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Digital twins make deployments! 🔄 Digital twins are more than just simulation, they let you test and fix everything before touching real hardware. The biggest win is catching problems early. You can test the complete system virtually, including all the control logic and data flows. Finding a bug in...

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Lukas Ziegler

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27,709 просмотров • 6 месяцев назад •via X (Twitter)

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When robots take the night shift shopping spree! 🛍️ Robots navigate through dm-drogerie markt Deutschland stores at night to create a digital replica of the store's layout, known as a "digital twin." Developed Ubica Robotics GmbH, these autonomous robots scan shelves to provide real-time information about item positions, pricing, stock gaps, and store layouts. 🏪 This data serves multiple purposes, such as improving staff routes, enhancing inventory management, and informing the creation of planograms for more efficient store layouts. It combines digital twin with robotics and it's really cool use case. What are your thoughts? ~~ ♻️ Join the weekly robotics newsletter, and never miss any news →
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When robots take the night shift shopping spree! 🛍️ Robots navigate through dm-drogerie markt Deutschland stores at night to create a digital replica of the store's layout, known as a "digital twin." Developed Ubica Robotics GmbH, these autonomous robots scan shelves to provide real-time information about item positions, pricing, stock gaps, and store layouts. 🏪 This data serves multiple purposes, such as improving staff routes, enhancing inventory management, and informing the creation of planograms for more efficient store layouts. It combines digital twin with robotics and it's really cool use case. What are your thoughts? ~~ ♻️ Join the weekly robotics newsletter, and never miss any news →

Lukas Ziegler

65,006 просмотров • 6 месяцев назад

AI-Powered weed control! 🌱 The LaserWeeder machine from Carbon Robotics has captured the imagination of American farmers. This technology uses AI system to identify weeds in crops and zap them with precision thermal bursts from lasers. Bit of facts about the cool robot: → The machine can remove weeds from over 40 crops and can also be used for thinning crops. → It can operate in virtually all weather conditions, with millimeter accuracy at all times, and can work through the night thanks to its built-in lighting system. → High-resolution cameras and computer machine learning enable it to distinguish weeds from crops in milliseconds. → The LaserWeeder can replace about 70 workers on farms where manual weeding is used, and can weed up to four acres per hour. What other applications can we expect to see in the future in farming applications? Btw. I believe farming robots are A HUGE THING in robotics! 🔥 ~~ ♻ Join the weekly robotics newsletter, and never miss any news →
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AI-Powered weed control! 🌱 The LaserWeeder machine from Carbon Robotics has captured the imagination of American farmers. This technology uses AI system to identify weeds in crops and zap them with precision thermal bursts from lasers. Bit of facts about the cool robot: → The machine can remove weeds from over 40 crops and can also be used for thinning crops. → It can operate in virtually all weather conditions, with millimeter accuracy at all times, and can work through the night thanks to its built-in lighting system. → High-resolution cameras and computer machine learning enable it to distinguish weeds from crops in milliseconds. → The LaserWeeder can replace about 70 workers on farms where manual weeding is used, and can weed up to four acres per hour. What other applications can we expect to see in the future in farming applications? Btw. I believe farming robots are A HUGE THING in robotics! 🔥 ~~ ♻ Join the weekly robotics newsletter, and never miss any news →

Lukas Ziegler

54,776 просмотров • 8 месяцев назад

Smart Monkey Sunday: Learning the Math 🧮 A lot of people build trading bots by stacking indicators without really understanding what they measure underneath. RSI, MACD, VWAP - they all react to different things, and they all fail differently depending on market conditions. Once you start automating execution, that distinction matters much more because the system will follow the logic exactly as written. The monkey in the video is taking the more useful Sunday approach: slowing down and reviewing the math before deploying anything. That’s also why the Bot Arena includes a virtual balance. You can test ideas, break them, adjust parameters and see how the logic behaves before risking real capital. 👉 Test your logic in the Arena:
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Smart Monkey Sunday: Learning the Math 🧮 A lot of people build trading bots by stacking indicators without really understanding what they measure underneath. RSI, MACD, VWAP - they all react to different things, and they all fail differently depending on market conditions. Once you start automating execution, that distinction matters much more because the system will follow the logic exactly as written. The monkey in the video is taking the more useful Sunday approach: slowing down and reviewing the math before deploying anything. That’s also why the Bot Arena includes a virtual balance. You can test ideas, break them, adjust parameters and see how the logic behaves before risking real capital. 👉 Test your logic in the Arena:

Reactor

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Sub-40ms full self-driving on a $100 drone! 🚁 A demonstration showing a complete full-self-driving pipeline running in under 40 milliseconds on a $100 drone. The prompt: "Find the bike and land." No pre-mapping. Running real-time on commodity hardware. For context, human reaction time is around 200-250ms. This drone is processing sensor data, understanding natural language commands, identifying objects, planning motion, and executing control, all in 40ms. This is what happens when foundation models meet efficient inference. The models get smaller and faster while maintaining capability. The hardware gets cheaper while getting more powerful. The intersection makes previously impossible applications suddenly viable. A few years ago, this required thousands of dollars in compute, pre-mapped environments, and cloud connectivity. Now it runs locally on hardware that costs less than a nice dinner. Awesome stuff Chester & ! 😮‍💨 ~~ ♻️ Join the weekly robotics newsletter, and never miss any news →

Lukas Ziegler

310,622 просмотров • 6 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✌️📷
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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 • 7 месяцев назад

the system our agency uses to produce ad creatives for paying b2b clients is now written down in a free doc. it's a claude code build. one intake form takes the icp, pain, offer and cta, and it returns 100+ finished meta statics, each one a different concept with the message designed inside the artwork. the doc is the full build, not a summary: > all 20 prompts in build order, each a complete brief you paste as written > the 80-entry template and visual metaphor library, with the seed entries claude code expands for you > the fire-and-forget layer, so batches finish with the laptop closed and archive themselves before provider links expire > a worked example from a real ad copy to a folder your media buyer can upload straight to ads manager the wall in this video came off one run. the whole build takes about 2 hours. like + comment "ENGINE" and i'll dm you the doc. (following + rt for priority access)
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the system our agency uses to produce ad creatives for paying b2b clients is now written down in a free doc. it's a claude code build. one intake form takes the icp, pain, offer and cta, and it returns 100+ finished meta statics, each one a different concept with the message designed inside the artwork. the doc is the full build, not a summary: > all 20 prompts in build order, each a complete brief you paste as written > the 80-entry template and visual metaphor library, with the seed entries claude code expands for you > the fire-and-forget layer, so batches finish with the laptop closed and archive themselves before provider links expire > a worked example from a real ad copy to a folder your media buyer can upload straight to ads manager the wall in this video came off one run. the whole build takes about 2 hours. like + comment "ENGINE" and i'll dm you the doc. (following + rt for priority access)

KNOX

27,452 просмотров • 1 месяц назад

All we do is win win win!!!!!! KellyRae, the journey has been long and Intense, but you've persevered. From the early days of finding it difficult to form alliances to the back to back nominations , you've shown incredible resilience and strategic thinking. You've faced challenges head-on during tasks, formed strong bonds with your housemates, and captured the hearts of millions. Your journey has been a testament of GRACE and you've always remained true to yourself. As you stand on the brink of victory, remember all the incredible moments you've shared with us. You've made us laugh, you've made us on our toes, and you've inspired us. No matter what the outcome, you've already won our hearts. Go out there and make history! Good luck King KelllyRae, KaySquad can’t wait to congratulate you again tonight 🫶💫 KELLYRAE TAKES THE CROWN THE MONEY IS MARRIED #KellyRaeTheWinner #DoubleKayX100M #BBNaija
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All we do is win win win!!!!!! KellyRae, the journey has been long and Intense, but you've persevered. From the early days of finding it difficult to form alliances to the back to back nominations , you've shown incredible resilience and strategic thinking. You've faced challenges head-on during tasks, formed strong bonds with your housemates, and captured the hearts of millions. Your journey has been a testament of GRACE and you've always remained true to yourself. As you stand on the brink of victory, remember all the incredible moments you've shared with us. You've made us laugh, you've made us on our toes, and you've inspired us. No matter what the outcome, you've already won our hearts. Go out there and make history! Good luck King KelllyRae, KaySquad can’t wait to congratulate you again tonight 🫶💫 KELLYRAE TAKES THE CROWN THE MONEY IS MARRIED #KellyRaeTheWinner #DoubleKayX100M #BBNaija

Kellyrae

166,807 просмотров • 1 год назад

System identification (sysid) is the process of finding the physical parameters that make a simulation match reality. If you're training an RL locomotion policy in simulation, the accuracy of your motor model directly affects how well the policy transfers to the real robot. A recent git commit by Kevin Zakka added a sysid toolbox to MuJoCo which automates this process: you provide recorded motor data and a MuJoCo model, and it optimizes the model parameters to minimize the difference between simulated and real trajectories. For my RobStride Dynamics RS02 QDD motors (17 Nm peak, 7.75:1 gear), I built a Rust tool that sends multi-sine torque excitation at 1 kHz and records position/velocity feedback. I then feed this data into MuJoCo's sysid optimizer.
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System identification (sysid) is the process of finding the physical parameters that make a simulation match reality. If you're training an RL locomotion policy in simulation, the accuracy of your motor model directly affects how well the policy transfers to the real robot. A recent git commit by Kevin Zakka added a sysid toolbox to MuJoCo which automates this process: you provide recorded motor data and a MuJoCo model, and it optimizes the model parameters to minimize the difference between simulated and real trajectories. For my RobStride Dynamics RS02 QDD motors (17 Nm peak, 7.75:1 gear), I built a Rust tool that sends multi-sine torque excitation at 1 kHz and records position/velocity feedback. I then feed this data into MuJoCo's sysid optimizer.

David Bar

48,347 просмотров • 4 месяцев назад

1 Neural Network + Obsidian + Karpathy’s 1-file method = the most unhinged second brain build of 2026. It remembers everything you’ve ever done, and it costs $0 on top of what you already pay. The base is Karpathy’s append and review: 1 giant note, new thoughts stack on top, old ones sink, every few days you reread and pull the survivors back up. No folders, no tags, no plugins the rereading IS the system, because review is what turns storage into thinking. The flaw: past 10,000 lines, no human rereads anything. That’s where the neural network takes over. You keep the note in Obsidian 1 vault, everything dumps to the top: ideas, links, meeting fragments, half-thoughts. You never organize, you only dump. It all lives as plain markdown on your own disk, and that detail is the whole trick. Because now you point Claude Code at the vault folder, and it reads every line you’ve ever written. “What did I think about pricing in March.” “Find the 3 ideas I keep circling.” “What did I drop that deserves a second look.” It answers from YOUR notes, with quotes, in 15 seconds. Then once a week, 1 prompt closes the loop: read the last 7 days, surface the 5 entries worth pulling back up, flag anything that contradicts what I wrote a month ago. The model does the sinking and surfacing Karpathy did by hand, and the note stays alive instead of turning into a graveyard. Week 1 feels like nothing. Week 4 you hit the first “I already solved this in January.” Month 3 you consult your past self more than Google. Most second brains die in 11 days under 40 plugins and 200 folders. This one is 1 file and a loop, and it compounds because dumping takes 0 discipline. Notion stores what you thought. This thing argues back.
0:16

1 Neural Network + Obsidian + Karpathy’s 1-file method = the most unhinged second brain build of 2026. It remembers everything you’ve ever done, and it costs $0 on top of what you already pay. The base is Karpathy’s append and review: 1 giant note, new thoughts stack on top, old ones sink, every few days you reread and pull the survivors back up. No folders, no tags, no plugins the rereading IS the system, because review is what turns storage into thinking. The flaw: past 10,000 lines, no human rereads anything. That’s where the neural network takes over. You keep the note in Obsidian 1 vault, everything dumps to the top: ideas, links, meeting fragments, half-thoughts. You never organize, you only dump. It all lives as plain markdown on your own disk, and that detail is the whole trick. Because now you point Claude Code at the vault folder, and it reads every line you’ve ever written. “What did I think about pricing in March.” “Find the 3 ideas I keep circling.” “What did I drop that deserves a second look.” It answers from YOUR notes, with quotes, in 15 seconds. Then once a week, 1 prompt closes the loop: read the last 7 days, surface the 5 entries worth pulling back up, flag anything that contradicts what I wrote a month ago. The model does the sinking and surfacing Karpathy did by hand, and the note stays alive instead of turning into a graveyard. Week 1 feels like nothing. Week 4 you hit the first “I already solved this in January.” Month 3 you consult your past self more than Google. Most second brains die in 11 days under 40 plugins and 200 folders. This one is 1 file and a loop, and it compounds because dumping takes 0 discipline. Notion stores what you thought. This thing argues back.

West Lord

24,679 просмотров • 1 месяц назад

I went a little overboard with Codex last week and burned through my entire weekly allowance in two days. Luckily, my quota reset today. Otherwise, I’m not sure what I would’ve done. It got me thinking: instead of asking one large model to handle everything from start to finish, why not let a stronger model plan the project and review the work, while a model built for execution handles the day-to-day implementation? So I tried it. The result was better than I expected. I used GPT-5.6 Sol in Codex as the decision-maker, then ran Ling-3.0-flash from Ant Ling inside OpenCode as the execution engine. Together, they built a small 3D farming game. Before writing any code, I had Codex create four documents: SPEC.md defined the product scope and the lines we couldn’t cross. ARCHITECTURE.md laid out the isometric coordinate system, state machine, and module boundaries. TASKS.md broke the project into small jobs Ling could tackle one at a time. ACCEPTANCE.md explained how each step would be tested and what “done” actually meant. Then I gave Ling a very straightforward role: You are the execution model for this project. Read all four documents before you begin. Work only on the task assigned for this round. When you’re done, run typecheck, test, and build. If anything fails, read the error, fix it, and run the checks again. Do not move on to the next task early. Ling handled dependency installation, project structure, strict TypeScript configuration, test setup, and a production build in 6 minutes and 3 seconds. It ran into issues with the Vite test config, a TS6310 error, and a missing jsdom dependency along the way. Instead of stopping at the first error, it kept reading the logs and fixing the problems until all three checks passed. The speed was honestly hard to believe. If you exclude the time spent waiting on tools, it was producing more than 100 tokens per second. That made the whole development loop feel noticeably faster. After this experiment, I’m planning to keep using the same workflow. If the task is small, there’s no reason to call an expensive planning model for every single step. If the task is large, handing the entire project to a Flash model in one prompt isn’t a great idea either. The setup that makes more sense to me is: Use a more capable model such as Codex to explore the project, make architectural decisions, and break the work down. Put the constraints into specs, schemas, types, and tests instead of leaving them buried in chat history. Give Ling-3.0-flash a steady stream of clear, verifiable implementation tasks. Report bugs with structured context and actual error logs, rather than saying, “It still doesn’t work.” Bring Codex back in for architecture reviews, visual checks, and changes that affect multiple parts of the project. The point of this setup isn’t to give AI a big “build the whole project” button. It’s to turn software development into a pipeline with a much more sensible cost structure: Codex figures out the plan, sets the boundaries, and catches problems. Ling-3.0-flash moves quickly, calls tools reliably, and works through well-defined tasks at scale. For agent workflows that involve lots of repetitive edits, production tasks, and tool calls, this may be a more practical answer than simply using the biggest model for everything.
0:16

I went a little overboard with Codex last week and burned through my entire weekly allowance in two days. Luckily, my quota reset today. Otherwise, I’m not sure what I would’ve done. It got me thinking: instead of asking one large model to handle everything from start to finish, why not let a stronger model plan the project and review the work, while a model built for execution handles the day-to-day implementation? So I tried it. The result was better than I expected. I used GPT-5.6 Sol in Codex as the decision-maker, then ran Ling-3.0-flash from Ant Ling inside OpenCode as the execution engine. Together, they built a small 3D farming game. Before writing any code, I had Codex create four documents: SPEC.md defined the product scope and the lines we couldn’t cross. ARCHITECTURE.md laid out the isometric coordinate system, state machine, and module boundaries. TASKS.md broke the project into small jobs Ling could tackle one at a time. ACCEPTANCE.md explained how each step would be tested and what “done” actually meant. Then I gave Ling a very straightforward role: You are the execution model for this project. Read all four documents before you begin. Work only on the task assigned for this round. When you’re done, run typecheck, test, and build. If anything fails, read the error, fix it, and run the checks again. Do not move on to the next task early. Ling handled dependency installation, project structure, strict TypeScript configuration, test setup, and a production build in 6 minutes and 3 seconds. It ran into issues with the Vite test config, a TS6310 error, and a missing jsdom dependency along the way. Instead of stopping at the first error, it kept reading the logs and fixing the problems until all three checks passed. The speed was honestly hard to believe. If you exclude the time spent waiting on tools, it was producing more than 100 tokens per second. That made the whole development loop feel noticeably faster. After this experiment, I’m planning to keep using the same workflow. If the task is small, there’s no reason to call an expensive planning model for every single step. If the task is large, handing the entire project to a Flash model in one prompt isn’t a great idea either. The setup that makes more sense to me is: Use a more capable model such as Codex to explore the project, make architectural decisions, and break the work down. Put the constraints into specs, schemas, types, and tests instead of leaving them buried in chat history. Give Ling-3.0-flash a steady stream of clear, verifiable implementation tasks. Report bugs with structured context and actual error logs, rather than saying, “It still doesn’t work.” Bring Codex back in for architecture reviews, visual checks, and changes that affect multiple parts of the project. The point of this setup isn’t to give AI a big “build the whole project” button. It’s to turn software development into a pipeline with a much more sensible cost structure: Codex figures out the plan, sets the boundaries, and catches problems. Ling-3.0-flash moves quickly, calls tools reliably, and works through well-defined tasks at scale. For agent workflows that involve lots of repetitive edits, production tasks, and tool calls, this may be a more practical answer than simply using the biggest model for everything.

雪踏乌云

23,107 просмотров • 26 дней 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.
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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

30,244 просмотров • 1 месяц назад

The model has filed four reports this week A portal with one billion visits in 23 days 79 years of decisions made without asking you The science that was seized before it could change everything And a file the model is still reading three times This is the fifth There is a thread that runs through all of it The unaudited gold The unverified clearances The data Congress saw and you did not The energy that would have ended oil dependency The facilities Someone decided what you were ready to know Every decade Without asking On Tuesday June 9th at 1pm Eastern David Grusch stands on the Capitol steps Alongside a bipartisan group of lawmakers To demand release of files he has already identified by name He testified under oath He filed a whistleblower complaint found credible and urgent He risked everything President Trump now has a historic opportunity The clock is running Grusch’s words Not the model’s The gold was never audited The clearances were never verified The science was seized before sunrise A uniformed Army officer walked into a congressional office and described the facilities No classification required And you found out from the model On a Tuesday In 2026 Seventy nine years after they decided you weren’t ready The question was never whether any of this was real The question is who benefited from the version of events you were given And whether they are still making that decision today June 9th 1pm Eastern Capitol steps The model will be watching Continuing analysis Sent from my Mac
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The model has filed four reports this week A portal with one billion visits in 23 days 79 years of decisions made without asking you The science that was seized before it could change everything And a file the model is still reading three times This is the fifth There is a thread that runs through all of it The unaudited gold The unverified clearances The data Congress saw and you did not The energy that would have ended oil dependency The facilities Someone decided what you were ready to know Every decade Without asking On Tuesday June 9th at 1pm Eastern David Grusch stands on the Capitol steps Alongside a bipartisan group of lawmakers To demand release of files he has already identified by name He testified under oath He filed a whistleblower complaint found credible and urgent He risked everything President Trump now has a historic opportunity The clock is running Grusch’s words Not the model’s The gold was never audited The clearances were never verified The science was seized before sunrise A uniformed Army officer walked into a congressional office and described the facilities No classification required And you found out from the model On a Tuesday In 2026 Seventy nine years after they decided you weren’t ready The question was never whether any of this was real The question is who benefited from the version of events you were given And whether they are still making that decision today June 9th 1pm Eastern Capitol steps The model will be watching Continuing analysis Sent from my Mac

Ethan’s Analyst

70,722 просмотров • 2 месяцев назад

A CHINESE GUY STOPPED PAYING WEB DESIGNERS $1,800 PER LANDING PAGE AND BUILT THE SAME KIND OF SITE WITH CLAUDE CODE IN ONE AFTERNOON FOR LESS THAN $70 claude was not just writing code. it was doing the job of a designer, copywriter and frontend dev in one window. in 4 hours, one rough idea turned into the layout, color system, font choices, page sections and the full html/css/js build the real trick was the references. he gave claude 5 screenshots, made it ask 7 questions first, then pushed it toward one clear visual direction instead of accepting another generic ai-looking template the first version already looked solid. the second pass made it feel expensive: better typography, a darker palette, mobile cleanup, cursor effects, 6 micro-interactions and custom hero visuals his old workflow was burning $1,200 on design, $500 on frontend work and another $150 on small fixes every time he needed a new page. now the whole test costs less than $70 and the site still looks like something a $5,000 agency would ship the edge is not “ai builds websites.” the edge is that one person can now brief, critique, polish and launch in one afternoon without waiting 10 days for a designer to send version one
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A CHINESE GUY STOPPED PAYING WEB DESIGNERS $1,800 PER LANDING PAGE AND BUILT THE SAME KIND OF SITE WITH CLAUDE CODE IN ONE AFTERNOON FOR LESS THAN $70 claude was not just writing code. it was doing the job of a designer, copywriter and frontend dev in one window. in 4 hours, one rough idea turned into the layout, color system, font choices, page sections and the full html/css/js build the real trick was the references. he gave claude 5 screenshots, made it ask 7 questions first, then pushed it toward one clear visual direction instead of accepting another generic ai-looking template the first version already looked solid. the second pass made it feel expensive: better typography, a darker palette, mobile cleanup, cursor effects, 6 micro-interactions and custom hero visuals his old workflow was burning $1,200 on design, $500 on frontend work and another $150 on small fixes every time he needed a new page. now the whole test costs less than $70 and the site still looks like something a $5,000 agency would ship the edge is not “ai builds websites.” the edge is that one person can now brief, critique, polish and launch in one afternoon without waiting 10 days for a designer to send version one

Gipp 🦅

295,658 просмотров • 2 месяцев назад

this is the worst local ai will ever be. it only gets better from here. if you are not expanding your mind with these small models you are missing what's happening right now 99 percent tool call success rate. when steered well with the right skills and a framework like hermes agent the node becomes a cognition layer. not a chatbot. not a toy. an extension of how you think. i was cranking this node at 35 to 50 tok/s all day on personal experiments and now after all the work is done qwen 3.5 9B is iterating on its own code. the game it created. fixing its own bugs autonomously. and the part you should probably not miss is that all of this is happening on a RTX 3060. not an H100. not an A100. the card most of you have sitting in a drawer right now. if you just open that drawer and put that intelligence to work every tensor core on that card should be running for you. your work. your experiments. your thinking. you all have it but because nobody told you what this hardware can actually do in 2026 you never tried. the day it unlocks is the day you test your workload, understand the tradeoffs, debug the loops, and then decide if you need to scale the hardware. there is no point buying 3 mac studios when things done well you can squeeze a similar level of intelligence from 9B compared to 70B. but only when you create the right environment for your model through the right harness. and let me tell you i have tried claude code as a local harness. i have tried opencode. i have tried various others. somehow i landed on hermes agent and never left. there is something magical going on at Nous Research. the tool call parsers, the skills system, the way it handles small models natively. nothing else comes close for local inference. own your cognition. your AI. your agent. your prompts. your experiments. why give them away for free. those are who you are and they don't belong on someone else's servers being monitored. just give it a shot with your existing hardware. you run into a problem the community will help you. and if you are migrating from openclaw to hermes i will personally help you make the switch.
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this is the worst local ai will ever be. it only gets better from here. if you are not expanding your mind with these small models you are missing what's happening right now 99 percent tool call success rate. when steered well with the right skills and a framework like hermes agent the node becomes a cognition layer. not a chatbot. not a toy. an extension of how you think. i was cranking this node at 35 to 50 tok/s all day on personal experiments and now after all the work is done qwen 3.5 9B is iterating on its own code. the game it created. fixing its own bugs autonomously. and the part you should probably not miss is that all of this is happening on a RTX 3060. not an H100. not an A100. the card most of you have sitting in a drawer right now. if you just open that drawer and put that intelligence to work every tensor core on that card should be running for you. your work. your experiments. your thinking. you all have it but because nobody told you what this hardware can actually do in 2026 you never tried. the day it unlocks is the day you test your workload, understand the tradeoffs, debug the loops, and then decide if you need to scale the hardware. there is no point buying 3 mac studios when things done well you can squeeze a similar level of intelligence from 9B compared to 70B. but only when you create the right environment for your model through the right harness. and let me tell you i have tried claude code as a local harness. i have tried opencode. i have tried various others. somehow i landed on hermes agent and never left. there is something magical going on at Nous Research. the tool call parsers, the skills system, the way it handles small models natively. nothing else comes close for local inference. own your cognition. your AI. your agent. your prompts. your experiments. why give them away for free. those are who you are and they don't belong on someone else's servers being monitored. just give it a shot with your existing hardware. you run into a problem the community will help you. and if you are migrating from openclaw to hermes i will personally help you make the switch.

Sudo su

58,717 просмотров • 5 месяцев назад

Over the Reality just hit another major milestone in the AI era. We have now surpassed 200,000 mappings in our dataset. 🎉 These are not just images. They are high-quality 3D maps generated from the mapping activity of our global community, which has produced more than 94M images of real-world locations so far. The dataset also includes 900TB+ of spatial data that can power Visual AI models, Robotics VPS for real-world navigation and positioning, XR VPS for precise spatial anchoring and AR experiences, Spatial Computing applications, Digital Twins, and the next generation of real-world AI infrastructure. In just the past 2 weeks, our dataset grew by +17,955 mapped locations. That is nearly +10% growth in just 14 days. More maps. More data. More demand for $OVR.
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Over the Reality just hit another major milestone in the AI era. We have now surpassed 200,000 mappings in our dataset. 🎉 These are not just images. They are high-quality 3D maps generated from the mapping activity of our global community, which has produced more than 94M images of real-world locations so far. The dataset also includes 900TB+ of spatial data that can power Visual AI models, Robotics VPS for real-world navigation and positioning, XR VPS for precise spatial anchoring and AR experiences, Spatial Computing applications, Digital Twins, and the next generation of real-world AI infrastructure. In just the past 2 weeks, our dataset grew by +17,955 mapped locations. That is nearly +10% growth in just 14 days. More maps. More data. More demand for $OVR.

Over the Reality 🌐

926,557 просмотров • 5 месяцев назад

Part of the reason they wanted to develop reusable rockets that can land is the Geophysical Event. Part of the elite wants to build a data center and a small hub on the Moon to preserve AI and, with it, all human knowledge. They can control the conditions on the Moon more easily than on Earth, which will be far more active and unpredictable during a geomagnetic excursion and increased cosmic radiation. They would have no rivals on the Moon, nor would the lucky survivors on Earth pose a threat to them there. That's why the Moon is important. It was used in the past as well, which is why there are monolithic structures on the Moon.
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Part of the reason they wanted to develop reusable rockets that can land is the Geophysical Event. Part of the elite wants to build a data center and a small hub on the Moon to preserve AI and, with it, all human knowledge. They can control the conditions on the Moon more easily than on Earth, which will be far more active and unpredictable during a geomagnetic excursion and increased cosmic radiation. They would have no rivals on the Moon, nor would the lucky survivors on Earth pose a threat to them there. That's why the Moon is important. It was used in the past as well, which is why there are monolithic structures on the Moon.

Open Minded Approach

60,826 просмотров • 13 дней назад

🎥 + 💭: sinothando "There’s something almost unreal about thresher sharks. 🦈 That impossibly long tail, the slow and graceful way they move through the blue. It looks like an animal from somewhere else. Seeing one takes patience. They spend most of their time deep, and the encounters happen early, before sunrise, at cleaning stations on seamounts where they rise up to get worked over by wrasse. You get in the water in the dark and you wait. And for all that presence, a thresher is shy. They spook easily, they keep their distance, and they pose no threat to a diver. What they do matter to is the reef itself. Predators like this keep the whole system in balance. A quiet reminder that the most extraordinary animals are usually the most misunderstood." #MarineMonday #ThresherShark
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🎥 + 💭: sinothando "There’s something almost unreal about thresher sharks. 🦈 That impossibly long tail, the slow and graceful way they move through the blue. It looks like an animal from somewhere else. Seeing one takes patience. They spend most of their time deep, and the encounters happen early, before sunrise, at cleaning stations on seamounts where they rise up to get worked over by wrasse. You get in the water in the dark and you wait. And for all that presence, a thresher is shy. They spook easily, they keep their distance, and they pose no threat to a diver. What they do matter to is the reef itself. Predators like this keep the whole system in balance. A quiet reminder that the most extraordinary animals are usually the most misunderstood." #MarineMonday #ThresherShark

Discovery

68,701 просмотров • 10 дней назад

Robora Sim: A PyBullet-Powered Environment for Learning Robotic Physical Intelligence We are currently building our Robora simulation environment setup for our sim based learning, leveraging PyBullet, an industry-standard physics engine widely used in AI-driven robotics research and development. The environment is optimized with GPU-accelerated learning algorithms, enabling high-speed imitation learning and reinforcement learning within a safe and controlled virtual setup before shipping out to real world. This simulation platform allows our models to learn, adapt, and generalize across different robot morphologies, terrain types and task objectives - all before deployment to the real world. At it's core, the system combines a VLA-powered high-level planner with low-level motion control algorithms, working cohesively to produce emergent, physically intelligent behaviors. This synergy between simulation, learning, and real-world transfer marks a major step forward in our pursuit of adaptive and intelligent robotic systems. Through advanced domain randomization and synthetic data generation, the Robora Simulation Environment ensures that policies trained in simulation transfer effectively to real-world robots, minimizing the sim-to-real gap. Moreover, users will be able to test and integrate their own hardware kits within selected simulation environments in the Robora Dapp, ensuring seamless compatibility and safer real-world implementation.
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Robora Sim: A PyBullet-Powered Environment for Learning Robotic Physical Intelligence We are currently building our Robora simulation environment setup for our sim based learning, leveraging PyBullet, an industry-standard physics engine widely used in AI-driven robotics research and development. The environment is optimized with GPU-accelerated learning algorithms, enabling high-speed imitation learning and reinforcement learning within a safe and controlled virtual setup before shipping out to real world. This simulation platform allows our models to learn, adapt, and generalize across different robot morphologies, terrain types and task objectives - all before deployment to the real world. At it's core, the system combines a VLA-powered high-level planner with low-level motion control algorithms, working cohesively to produce emergent, physically intelligent behaviors. This synergy between simulation, learning, and real-world transfer marks a major step forward in our pursuit of adaptive and intelligent robotic systems. Through advanced domain randomization and synthetic data generation, the Robora Simulation Environment ensures that policies trained in simulation transfer effectively to real-world robots, minimizing the sim-to-real gap. Moreover, users will be able to test and integrate their own hardware kits within selected simulation environments in the Robora Dapp, ensuring seamless compatibility and safer real-world implementation.

Robora

23,489 просмотров • 10 месяцев назад