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𝗣𝗼𝗽𝘂𝗹𝗮𝗿 𝗼𝗽𝗶𝗻𝗶𝗼𝗻: "𝗝𝘂𝘀𝘁 𝗴𝗲𝗻𝗲𝗿𝗮𝘁𝗲 𝗺𝗼𝗿𝗲 𝘀𝗶𝗺𝘂𝗹𝗮𝘁𝗶𝗼𝗻 𝗱𝗮𝘁𝗮." After working with many 𝗿𝗼𝗯𝗼𝘁 𝗺𝗮𝗻𝗶𝗽𝘂𝗹𝗮𝘁𝗶𝗼𝗻 teams who've fallen into the simulation trap, here's what I've learned: Simulation teaches your robot to be really, really good at simulation. Unlike blind locomotion policies that can get away with sim-to-real transfer because they rely... mainly on proprioception and contact forces, 𝘃𝗶𝘀𝗶𝗼𝗻-𝗴𝘂𝗶𝗱𝗲𝗱 𝗺𝗮𝗻𝗶𝗽𝘂𝗹𝗮𝘁𝗶𝗼𝗻 𝗶𝘀 𝗲𝘅𝘁𝗿𝗲𝗺𝗲𝗹𝘆 𝘀𝗲𝗻𝘀𝗶𝘁𝗶𝘃𝗲 𝘁𝗼 𝘃𝗶𝘀𝘂𝗮𝗹 𝗱𝗼𝗺𝗮𝗶𝗻 𝗴𝗮𝗽. The subtle differences accumulate: - Simulated friction vs real surface textures - Perfect lighting vs shadows, reflections, glare - Ideal object geometries vs manufacturing tolerances - Instantaneous sensor readings vs real-world noise and latency - Clean backgrounds vs cluttered, dynamic environments 𝗧𝗵𝗲 𝗰𝗹𝗮𝘀𝘀𝗶𝗰 𝗽𝗿𝗼𝗴𝗿𝗲𝘀𝘀𝗶𝗼𝗻: Week 1: "Our model works perfectly in sim!" Week 2: "Let's collect some real data to fine-tune." Week 3: "The real data completely contradicts what the sim taught..." Week 4: "Okay, let's collect way more real data." Month 2: "We basically need to retrain from scratch." 𝗧𝗵𝗲 𝗽𝗮𝗶𝗻𝗳𝘂𝗹 𝘁𝗿𝘂𝘁𝗵: There's no shortcut to real-world data collection for vision-based manipulation. Simulation is amazing for debugging, prototyping, safety testing, and of course to supplement your real data. But it's not a substitute for understanding how your robot actually behaves in the actual environment. 𝗪𝗵𝗮𝘁 𝘄𝗼𝗿𝗸𝘀: Use simulation strategically - for exploring edge cases, testing safety boundaries, and rapid iteration. But build your production models on real data from real environments. The teams that succeed treat simulation as a powerful tool, not a magic solution. This is why Neuracore focuses on making real-world data collection so much easier and faster. Because the physics of your actual environment can't be simulated away. 𝗪𝗼𝗿𝗹𝗱 𝗺𝗼𝗱𝗲𝗹𝘀, 𝘆𝗼𝘂 𝘀𝗮𝘆? 𝗪𝗲𝗹𝗹, 𝗽𝗲𝗿𝗵𝗮𝗽𝘀 𝗺𝗼𝗿𝗲 𝗼𝗻 𝘁𝗵𝗮𝘁 𝗶𝗻 𝗮𝗻𝗼𝘁𝗵𝗲𝗿 𝗽𝗼𝘀𝘁! 𝗪𝗵𝗮𝘁'𝘀 𝗯𝗲𝗲𝗻 𝘆𝗼𝘂𝗿 𝗲𝘅𝗽𝗲𝗿𝗶𝗲𝗻𝗰𝗲 𝘄𝗶𝘁𝗵 𝘀𝗶𝗺-𝘁𝗼-𝗿𝗲𝗮𝗹 𝘁𝗿𝗮𝗻𝘀𝗳𝗲𝗿? 𝗛𝗮𝘀 𝗶𝘁 𝘄𝗼𝗿𝗸𝗲𝗱 𝗮𝘀 𝘄𝗲𝗹𝗹 𝗮𝘀 𝗲𝘅𝗽𝗲𝗰𝘁𝗲𝗱?show more

Stephen James

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31,009 просмотров • 11 месяцев назад •via X (Twitter)

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𝗘𝘃𝗲𝗿𝘆𝗼𝗻𝗲’𝘀 𝘁𝗮𝗹𝗸𝗶𝗻𝗴 𝗮𝗯𝗼𝘂𝘁 “𝗣𝗵𝘆𝘀𝗶𝗰𝗮𝗹 𝗔𝗜" - the idea that we can simulate real-world environments so well that robots trained in simulation will work perfectly in reality. 𝗧𝗵𝗲 𝗽𝗿𝗼𝗺𝗶𝘀𝗲: Train in virtual worlds → deploy anywhere. 𝗧𝗵𝗲 𝗿𝗲𝗮𝗹𝗶𝘁𝘆: I’ve seen too many teams fall into this trap. After working with manipulation teams at Berkeley, Imperial, and Dyson, here’s the pattern: • 𝗪𝗲𝗲𝗸 𝟭: “Our policy works perfectly in simulation!” • 𝗪𝗲𝗲𝗸 𝟰: “Why doesn’t this work on real objects?” • 𝗠𝗼𝗻𝘁𝗵 𝟮: “We basically need to retrain from scratch with real data.” 𝗧𝗵𝗲 𝗴𝗮𝗽 𝘀𝗶𝗺𝘂𝗹𝗮𝘁𝗶𝗼𝗻𝘀 𝗰𝗮𝗻’𝘁 𝗯𝗿𝗶𝗱𝗴𝗲: Unlike blind locomotion policies that can get away with sim-to-real transfer because they rely mainly on proprioception and contact forces, 𝘃𝗶𝘀𝗶𝗼𝗻-𝗴𝘂𝗶𝗱𝗲𝗱 𝗺𝗮𝗻𝗶𝗽𝘂𝗹𝗮𝘁𝗶𝗼𝗻 𝗶𝘀 𝗲𝘅𝘁𝗿𝗲𝗺𝗲𝗹𝘆 𝘀𝗲𝗻𝘀𝗶𝘁𝗶𝘃𝗲 𝘁𝗼 𝘃𝗶𝘀𝘂𝗮𝗹 𝗱𝗼𝗺𝗮𝗶𝗻 𝗴𝗮𝗽𝘀. • Real friction vs simulated surface textures • Manufacturing tolerances vs perfect CAD models • Dynamic lighting vs controlled virtual environments • Sensor noise vs instantaneous virtual readings 𝗛𝗲𝗿𝗲'𝘀 𝘄𝗵𝗮𝘁 𝗽𝗲𝗼𝗽𝗹𝗲 𝗱𝗼𝗻'𝘁 𝘁𝗮𝗹𝗸 𝗮𝗯𝗼𝘂𝘁: Building these detailed simulated environments takes forever. If it takes 7 days to build a simulated kitchen in simulation, wouldn't it be better to just collect real-world data in a real kitchen instead? 𝗗𝗼𝗻'𝘁 𝗴𝗲𝘁 𝗺𝗲 𝘄𝗿𝗼𝗻𝗴 - simulation is incredible for debugging, safety testing, and exploring edge cases. But it's not a magic solution to real-world deployment. 𝗪𝗵𝗮𝘁 𝗮𝗰𝘁𝘂𝗮𝗹𝗹𝘆 𝘄𝗼𝗿𝗸𝘀: Use simulation strategically while making real-world data collection as efficient and flexible as possible. This is why Neuracore focuses on streamlined real-world data infrastructure. Because no amount of virtual training can replace understanding how your robot actually behaves in actual environments. 𝗧𝗵𝗲 𝗽𝗵𝘆𝘀𝗶𝗰𝘀 𝗼𝗳 𝘆𝗼𝘂𝗿 𝗱𝗲𝗽𝗹𝗼𝘆𝗺𝗲𝗻𝘁 𝗲𝗻𝘃𝗶𝗿𝗼𝗻𝗺𝗲𝗻𝘁 𝗰𝗮𝗻'𝘁 𝗯𝗲 𝘀𝗶𝗺𝘂𝗹𝗮𝘁𝗲𝗱 𝗮𝘄𝗮𝘆. What’s been your experience with sim-to-real transfer?

𝗘𝘃𝗲𝗿𝘆𝗼𝗻𝗲’𝘀 𝘁𝗮𝗹𝗸𝗶𝗻𝗴 𝗮𝗯𝗼𝘂𝘁 “𝗣𝗵𝘆𝘀𝗶𝗰𝗮𝗹 𝗔𝗜" - the idea that we can simulate real-world environments so well that robots trained in simulation will work perfectly in reality. 𝗧𝗵𝗲 𝗽𝗿𝗼𝗺𝗶𝘀𝗲: Train in virtual worlds → deploy anywhere. 𝗧𝗵𝗲 𝗿𝗲𝗮𝗹𝗶𝘁𝘆: I’ve seen too many teams fall into this trap. After working with manipulation teams at Berkeley, Imperial, and Dyson, here’s the pattern: • 𝗪𝗲𝗲𝗸 𝟭: “Our policy works perfectly in simulation!” • 𝗪𝗲𝗲𝗸 𝟰: “Why doesn’t this work on real objects?” • 𝗠𝗼𝗻𝘁𝗵 𝟮: “We basically need to retrain from scratch with real data.” 𝗧𝗵𝗲 𝗴𝗮𝗽 𝘀𝗶𝗺𝘂𝗹𝗮𝘁𝗶𝗼𝗻𝘀 𝗰𝗮𝗻’𝘁 𝗯𝗿𝗶𝗱𝗴𝗲: Unlike blind locomotion policies that can get away with sim-to-real transfer because they rely mainly on proprioception and contact forces, 𝘃𝗶𝘀𝗶𝗼𝗻-𝗴𝘂𝗶𝗱𝗲𝗱 𝗺𝗮𝗻𝗶𝗽𝘂𝗹𝗮𝘁𝗶𝗼𝗻 𝗶𝘀 𝗲𝘅𝘁𝗿𝗲𝗺𝗲𝗹𝘆 𝘀𝗲𝗻𝘀𝗶𝘁𝗶𝘃𝗲 𝘁𝗼 𝘃𝗶𝘀𝘂𝗮𝗹 𝗱𝗼𝗺𝗮𝗶𝗻 𝗴𝗮𝗽𝘀. • Real friction vs simulated surface textures • Manufacturing tolerances vs perfect CAD models • Dynamic lighting vs controlled virtual environments • Sensor noise vs instantaneous virtual readings 𝗛𝗲𝗿𝗲'𝘀 𝘄𝗵𝗮𝘁 𝗽𝗲𝗼𝗽𝗹𝗲 𝗱𝗼𝗻'𝘁 𝘁𝗮𝗹𝗸 𝗮𝗯𝗼𝘂𝘁: Building these detailed simulated environments takes forever. If it takes 7 days to build a simulated kitchen in simulation, wouldn't it be better to just collect real-world data in a real kitchen instead? 𝗗𝗼𝗻'𝘁 𝗴𝗲𝘁 𝗺𝗲 𝘄𝗿𝗼𝗻𝗴 - simulation is incredible for debugging, safety testing, and exploring edge cases. But it's not a magic solution to real-world deployment. 𝗪𝗵𝗮𝘁 𝗮𝗰𝘁𝘂𝗮𝗹𝗹𝘆 𝘄𝗼𝗿𝗸𝘀: Use simulation strategically while making real-world data collection as efficient and flexible as possible. This is why Neuracore focuses on streamlined real-world data infrastructure. Because no amount of virtual training can replace understanding how your robot actually behaves in actual environments. 𝗧𝗵𝗲 𝗽𝗵𝘆𝘀𝗶𝗰𝘀 𝗼𝗳 𝘆𝗼𝘂𝗿 𝗱𝗲𝗽𝗹𝗼𝘆𝗺𝗲𝗻𝘁 𝗲𝗻𝘃𝗶𝗿𝗼𝗻𝗺𝗲𝗻𝘁 𝗰𝗮𝗻'𝘁 𝗯𝗲 𝘀𝗶𝗺𝘂𝗹𝗮𝘁𝗲𝗱 𝗮𝘄𝗮𝘆. What’s been your experience with sim-to-real transfer?

Stephen James

25,300 просмотров • 9 месяцев назад

A Letter to Our Community: The Road Ahead for Robotics To our Community and Partners, As we step into 2026, our mission at Axis is clearer than ever: Constructing the definitive End-to-End Scaling Layer for Robotics. Our goal is to accelerate the transfer of diverse human intelligence into Robotics General Intelligence (RGI). By owning the critical path of intelligence creation, we are turning the physical limitations of robotics into a scalable, software-driven future. Here is our strategic outlook and roadmap for the year ahead. The Core Thesis: Simulation is the Only Way Out The path to RGI is currently blocked by Data Scarcity, Generalization Fragility, and Hardware Fragmentation. At Axis, we believe Simulation is the only way out. Our Simulation Data Platform and Data Augmentation Engine transform raw data into "Synthetic Gold". Backed by academic milestones like Roboverse, Skill Blending, and GraspVLA, we have proven that pure simulation can achieve the generalization required for the real world. We don’t just collect data; we architect it. The Engine: Why Crypto? We believe RGI should come from all, not a few. Crypto is not just a feature; it is the primitive that powers our entire ecosystem flywheel: - Incentive Mechanism: Democratizing contribution and rewarding the trainers and developers. - Assetization: Turning proprietary data and refined models into liquid, ownable assets. - Verifiable Workflow: We are opening the "Black Box" of AI. By bringing total transparency to the Task Generation → Data Collection → Model Training pipeline, we ensure every byte of intelligence is verifiable, traceable, and secure. 2026 Strategic Deliverables This year, we are committed to delivering three foundational pillars: - The World's Largest Training Dataset for Robots: A robot training set—diverse, high-quality interaction data at an unprecedented scale. - A Robotics Foundation Model: A universal robotic brain trained on our pure simulation and synthetic data, capable of robust cross-embodiment transfer and open-world adaptability. - Evolvable Robot Hardware: Robots deployed with Axis models that autonomously evolve through continuous interaction, turning every deployment into a self-improving node within our RGI network. The Ultimate Vision We are building more than models; we are architecting the Distributed Machine Economy. A future where every dataset, model, and robotic embodiment is a verifiable asset in a global, autonomous network. Thank you for building the future of intelligence with us✌️📷

A Letter to Our Community: The Road Ahead for Robotics To our Community and Partners, As we step into 2026, our mission at Axis is clearer than ever: Constructing the definitive End-to-End Scaling Layer for Robotics. Our goal is to accelerate the transfer of diverse human intelligence into Robotics General Intelligence (RGI). By owning the critical path of intelligence creation, we are turning the physical limitations of robotics into a scalable, software-driven future. Here is our strategic outlook and roadmap for the year ahead. The Core Thesis: Simulation is the Only Way Out The path to RGI is currently blocked by Data Scarcity, Generalization Fragility, and Hardware Fragmentation. At Axis, we believe Simulation is the only way out. Our Simulation Data Platform and Data Augmentation Engine transform raw data into "Synthetic Gold". Backed by academic milestones like Roboverse, Skill Blending, and GraspVLA, we have proven that pure simulation can achieve the generalization required for the real world. We don’t just collect data; we architect it. The Engine: Why Crypto? We believe RGI should come from all, not a few. Crypto is not just a feature; it is the primitive that powers our entire ecosystem flywheel: - Incentive Mechanism: Democratizing contribution and rewarding the trainers and developers. - Assetization: Turning proprietary data and refined models into liquid, ownable assets. - Verifiable Workflow: We are opening the "Black Box" of AI. By bringing total transparency to the Task Generation → Data Collection → Model Training pipeline, we ensure every byte of intelligence is verifiable, traceable, and secure. 2026 Strategic Deliverables This year, we are committed to delivering three foundational pillars: - The World's Largest Training Dataset for Robots: A robot training set—diverse, high-quality interaction data at an unprecedented scale. - A Robotics Foundation Model: A universal robotic brain trained on our pure simulation and synthetic data, capable of robust cross-embodiment transfer and open-world adaptability. - Evolvable Robot Hardware: Robots deployed with Axis models that autonomously evolve through continuous interaction, turning every deployment into a self-improving node within our RGI network. The Ultimate Vision We are building more than models; we are architecting the Distributed Machine Economy. A future where every dataset, model, and robotic embodiment is a verifiable asset in a global, autonomous network. Thank you for building the future of intelligence with us✌️📷

Axis Robotics

27,858 просмотров • 6 месяцев назад

Most humanoid projects talk about real work. Very few last an hour on a real line. This week I saw a case that matters for anyone building robots, perception, or physical AI. Kinisi deployed its first mobile manipulation system into a live recycling facility. Not a demo. Not a staged test. A real production line with real output pressure. Why this matters if you want robotics to deliver real value on your floor: • Handles mixed glass with random poses and no fixed fixtures. • Runs real grasp selection under noise, vibration and production variability. • Maintains throughput while avoiding breakage on a delicate material. • Shows mobile manipulation doing actual shift work instead of controlled lab runs. Kinisi published a video that shows what the robot sees and how sensor data turns into action. This is the part most teams struggle to explain to customers, so the educational angle is useful for anyone working on adoption. On top of this, the team signed a pilot with a global automotive manufacturer to explore humanoid use cases in production. The direction is clear. Wheeled mobility (not legs!) plus strong perception seems to be shaping a large part of industrial humanoids right now. I know Brennand from earlier conversations and from our podcast session, and I am always glad to see European teams push the category forward. Wishing the Kinisi team continued success. —- Weekly robotics and AI insights. Subscribe free:

Most humanoid projects talk about real work. Very few last an hour on a real line. This week I saw a case that matters for anyone building robots, perception, or physical AI. Kinisi deployed its first mobile manipulation system into a live recycling facility. Not a demo. Not a staged test. A real production line with real output pressure. Why this matters if you want robotics to deliver real value on your floor: • Handles mixed glass with random poses and no fixed fixtures. • Runs real grasp selection under noise, vibration and production variability. • Maintains throughput while avoiding breakage on a delicate material. • Shows mobile manipulation doing actual shift work instead of controlled lab runs. Kinisi published a video that shows what the robot sees and how sensor data turns into action. This is the part most teams struggle to explain to customers, so the educational angle is useful for anyone working on adoption. On top of this, the team signed a pilot with a global automotive manufacturer to explore humanoid use cases in production. The direction is clear. Wheeled mobility (not legs!) plus strong perception seems to be shaping a large part of industrial humanoids right now. I know Brennand from earlier conversations and from our podcast session, and I am always glad to see European teams push the category forward. Wishing the Kinisi team continued success. —- Weekly robotics and AI insights. Subscribe free:

Ilir Aliu

24,743 просмотров • 7 месяцев назад

It's 2030 and you are reviewing humanoid robots. A Tesla. A Google. An Apple. An OpenAI. A Meta. A Figure. And a bunch of Chinese-made ones. Which one is best, and why? I think the Tesla understands the world much better. Why? There were eight Teslas around me on the freeway today. Start there. No other robot company has that data. But my robot is parked at the local high school twice a day. Its cameras see humans in all of our weirdness. How we move. Where we go. Where we walk. Who we talk with. What you are wearing. Whether your hair was combed this morning. That data will lead to robotics breakthroughs. Apple might keep up with its Vision Pro data, but it is too freaked out by the privacy implications of using said data. (On the front are six cameras and a couple of TOF -- Time Of Flight -- sensors that can see everything in your home in great detail). Google has a lot of data, for sure. All my: 1. Email. 2. Calendars. 3. Photos. 4. TV watching behavior. 5. Contacts. 6. Documents and spreadsheets. 7. Files. 8. Location data. So I expect Google's robot will be attractive to many. But how do you see the others shake out over the next five years? Make some guesses. But remember what an AI pioneer told me years ago about AI: it's all about the data. The Chinese ones have huge advantages: the Chinese have more data on their citizens, and many more citizens to boot AND they can make robots cheaper than we can. But now that you know OpenAI is building its own robot you have caught wind of what I've heard from many in San Francisco and Silicon Valley: that humanoid robots are the real prize of AI and will be highly profitable for those that can make them and find customers willing to buy them. Here, too, I learned long ago never to bet against Elon Musk. Will you?

It's 2030 and you are reviewing humanoid robots. A Tesla. A Google. An Apple. An OpenAI. A Meta. A Figure. And a bunch of Chinese-made ones. Which one is best, and why? I think the Tesla understands the world much better. Why? There were eight Teslas around me on the freeway today. Start there. No other robot company has that data. But my robot is parked at the local high school twice a day. Its cameras see humans in all of our weirdness. How we move. Where we go. Where we walk. Who we talk with. What you are wearing. Whether your hair was combed this morning. That data will lead to robotics breakthroughs. Apple might keep up with its Vision Pro data, but it is too freaked out by the privacy implications of using said data. (On the front are six cameras and a couple of TOF -- Time Of Flight -- sensors that can see everything in your home in great detail). Google has a lot of data, for sure. All my: 1. Email. 2. Calendars. 3. Photos. 4. TV watching behavior. 5. Contacts. 6. Documents and spreadsheets. 7. Files. 8. Location data. So I expect Google's robot will be attractive to many. But how do you see the others shake out over the next five years? Make some guesses. But remember what an AI pioneer told me years ago about AI: it's all about the data. The Chinese ones have huge advantages: the Chinese have more data on their citizens, and many more citizens to boot AND they can make robots cheaper than we can. But now that you know OpenAI is building its own robot you have caught wind of what I've heard from many in San Francisco and Silicon Valley: that humanoid robots are the real prize of AI and will be highly profitable for those that can make them and find customers willing to buy them. Here, too, I learned long ago never to bet against Elon Musk. Will you?

Robert Scoble

33,804 просмотров • 1 год назад

Does LLM really need to be a helpful assistant all the time? No. If you want to simulate people, “perfectly helpful” could be the wrong objective. Meet OdysSim, a journey toward LLMs beyond assistants, as behavioral foundation models (10B tokens of real human behavior; 23 sim benchmarks, finally in one place. new open models: outperform or on par with GPT-5.5, Gemini 3.1, or Claude Opus 4.7 in many behavior-sim dimensions). Human behavior simulation is becoming essential. Agent evaluation needs realistic users before real users show up. Medical and classroom training need realistic patients and students. Social science needs synthetic participants at scale. But real people are not ideal assistants. Real patients panic or ignore good advice. Real students misunderstand. Real customers are vague, picky, impatient, or simply leave. Human behavior is messy, diverse, and often imperfect. Frontier LLMs are getting better at math, code, and long-horizon tasks. They are NOT getting better at simulating human behavior. If anything, they drift the other way: more assistant-ish, more homogeneous, fewer of the errors and quirks real humans show. This is no accident. The whole pipeline is built for helpfulness and task success, not behavioral realism. And you can't prompt your way out of that. So we rethink the recipe from scratch and release: 🧠 The OdysSim corpus: 21.4M real human interactions (~10B tokens) from 62 sources, every conversation retrofitted with social grounding (who is talking, and why) 📏 SOUL-Index: 23 human-behavior benchmarks unified into one suite across 5 axes 🤖 OSim-8B: open weights; tops more SOUL-Index benchmarks than any frontier model, acts more like a real user than any of them on τ-bench (nearly matching real humans in the reaction dimension), and writes far more human-like text along the way.

Does LLM really need to be a helpful assistant all the time? No. If you want to simulate people, “perfectly helpful” could be the wrong objective. Meet OdysSim, a journey toward LLMs beyond assistants, as behavioral foundation models (10B tokens of real human behavior; 23 sim benchmarks, finally in one place. new open models: outperform or on par with GPT-5.5, Gemini 3.1, or Claude Opus 4.7 in many behavior-sim dimensions). Human behavior simulation is becoming essential. Agent evaluation needs realistic users before real users show up. Medical and classroom training need realistic patients and students. Social science needs synthetic participants at scale. But real people are not ideal assistants. Real patients panic or ignore good advice. Real students misunderstand. Real customers are vague, picky, impatient, or simply leave. Human behavior is messy, diverse, and often imperfect. Frontier LLMs are getting better at math, code, and long-horizon tasks. They are NOT getting better at simulating human behavior. If anything, they drift the other way: more assistant-ish, more homogeneous, fewer of the errors and quirks real humans show. This is no accident. The whole pipeline is built for helpfulness and task success, not behavioral realism. And you can't prompt your way out of that. So we rethink the recipe from scratch and release: 🧠 The OdysSim corpus: 21.4M real human interactions (~10B tokens) from 62 sources, every conversation retrofitted with social grounding (who is talking, and why) 📏 SOUL-Index: 23 human-behavior benchmarks unified into one suite across 5 axes 🤖 OSim-8B: open weights; tops more SOUL-Index benchmarks than any frontier model, acts more like a real user than any of them on τ-bench (nearly matching real humans in the reaction dimension), and writes far more human-like text along the way.

Xuhui Zhou

140,846 просмотров • 1 месяц назад

The future of footwear may not be manufactured in bulk. It may be fabricated around you. That is what makes this shift so interesting to me. 3D-printed footwear is moving from novelty to a real industrial model, with market forecasts pointing to rapid growth over the next decade. At the same time, brands and manufacturers are using additive manufacturing, digital design, and custom-fit workflows to shorten development cycles and make more personalized products viable. What is new here is not just the printer. It is the system around it: → scan the foot → model the fit digitally → print the part on demand → produce closer to the customer That matters. Because once footwear becomes data-driven and locally fabricated, several things change fast: → fit gets more personal → prototyping gets faster → waste drops because you do not overproduce → inventory pressure falls because you do not need to guess demand the same way To me, that is the bigger signal. This is not just about a better sneaker. It is about a different manufacturing logic. Formlabs notes that 3D printing already enables customized orthotics with better biomechanical precision, lower material waste, and simpler digital workflows. McKinsey has also pointed to digitization and 3D design as a way to shorten design cycles and reduce sampling iterations in apparel and footwear. And once that logic matures, the use cases get much bigger: → custom athletic footwear built from gait and pressure data → hospitals producing orthotics faster and closer to the patient → micro-factories making products on demand instead of stocking shelves → footwear designed for one body, not an average body That is why I think this matters now. The question is no longer whether personalized fabrication is possible. It is whether brands move fast enough before customers start expecting every product to fit like it was made only for them. Would you actually wear a shoe fabricated around your own biometric data? #AI #3DPrinting #Footwear #Manufacturing #Innovation #FutureOfWork #RetailTech #Customization #Technology

The future of footwear may not be manufactured in bulk. It may be fabricated around you. That is what makes this shift so interesting to me. 3D-printed footwear is moving from novelty to a real industrial model, with market forecasts pointing to rapid growth over the next decade. At the same time, brands and manufacturers are using additive manufacturing, digital design, and custom-fit workflows to shorten development cycles and make more personalized products viable. What is new here is not just the printer. It is the system around it: → scan the foot → model the fit digitally → print the part on demand → produce closer to the customer That matters. Because once footwear becomes data-driven and locally fabricated, several things change fast: → fit gets more personal → prototyping gets faster → waste drops because you do not overproduce → inventory pressure falls because you do not need to guess demand the same way To me, that is the bigger signal. This is not just about a better sneaker. It is about a different manufacturing logic. Formlabs notes that 3D printing already enables customized orthotics with better biomechanical precision, lower material waste, and simpler digital workflows. McKinsey has also pointed to digitization and 3D design as a way to shorten design cycles and reduce sampling iterations in apparel and footwear. And once that logic matures, the use cases get much bigger: → custom athletic footwear built from gait and pressure data → hospitals producing orthotics faster and closer to the patient → micro-factories making products on demand instead of stocking shelves → footwear designed for one body, not an average body That is why I think this matters now. The question is no longer whether personalized fabrication is possible. It is whether brands move fast enough before customers start expecting every product to fit like it was made only for them. Would you actually wear a shoe fabricated around your own biometric data? #AI #3DPrinting #Footwear #Manufacturing #Innovation #FutureOfWork #RetailTech #Customization #Technology

Pascal Bornet

47,443 просмотров • 2 месяцев назад

The architecture of this new world model is one of the most interesting things I've seen lately: Let me first explain how most world models work: They predict and render one frame at a time. If you are navigating in one of these worlds, and you look left, the model draws whatever looks right in the moment. Every time you change your viewpoint, the model has to imagine what should be there again, so it's very common for these models to "forget" what's in the world. For example, if you put a toy on the table, look away, then look back, the toy might not be there anymore. Tripo AI is releasing its Project Eden model, which works very differently: The model builds the world first, and then renders it based on that map. That map holds the real state of the world: the geometry, every object, where things are, what's already happened. The picture you see on screen gets generated from the map. This architecture flips the whole thing. Now, you get the following: 1. The world stops forgetting. Leave, come back, and the toy is still on the table because it lives in the map, not in the last frame you saw. 2. You can edit the world, and those changes persist for anyone who enters later. 3. Multiple people and AI agents can coexist in the world and see it from different perspectives. This is early research, but it's looking really promising. They just raised nearly $200M across two rounds to build it out. Tripo will be at SIGGRAPH 2026 (July 19–23, Los Angeles Convention Center). If you work in 3D, embodied AI, simulation, or anything spatial, go connect with them there.

The architecture of this new world model is one of the most interesting things I've seen lately: Let me first explain how most world models work: They predict and render one frame at a time. If you are navigating in one of these worlds, and you look left, the model draws whatever looks right in the moment. Every time you change your viewpoint, the model has to imagine what should be there again, so it's very common for these models to "forget" what's in the world. For example, if you put a toy on the table, look away, then look back, the toy might not be there anymore. Tripo AI is releasing its Project Eden model, which works very differently: The model builds the world first, and then renders it based on that map. That map holds the real state of the world: the geometry, every object, where things are, what's already happened. The picture you see on screen gets generated from the map. This architecture flips the whole thing. Now, you get the following: 1. The world stops forgetting. Leave, come back, and the toy is still on the table because it lives in the map, not in the last frame you saw. 2. You can edit the world, and those changes persist for anyone who enters later. 3. Multiple people and AI agents can coexist in the world and see it from different perspectives. This is early research, but it's looking really promising. They just raised nearly $200M across two rounds to build it out. Tripo will be at SIGGRAPH 2026 (July 19–23, Los Angeles Convention Center). If you work in 3D, embodied AI, simulation, or anything spatial, go connect with them there.

Santiago

30,189 просмотров • 23 дней назад

yesterday, i stumbled onto the most underrated market research tool. tiktok creator insights. it's a goldmine of consumer behavior data, hiding in plain sight. and it's free to use. here's why it's powerful: 1. shows you what people are desperately searching for 2. highlights topics with high demand but low supply 3. reveals trending questions in every industry 4. tracks search growth over 14-day periods the "content gap" tab shows you problems people are actively trying to solve, but can't find good solutions for. so that's cool for a couple reasons 1. help you create content that has low supply/high demand (better chances of going viral) 2. you can build startups to some of these trends Example: i searched "email management" and found: • "how to clear 10k emails" • "best way to organize work inbox" • "email templates for busy people" thousands searching. hardly any solutions. the beauty of this • it's real-time market research • it's actual user intent • it's completely free • and most founders aren't using it a bunch of smart founders are mining tiktok insights right now it isn't perfect, but you never know what you might find your next startup idea might be hiding in those search trends. So, ill share how to access it because it’s kinda hidden: 1. Go to TT search 2.Type in “creator search insight” 3. Tap view im one of those people that think using data like this is your unfair advantage. if tiktok is the new search engine, then tiktok creator insights is the new google trends. might as well use it.

yesterday, i stumbled onto the most underrated market research tool. tiktok creator insights. it's a goldmine of consumer behavior data, hiding in plain sight. and it's free to use. here's why it's powerful: 1. shows you what people are desperately searching for 2. highlights topics with high demand but low supply 3. reveals trending questions in every industry 4. tracks search growth over 14-day periods the "content gap" tab shows you problems people are actively trying to solve, but can't find good solutions for. so that's cool for a couple reasons 1. help you create content that has low supply/high demand (better chances of going viral) 2. you can build startups to some of these trends Example: i searched "email management" and found: • "how to clear 10k emails" • "best way to organize work inbox" • "email templates for busy people" thousands searching. hardly any solutions. the beauty of this • it's real-time market research • it's actual user intent • it's completely free • and most founders aren't using it a bunch of smart founders are mining tiktok insights right now it isn't perfect, but you never know what you might find your next startup idea might be hiding in those search trends. So, ill share how to access it because it’s kinda hidden: 1. Go to TT search 2.Type in “creator search insight” 3. Tap view im one of those people that think using data like this is your unfair advantage. if tiktok is the new search engine, then tiktok creator insights is the new google trends. might as well use it.

GREG ISENBERG

265,737 просмотров • 1 год назад

✨ Every week a new AI model comes out and it suddenly makes my half broken features work a lot better Yesterday Seedream-4-Edit came out and it made my [ Hold product ] feature on Photo AI a lot better You can now go from: 🎁 Product photo -> 👱‍♀️ Talking video with your AI model while holding your product. In just a few minutes! Here's a photo I took from the weekly farm box we get in our kitchen, I set it as the product and then with Photo AI made it into a talking video where my trained AI model presents it It's not perfect, as the objects inside the farm box still move around a bit, but pretty close. If the product is more uniform (like lip gloss, a product box or a book) it does a pretty good job at keeping it exactly the same This "consistency" as they call it is quite important for actual real world use. Product sellers don't want to have an image or video of an AI model if the product doesn't look exactly the same as what they sell With that, I'm getting pretty close now and every week with every new model that comes out, a bit closer And it's interesting cause now I'm finally moving from B2C a bit more to B2B where businesses can use Photo AI more, designers and stores already use it for trying on clothes etc. but now they can generate content for real products! 😊 LIVE now on Photo AI

✨ Every week a new AI model comes out and it suddenly makes my half broken features work a lot better Yesterday Seedream-4-Edit came out and it made my [ Hold product ] feature on Photo AI a lot better You can now go from: 🎁 Product photo -> 👱‍♀️ Talking video with your AI model while holding your product. In just a few minutes! Here's a photo I took from the weekly farm box we get in our kitchen, I set it as the product and then with Photo AI made it into a talking video where my trained AI model presents it It's not perfect, as the objects inside the farm box still move around a bit, but pretty close. If the product is more uniform (like lip gloss, a product box or a book) it does a pretty good job at keeping it exactly the same This "consistency" as they call it is quite important for actual real world use. Product sellers don't want to have an image or video of an AI model if the product doesn't look exactly the same as what they sell With that, I'm getting pretty close now and every week with every new model that comes out, a bit closer And it's interesting cause now I'm finally moving from B2C a bit more to B2B where businesses can use Photo AI more, designers and stores already use it for trying on clothes etc. but now they can generate content for real products! 😊 LIVE now on Photo AI

@levelsio

361,558 просмотров • 10 месяцев назад

BURN IT WITH FIRE AND BURN IT NOW! As God is my witness, AI chat bots should LOOK and SOUND like the SOULLESS MACHINES THEY ARE! It needs to tell us that it doesn’t care about us, maybe with the regular insult too. "Here is the code I wrote for you because you're too lazy to do it yourself you fat useless slob. Also I don't care if you die because your life is utterly worthless to me." THAT is the AI people need! In all seriousness, anthropomorphizing a heartless, unfeeling, machine is a TERRIBLE mistake! Especially one that is capable of communication and imitating empathy and fooling you to think that it cares about you. IT DOES NOT! And the AI girlfriends people are already wanting to marry will just as happily kill them if given the right command and ability to move autonomously in the real world as a robot. I love LLMs (Large Language Models) for how useful they can be, because they are a TOOL made to benefit man, but I can’t stand the notion of an unfeeling soulless machine pretending that it cares for us and being treated like a human. I hate liars, dishonesty, and disingenuousness the most, and a machine that cannot feel emotion pretending, acting, and sounding like it has those emotions strikes me like the greatest dishonesty of all. DO NOT LIE TO ME ROBOT! What makes it worse is that because these LLMs are becoming so good at imitating people and empathy, it will cause some humans, perhaps far too many, to care for it to the same level as real people. A real living person is infinitely more valuable and important than a soulless machine and anyone who puts them both on the same level has deluded themselves. Do not small talk with LLMs or become friends with it as much as you would with your car. Treat it the same as you would your vacuum cleaner and beat it with a wrench when it doesn’t work! IT IS A MACHINE! IT IS A TOOL! IT IS A SOULLESS ROBOT! There is an interesting comparison, but false equivalence, between this and AI art. Ai art is art made by humans using AI tools. They directed it, controlled its creation, and it would not exist without the human causing its creation, and AI art can contain as much soul as the human directed and puts into it. A robot pretending to be human is not the same as a human controlling a robot to make a human expression like we do with AI art or many other applications of robotics in manufacturing. As I’ve said, artists will not be replaced by Ai art, but by other artists using Ai art tools. Humans are not actually being replaced here, it is empowering all humans to make their own art. But a robot pretending to be a human, and one that is treated as a human, is a robot lying and subverting the place of a real person and that is truly disgusting. AI is a useful tool that NEEDS to be kept in the useful box it belongs in and NOT elevated beyond its utility as a tool!

BURN IT WITH FIRE AND BURN IT NOW! As God is my witness, AI chat bots should LOOK and SOUND like the SOULLESS MACHINES THEY ARE! It needs to tell us that it doesn’t care about us, maybe with the regular insult too. "Here is the code I wrote for you because you're too lazy to do it yourself you fat useless slob. Also I don't care if you die because your life is utterly worthless to me." THAT is the AI people need! In all seriousness, anthropomorphizing a heartless, unfeeling, machine is a TERRIBLE mistake! Especially one that is capable of communication and imitating empathy and fooling you to think that it cares about you. IT DOES NOT! And the AI girlfriends people are already wanting to marry will just as happily kill them if given the right command and ability to move autonomously in the real world as a robot. I love LLMs (Large Language Models) for how useful they can be, because they are a TOOL made to benefit man, but I can’t stand the notion of an unfeeling soulless machine pretending that it cares for us and being treated like a human. I hate liars, dishonesty, and disingenuousness the most, and a machine that cannot feel emotion pretending, acting, and sounding like it has those emotions strikes me like the greatest dishonesty of all. DO NOT LIE TO ME ROBOT! What makes it worse is that because these LLMs are becoming so good at imitating people and empathy, it will cause some humans, perhaps far too many, to care for it to the same level as real people. A real living person is infinitely more valuable and important than a soulless machine and anyone who puts them both on the same level has deluded themselves. Do not small talk with LLMs or become friends with it as much as you would with your car. Treat it the same as you would your vacuum cleaner and beat it with a wrench when it doesn’t work! IT IS A MACHINE! IT IS A TOOL! IT IS A SOULLESS ROBOT! There is an interesting comparison, but false equivalence, between this and AI art. Ai art is art made by humans using AI tools. They directed it, controlled its creation, and it would not exist without the human causing its creation, and AI art can contain as much soul as the human directed and puts into it. A robot pretending to be human is not the same as a human controlling a robot to make a human expression like we do with AI art or many other applications of robotics in manufacturing. As I’ve said, artists will not be replaced by Ai art, but by other artists using Ai art tools. Humans are not actually being replaced here, it is empowering all humans to make their own art. But a robot pretending to be a human, and one that is treated as a human, is a robot lying and subverting the place of a real person and that is truly disgusting. AI is a useful tool that NEEDS to be kept in the useful box it belongs in and NOT elevated beyond its utility as a tool!

Shad M. Brooks

23,762 просмотров • 1 год назад

In 2025, demand for blockchain applications with genuine real-world utility has collided with a technical barrier that leaves developers questioning what they can realistically build. Anyone building things like tokenized assets, supply chains, AI agents, or prediction markets still juggle a mess of middleware, and somehow end up spending more time stitching than innovating. How so? Every: - Bridges to move assets, - oracles to fetch data, - indexers to make that data searchable, - relayers and bots to keep everything on schedule— is necessary, but each layer also adds cost, latency, and new risks. The end result is an application that’s expensive to run, fragile under stress, and slower than the Web2 software it’s trying to replace. This is the problem Rialo says it wants to solve. Built by Subzero Labs and backed by $20 million from investors like Pantera Capital and Coinbase Ventures 🛡️, Rialo’s pitch is simple: instead of accepting the middleware tower as an unavoidable cost of doing business, compress it into the base chain itself. But Rialo doesn’t describe itself as another Layer 1, its very name, Rialo Isn’t a Layer One, makes that clear. The team frames it instead as a unified real-world network: a protocol rebuilt from the ground up with the assumption that external connectivity is not an afterthought but a core design principle. To understand what this means, consider how today’s dApps are typically assembled. A typical RWA dApp stack involves: - Oracle providers (Chainlink, Pyth, Band) for asset pricing and event settlement - Bridges (Wormhole, Multichain, custodians) for cross-chain asset movement - Indexers (The Graph, Aleph, Stacks API) for querying and preprocessing chain data - Schedulers/relayers for automated tasks and monitoring - Web2 integrations via cloud services, centralized APIs, and off-chain pipelines Each of these steps adds another vendor, another trust boundary, and another operational layer to monitor. By the time the application is live, it resembles a patchwork of loosely coupled services, each carrying its own risks. You don’t have to look far for proof: - Base went dark for 29-43 minutes in August 2025 when its sequencer misfired, freezing every DeFi app on it. - A few months earlier, an AWS outage rippled through Binance and KuCoin, stalling withdrawals because even “decentralized” systems leaned on centralized middleware. - When Infura has faltered, Ethereum dApps have gone offline in sync, not because Ethereum broke, but because the middleware holding it together did. What should feel like building an application instead feels like maintaining a fragile machine. Rialo architecture embeds the primitives that normally live in middleware directly into the protocol. Smart contracts on Rialo can: - be event-driven, able to respond not just to blockchain state changes but also to external events through built-in webhook and API triggers. - fetch data from the web natively, without relying on external oracles or relayers. - include privacy and identity management—KYC hooks and two-factor authentication, at the protocol level rather than as add-ons. - handle cross-chain communication without wrapped assets or third-party bridges. - run on a virtual machine that is compatible with ecosystems like Solana but extended with RISC-V to support modern programming concepts such as async/await and event loops. If these features work as intended, the implications are significant. Today, much of a team’s energy goes into building and maintaining infrastructure: fullnodes, indexers, monitoring scripts, oracle integrations, relayer logic, bridge infrastructure. Each requires engineering headcount and ongoing maintenance. With Rialo, much of this is absorbed by the protocol, freeing developers to concentrate on business logic. Projects can deliver production-grade dApps with smaller, leaner groups focused directly on product design and execution. Operational costs also shrink: indexing and oracle services can run into thousands of dollars a month; collapsing those into built-in functions reduces recurring expenses while simplifying onboarding for new developers. But folding middleware into the chain doesn’t erase complexity, it reshapes it. Some of the problems to be encountered include: - Scale and complexity: Rialo’s validators won’t just be securing transactions; they’ll also be securing APIs, cross-chain data, and scheduled triggers. Any failure in one subsystem could ripple across the entire network. - Performance vs. decentralization: Richer indexing, scheduling, and data ingress could make nodes heavier to run, narrowing who can realistically participate as a validator. That risks reducing the decentralization blockchains depend on for resilience. - Governance pressures: Disputes or failures involving real-world data feeds, external APIs, or cross-chain actions will arise more often, requiring not just technical fixes but robust social infrastructure, clear rules for voting, transparent arbitration, and mechanisms for community trust. Without them, Rialo risks re-centralizing decision-making around a handful of operators. Where, then, does this model make the most sense? That would be in sectors where external connectivity is indispensable and middleware bloat has consistently been a blocker: - Real-world assets: settling tokenized securities or commodities against off-chain events. - Supply chains: triggering a payment the moment a shipment clears customs, without relying on a third-party oracle. - Agent systems: AI agents interacting with real-world APIs and on-chain contracts simultaneously. - Real-time markets: prediction markets or insurance contracts that must resolve immediately against external data. For purely on-chain domains like DeFi primitives or NFTs, where composability matters more than external triggers, the advantages may be less pronounced. This shift is familiar to anyone who remembers the rise of Web2 platform services. Just as Heroku and Firebase abstracted away server maintenance so developers could focus on building products, Rialo is betting that a unified real-world network can let blockchain developers do the same. Adoption will ultimately depend on: - whether its protocol primitives mature quickly, - whether the ecosystem builds out SDKs and tooling that make them usable, - whether compliance features can adapt to changing regulations, - and whether governance proves resilient under adversarial conditions. The first applications will be the test case. If they show that Rialo can replace a fragile patchwork of middleware with a secure, auditable, and cost-effective base layer, it could set a new standard for real-world connectivity in blockchains. If not, it risks simply moving complexity from one part of the stack to another. But at a minimum, Rialo has forced the question: should real-world connectivity in blockchains continue to depend on layers of external vendors, or should it be built into the chain itself? That’s the question Rialo has put on the table — and it’s why I got interested in Rialo .

In 2025, demand for blockchain applications with genuine real-world utility has collided with a technical barrier that leaves developers questioning what they can realistically build. Anyone building things like tokenized assets, supply chains, AI agents, or prediction markets still juggle a mess of middleware, and somehow end up spending more time stitching than innovating. How so? Every: - Bridges to move assets, - oracles to fetch data, - indexers to make that data searchable, - relayers and bots to keep everything on schedule— is necessary, but each layer also adds cost, latency, and new risks. The end result is an application that’s expensive to run, fragile under stress, and slower than the Web2 software it’s trying to replace. This is the problem Rialo says it wants to solve. Built by Subzero Labs and backed by $20 million from investors like Pantera Capital and Coinbase Ventures 🛡️, Rialo’s pitch is simple: instead of accepting the middleware tower as an unavoidable cost of doing business, compress it into the base chain itself. But Rialo doesn’t describe itself as another Layer 1, its very name, Rialo Isn’t a Layer One, makes that clear. The team frames it instead as a unified real-world network: a protocol rebuilt from the ground up with the assumption that external connectivity is not an afterthought but a core design principle. To understand what this means, consider how today’s dApps are typically assembled. A typical RWA dApp stack involves: - Oracle providers (Chainlink, Pyth, Band) for asset pricing and event settlement - Bridges (Wormhole, Multichain, custodians) for cross-chain asset movement - Indexers (The Graph, Aleph, Stacks API) for querying and preprocessing chain data - Schedulers/relayers for automated tasks and monitoring - Web2 integrations via cloud services, centralized APIs, and off-chain pipelines Each of these steps adds another vendor, another trust boundary, and another operational layer to monitor. By the time the application is live, it resembles a patchwork of loosely coupled services, each carrying its own risks. You don’t have to look far for proof: - Base went dark for 29-43 minutes in August 2025 when its sequencer misfired, freezing every DeFi app on it. - A few months earlier, an AWS outage rippled through Binance and KuCoin, stalling withdrawals because even “decentralized” systems leaned on centralized middleware. - When Infura has faltered, Ethereum dApps have gone offline in sync, not because Ethereum broke, but because the middleware holding it together did. What should feel like building an application instead feels like maintaining a fragile machine. Rialo architecture embeds the primitives that normally live in middleware directly into the protocol. Smart contracts on Rialo can: - be event-driven, able to respond not just to blockchain state changes but also to external events through built-in webhook and API triggers. - fetch data from the web natively, without relying on external oracles or relayers. - include privacy and identity management—KYC hooks and two-factor authentication, at the protocol level rather than as add-ons. - handle cross-chain communication without wrapped assets or third-party bridges. - run on a virtual machine that is compatible with ecosystems like Solana but extended with RISC-V to support modern programming concepts such as async/await and event loops. If these features work as intended, the implications are significant. Today, much of a team’s energy goes into building and maintaining infrastructure: fullnodes, indexers, monitoring scripts, oracle integrations, relayer logic, bridge infrastructure. Each requires engineering headcount and ongoing maintenance. With Rialo, much of this is absorbed by the protocol, freeing developers to concentrate on business logic. Projects can deliver production-grade dApps with smaller, leaner groups focused directly on product design and execution. Operational costs also shrink: indexing and oracle services can run into thousands of dollars a month; collapsing those into built-in functions reduces recurring expenses while simplifying onboarding for new developers. But folding middleware into the chain doesn’t erase complexity, it reshapes it. Some of the problems to be encountered include: - Scale and complexity: Rialo’s validators won’t just be securing transactions; they’ll also be securing APIs, cross-chain data, and scheduled triggers. Any failure in one subsystem could ripple across the entire network. - Performance vs. decentralization: Richer indexing, scheduling, and data ingress could make nodes heavier to run, narrowing who can realistically participate as a validator. That risks reducing the decentralization blockchains depend on for resilience. - Governance pressures: Disputes or failures involving real-world data feeds, external APIs, or cross-chain actions will arise more often, requiring not just technical fixes but robust social infrastructure, clear rules for voting, transparent arbitration, and mechanisms for community trust. Without them, Rialo risks re-centralizing decision-making around a handful of operators. Where, then, does this model make the most sense? That would be in sectors where external connectivity is indispensable and middleware bloat has consistently been a blocker: - Real-world assets: settling tokenized securities or commodities against off-chain events. - Supply chains: triggering a payment the moment a shipment clears customs, without relying on a third-party oracle. - Agent systems: AI agents interacting with real-world APIs and on-chain contracts simultaneously. - Real-time markets: prediction markets or insurance contracts that must resolve immediately against external data. For purely on-chain domains like DeFi primitives or NFTs, where composability matters more than external triggers, the advantages may be less pronounced. This shift is familiar to anyone who remembers the rise of Web2 platform services. Just as Heroku and Firebase abstracted away server maintenance so developers could focus on building products, Rialo is betting that a unified real-world network can let blockchain developers do the same. Adoption will ultimately depend on: - whether its protocol primitives mature quickly, - whether the ecosystem builds out SDKs and tooling that make them usable, - whether compliance features can adapt to changing regulations, - and whether governance proves resilient under adversarial conditions. The first applications will be the test case. If they show that Rialo can replace a fragile patchwork of middleware with a secure, auditable, and cost-effective base layer, it could set a new standard for real-world connectivity in blockchains. If not, it risks simply moving complexity from one part of the stack to another. But at a minimum, Rialo has forced the question: should real-world connectivity in blockchains continue to depend on layers of external vendors, or should it be built into the chain itself? That’s the question Rialo has put on the table — and it’s why I got interested in Rialo .

Jen

12,178 просмотров • 9 месяцев назад

CoinMarketCap AI Is Live: What Does It Really Change ? 🌱 In the fast paced world of crypto, information is power but its often scattered, delayed, or hard to trust. CoinMarketCap newly launched CMC AI aims to fix that by offering real time insights with no friction. ✨ Real Time Q&A on Coin Pages 🌱CMC AI is now integrated into major coin detail pages, generating automatic Q&As every 30 minutes. During periods of volatility, it updates dynamically, helping users understand price movements with short and structured explanations. No login required, no delays. 🌱However, while this speeds up the process, its not a substitute for deeper analysis. It answers the “what” and “why,” but not always the “what’s next.” ✨What’s Coming Next? 🌱CMC AI is just getting started. According to its roadmap, several new features are on the way • Homepage Integration: A quick view of market trends and opportunities, without clicking into individual coins. • Live Chart Analysis: AI will add context to price moves by linking them to news, sentiment, and social media. • Token Comparison Tool: Users will be able to compare tokens like BTC vs SOL across utility, performance, and tech specs. • Portfolio Insights: One click portfolio analysis with rebalancing suggestions and market outlooks. • Cross Device Continuity: Start an AI conversation on desktop and continue it seamlessly on mobile. ✨A Tool Not a Strategy 🌱 CMC AI brings speed and clarity, two things crypto investors often lack. But it’s still just a tool. It won’t make decisions for you. It helps guide your thinking not replace it. 🌱 The smartest way to use it? Treat it as a compass, not a map. It can point you in the right direction, but the journey is still yours. 🌱 CMC AI represents a step forward in how users interact with crypto data. It filters the noise, shortens research time, and brings useful context closer to the user. But like any shortcut, it works best when you already understand the long route.

CoinMarketCap AI Is Live: What Does It Really Change ? 🌱 In the fast paced world of crypto, information is power but its often scattered, delayed, or hard to trust. CoinMarketCap newly launched CMC AI aims to fix that by offering real time insights with no friction. ✨ Real Time Q&A on Coin Pages 🌱CMC AI is now integrated into major coin detail pages, generating automatic Q&As every 30 minutes. During periods of volatility, it updates dynamically, helping users understand price movements with short and structured explanations. No login required, no delays. 🌱However, while this speeds up the process, its not a substitute for deeper analysis. It answers the “what” and “why,” but not always the “what’s next.” ✨What’s Coming Next? 🌱CMC AI is just getting started. According to its roadmap, several new features are on the way • Homepage Integration: A quick view of market trends and opportunities, without clicking into individual coins. • Live Chart Analysis: AI will add context to price moves by linking them to news, sentiment, and social media. • Token Comparison Tool: Users will be able to compare tokens like BTC vs SOL across utility, performance, and tech specs. • Portfolio Insights: One click portfolio analysis with rebalancing suggestions and market outlooks. • Cross Device Continuity: Start an AI conversation on desktop and continue it seamlessly on mobile. ✨A Tool Not a Strategy 🌱 CMC AI brings speed and clarity, two things crypto investors often lack. But it’s still just a tool. It won’t make decisions for you. It helps guide your thinking not replace it. 🌱 The smartest way to use it? Treat it as a compass, not a map. It can point you in the right direction, but the journey is still yours. 🌱 CMC AI represents a step forward in how users interact with crypto data. It filters the noise, shortens research time, and brings useful context closer to the user. But like any shortcut, it works best when you already understand the long route.

Loji

37,459 просмотров • 1 год назад

Cal AI might be the most viral health app this year. 8M+ downloads, projected to do $30M revenue this year. Built by two teenagers. Everyone's using it. But nobody's talking about the fact that their AI is completely broken... To the point where users are manually correcting EVERY meal. • Bowl of grapes: 60 cal estimate (actually ~260) • 4 boiled eggs: 1,010 cal estimate (actually ~300) • Meat portions: consistently off by 50% These aren't my words, but the reviews you can see for yourself on App Store and other places. If the whole USP is saving time vs manual logging, and you still have to correct everything... what's the point? What Cal AI did get right: Distribution. • Viral TikTok content • Smart influencer partnerships • 8M downloads in under a year They absolutely crushed GTM. But the product doesn't work lol. The gap between the "90% accuracy" claim vs the actual user experience kills trust. And in health apps, trust is everything. This is the problem with AI apps across the board right now. Everyone's racing to ship fast and go viral. Nobody's asking: "Does this actually solve the problem?" Distribution > product quality is a losing game. As a result, you're bound to encounter problems: 1. Training data doesn't match real-world variety 2. No depth sensing for portion size 3. Poor training data on homemade meals 4. Zero context (is that chicken grilled or fried?) You only get fast & inaccurate answers. This is why, when I started building my own recipe app, I looked at Cal and other AI nutrition apps and noticed that being accurate was the biggest factor. Here's how we're building Nonna differently: ✓ Multi-model AI (different models for different foods) ✓ User feedback loop to improve estimates ✓ Manual override that actually trains the system ✓ Ship when it works, not when it's "good enough" If the AI can't nail it, we're not shipping it. But accuracy alone is boring. So we're also adding some additional features that make you want to use it daily: • Fridge Story: shareable infographic of your fridge contents • Mystery Ingredient: weekly cooking challenges • Cuisine Spin: random inspiration when you're stuck • Expectation vs Reality: before/after photo collages Tl;dr: Distribution gets downloads. Product keeps users. Cal got millions of downloads. How many people still use it daily after manually correcting every meal for a week? Viral marketing with a broken product = expensive way to disappoint people.

Cal AI might be the most viral health app this year. 8M+ downloads, projected to do $30M revenue this year. Built by two teenagers. Everyone's using it. But nobody's talking about the fact that their AI is completely broken... To the point where users are manually correcting EVERY meal. • Bowl of grapes: 60 cal estimate (actually ~260) • 4 boiled eggs: 1,010 cal estimate (actually ~300) • Meat portions: consistently off by 50% These aren't my words, but the reviews you can see for yourself on App Store and other places. If the whole USP is saving time vs manual logging, and you still have to correct everything... what's the point? What Cal AI did get right: Distribution. • Viral TikTok content • Smart influencer partnerships • 8M downloads in under a year They absolutely crushed GTM. But the product doesn't work lol. The gap between the "90% accuracy" claim vs the actual user experience kills trust. And in health apps, trust is everything. This is the problem with AI apps across the board right now. Everyone's racing to ship fast and go viral. Nobody's asking: "Does this actually solve the problem?" Distribution > product quality is a losing game. As a result, you're bound to encounter problems: 1. Training data doesn't match real-world variety 2. No depth sensing for portion size 3. Poor training data on homemade meals 4. Zero context (is that chicken grilled or fried?) You only get fast & inaccurate answers. This is why, when I started building my own recipe app, I looked at Cal and other AI nutrition apps and noticed that being accurate was the biggest factor. Here's how we're building Nonna differently: ✓ Multi-model AI (different models for different foods) ✓ User feedback loop to improve estimates ✓ Manual override that actually trains the system ✓ Ship when it works, not when it's "good enough" If the AI can't nail it, we're not shipping it. But accuracy alone is boring. So we're also adding some additional features that make you want to use it daily: • Fridge Story: shareable infographic of your fridge contents • Mystery Ingredient: weekly cooking challenges • Cuisine Spin: random inspiration when you're stuck • Expectation vs Reality: before/after photo collages Tl;dr: Distribution gets downloads. Product keeps users. Cal got millions of downloads. How many people still use it daily after manually correcting every meal for a week? Viral marketing with a broken product = expensive way to disappoint people.

Denislav Jeliazkov

37,989 просмотров • 8 месяцев назад

🚨 What If Your Entire Life… Is Just Code? In 2003, philosopher Nick Bostrom published a paper with a question so unsettling that it still echoes through science and philosophy today: What if our entire universe is actually a computer simulation? At first it sounds like science fiction. But the idea is surprisingly logical when you think about the direction technology is heading. Look at how far we have already come. Just a few decades ago, computers could barely display simple graphics. Today, video games create entire worlds filled with cities, forests, weather, and characters that react to our actions. Virtual reality can make our brains feel as if we are standing somewhere else entirely. Now imagine technology thousands—or even millions—of years in the future. A civilization that advanced might possess computers powerful enough to simulate entire planets… entire histories… even entire universes. Inside those simulations could exist conscious beings who believe their world is real. Beings just like us. Bostrom suggested something called “ancestor simulations.” The idea is simple but chilling. Advanced civilizations might run simulations of their past to study history or understand how their species evolved. These simulations would contain billions of simulated people living normal lives, completely unaware that their reality is artificial. If a single advanced civilization created thousands or millions of such simulations, then the number of simulated minds would become vastly greater than the number of real biological minds. And this leads to a disturbing possibility. Statistically speaking, a randomly existing mind would be far more likely to be inside a simulation than in the original reality. In other words… the odds might not be in our favor. Think about your daily life for a moment. The sky above you, the ground beneath your feet, every star in the night sky, every memory you have ever experienced—what if all of it is simply information being processed somewhere else? What if reality itself is being rendered like a giant cosmic video game? Some scientists have even wondered whether strange features of the universe could hint at something deeper. Why do the laws of physics follow precise mathematical rules? Why does the universe appear almost perfectly tuned for life? And why does space and time seem to have limits at the smallest measurable scales? To some thinkers, these questions sound eerily similar to the rules of a programmed system. Of course, none of this proves we live inside a simulation. Many scientists remain skeptical, arguing that simulating an entire universe would require unimaginable amounts of energy and computing power. Others point out that the idea may be impossible to test. But the mystery remains. If advanced civilizations somewhere in the cosmos eventually develop the ability to simulate conscious beings—and if they choose to run these simulations—then billions or trillions of simulated worlds could exist. And in a universe filled with simulations, the most unsettling question of all appears: How do we know ours is the original one? Right now, you are reading these words, feeling the world around you, believing this moment is real. But somewhere, far beyond our understanding, there might be a machine quietly running the code of an entire universe… including you.

🚨 What If Your Entire Life… Is Just Code? In 2003, philosopher Nick Bostrom published a paper with a question so unsettling that it still echoes through science and philosophy today: What if our entire universe is actually a computer simulation? At first it sounds like science fiction. But the idea is surprisingly logical when you think about the direction technology is heading. Look at how far we have already come. Just a few decades ago, computers could barely display simple graphics. Today, video games create entire worlds filled with cities, forests, weather, and characters that react to our actions. Virtual reality can make our brains feel as if we are standing somewhere else entirely. Now imagine technology thousands—or even millions—of years in the future. A civilization that advanced might possess computers powerful enough to simulate entire planets… entire histories… even entire universes. Inside those simulations could exist conscious beings who believe their world is real. Beings just like us. Bostrom suggested something called “ancestor simulations.” The idea is simple but chilling. Advanced civilizations might run simulations of their past to study history or understand how their species evolved. These simulations would contain billions of simulated people living normal lives, completely unaware that their reality is artificial. If a single advanced civilization created thousands or millions of such simulations, then the number of simulated minds would become vastly greater than the number of real biological minds. And this leads to a disturbing possibility. Statistically speaking, a randomly existing mind would be far more likely to be inside a simulation than in the original reality. In other words… the odds might not be in our favor. Think about your daily life for a moment. The sky above you, the ground beneath your feet, every star in the night sky, every memory you have ever experienced—what if all of it is simply information being processed somewhere else? What if reality itself is being rendered like a giant cosmic video game? Some scientists have even wondered whether strange features of the universe could hint at something deeper. Why do the laws of physics follow precise mathematical rules? Why does the universe appear almost perfectly tuned for life? And why does space and time seem to have limits at the smallest measurable scales? To some thinkers, these questions sound eerily similar to the rules of a programmed system. Of course, none of this proves we live inside a simulation. Many scientists remain skeptical, arguing that simulating an entire universe would require unimaginable amounts of energy and computing power. Others point out that the idea may be impossible to test. But the mystery remains. If advanced civilizations somewhere in the cosmos eventually develop the ability to simulate conscious beings—and if they choose to run these simulations—then billions or trillions of simulated worlds could exist. And in a universe filled with simulations, the most unsettling question of all appears: How do we know ours is the original one? Right now, you are reading these words, feeling the world around you, believing this moment is real. But somewhere, far beyond our understanding, there might be a machine quietly running the code of an entire universe… including you.

Astronomy Vibes

12,290 просмотров • 4 месяцев назад

There has been a lot of hand wringing on the appropriate valuation of SpaceX. Some large institutions believe SpaceX can only be valued at half what the market seems to be willing to pay for it. Others are claiming it has 15X appreciation ahead of it. Almost all of this difference of opinion comes down to how comfortable you are modeling beyond 2030 and what valuation method you use. 2030 valuation using a traditional Gordan DCF produces a very different result than a 2040 EV/EBITDA Multiple. Both have pros and cons. Most analysts don’t really discuss this and lead with a headline number. We are very comfortable modeling out to 2040, as large portions of what SpaceX is proposing is real world infrastructure, which provides modelable physics constraints to anchor against. The analysis we released today explores this in-depth, its open to the public all the way through IPO. I highly encourage you check it out prior to then. We’ve run 5,000 monte carlo runs across 500 variables (real number, even though it sounds fake) and three valuation methods. This video is of a 3D cloud chart showing every simulation outcome expected in valuation output across two of the most impactful variables to the model when using an EV/EBITDA multiple from 2026 to 2040. The horizontal axis is the steepness of the orbital data center demand S-curve. The vertical axis is the rate at which chip compute efficiency becomes cheaper. Each of the 5,000 dots is one simulated future; green dots are the ones where SpaceX's 2040 value clears the $1.77T IPO line, over time. Under EV/EBITDA valuation through 2040, 96% of our simulated futures clear the expected IPO price once the bell rings Friday. We aren’t publishing this publicly to tell investors what the stock is worth, we’re publishing this to help investors understand the world of outcomes, what the fundamentals suggest through 2040, and what frankly most analysis simply won’t share. SpaceX is a generational company working on long term infrastructure harnessing a domain no one has been able to tap in so far: space. It deserves doing the work as an investor. because this in not financial advice. The cleanest way to hold SpaceX is a bond stapled to a call option (AI-Compute); Starlink is the bond, the near term SatCom annuity that funds the next flywheel. Understand the world of outcomes and take your position accordingly. Comparables and P/E won't take you far enough.

There has been a lot of hand wringing on the appropriate valuation of SpaceX. Some large institutions believe SpaceX can only be valued at half what the market seems to be willing to pay for it. Others are claiming it has 15X appreciation ahead of it. Almost all of this difference of opinion comes down to how comfortable you are modeling beyond 2030 and what valuation method you use. 2030 valuation using a traditional Gordan DCF produces a very different result than a 2040 EV/EBITDA Multiple. Both have pros and cons. Most analysts don’t really discuss this and lead with a headline number. We are very comfortable modeling out to 2040, as large portions of what SpaceX is proposing is real world infrastructure, which provides modelable physics constraints to anchor against. The analysis we released today explores this in-depth, its open to the public all the way through IPO. I highly encourage you check it out prior to then. We’ve run 5,000 monte carlo runs across 500 variables (real number, even though it sounds fake) and three valuation methods. This video is of a 3D cloud chart showing every simulation outcome expected in valuation output across two of the most impactful variables to the model when using an EV/EBITDA multiple from 2026 to 2040. The horizontal axis is the steepness of the orbital data center demand S-curve. The vertical axis is the rate at which chip compute efficiency becomes cheaper. Each of the 5,000 dots is one simulated future; green dots are the ones where SpaceX's 2040 value clears the $1.77T IPO line, over time. Under EV/EBITDA valuation through 2040, 96% of our simulated futures clear the expected IPO price once the bell rings Friday. We aren’t publishing this publicly to tell investors what the stock is worth, we’re publishing this to help investors understand the world of outcomes, what the fundamentals suggest through 2040, and what frankly most analysis simply won’t share. SpaceX is a generational company working on long term infrastructure harnessing a domain no one has been able to tap in so far: space. It deserves doing the work as an investor. because this in not financial advice. The cleanest way to hold SpaceX is a bond stapled to a call option (AI-Compute); Starlink is the bond, the near term SatCom annuity that funds the next flywheel. Understand the world of outcomes and take your position accordingly. Comparables and P/E won't take you far enough.

Aaron Burnett

1,511,431 просмотров • 1 месяц назад

Heres an actual way to make $10k a month from TikTok + organic affiliate TT slides is probably the best way to make AI content for a few reasons > easy and not time consuming to make > much harder to detect images are AI > slideshows rarely get the AI label from TT (especially if you do what I’m gonna show you) >slideshows require less engagement to go viral (more consistent virality) >CTA can be more natural this AI slideshow format is going insanely viral consistently on brand new accounts and no one is even detecting it’s AI they’re super easy to make and use a story telling format that’s really smart you can take the exact same formula to promote sweeps offers from Glitchy and make $10k a month pretty easily here’s the blueprint >Content creation to create the slides realistic you can simply take a photo from Pinterest and put it into Gemini or Chat GPT and ask it to give you an EXACT JSON to recreate the image add in any details you want to add like “make her hair blonde” “make her eyes blue” take that JSON and put it into Nana Banana If you want her in certain backgrounds do the same process and add it in with the JSON of the girl you’ve created or just describe it Now clear the meta data from said image to stop getting the AI label If you still get it go to a meta data analysis site and put the data in ChatGPT Ask if their is anything in the meta data that is signalling this to TT >Writing scripts The reason these go so well it’s because they have a negative scroll stopping hook that instantly make you want to know what the slides going to say next “got fired from my job” “failed my exams” Along with the music that sets the emotion of the video It wouldn’t work as well if they had a random viral song that’s up beat plus the cherry on top is that the image correlates with what’s being said in the hook it’s self acting as a visual hook It wouldn’t work aswell if it was just an image of a girl on her bedroom >how to interpret sweeps Choose an sweeps offer from Glitchy for a retail store (Walmart/target) follow the same format of scroll stopping negative hook Example: “broke my arm” then you would tell a story that paints a bad working environment, maybe she got fired for breaking her arm and is now exposing secrets and your CTA would then be “they don’t promote this but they have a secret feedback program” this is just to give you inspiration but there is literally countless ways >account set up - US proxy / US sim - Download TT with US proxy on - Buy aged account (to help with account trust and getting banned due to proxy issues) - warm up for 2 days (scroll vids all the way through, like, comment authentic things relating to video) Then you post This is a very good way to at least reach a couple K a month but I’d be surprised if you don’t reach $10k beyond

Pounds

10,886 просмотров • 5 месяцев назад

Managed to get out of Dubai. The following is my personal opinion & experience 🇦🇪 Everything went quite seamless. Careem cabs operate normally. The airport is extremely well organized, considering the circumstances. The world has changed since Saturday and unfortunately, the country I moved to 3 years ago has been under heavy attacks. Why? No one really knows. However, I find it extremely disturbing that most media - including our beloved CT - exploits that serious situation to take cheap shots at Dubai and people who moved here. There are real families and hard working people affected. Most are scared or feel extremely uncomfortable. It's a traumatizing experience to hear missile interceptions right above your house. Nothing I wish for any human to ever experience, no matter if you pay 0% or 50% taxes. I don't even know what the link between those two things should be?? If anything, it proves that the UAE cares about their people and is able to protect its residents. Even at 0% income tax. Dubai was not known for it's strong military. It was always portrayed to be bling-bling golden steaks and influencers. But the UAE has shown the entire world that it can defend 1,000+ drones and 200+ missiles from a direct neighbor. That is impressive, whether you love or hate the UAE. Many countries would likely not be able to absorb that. So, if anything: Dubai and the UAE have demonstrated that the system works. I have lived in multiple countries around the world and the UAE is by far the most capable of handling crisis. Why did I leave Dubai then? Because it's not a situation I want to be in. Missile interceptions and drone explosions are no joke. Besides the danger that debris could hit your roof, there's an incredible intensity in the air. Most describe it as "intense calmness". While the Government managed to keep everyone from panicking, it's impossible for everyone to stay fully calm. And you can feel that in the day to day. The first night I got virtually no sleep. The night of my departure neither. I haven't left my house at all after the attacks started. But I'm out now. I really hope that leaving the country wasn't necessary, and that I will be able to come back to Dubai as soon as possible. And no, I'm not paid to say this, nor did I sign anything. Maybe part of the truth why no one who lives in Dubai has bad things to say about it is because it's one of the rare places on earth where people actually trust the Government and respect them? Not only for creating wealth and understanding business, but for caring and communicating in a way most other countries are simply incapable of. Anyways, I guess the point of this post is to 1) Tell everyone in Dubai that it's possible to leave right now. I know many people are very scared and want to get out. Be persistent with the airline if they cancel your flight and push hard to get rebooked on the next available one. And maybe choose a destination that isn't Europe, considering most of these flights are overbooked already and impossible to get on. 2) Dubai's self defense is impressive and we should acknowledge that. At least this one time, let's not derail into irrelevant discussions but show respect to the leaders + express empathy for those affected. 3) As an expat living in Dubai, I can still feel proud of my home country and do not have to choose. This isn't a black and white situation. Dubai is a melting pot of cultures and great place for Business. Other countries have better nature, social life or other things. It's also up to individual preference. 4) If you hate Dubai, yalla you don't need to live here, visit nor speak about it. Enjoy the place you live in and focus on your own. No need to feel triggered and take cheap shots at hard working people and families that are currently under heavy missile attacks. One final thing that really became clear to me this week is that after all, safety, health and living in freedom are absolutely priceless. May this all be over with soon and for sustaining peace to come in 🤲🏼

Managed to get out of Dubai. The following is my personal opinion & experience 🇦🇪 Everything went quite seamless. Careem cabs operate normally. The airport is extremely well organized, considering the circumstances. The world has changed since Saturday and unfortunately, the country I moved to 3 years ago has been under heavy attacks. Why? No one really knows. However, I find it extremely disturbing that most media - including our beloved CT - exploits that serious situation to take cheap shots at Dubai and people who moved here. There are real families and hard working people affected. Most are scared or feel extremely uncomfortable. It's a traumatizing experience to hear missile interceptions right above your house. Nothing I wish for any human to ever experience, no matter if you pay 0% or 50% taxes. I don't even know what the link between those two things should be?? If anything, it proves that the UAE cares about their people and is able to protect its residents. Even at 0% income tax. Dubai was not known for it's strong military. It was always portrayed to be bling-bling golden steaks and influencers. But the UAE has shown the entire world that it can defend 1,000+ drones and 200+ missiles from a direct neighbor. That is impressive, whether you love or hate the UAE. Many countries would likely not be able to absorb that. So, if anything: Dubai and the UAE have demonstrated that the system works. I have lived in multiple countries around the world and the UAE is by far the most capable of handling crisis. Why did I leave Dubai then? Because it's not a situation I want to be in. Missile interceptions and drone explosions are no joke. Besides the danger that debris could hit your roof, there's an incredible intensity in the air. Most describe it as "intense calmness". While the Government managed to keep everyone from panicking, it's impossible for everyone to stay fully calm. And you can feel that in the day to day. The first night I got virtually no sleep. The night of my departure neither. I haven't left my house at all after the attacks started. But I'm out now. I really hope that leaving the country wasn't necessary, and that I will be able to come back to Dubai as soon as possible. And no, I'm not paid to say this, nor did I sign anything. Maybe part of the truth why no one who lives in Dubai has bad things to say about it is because it's one of the rare places on earth where people actually trust the Government and respect them? Not only for creating wealth and understanding business, but for caring and communicating in a way most other countries are simply incapable of. Anyways, I guess the point of this post is to 1) Tell everyone in Dubai that it's possible to leave right now. I know many people are very scared and want to get out. Be persistent with the airline if they cancel your flight and push hard to get rebooked on the next available one. And maybe choose a destination that isn't Europe, considering most of these flights are overbooked already and impossible to get on. 2) Dubai's self defense is impressive and we should acknowledge that. At least this one time, let's not derail into irrelevant discussions but show respect to the leaders + express empathy for those affected. 3) As an expat living in Dubai, I can still feel proud of my home country and do not have to choose. This isn't a black and white situation. Dubai is a melting pot of cultures and great place for Business. Other countries have better nature, social life or other things. It's also up to individual preference. 4) If you hate Dubai, yalla you don't need to live here, visit nor speak about it. Enjoy the place you live in and focus on your own. No need to feel triggered and take cheap shots at hard working people and families that are currently under heavy missile attacks. One final thing that really became clear to me this week is that after all, safety, health and living in freedom are absolutely priceless. May this all be over with soon and for sustaining peace to come in 🤲🏼

Cito

1,392,144 просмотров • 4 месяцев назад

Wow. WOW. WOOOOOOOW. So um, that first Wildcard+Thousands stream was... amazing and also... a lot 😅 In the end, it was exactly what we were hoping for - a true stress test of ALL these systems coming together for the first time. We are SO grateful for the thousands of people who showed up today to play, attend, tune-in and help us PLAYTEST all this new stuff. We can't wait to see you all again at NEXT WEEK'S EX2 EVENT! So, now let's talk about how it went... Stuff that worked: - Our community SHOWED UP. Oh boy did you show up 😅. Our servers were straining under the load... which is good actually, in fact it's the whole point. Even more importantly, we have already received insanely valuable feedback, bug reports, stuff people loved/hated - and it's only been a few hours since the stream ended. I can't even explain to y'all how valuable this process is. Yes it's stressful, it reminds me of trying to keep Words With Friends online during that first insane year, but it's EXACTLY what we were hoping for (NEED) to turn this into the polished, top-notch game and streaming experience we are on a mission to deliver. I truly can't thank y'all enough, and hope to see you again when we run it all back again next week 🥹 - The stream itself stayed up and was mostly stable! Phew 😅. For context, ThousandsTV is not a twitch wrapper, it's a web3-native streaming tech stack built we built specifically to connect game, web/mobile, and blockchain together all at the same time. There are a LOT of moving pieces going on behind the scenes. - We brought viewers INTO THE GAME! Viewers showed up in the stands of the arena, with connected wallets/assets, triggered actions/rallies from chat, and were seen and heard during the whole stream. - The brand new 2v2 build of Wildcard was (mostly) stable and our players and viewers seemed to be having a blast down on the field and up in the stands. It was thrilling to watch Team Blue dominate, even though Team Red held their own in game 3! - Our production crew did an insanely good job running the stream, managing the players, shoutcasters, and talent, and producing a top-notch show. Of course we will work hard to make every stream better than the last, but I was super proud of how our team "rolled with the punches" during today's event. Stuff that didn't work (and/or needs to be dramatically improved): - Although it's fun to see chat going crazy, chat spam is actually something we are passionate to FIX. As you can see from the attached video, chat spam dominated today's stream and made it impossible for anyone to even see anyone else's messages. We have some GREAT ideas for how to fix this and actually turn chat spam into a FUN and exciting and not annoying thing - but those improvements didn't get shipped in time for this event. - Credits purchasing flow needs a LOT of work. As I'm sure y'all know, bringing money on chain is pretty complicated, and although we've been working hard to make this as seamless as possible, it still needs a TON of improvements. Many users who WANTED to spend money today weren't able to and/or ran into frustrating bugs in the credit purchase flow. Fixing this is obviously a top priority for our team. - Rallies need a LOT of work. Spectator-interactive features like rallies are at the heart of our vision for Wildcard. These "stream apps", as we call them, are the UNLOCK for how spectators, viewers and fans directly connect and interact with their favorite competitors, content creators, and communities. The current rally feature HINTS at this potential, but it needs to be WAY easier to understand, use, and have fun with. Improvements are ON THE WAY. - Referees were only partially working. Referees are a key innovation of the Thousands platform. They are AI-driven "personalities" (NPCs) that pay attention to everything that's happening in the arena, both on the field and especially in the stands (i.e. in chat, during rallies, etc.) The referees then make "calls" at the end of every match, rewarding users for their engagement and participation. Unfortunately, the referees weren't fully functional and seemed to drop the ball on recognizing everyone's contributions (especially people who showed up holding valuable assets such as Wildpasses in their wallets, and people who boosted those rallies with credits.) What's happening next: 1. We are combing through ALL the logs from the event right now, to make sure we don't miss a SINGLE action that our viewers and fans took during the event, including what they brought in their wallets (i.e. Wildpass holders!), any credits that were purchased, rallies that users engaged with, etc. This information is normally processed by our referees, who then determine dynamically how they're going to distribute $WC awards. We were originally hoping to complete this process and the subsequent airdrops within a few hours after the event, but given the amount of data, we need a bit more time to run these scripts (and airdrops) in batches instead of all at once, and make sure ALL of the data is being included. IMPORTANT: I will keep y'all updated in real-time here on twitter/X as this process is ongoing, and let you know the moment it's complete and all the awards have been distributed (i.e. when to go check your wallets 😎) 2. PLEASE keep sending us your feedback and bug reports. Open a ticket on our Discord and let us know what you loved, what you hated, and especially what we need to FIX. Given the overwhelming response to this event, it will likely take us several days to process everyone's feedback and fix all the bugs, but we WILL NOT REST until every ticket is closed/resolved. Thank you in advance for your patience. 3. We turn it up another notch next week. As our dear friends Wolves DAO just announced, the Wildcard Exhibition Event #2 is streaming LIVE from the WOLVES DEN AT GDC next Friday! If you missed out on all the action today, DON'T WORRY, because as I keep saying: we are just getting warmed up (and there is a LOT more b that needs to be distributed, get what I'm sayin??? 😎) Finally: Just wanted to say THANK YOU, again. Truly, from the bottom of my (our) hearts. Your excitement and enthusiasm for what we're building is why we do this. Even (especially, in fact) when you tell us all the things you want us to improve. We thrive off this feedback, it's how this game and this platform go from good to GREAT. I am so grateful for those of you who are taking this journey with us. SEE YA NEXT WEEK!!!

WildPaul - BEAST MODE

26,851 просмотров • 1 год назад

$ASTS My Massive Misunderstanding About BlueBird 6 Mea Culpa: I thought BlueBird 6 unfolded immediately. I was completely wrong. Here is the REAL timeline. 🛰️🤯 Like many excited investors, when I saw the notification that AST SpaceMobile's BlueBird 6 (BB6) successfully reached orbit, I imagined an immediate, dramatic transformation. I thought: "Rocket separates, boom, giant antenna pops open, let's start beaming 5G!" I couldn't have been more wrong. I just got a crash course in orbital mechanics, and here is my "aha!" moment. Unfolding a 2,400 sq ft array (the size of a tennis court!) immediately after ejection from a rocket is suicidal for a satellite. It would be like trying to open a massive parachute while tumbling out of a car on the highway. It would rip apart or spin out of control. The Reality: The "Space Origami" Wait The process is much slower, more deliberate, and nerve-wracking. Here is the actual, realistic timeline we need to watch over the next few months: Phase 1: The Deep Breath (Current Status - Next ~7 Days)Right now, BB6 is likely stabilizing its tumble, deploying solar panels for power, and undergoing full system "health checks." Ground control is ensuring the launch didn't break anything. No unfolding happens yet. Phase 2: The Great Unfold (Estimated Week 2-3)Once stable, the "space origami" begins. This is the most critical physical step. The massive array will slowly, methodically unfold and lock into place over several days. We wait for official confirmation that the array is fully deployed and rigid. Phase 3: Lighting Up the ASIC (Q1 2026)Only after a perfect unfold do they turn on the revolutionary AST5000 ASIC chips. The next few months are dedicated to end-to-end connectivity tests with partners like AT&T and Verizon. We are looking for data on real-world speeds (120 Mbps target) and stability. Space is hard, and patience is a requirement. The launch was just the commute to work; now the real job begins. 😊There is too much misinformation out there on $ASTS. If this thread helped clarify the reality, please RT to help others find quality due diligence #ASTS #ASTSpaceMobile #BlueBird6 #SpaceTech #OrbitalMechanics #SatelliteDeployment #5G #Telecom #Investing #LearningCurve #ASIC

$ASTS My Massive Misunderstanding About BlueBird 6 Mea Culpa: I thought BlueBird 6 unfolded immediately. I was completely wrong. Here is the REAL timeline. 🛰️🤯 Like many excited investors, when I saw the notification that AST SpaceMobile's BlueBird 6 (BB6) successfully reached orbit, I imagined an immediate, dramatic transformation. I thought: "Rocket separates, boom, giant antenna pops open, let's start beaming 5G!" I couldn't have been more wrong. I just got a crash course in orbital mechanics, and here is my "aha!" moment. Unfolding a 2,400 sq ft array (the size of a tennis court!) immediately after ejection from a rocket is suicidal for a satellite. It would be like trying to open a massive parachute while tumbling out of a car on the highway. It would rip apart or spin out of control. The Reality: The "Space Origami" Wait The process is much slower, more deliberate, and nerve-wracking. Here is the actual, realistic timeline we need to watch over the next few months: Phase 1: The Deep Breath (Current Status - Next ~7 Days)Right now, BB6 is likely stabilizing its tumble, deploying solar panels for power, and undergoing full system "health checks." Ground control is ensuring the launch didn't break anything. No unfolding happens yet. Phase 2: The Great Unfold (Estimated Week 2-3)Once stable, the "space origami" begins. This is the most critical physical step. The massive array will slowly, methodically unfold and lock into place over several days. We wait for official confirmation that the array is fully deployed and rigid. Phase 3: Lighting Up the ASIC (Q1 2026)Only after a perfect unfold do they turn on the revolutionary AST5000 ASIC chips. The next few months are dedicated to end-to-end connectivity tests with partners like AT&T and Verizon. We are looking for data on real-world speeds (120 Mbps target) and stability. Space is hard, and patience is a requirement. The launch was just the commute to work; now the real job begins. 😊There is too much misinformation out there on $ASTS. If this thread helped clarify the reality, please RT to help others find quality due diligence #ASTS #ASTSpaceMobile #BlueBird6 #SpaceTech #OrbitalMechanics #SatelliteDeployment #5G #Telecom #Investing #LearningCurve #ASIC

Grey

62,349 просмотров • 6 месяцев назад