Monte, you know what I’m saying? Even while you’re on your break. By the way, Steph, I’m just going to break here just posting things, right? And I’m saying I’m not even supposed to be riled up right now. We’re going to go back to reading the indictment in a little bit. I’m just a little towel. I’m on one, you know what I’m saying? Absolutely not. I don’t know which Steph you are. I don’t know if you’re that Steph or whatever, the fake Canadian. You’re not going to come on here and tell me I cannot call her Leigha Bathtub Genduso. I’m going to triple down. I’m going to call Leigha Bathtub Genduso more now. Thank you for all the comments, by the way. It helps the stream get attention in the Kate Peter sucks. Remember that? Yes, that I want you to get this tattooed on your arm: Kate Peter sucks. I’ll help you spell it: K-A-T-E P-E-T-E-R, no S at the end, just Kate Peter, now a new word, sucks, S-U-C-K-S. Everybody on the same page? All right, it’s artistic expression, bro. What do you want to say? Oh no, she’s gone. Steph, I was enjoying all your comments. Yes, Steph, that’s exactly what I want. I want you to keep interacting in the comments because it gets the stream more attention in the feed. I want that. I want you to continue to engage, and I’m going to keep calling her Leigha Bathtub Genduso. It’s not an obsession. It is the product of multiple years of work on the story to uncover something hidden that you don’t want to be talked about in public. That’s the reality. Is that not right Steph, you’re concerned that Kate Peter compromised the cases against Aiden Kearney because she worked as a PI for Marty Craft, who’s now lost his license because of what she was up to according to people’s reports in this chat, and you feel that it’s uncomfortable to have to hold her to the same moral standard that you do Aiden because you’re biased, right? Fine, I don’t care. I’ll tell it to your face yes. No, Steph, you have something to say? You say it right here, one-on-one. Let’s debate. We can do it. I have all the evidence now. We can talk about it all. That’s correct. I don’t create realities, Steph. I bring them to light. Your normative moral framework and what you want to happen is just that. The descriptive reality is independent of what any of us want. It is simply a factual record. In the context of our asymptotic relationship with that factual record, notwithstanding, I was interested in the truth, and you are who is afraid of it, let’s be clear. I wouldn’t say you’re debating me, Steph. You can’t debate on the merit of the facts. You want to know why? Because, for example, it would be very hard for you to counter something like this paragraph right here, right? Where Robert Cosgrove says that any data missing from Lindsey Gaetani’s phone was not on the phone at the time Brian Tully did the extraction. And you might be saying to yourself, Grant, how can you know? How can you know that Brian Tully intentionally released the phone unredacted after only removing messages from Kate to Lindsey and from Tully to Lindsey and after removing things like audio messages from Leigha Genduso? How do I know? Well, because how else would Lindsey have posted it on social media? My word, Steph. It’s almost like there’s proof that Robert Cosgrove was withholding material information related to the sum and substance of Kate Peter’s communications with various members of the prosecution team and/or witnesses and/or the handling of evidence in order to insulate certain charges from Kate Peter touching that evidence so that they could continue to trial, notwithstanding the discovery obligations of the state under the new updated Rule 14 as implemented on March 1, 2025. And towel is in a snarky mood indeed. And you’re not going to be able to do anything about it—oh, please, you're not saying to yourself, "what’s wrong with towel, Steph?" You’re basically saying, "why are you crossing the thin blue line?" And I would like to respond to you by saying, in the least unloving way, but the fact that you would ask me, “What is Grant doing?” because I won’t adhere to your thin blue line? Get the fuck out of here. Go climb up somebody else’s tree. Go find your own treehouse. Not happening. Absolutely not happening. You will look this factual record in the eye. You will confront your moral problems with the various actions of different people involved on your own time. And Leigha Bathtub Genduso will be central to this moral reckoning. And there’s not a damn thing you or your fake Canadian ass can do about it. I’m on one. I told you. Listen, you want it? You want it to be on record? We’ll do it. No, no, I’m just not loyal to your interests, Steph. I’m loyal to truth. I’m loyal to the people who are actually harmed. I’m not loyal to you or any of your friends or Kate Peter or the thin blue line or the thin green line or the thin pink line for that matter. All of you can take your lines and go fuck yourselves. Fake Canadian. Yeah, right, Steph. Yeah, let’s go with that. Yep, let’s go with fake Canadian, because why would you want me looking more in to you? A reporter? You want me to look more into you? No. God, take the L, man, just move on. That’s correct. No, listen, Steph, you want to talk about Michael Proctor’s family’s relationship to my mother? You want to be the person who draws that line? I’ll tell you about it. You sure you want to talk about it? You damn fake Canadian. We may have to get this fake Canadian out of here. She’s riling me up. You’re riling me up by trying to defend Kate Peter. I knew you were a rat the whole time. Goddamn Kate Peter mole. I knew it. I saw through that shit. "I just heard you acknowledge me about the AI. No hate. I appreciate you reading this. Good content." Thank you, sir. Thank you, to the person who said that! You see what I’m saying, Steph? You know what? I think we should just let Steph talk to herself, all right? She can just keep promoting the stream and the algorithm. Let her talk to herself. But Steph, even if you’re talking to yourself, I still have to write the post, okay? Damn fake Canadians. Steph is a fake Canadian and she may or may not be a communist. What you gonna' do about it? You damn fake Canadian. All right, no, I actually have to write this follow-up post. Stop it, Steph. Stop trying to gaslight to protect Kate Peter. You’ll be thrown out of here faster than someone with a cannabis conviction trying to enter Canada who doesn’t actually live there. Damn fake Canadians. Thank you, Kristina. I appreciate it. Yes, and Kristina, you ever wonder if maybe people come in here specifically to derail the conversation because we’re talking about very damning things as to Kate Peter? Well then, let me write my other post, by the way. I’ll help. I will put it up on the screen for you in one second. I just got to get the video loading before I start typing. Oh, Steph, you were on assignment. Stop bitching. I hope they paid you well for it. Don’t bark up my towel tree about you had to spend time with me so you could run intel to all the Kate Peter people. I don’t care. I knew what you were doing. Do you think I was born yesterday? Come on. You all insult my intelligence routinely—not you in the chat. Some of you moles are just like, “He won’t know.” What, are you just going to tell me I’m the greatest thing ever and then it’s going to go along? I’m just saying, I’ve been posting on social media being like, “Aidan, if people tell you that you’re the greatest thing ever, that might be true, but some of them are going to tell you that because they’re moles.” Come on. This is very basic-level intel stuff here. Steph, that was very nice of you. I am never going to degrade you for supporting people in need. What I’m concerned about, okay—I’m not concerned about who you are as a person. I’m concerned about what you didn’t tell us. All right? Yeah? And that's my right. No, absolutely not, Steph. You know exactly what happened. You flipped on a dime as soon as I started asking questions about Kate Peter because she has a lot of moles in her orbit. And then as soon as we started talking about her today, coincidentally enough, you popped right back up. Oh, what’s this? Robert Cosgrove represented in a sworn affidavit that any material missing from Lindsey Gaetani’s—see what I’m doing, Steph? This is, uh, this is for you—Lindsey Gaetani’s phone extraction was not on the phone when MSP did that extraction. And then Brian Tully leaked that extraction unredacted. That’s a message from Leigha Bathtub Genduso proves Tully failed to include material that was indeed on Lindsey's phone. That was for you too, Steph. It’s weird that you know Bathtub, by the way. That’s just odd. Like she’s known Kate Peter for years too. If this Steph, who I watched Sandlot with, is the same Steph as the one who’s a second cousin of John O’Keefe, then she lied to me. She lied to me. If we can prove that this is the same, same Steph, then she lied to me. She told me she was from Canada, Saskatchewan, whatever the fuck. That’s what I’m saying. So Steph, if you are that Steph from wherever the fuck you live, bro—if you are that Steph—you lied to us all. You told us you were fucking Canadian. Wait a minute, wait a minute, wait a minute, wait a minute, wait a minute—are you actually that Steph? No fucking way. You lied to all of us this whole time and pretended to be Canadian? No, that was not—I didn’t ask if you were from Canada. I said, are you the same Steph who was second cousins with John O’Keefe and did you come on this channel and go on a Zoom call with me representing yourself to be Canadian from Saskatchewan? I don’t even have—no, that is not the question I’m asking you. Are you the same Steph that is second cousins with John O’Keefe? Thanks for letting us know. See what I mean? Kristina, it’s not the same Steph. It’s just some random person who really likes Leigha Genduso, Leigha Bathtub Genduso, and Kate Peter. Random coincidence! Just totally random. Come on. I’m rolling my eyes so hard I’m laughing. This has been really interesting though. I know you said no. That makes it even weirder. If you’re not that Steph, your fervent defense of Kate Peter and Leigha Bathtub Genduso is even more weird. Go back to Discord. Come on now, shoo. You’re bothering me. If you bother me too much, I’m just going to go on a 45-minute rant eviscerating Kate Peter with facts, all right? So it’s better to just go. Like I told Benny Sweatpants the other day. Send him my regards, all right? No, I like calling out your hypocrisy. You wouldn’t say a negative word about Kate Peter if I demonstrated the factual record for you in real time. Live! Which I’m doing. You haven’t addressed one element of it on substance. All you’ve done is gaslight, and frankly you’re going to find yourself removed if you continue to fail to adhere to the rules of Towel Channel. As you know, the rules of Towel Channel are pretty simple, which is: one, don’t be discriminatory; two, don’t be derogatory; three, don’t sealion; four, don’t gaslight; and five, no Kate Peters. All right? Jay’s like, “I’m aboard the Grant train.” Thanks, Jay. It wasn’t one question, Steph. It was three questions. Let me reiterate them to you very quickly. Number one, Steph, please address the fact—please address why Kate Peter’s February 24, 2024 email to Ken Mello was not turned over in the 5,000 pages of emails that Robert Cosgrove spent seven months putting together that were between Kate Peter and Ken Mello and Kate Peter and Brian Tully. Why was that February 24, 2024 email not turned over? Secondly, is the fact that those emails were turned over—despite the fact that it wasn’t a full turnover of emails—in August of 2025 tie into why the Lindsey Gaetani charges involving Aiden were dismissed? Second question: is the fact that Kate Peter—now we know from these documents—directly handled two pieces of key evidence in the Gaetani indictments involving Kearney the reason why, coupled with the August 2025 disclosure of those manipulated email records between Tully and Kate and Kate Peter and Ken Mello, was that the reason why the 2024 indictments involving Lindsey Gaetani were actually null-prossed? Time to answer some tough questions, Steph. And furthermore, why was that audio of Leigha Genduso not included in the extraction that Brian Tully released completely unredacted in April of 2024? And why have you never said a word about how Tully manipulated that extraction to remove messages from Tully to Lindsey and from Kate to Lindsey before releasing it? And Tully apparently didn’t include Leigha Genduso’s audio message that is now part of the public court record. Yes, Steph, you can’t address it on merit, you can’t, because you’re not here to do that, are you? You’re here to vacuously distract with nonsensical emotional rhetoric. And I will not stand for it. No, I’ll continue reading. It’ll get worse before it gets better, Steph. I’ll tell you that right now. No, she did not, Steph. I’ll tell you what, right now. You know how I know? Because look at Steph, it was posted on social media. Oh, Steph, it was posted on social media and not included in the extraction. So how could Lindsey have deleted it? Lindsey saved it, because Tully didn’t include it in the extraction, and then Lindsey dropped it on social media. And that proves it. That absolutely proves it. All right, so Steph, if you don’t know and don’t care, that’s the end of this discussion. If we have to move you on begrudgingly, we will. But as of now, you can’t address any of this on merit. You don’t know the factual record. You’re getting humiliated. And furthermore, I’m sending a message through you to Kate that her moles are not welcome here. So, well, yeah, but no, that’s not—hold on, do you realize, Steph, the point is not where it was posted. It was that the audio file exists. If it was not on Lindsey’s phone when they did the extraction, she couldn’t have it. But she still has it. There you go. So, listen, oh, I knew we were onto something. I didn’t know it was this bad, Steph. You shouldn’t have tipped Kate’s hand like this, by the way. The reacting that way is only making me aware that this is the whole kitten caboodle. No, Steph, again, you have no standing to stand up for anyone, call anyone anything, or otherwise say anything here, because you will not address the merits of the argument. You just admitted you don’t care about the filings, you don’t know the details, and you refuse to engage. So therefore, we’re done. Oh, it’s such a shame. All right, I gotta move her on. All right, Steph, it was great. We’ll put you in a little timeout. You can come back tomorrow, okay? I’m glad you spent some time with us, but the reality is I just don’t—I don’t wanna play that type of Kate Peter game, all right? Yep, now, Christina, you, as you know, this channel in Br… every possible perspective. I don’t care what you want to come in here and believe, you know you and I align on a lot of the factual record about a lot of these different cases. It’s not that. I’ll never ever have a problem with that. It’s the bad faith—and it’s not you, Christina. You are wonderful. You’ve never done it—but it’s the people who get too close to Kate Peter and then as embodied in that colloquy with Steph right there, whoever the fuck she is, we still don’t know. As embodied in that colloquy, you have a situation where when confronted with the facts instead of responding or even giving the time of day to what Kate Peter or Tully or Cosgrove might have done wrong, immediately it starts with the emotional manipulation, the attacks, the distraction. So I hope that—I hope that tells us all something. But yes, let’s keep reading because before I got in that fun colloquy, we were—I bet Steph was sent here to try to derail me. Nice try, Steph, take it elsewhere. All right, so we got those two posts up, by the way. All right, following service. Do you remember where we were in all this? The very last—so we just read about the Kate emails. By the way, now we know the whole Kate and Kaboodle is the Kate emails. We just read about the Kate emails and take a look where it goes next. All right, it just keeps going and going. Oh, do you think I should add Kate, Steph to the chart, by the way? Where should she go on the chart? Should she go under the Trollhollmio section? I feel like that’s appropriate. You know, this is just my opinion of how all these people tie together. Say you got Kate Peter, the Lord of Darkness in the middle—that’s my opinion. Then you got Jamz up there, Llama over there, Jason Broyles down here, Gaffney over here, Trollhollomio here. Then you got people like Critical Mass, Virgil, that—I don’t know who that is. And then you got Tully, Michael Morrissey, and Michael Proctor. Then you got Jake Sun, Twisted Tragedies tied to Gaffney. Then you got that guy, Jason Broyles, who thinks—who pretends to be a woman online. You got him, I think he’s tied to Barry Lewis and this weird woman from Connecticut that Kate keeps working with. She used to pretend to be like an advocate for medical patients, but now apparently she’s a big advocate of prednisone. I don’t really understand. She’s been going online telling people that people with colitis have to use prednisone apparently and they can’t use cannabis. I’m baffled by it. I didn’t know she was a doctor. Listen, if I knew that this woman was a doctor, I would start looking to whether she’s received payments from the pharmaceutical industry because I’ve never met a cannabis advocate who tells people they have to use prednisone for colitis. So that woman baffles me. Also, she’s the reason consumption event in Massachusetts are now regulated by the CCC. So listen, you all think that Kate Peter’s just some kind of like moron. She just plays that role, okay? Like she plays like she doesn’t know what she’s talking about and she doesn’t mostly with these court developments. But look at her network. Like people fawn over her like TurtleBoy. She is the female TurtleBoy in so many ways. And what makes her scary is she doesn’t own it.

Grant Smith Ellis

13,617 Aufrufe • vor 8 Monaten

I IGNORED IGAMING FOR TWO YEARS. THEN I DID THE MATH Here is what changed my mind Imagine a debate: Everyone is arguing about the next big narrative - AI agents? - Meme coins? - Another DeFi twist? But suddenly someone mentions iGaming - and the conversation does not die. It grows The same thing started happening on Crypto Twitter recently I used to be the one who always changed the topic until recently Let me explain WHAT I HAD TO ADMIT iGaming is not a new hype It is a multi billion machine that has been generating real cash for years 13 percent annual growth Crypto volumes in the sector are doubling year over year The money is already flowing The only thing that changes is who captures it on-chain Looking at it now I do not understand how I ignored it before BUT FIRST WHY I DID NOT TRUST THIS SECTOR All iGaming tokens sounded the same: - Huge market - Loud promises - Token detached from real usage I thought it was just noise but that assumption was my mistake What changed my position was not hype or anyone’s recommendation It was structure concrete verifiable numbers THIS IS AN INDUSTRY THAT NEVER NEEDED CRYPTO Here is the right question I should have asked from the start Can a token connect to activity that already exists instead of trying to create it This is a completely different starting point iGaming answers this question YES Millions of people go on platforms every day not to farm points and not to speculate but to play bet and interact This activity generates what crypto rarely has at scale - stable revenue independent of sentiment A PLATFORM THAT EXISTED BEFORE THE TOKEN? Most crypto projects follow this scheme: 1. Launch a token 2. Distribute incentives 3. Try to find users But I found a project that positions itself differently - 1win Token 1win did the opposite First nearly a decade of operations: - 30M+ users in 50+ countries - Systems for engagement and monetization - Around 1B USD annual revenue - Global celebrity advertising - Nine years of operations before the first token The platform existed long before the token did And that order matters more than it seems WHERE TOKENOMICS FINALLY BECAME INTERESTING Most buyback mechanisms are just marketing in a whitepaper Here it is different The mechanism is deterministic and built into code: - A portion of real platform revenue goes to weekly buybacks from the market - Bought tokens are returned to users as cashback - 10 percent of every spent 1WIN is burned daily - Fixed supply 10B No inflation No additional issuance The loop looks like this: ACTIVITY -> REVENUE -> BUYBACK -> REWARDS -> BURN -> LOWER SUPPLY Activity affects the token Not sentiment This is a flywheel tied to real human behavior not the price of BTC THE ARCHITECTURE MOST PEOPLE MISS $1WIN is natively deployed on BNB Chain and Solana via LayerZero When tokens move between chains they are burned on source and minted on destination Total supply remains constant This removes typical bridge risks that have already killed many projects And it gives access to: - BNB liquidity and retail base - Solana speed and DeFi activity This level of design is not accidental THE SIGNAL THAT MADE ME LOOK TWICE Then I saw the collaboration with Jupiter Not a vague partnership announcement but a live reward campaign inside the Jupiter ecosystem Jupiter is the core infrastructure hub of Solana DeFi with billions in daily volume Serious infrastructure platforms do not align with projects without substance This added another layer of confidence before the tokensale ATTENTION IS ALREADY BOUGHT INTO CRYPTO There is another layer most people ignore Before any token before any on-chain narrative 1win already built global distribution through sports and entertainment - Jon Jones: UFC legend and youngest champion in history - Canelo Alvarez: Mexican boxing superstar - David Warner: elite international cricket star You do not get athletes of this level unless the business under the hood generates serious stable cashflow This is the key signal The token is not trying to create attention from zero It is plugging into attention that already exists WHY TIMING MATTERS RIGHT NOW Recently I started noticing a shift Conversations about iGaming tokens are becoming more frequent and more serious Less noise more people with real arguments This is how early narratives usually form And right when this shift is happening $1WIN is approaching its tokensale No price history No price discovery Only users revenue and mechanics already in place This timing is rare BUT REMEMBER THE RISKS Regulation execution liquidity after launch These risks are real and I am not going to pretend they are not But the key difference compared to most pre TGE projects is: - Product already exists - Users already exist - Revenue is already generated Most crypto projects do not start this way WHY MY VIEW CHANGED Not because of shill Not because of anyone’s recommendation Because the puzzle finally made sense 1. Large existing industry with real cashflow 2. Platform with real users and revenue before token 3. Token mechanically tied to activity not sentiment 4. Architecture solving real problems 5. Tier 1 partnerships as a catalyst 6. Timing where attention is just starting to form This combination is rare The best opportunities rarely look obvious at first They sit in categories people underestimate while the structure quietly builds underneath iGaming is already a cashflow machine What changes is how that value is represented on-chain This is not a token looking for users This is a working system inviting the token inside And right now it still feels early Usually that is where the edge is Good luck!
0:39

I IGNORED IGAMING FOR TWO YEARS. THEN I DID THE MATH Here is what changed my mind Imagine a debate: Everyone is arguing about the next big narrative - AI agents? - Meme coins? - Another DeFi twist? But suddenly someone mentions iGaming - and the conversation does not die. It grows The same thing started happening on Crypto Twitter recently I used to be the one who always changed the topic until recently Let me explain WHAT I HAD TO ADMIT iGaming is not a new hype It is a multi billion machine that has been generating real cash for years 13 percent annual growth Crypto volumes in the sector are doubling year over year The money is already flowing The only thing that changes is who captures it on-chain Looking at it now I do not understand how I ignored it before BUT FIRST WHY I DID NOT TRUST THIS SECTOR All iGaming tokens sounded the same: - Huge market - Loud promises - Token detached from real usage I thought it was just noise but that assumption was my mistake What changed my position was not hype or anyone’s recommendation It was structure concrete verifiable numbers THIS IS AN INDUSTRY THAT NEVER NEEDED CRYPTO Here is the right question I should have asked from the start Can a token connect to activity that already exists instead of trying to create it This is a completely different starting point iGaming answers this question YES Millions of people go on platforms every day not to farm points and not to speculate but to play bet and interact This activity generates what crypto rarely has at scale - stable revenue independent of sentiment A PLATFORM THAT EXISTED BEFORE THE TOKEN? Most crypto projects follow this scheme: 1. Launch a token 2. Distribute incentives 3. Try to find users But I found a project that positions itself differently - 1win Token 1win did the opposite First nearly a decade of operations: - 30M+ users in 50+ countries - Systems for engagement and monetization - Around 1B USD annual revenue - Global celebrity advertising - Nine years of operations before the first token The platform existed long before the token did And that order matters more than it seems WHERE TOKENOMICS FINALLY BECAME INTERESTING Most buyback mechanisms are just marketing in a whitepaper Here it is different The mechanism is deterministic and built into code: - A portion of real platform revenue goes to weekly buybacks from the market - Bought tokens are returned to users as cashback - 10 percent of every spent 1WIN is burned daily - Fixed supply 10B No inflation No additional issuance The loop looks like this: ACTIVITY -> REVENUE -> BUYBACK -> REWARDS -> BURN -> LOWER SUPPLY Activity affects the token Not sentiment This is a flywheel tied to real human behavior not the price of BTC THE ARCHITECTURE MOST PEOPLE MISS $1WIN is natively deployed on BNB Chain and Solana via LayerZero When tokens move between chains they are burned on source and minted on destination Total supply remains constant This removes typical bridge risks that have already killed many projects And it gives access to: - BNB liquidity and retail base - Solana speed and DeFi activity This level of design is not accidental THE SIGNAL THAT MADE ME LOOK TWICE Then I saw the collaboration with Jupiter Not a vague partnership announcement but a live reward campaign inside the Jupiter ecosystem Jupiter is the core infrastructure hub of Solana DeFi with billions in daily volume Serious infrastructure platforms do not align with projects without substance This added another layer of confidence before the tokensale ATTENTION IS ALREADY BOUGHT INTO CRYPTO There is another layer most people ignore Before any token before any on-chain narrative 1win already built global distribution through sports and entertainment - Jon Jones: UFC legend and youngest champion in history - Canelo Alvarez: Mexican boxing superstar - David Warner: elite international cricket star You do not get athletes of this level unless the business under the hood generates serious stable cashflow This is the key signal The token is not trying to create attention from zero It is plugging into attention that already exists WHY TIMING MATTERS RIGHT NOW Recently I started noticing a shift Conversations about iGaming tokens are becoming more frequent and more serious Less noise more people with real arguments This is how early narratives usually form And right when this shift is happening $1WIN is approaching its tokensale No price history No price discovery Only users revenue and mechanics already in place This timing is rare BUT REMEMBER THE RISKS Regulation execution liquidity after launch These risks are real and I am not going to pretend they are not But the key difference compared to most pre TGE projects is: - Product already exists - Users already exist - Revenue is already generated Most crypto projects do not start this way WHY MY VIEW CHANGED Not because of shill Not because of anyone’s recommendation Because the puzzle finally made sense 1. Large existing industry with real cashflow 2. Platform with real users and revenue before token 3. Token mechanically tied to activity not sentiment 4. Architecture solving real problems 5. Tier 1 partnerships as a catalyst 6. Timing where attention is just starting to form This combination is rare The best opportunities rarely look obvious at first They sit in categories people underestimate while the structure quietly builds underneath iGaming is already a cashflow machine What changes is how that value is represented on-chain This is not a token looking for users This is a working system inviting the token inside And right now it still feels early Usually that is where the edge is Good luck!

Linton Worm (🍏,🪱)

14,345 Aufrufe • vor 3 Monaten

Technically is dead; long live Technically Some bittersweet news for you all today: after 5 years writing Technically and more than 100 posts about everything from APIs to data warehouses to Facebook DNS hacks, today I am (for the most part) shutting the Technically Substack down… …and replacing it with Technically 2.0, an amazing software product I’ve been working with David Krevitt on for the past 6 months. But first… For a newsletter that started with an innocent tweet while I was bored in Haneda airport, this thing has come pretty far. 70K+ subscribers, yada yada. But you’re here for the story so here it is. It was December 2019, I was traveling before moving to SF to start at Retool, and like I said, I was bored. Late 2019 – what an amazing time to start a newsletter! There weren’t that many of them out there. And then came 2020. Everyone was stuck at home with nothing to do but sign up for more and more Substacks, and talk about them on the internet. Every day, another one of your friends was announcing a newsletter on Twitter. It was the golden era, no doubt, and many people like me combined hard work, a good idea, and the old fashioned “right place right time” streak of luck to build a really nice Substack business. I’ll never forget the day Ben Thompson referenced Technically in Stratechery. I must have gotten 50 texts from friends. Everyone was reading Stratechery at the time…it was like becoming a made man. There was this almost communal vibe in the air with these newsletters. Everyone was reading the same stuff, talking about the same stuff. It was a scene is what it was. But by 2022 things were changing. People were outside again, and had less free time to read newsletters. Interest rates were going up, and people were working more (even in offices). In the paid Substack group chats, most of us were reporting stalling or negative growth, even though we hadn’t changed anything on our end. And with 50% churn rates on these subscriptions, if you weren’t growing you were dying. I’m listening to Neil Young’s “After the Gold Rush” as I write this and it couldn’t be more fitting. Although I imagine Mr. Young himself would disapprove of the whole paid newsletter endeavor. What happened to newsletters? David and I call what’s going on “Substack Fatigue” – people are just tired of reading yet another newsletter, let alone paying for one. Newsletters are just not the thing anymore. There are some fast growing news ones focused on AI, but the same story is going to play out in a few years when everything cools down. Political newsletters are fully investing in video and podcasts. Newsletters are not the thing anymore. We rode a cultural wave and the wave is over. You don’t have to die, but you have to adjust. The wrench in this whole story is that Technically was only (very) part time for me. I’m pretty sure at one point I was generating the most revenue on Substack for someone who wasn’t focusing on their newsletter full time. Which is a sick flex no doubt, but was also a huge problem, because I just didn’t have the time or mental capacity to make the big moves required to reverse the trend. Technically started to slowly lose paid subscribers every month, but I was at peace with that. I was entering a new phase in my life, settling down a bit, enjoying spending time on cooking, cocktails, and music. Approaching 30 and feeling really good about everything. It’s OK for some things to be temporary, and I was content with Technically to continue to be useful to people…just not make as much money. But in the back of my head, I always knew that Technically had a lot more potential, and deserved more than I could give it. That there’s no reason this thing couldn’t be a $1M+/year business. I continue to believe that technical literacy is going to be one of the defining social problems of our era, and the progress in AI only makes this even more critical. It should be way more than a newsletter, it should be how everyone learns what the fuck is going on in this digital world. I needed some help. But I had a bad track record of getting people to work on Technically with me. It’s hard to share custody of your child. And I am extremely particular. I’m not always the easiest to work with. Worked with some contractors here and there, but never found a more long term partner. I’m extremely grateful that David Krevitt reached out to me when he did or this paragraph would end here. Instead, after regaling you with my boring tale for many paragraphs now, I can finally share what we’ve been working on since last year. It’s called Technically 2.0. It’s all of the content you know and love, but built as a piece of software specifically aimed at helping people get more technical. No more newsletter – it’s a learning platform now, complete with reading lists, bookmarks, a dictionary, and guided learning tracks. Our goal was to make a Wikipedia kind of experience: click around to follow your curiosity on whatever software you’re learning about. You can sign up on the Technically site ( You’re going to have to pay for it, but if your experience is anything like that of the other thousands of people who already have, you won’t regret it. Enjoy the soothing sounds of David's voice as he walks you through it in the video below. It’s hard to say goodbye completely to something you’ve been doing every week for 5 years. Any readers with their own long running newsletters will understand the odd, para-social relationship you develop with your audience. So I’m going to keep publishing on Substack a little – monthly roundups of the new stuff we’re publishing on Technically 2.0, plus some good sponsored posts. So while the newsletter might be dead, it is also only just beginning (or something). Hope to see you on the other side, ~ ❤️ Justin
1:14

Technically is dead; long live Technically Some bittersweet news for you all today: after 5 years writing Technically and more than 100 posts about everything from APIs to data warehouses to Facebook DNS hacks, today I am (for the most part) shutting the Technically Substack down… …and replacing it with Technically 2.0, an amazing software product I’ve been working with David Krevitt on for the past 6 months. But first… For a newsletter that started with an innocent tweet while I was bored in Haneda airport, this thing has come pretty far. 70K+ subscribers, yada yada. But you’re here for the story so here it is. It was December 2019, I was traveling before moving to SF to start at Retool, and like I said, I was bored. Late 2019 – what an amazing time to start a newsletter! There weren’t that many of them out there. And then came 2020. Everyone was stuck at home with nothing to do but sign up for more and more Substacks, and talk about them on the internet. Every day, another one of your friends was announcing a newsletter on Twitter. It was the golden era, no doubt, and many people like me combined hard work, a good idea, and the old fashioned “right place right time” streak of luck to build a really nice Substack business. I’ll never forget the day Ben Thompson referenced Technically in Stratechery. I must have gotten 50 texts from friends. Everyone was reading Stratechery at the time…it was like becoming a made man. There was this almost communal vibe in the air with these newsletters. Everyone was reading the same stuff, talking about the same stuff. It was a scene is what it was. But by 2022 things were changing. People were outside again, and had less free time to read newsletters. Interest rates were going up, and people were working more (even in offices). In the paid Substack group chats, most of us were reporting stalling or negative growth, even though we hadn’t changed anything on our end. And with 50% churn rates on these subscriptions, if you weren’t growing you were dying. I’m listening to Neil Young’s “After the Gold Rush” as I write this and it couldn’t be more fitting. Although I imagine Mr. Young himself would disapprove of the whole paid newsletter endeavor. What happened to newsletters? David and I call what’s going on “Substack Fatigue” – people are just tired of reading yet another newsletter, let alone paying for one. Newsletters are just not the thing anymore. There are some fast growing news ones focused on AI, but the same story is going to play out in a few years when everything cools down. Political newsletters are fully investing in video and podcasts. Newsletters are not the thing anymore. We rode a cultural wave and the wave is over. You don’t have to die, but you have to adjust. The wrench in this whole story is that Technically was only (very) part time for me. I’m pretty sure at one point I was generating the most revenue on Substack for someone who wasn’t focusing on their newsletter full time. Which is a sick flex no doubt, but was also a huge problem, because I just didn’t have the time or mental capacity to make the big moves required to reverse the trend. Technically started to slowly lose paid subscribers every month, but I was at peace with that. I was entering a new phase in my life, settling down a bit, enjoying spending time on cooking, cocktails, and music. Approaching 30 and feeling really good about everything. It’s OK for some things to be temporary, and I was content with Technically to continue to be useful to people…just not make as much money. But in the back of my head, I always knew that Technically had a lot more potential, and deserved more than I could give it. That there’s no reason this thing couldn’t be a $1M+/year business. I continue to believe that technical literacy is going to be one of the defining social problems of our era, and the progress in AI only makes this even more critical. It should be way more than a newsletter, it should be how everyone learns what the fuck is going on in this digital world. I needed some help. But I had a bad track record of getting people to work on Technically with me. It’s hard to share custody of your child. And I am extremely particular. I’m not always the easiest to work with. Worked with some contractors here and there, but never found a more long term partner. I’m extremely grateful that David Krevitt reached out to me when he did or this paragraph would end here. Instead, after regaling you with my boring tale for many paragraphs now, I can finally share what we’ve been working on since last year. It’s called Technically 2.0. It’s all of the content you know and love, but built as a piece of software specifically aimed at helping people get more technical. No more newsletter – it’s a learning platform now, complete with reading lists, bookmarks, a dictionary, and guided learning tracks. Our goal was to make a Wikipedia kind of experience: click around to follow your curiosity on whatever software you’re learning about. You can sign up on the Technically site ( You’re going to have to pay for it, but if your experience is anything like that of the other thousands of people who already have, you won’t regret it. Enjoy the soothing sounds of David's voice as he walks you through it in the video below. It’s hard to say goodbye completely to something you’ve been doing every week for 5 years. Any readers with their own long running newsletters will understand the odd, para-social relationship you develop with your audience. So I’m going to keep publishing on Substack a little – monthly roundups of the new stuff we’re publishing on Technically 2.0, plus some good sponsored posts. So while the newsletter might be dead, it is also only just beginning (or something). Hope to see you on the other side, ~ ❤️ Justin

sisyphus bar and grill

29,390 Aufrufe • vor 1 Jahr

Maple is preparing for the release of a co-working agent. You install it locally and it works with your files, whether it's office work or building websites and apps. It's a turnkey solution, as easy as Claude Code, that keeps your data secure and private, no data sharing with closed AI labs. This is THE sovereign AI app for individuals and businesses who want powerful AI while retaining ownership of their information. Why build an agent into the Maple app when other agents already exist? Easy, we want to give you control over your work. We don't have a business plan that incorporates making money off our users' data. In the age of AI, your information, whether it's personal or company trade secrets, is the single thing that differentiates you from everyone else. We all have access to AI that can build a professional website for selling shoes. But your strategy and network for how you sell shoes should not be shared with your competitors. Sovereignty is the path to protecting what makes you, you. Maple sits at the intersection of Usability and Sovereignty. Maple gives you the best tools that are both easy to use and maintain your data sovereignty. Sovereign for one, sovereign for all. It has been a journey to get here. We brought to market the very first personal chatbot with end-to-end encryption using TEEs in late 2024. Prior to that there were proofs of concept but no full product offerings. Every other AI chat product on the market handled your data in plain text, either selling you a service to get your data or asking you to trust that they won't snoop on you. Quickly people found Maple and latched onto its open-source code and verifiable encryption. We didn't stop there. You may remember earlier this year we teased a product called "Maple Agent" and opened up a waiting list. That product is a mobile app that acts as your AI "friend", maintaining one long continuous chat, and getting to know you over time. I dislike using the word "friend" there, but it's the best way to convey the UX in a few words. AI is a tool, always has been, always will be. Any kind of friendly personality on top is just synthetic. In our testing, the UX of Maple Agent is really powerful for what it does. Think about the many short AI chats you have in your favorite app, whether it's looking up a historical fact or asking advice about a topic. With Maple Agent, those all go away in favor of the long-running chat with the friendly agent. It's like you have your own personal assistant who knows you so well and can look up anything for you. When I ask AI certain questions, I want to ask an expert who already understands my situation so I'm not repeating myself for the 100th time. That's the amazing value the personal agent brings to the table. We still see great utility for a personal agent like the "Maple Agent". Thousands of people on the waiting list, hoping to get their hands on it, agree that the concept is worth exploring and trying out. We were constrained in launching it due to a few circumstances, one of them being access to the scale of compute needed to power it. We have a clear path laid out for how to get there, but today is not the day to execute on that. It will be in the near future. Instead we have a different agent ready to go that we think is also incredible. We now have an agentic harness inside of the Maple Research app. This thing is a powerhouse. It even builds and publishes its own software releases. The agent in Maple Research works with your local filesystem, speaks to the largest open models running in TEEs, utilizes local models for certain tasks, is compatible with MCP tools, has an API for connecting to anything you need, and also supports the ACP protocol, which means it can be extended in the future to speak to other tools like Claude Code, Codex, and local models running on your own hardware. A big unlock for us was the Goose Development Kit, which powers the core of our agent harness. More on that to come as we publish articles and documentation later about the agent. The agent inside Maple Research doesn't have a name. At least not yet, not sure if it ever will. For now we call it "Chat Mode" and "Agent Mode". Think of this as the workhorse, the truck, the heavy lifter. Our other "Agent", the phone app, is your sidekick in your pocket, ready to help with quick things and ongoing conversations about life. I am incredibly excited about the Maple Research Agent. While I'm already seeing great results using it for internal work items, I'm especially thrilled about the personal health and wellness work it's doing for me. I know there are plenty of apps out there for compiling wellness data, but I'm having it build a tool tailored specifically for what I need, without the extra fluff. And none of my health data is being donated to the closed AI labs or sent to advertisers. I know that the AI logic is not being silently adjusted to fit the whims of a large corporation that has paid for product placement. It's me, state of the art AI, and my data. That's how I want it. Maple's new agent makes that possible. We can't wait for you to try it out. If you want early access, comment here, email us, reach out in some way. To those on the other agent waitlist, you're already in the queue. Thanks for reading this lengthy update. :)
0:49

Maple is preparing for the release of a co-working agent. You install it locally and it works with your files, whether it's office work or building websites and apps. It's a turnkey solution, as easy as Claude Code, that keeps your data secure and private, no data sharing with closed AI labs. This is THE sovereign AI app for individuals and businesses who want powerful AI while retaining ownership of their information. Why build an agent into the Maple app when other agents already exist? Easy, we want to give you control over your work. We don't have a business plan that incorporates making money off our users' data. In the age of AI, your information, whether it's personal or company trade secrets, is the single thing that differentiates you from everyone else. We all have access to AI that can build a professional website for selling shoes. But your strategy and network for how you sell shoes should not be shared with your competitors. Sovereignty is the path to protecting what makes you, you. Maple sits at the intersection of Usability and Sovereignty. Maple gives you the best tools that are both easy to use and maintain your data sovereignty. Sovereign for one, sovereign for all. It has been a journey to get here. We brought to market the very first personal chatbot with end-to-end encryption using TEEs in late 2024. Prior to that there were proofs of concept but no full product offerings. Every other AI chat product on the market handled your data in plain text, either selling you a service to get your data or asking you to trust that they won't snoop on you. Quickly people found Maple and latched onto its open-source code and verifiable encryption. We didn't stop there. You may remember earlier this year we teased a product called "Maple Agent" and opened up a waiting list. That product is a mobile app that acts as your AI "friend", maintaining one long continuous chat, and getting to know you over time. I dislike using the word "friend" there, but it's the best way to convey the UX in a few words. AI is a tool, always has been, always will be. Any kind of friendly personality on top is just synthetic. In our testing, the UX of Maple Agent is really powerful for what it does. Think about the many short AI chats you have in your favorite app, whether it's looking up a historical fact or asking advice about a topic. With Maple Agent, those all go away in favor of the long-running chat with the friendly agent. It's like you have your own personal assistant who knows you so well and can look up anything for you. When I ask AI certain questions, I want to ask an expert who already understands my situation so I'm not repeating myself for the 100th time. That's the amazing value the personal agent brings to the table. We still see great utility for a personal agent like the "Maple Agent". Thousands of people on the waiting list, hoping to get their hands on it, agree that the concept is worth exploring and trying out. We were constrained in launching it due to a few circumstances, one of them being access to the scale of compute needed to power it. We have a clear path laid out for how to get there, but today is not the day to execute on that. It will be in the near future. Instead we have a different agent ready to go that we think is also incredible. We now have an agentic harness inside of the Maple Research app. This thing is a powerhouse. It even builds and publishes its own software releases. The agent in Maple Research works with your local filesystem, speaks to the largest open models running in TEEs, utilizes local models for certain tasks, is compatible with MCP tools, has an API for connecting to anything you need, and also supports the ACP protocol, which means it can be extended in the future to speak to other tools like Claude Code, Codex, and local models running on your own hardware. A big unlock for us was the Goose Development Kit, which powers the core of our agent harness. More on that to come as we publish articles and documentation later about the agent. The agent inside Maple Research doesn't have a name. At least not yet, not sure if it ever will. For now we call it "Chat Mode" and "Agent Mode". Think of this as the workhorse, the truck, the heavy lifter. Our other "Agent", the phone app, is your sidekick in your pocket, ready to help with quick things and ongoing conversations about life. I am incredibly excited about the Maple Research Agent. While I'm already seeing great results using it for internal work items, I'm especially thrilled about the personal health and wellness work it's doing for me. I know there are plenty of apps out there for compiling wellness data, but I'm having it build a tool tailored specifically for what I need, without the extra fluff. And none of my health data is being donated to the closed AI labs or sent to advertisers. I know that the AI logic is not being silently adjusted to fit the whims of a large corporation that has paid for product placement. It's me, state of the art AI, and my data. That's how I want it. Maple's new agent makes that possible. We can't wait for you to try it out. If you want early access, comment here, email us, reach out in some way. To those on the other agent waitlist, you're already in the queue. Thanks for reading this lengthy update. :)

Mark

29,937 Aufrufe • vor 11 Tagen

The 40,000% ROI "Bug": How Claude Code Cracked the TradingView Holy Grail most people think the elite traders at the top of the mountain have some secret indicator or a hidden math formula that gives them a forty thousand percent return. they assume the game is rigged against the small player and that you need a multi million dollar budget just to get a seat at the table. the truth is that the holy grail of trading is actually hidden in plain sight inside a community tab that most people scroll past every single day i spent years losing money to liquidations and over trading because i thought i had to manually predict where the price was going next. i even spent hundreds of thousands of dollars on developers to build apps for me because i was convinced that i would never be able to code the systems myself. it turns out that once you stop trying to be a genius and start using the tools that are already available you can crack the code to unlimited trading strategies the secret is not in a single indicator but in the process of research back test and implement. if you go to the community section of trading view you will find an endless stream of source code for indicators that people have built over decades. most traders just slap these on a chart and hope for the best but if you are a data dog like me you know that a chart is just a pretty picture that lies to you i believe that code is the great equalizer because it allows us to take these public ideas and turn them into fully automated systems that trade for us while we sleep. i decided to learn to code live on youtube to show everyone that you can iterate your way to success without being a math wizard or a stanford graduate. now i have fully automated systems that manage my capital instead of getting liquidated by emotional decisions in the middle of the night the biggest trap in the trading world is something called repainting and it is the reason why so many strategy back tests look like they are printing money when they are actually just a scam. repainting happens when an indicator looks at future data to tell you what happened in the past which makes every buy and sell signal look like a perfect entry at the top and bottom. if you trust a back test on a basic chart without understanding the logic underneath you are just building a house on a foundation of sand this is why i transitioned all of my serious work into python because python does not lie to you. in python you can control the data flow tick by tick and bar by bar to ensure that no future data is leaking into your strategy. i built a back test architect which is a specialized sub agent that knows exactly how to take a simple idea and test it against twenty five different data sources all at once when you run a strategy across btc eth apple google and tesla you start to see the real truth about whether a strategy has an edge or if it was just a lucky fluke on one chart. i saw one strategy this week that showed a one million percent return which sounds like a total lie but the data does not have an ego. even if a number looks insane you have to investigate it and incubate it with tiny size to see if it holds up in the live market you must treat your trading like a business where you are the manager and the code is your team of tireless employees. i have sub agents running for me right now that act as masters of specific tasks like converting pine script into python or optimizing exit logic. if you are not using these specialized ai assistants in your workflow you are essentially trying to build a skyscraper with a hand saw while everyone else is using heavy machinery most people get stuck in the beginner phase because they think they need to write every single line of code from scratch. the reality is that the best developers are just really good at importing the hard work of others and connecting it like lego blocks. i use a library called ccxt that allows my bots to communicate with every major exchange in the world with just a few lines of script which saves me months of development time the reason i show everything live is because the industry is filled with gatekeepers who want to keep the secrets of automation to themselves. they want you to stay as a manual trader who pays high fees and provides liquidity for their algorithms. once you learn to automate you are no longer a victim of the market but a participant in the architecture of the financial system if you are sitting there right now feeling defeated because you just got smoked on a trade or you missed a massive pump you have to realize that those emotions are your greatest enemy. a computer does not feel fomo and it does not get tilted after a loss; it just waits for the next signal that fits the parameters you defined. my mission is to help you get to a place where you can walk away from the screen and let the machines do the heavy lifting learning to code is actually much easier than learning a second language because the syntax is logical and the feedback is immediate. i spent ten years in tech scared to touch a keyboard for anything other than emails because i thought i was not smart enough for engineering. once i realized that code is just logic i was able to build my first profitable bot within a few months and i have never looked back the transition from a manual trader to an algorithmic expert is about building a robust framework for testing your ideas as fast as possible. you want to be able to find an indicator on trading view convert it to python and run it against years of historical data in less than five minutes. if you can do that you have a higher chance of success than ninety nine percent of the people who are just drawing lines on a screen one of the most powerful strategies i found recently combines the squeeze momentum indicator with smart money concepts. when you test these individually they might show a decent return but when you combine them and add a filter like the adx you can find setups that have a massive expectancy. the key is to look for strategies that show positive returns across multiple different asset classes and time frames simultaneously even if a strategy looks like it is printing a forty thousand percent return you must always remain skeptical and look for the catch. i always incubate my new ideas with tiny capital for at least a few weeks to see how they handle real world slippage and fees. a back test is a map of the past but the live market is a wilderness that changes every single day this is why i believe in the rbi method which stands for research back test and implement. you spend your mornings looking for new ideas your afternoons stress testing them with ai and your evenings deploying the winners to the market. it is a systematic approach to wealth that removes the need for luck or guessing what a celebrity is going to tweet next the most successful traders in history like jim simons did not sit around looking at rsi levels on a fifteen minute chart. they built systems that identified mathematical edges and then scaled those systems until they were managing billions of dollars. you do not need thirty one billion dollars to change your life but you do need the discipline to stop trading like a human and start thinking like a system i give away so much for free on youtube because i want to build a community of data dogs who are all chasing the same goal of financial freedom through automation. when we work together and share our findings we can collectively identify edges that nobody else is looking at. the world is moving towards an ai dominated economy and if you are not learning to control the machines you are going to be controlled by them the road to automation is not a straight line and you will run into bugs that make you want to throw your computer out the window. but every time you fix an error and every time you optimize a script you are getting one step closer to a life where you own your time. code really is the great equalizer and it is waiting for you to pick it up and start building your own future if you can fly then run and if you can run then walk but whatever you do you must keep moving forward in this journey. trading can be heartless but the logic of code is always fair and consistent. stop being the liquidity for someone else's bot and start building the walls that will protect your capital forever
2:02:43

The 40,000% ROI "Bug": How Claude Code Cracked the TradingView Holy Grail most people think the elite traders at the top of the mountain have some secret indicator or a hidden math formula that gives them a forty thousand percent return. they assume the game is rigged against the small player and that you need a multi million dollar budget just to get a seat at the table. the truth is that the holy grail of trading is actually hidden in plain sight inside a community tab that most people scroll past every single day i spent years losing money to liquidations and over trading because i thought i had to manually predict where the price was going next. i even spent hundreds of thousands of dollars on developers to build apps for me because i was convinced that i would never be able to code the systems myself. it turns out that once you stop trying to be a genius and start using the tools that are already available you can crack the code to unlimited trading strategies the secret is not in a single indicator but in the process of research back test and implement. if you go to the community section of trading view you will find an endless stream of source code for indicators that people have built over decades. most traders just slap these on a chart and hope for the best but if you are a data dog like me you know that a chart is just a pretty picture that lies to you i believe that code is the great equalizer because it allows us to take these public ideas and turn them into fully automated systems that trade for us while we sleep. i decided to learn to code live on youtube to show everyone that you can iterate your way to success without being a math wizard or a stanford graduate. now i have fully automated systems that manage my capital instead of getting liquidated by emotional decisions in the middle of the night the biggest trap in the trading world is something called repainting and it is the reason why so many strategy back tests look like they are printing money when they are actually just a scam. repainting happens when an indicator looks at future data to tell you what happened in the past which makes every buy and sell signal look like a perfect entry at the top and bottom. if you trust a back test on a basic chart without understanding the logic underneath you are just building a house on a foundation of sand this is why i transitioned all of my serious work into python because python does not lie to you. in python you can control the data flow tick by tick and bar by bar to ensure that no future data is leaking into your strategy. i built a back test architect which is a specialized sub agent that knows exactly how to take a simple idea and test it against twenty five different data sources all at once when you run a strategy across btc eth apple google and tesla you start to see the real truth about whether a strategy has an edge or if it was just a lucky fluke on one chart. i saw one strategy this week that showed a one million percent return which sounds like a total lie but the data does not have an ego. even if a number looks insane you have to investigate it and incubate it with tiny size to see if it holds up in the live market you must treat your trading like a business where you are the manager and the code is your team of tireless employees. i have sub agents running for me right now that act as masters of specific tasks like converting pine script into python or optimizing exit logic. if you are not using these specialized ai assistants in your workflow you are essentially trying to build a skyscraper with a hand saw while everyone else is using heavy machinery most people get stuck in the beginner phase because they think they need to write every single line of code from scratch. the reality is that the best developers are just really good at importing the hard work of others and connecting it like lego blocks. i use a library called ccxt that allows my bots to communicate with every major exchange in the world with just a few lines of script which saves me months of development time the reason i show everything live is because the industry is filled with gatekeepers who want to keep the secrets of automation to themselves. they want you to stay as a manual trader who pays high fees and provides liquidity for their algorithms. once you learn to automate you are no longer a victim of the market but a participant in the architecture of the financial system if you are sitting there right now feeling defeated because you just got smoked on a trade or you missed a massive pump you have to realize that those emotions are your greatest enemy. a computer does not feel fomo and it does not get tilted after a loss; it just waits for the next signal that fits the parameters you defined. my mission is to help you get to a place where you can walk away from the screen and let the machines do the heavy lifting learning to code is actually much easier than learning a second language because the syntax is logical and the feedback is immediate. i spent ten years in tech scared to touch a keyboard for anything other than emails because i thought i was not smart enough for engineering. once i realized that code is just logic i was able to build my first profitable bot within a few months and i have never looked back the transition from a manual trader to an algorithmic expert is about building a robust framework for testing your ideas as fast as possible. you want to be able to find an indicator on trading view convert it to python and run it against years of historical data in less than five minutes. if you can do that you have a higher chance of success than ninety nine percent of the people who are just drawing lines on a screen one of the most powerful strategies i found recently combines the squeeze momentum indicator with smart money concepts. when you test these individually they might show a decent return but when you combine them and add a filter like the adx you can find setups that have a massive expectancy. the key is to look for strategies that show positive returns across multiple different asset classes and time frames simultaneously even if a strategy looks like it is printing a forty thousand percent return you must always remain skeptical and look for the catch. i always incubate my new ideas with tiny capital for at least a few weeks to see how they handle real world slippage and fees. a back test is a map of the past but the live market is a wilderness that changes every single day this is why i believe in the rbi method which stands for research back test and implement. you spend your mornings looking for new ideas your afternoons stress testing them with ai and your evenings deploying the winners to the market. it is a systematic approach to wealth that removes the need for luck or guessing what a celebrity is going to tweet next the most successful traders in history like jim simons did not sit around looking at rsi levels on a fifteen minute chart. they built systems that identified mathematical edges and then scaled those systems until they were managing billions of dollars. you do not need thirty one billion dollars to change your life but you do need the discipline to stop trading like a human and start thinking like a system i give away so much for free on youtube because i want to build a community of data dogs who are all chasing the same goal of financial freedom through automation. when we work together and share our findings we can collectively identify edges that nobody else is looking at. the world is moving towards an ai dominated economy and if you are not learning to control the machines you are going to be controlled by them the road to automation is not a straight line and you will run into bugs that make you want to throw your computer out the window. but every time you fix an error and every time you optimize a script you are getting one step closer to a life where you own your time. code really is the great equalizer and it is waiting for you to pick it up and start building your own future if you can fly then run and if you can run then walk but whatever you do you must keep moving forward in this journey. trading can be heartless but the logic of code is always fair and consistent. stop being the liquidity for someone else's bot and start building the walls that will protect your capital forever

Moon Dev

245,471 Aufrufe • vor 5 Monaten

Here's a devlog made by an anonymous Chinese fan replicating the surprisingly brand new technique that I developed for detecting asteroids which wound up being so powerful that it can easily track Stealth Fighters from over 100km away even when it’s only using three $30 webcams as sensors meaning it easily outperforms all modern stealth tracking techniques in precision, range and cost. And while this demo is using optical light, this same technique which I call pixel motion to voxel projection, can be used interchangeably with thermal infrared cameras to work at night and also majorly boosts the effectiveness of radar allowing you to track fighters much more effectively through clouds and over the horizon. This technique will also always eventually give the exact location of the target even if the image is blurry as those blurs will always average out from the different perspectives into revealing the precise location of the target in the voxel grid. There is definitely a Mandela effect with this technique as it feels as though it should already exist, especially because at first as it sounds like it is performing triangulation (which has existed for years and is what we do for mocap and tennis ball tracking). But triangulation is entirely separate to this as triangulations only works if you have already identified where the ball is in a 2D image because you’re able to rely on being able to use at least 2 separate high quality cameras which are much closer to the ball making the ball’s apparent size much much bigger and therefore gives you hundreds of pixels to work with which makes it much easier to use object recognition techniques to recognize where it is in the image aka in 2D and then you’re just using the other cameras view to project out lines which intersect in 3D to find out where the ball is in 3D. The major difference is that pixel motion to voxel projection allows you to find where the object is in 3D without having already found it in 2D which is an unbelievable difference as it allows you to use much lower quality cameras together to accumulate data together into 3D space. If this seem like it doesn’t mean much then what it actually means is that you don’t understand what I’m saying as what I’m saying means a LOT in practical terms as it means you go from having to use an imaging system that has to be able to image the object to the point that it is over a hundred total pixels in surface area to have enough data to recognize it to instead be able to use something that is only images the object to be 1 pixel in surface area and only changes the brightness value by 1 value every now and then. I’d recommend an amazing video by DST studios called “Lowlight cameras can’t defeat stealth” if you want a great video which goes over the difficulty of even using telescopes to recognize stealth fighters and why this is so impressive compared to other techniques and ironically it is what inspired me to realize the asteroid tracker I was working on actually could do this. Which brings me to the point that if this wasn’t a new technique then not only would there be at least one example of an asteroid survey that points distant telescopes at the same place at the same time in order to be able to add the light together to detect asteroids which as I was shocked to learn isn’t a thing despite the fact that it would make detecting asteroids trivial by comparison to modern 2D imaging while also having no impact on the normal scientific operations of those surveys other than small changes to scheduling. But there would also be an example of a drone tracker that uses this instead of using the aforementioned high quality zoomable telescope which has to be able to zoom in close enough to be able to recognize a drone. If you want to tell me that this is something that already exists give me an exact example of a product that uses it, not the general outline of a concept that you think it is, the actual product and then also tell me the asteroid survey that uses distant telescopes that point at the exact same place at the exact same time because I can guarantee that if you google what you think uses this you won’t even find the steps of subtracting the images from each other to get motion and will definitely not get the added step of projecting that motion into a voxel grid (It would blow your mind if you found out how Xbox kinect cameras work.) Also I want to make it clear, I’m not saying you should just use web cams to do this, I’m just using them as an example to show you the power of this in reality you would probably want to use 5 high quality zoomable thermal cameras which pan across the sky in sync with each other which due to using lower frequency are much less prone to the Rayleigh scattering that scatters visible light at 150 or so km away and again, you can also use this to majorly upgrade radar. Pretty much all of the problems you could think of for this are incredibly easy to overcome if you apply even a small amount of brainpower into fixing the problem. And yes, this gives you the exact location down to the meter of whatever you are tracking even if the image is blurry as those blurs will always average out to the exact location down to the meter in the voxel grid. Which is what makes this technique so powerful since the cost of adding each camera to The network grows linearly while the rate at which each camera gives more information grows exponentially due to the increasing unlikeliness of all of them having more movement in the same place. And given the size of the cameras it really wouldn’t be that hard to hide and network these cameras together in other countries and on sea buoys to know where planes are everywhere in the world. Which brings me to the point that I personally really don’t care about the military uses of this technology, if all it could do is precisely track stealth fighters then I wouldn’t have cared enough to work on it, I could have used any of the many other life saving techniques as the subject of the video, stealth fighters just sounds the most clickable and the scale of the problem is more intuitive to most people and if I did use any of those as subjects for the demo it would inevitably result in the stealth fighter technique being figured out anyway and all of the other uses are so useful that I don't think anyone would reasonably complain about the upside. The real purpose of this video is that since this is a new technique that hasn’t been used to detect stealth fighters despite the billions we have spent on that, then what else can you apply this to that could go on to improve billions of people’s lives that you or others are working on. For example this also allows you to majorly improve the effectiveness of cryo electron microscopy and CT scanners. This part also is kind of hard to explain as it also sounds like it exists but again, when you look through all of the places where you think it is being used you will find that it wasn’t. What I’m saying here isn’t that this is a Radon transform or gaussian splat or whatever, I’m saying that this is able to get new information that wasn’t being accessed before due to the added information about depth you get from the correlation of movement between each perspective which adds to the information that you already have. This allows you to directly subtract foreground and background objects as well as noise faster than you would be able to before and works better than super resolution for your images since super resolution won’t remove foreground and background objects like this does and instead just scales up target, foreground and background objects indiscriminately. And while with enough data Radon transforms or other scanning techniques would eventually get you a correct answer this will get you there a lot faster since those are mostly averaging techniques which average out noise whereas this gets you the ability to directly subtract noise. I’m not expecting you to think that this would do anything but if you try it for yourself you will find that it does majorly improve your ability to perform 3d scans. Again, cryo EM is a field where you would expect this technique to exist but when you look through all the papers on the topic there is no mention of tilting the grid slightly in order to be able to change your perspective slightly on the order of the feature size (if you tilt the grid then you only need precision on the order of an arc minute to do this) and doing multiple exposures from multiple different known tilts and then using those difference images to correlate depth from motion. In fact, in cryo EM you would normally want to do the opposite of this and have your exposures all taken from the same grid angle and just use the variations in how many of the same proteins are oriented in order to be able to scan them for a 3D model but this will generate you far more data faster. There is so much information that I can’t really explain in text so if you have any questions such as why this hasn’t been made before then they will most likely be answered in the video I originally posted which I have added to the end of the first Devlog for your convenience. And again, pretty much all of the problems with the technique can be fixed with a little bit of brainpower, in reality you would probably want to use 5 high quality zoomable thermal cameras which pan across the sky in sync with each other which due to using lower frequency are much less prone to the Rayleigh scattering that scatters visible light at 150 or so km away and again, you can also use this to majorly upgrade radar.
18:48

Here's a devlog made by an anonymous Chinese fan replicating the surprisingly brand new technique that I developed for detecting asteroids which wound up being so powerful that it can easily track Stealth Fighters from over 100km away even when it’s only using three $30 webcams as sensors meaning it easily outperforms all modern stealth tracking techniques in precision, range and cost. And while this demo is using optical light, this same technique which I call pixel motion to voxel projection, can be used interchangeably with thermal infrared cameras to work at night and also majorly boosts the effectiveness of radar allowing you to track fighters much more effectively through clouds and over the horizon. This technique will also always eventually give the exact location of the target even if the image is blurry as those blurs will always average out from the different perspectives into revealing the precise location of the target in the voxel grid. There is definitely a Mandela effect with this technique as it feels as though it should already exist, especially because at first as it sounds like it is performing triangulation (which has existed for years and is what we do for mocap and tennis ball tracking). But triangulation is entirely separate to this as triangulations only works if you have already identified where the ball is in a 2D image because you’re able to rely on being able to use at least 2 separate high quality cameras which are much closer to the ball making the ball’s apparent size much much bigger and therefore gives you hundreds of pixels to work with which makes it much easier to use object recognition techniques to recognize where it is in the image aka in 2D and then you’re just using the other cameras view to project out lines which intersect in 3D to find out where the ball is in 3D. The major difference is that pixel motion to voxel projection allows you to find where the object is in 3D without having already found it in 2D which is an unbelievable difference as it allows you to use much lower quality cameras together to accumulate data together into 3D space. If this seem like it doesn’t mean much then what it actually means is that you don’t understand what I’m saying as what I’m saying means a LOT in practical terms as it means you go from having to use an imaging system that has to be able to image the object to the point that it is over a hundred total pixels in surface area to have enough data to recognize it to instead be able to use something that is only images the object to be 1 pixel in surface area and only changes the brightness value by 1 value every now and then. I’d recommend an amazing video by DST studios called “Lowlight cameras can’t defeat stealth” if you want a great video which goes over the difficulty of even using telescopes to recognize stealth fighters and why this is so impressive compared to other techniques and ironically it is what inspired me to realize the asteroid tracker I was working on actually could do this. Which brings me to the point that if this wasn’t a new technique then not only would there be at least one example of an asteroid survey that points distant telescopes at the same place at the same time in order to be able to add the light together to detect asteroids which as I was shocked to learn isn’t a thing despite the fact that it would make detecting asteroids trivial by comparison to modern 2D imaging while also having no impact on the normal scientific operations of those surveys other than small changes to scheduling. But there would also be an example of a drone tracker that uses this instead of using the aforementioned high quality zoomable telescope which has to be able to zoom in close enough to be able to recognize a drone. If you want to tell me that this is something that already exists give me an exact example of a product that uses it, not the general outline of a concept that you think it is, the actual product and then also tell me the asteroid survey that uses distant telescopes that point at the exact same place at the exact same time because I can guarantee that if you google what you think uses this you won’t even find the steps of subtracting the images from each other to get motion and will definitely not get the added step of projecting that motion into a voxel grid (It would blow your mind if you found out how Xbox kinect cameras work.) Also I want to make it clear, I’m not saying you should just use web cams to do this, I’m just using them as an example to show you the power of this in reality you would probably want to use 5 high quality zoomable thermal cameras which pan across the sky in sync with each other which due to using lower frequency are much less prone to the Rayleigh scattering that scatters visible light at 150 or so km away and again, you can also use this to majorly upgrade radar. Pretty much all of the problems you could think of for this are incredibly easy to overcome if you apply even a small amount of brainpower into fixing the problem. And yes, this gives you the exact location down to the meter of whatever you are tracking even if the image is blurry as those blurs will always average out to the exact location down to the meter in the voxel grid. Which is what makes this technique so powerful since the cost of adding each camera to The network grows linearly while the rate at which each camera gives more information grows exponentially due to the increasing unlikeliness of all of them having more movement in the same place. And given the size of the cameras it really wouldn’t be that hard to hide and network these cameras together in other countries and on sea buoys to know where planes are everywhere in the world. Which brings me to the point that I personally really don’t care about the military uses of this technology, if all it could do is precisely track stealth fighters then I wouldn’t have cared enough to work on it, I could have used any of the many other life saving techniques as the subject of the video, stealth fighters just sounds the most clickable and the scale of the problem is more intuitive to most people and if I did use any of those as subjects for the demo it would inevitably result in the stealth fighter technique being figured out anyway and all of the other uses are so useful that I don't think anyone would reasonably complain about the upside. The real purpose of this video is that since this is a new technique that hasn’t been used to detect stealth fighters despite the billions we have spent on that, then what else can you apply this to that could go on to improve billions of people’s lives that you or others are working on. For example this also allows you to majorly improve the effectiveness of cryo electron microscopy and CT scanners. This part also is kind of hard to explain as it also sounds like it exists but again, when you look through all of the places where you think it is being used you will find that it wasn’t. What I’m saying here isn’t that this is a Radon transform or gaussian splat or whatever, I’m saying that this is able to get new information that wasn’t being accessed before due to the added information about depth you get from the correlation of movement between each perspective which adds to the information that you already have. This allows you to directly subtract foreground and background objects as well as noise faster than you would be able to before and works better than super resolution for your images since super resolution won’t remove foreground and background objects like this does and instead just scales up target, foreground and background objects indiscriminately. And while with enough data Radon transforms or other scanning techniques would eventually get you a correct answer this will get you there a lot faster since those are mostly averaging techniques which average out noise whereas this gets you the ability to directly subtract noise. I’m not expecting you to think that this would do anything but if you try it for yourself you will find that it does majorly improve your ability to perform 3d scans. Again, cryo EM is a field where you would expect this technique to exist but when you look through all the papers on the topic there is no mention of tilting the grid slightly in order to be able to change your perspective slightly on the order of the feature size (if you tilt the grid then you only need precision on the order of an arc minute to do this) and doing multiple exposures from multiple different known tilts and then using those difference images to correlate depth from motion. In fact, in cryo EM you would normally want to do the opposite of this and have your exposures all taken from the same grid angle and just use the variations in how many of the same proteins are oriented in order to be able to scan them for a 3D model but this will generate you far more data faster. There is so much information that I can’t really explain in text so if you have any questions such as why this hasn’t been made before then they will most likely be answered in the video I originally posted which I have added to the end of the first Devlog for your convenience. And again, pretty much all of the problems with the technique can be fixed with a little bit of brainpower, in reality you would probably want to use 5 high quality zoomable thermal cameras which pan across the sky in sync with each other which due to using lower frequency are much less prone to the Rayleigh scattering that scatters visible light at 150 or so km away and again, you can also use this to majorly upgrade radar.

ConsistentlyInconsistent

50,687 Aufrufe • vor 11 Monaten