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Artificial intelligence software just destroyed an entire farm harvest after generating a toxic chemical mix. The user followed a chat tool recommendation that mixed incompatible crop sprays. Because previous weather predictions from the app worked well, the farm owner applied the toxic formula without asking a real agricultural expert... show more

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AI Is Moving Beyond “Generating Videos” — Toward “Generating Worlds” Over the past two years, AI video models have advanced at an astonishing pace. From Runway and Pika to Sora and Veo, AI-generated videos have become increasingly realistic and more consistent with the physical laws of the real world. Many people believe the next objective is simply to generate videos that are longer, sharper, and more lifelike. But if we take a step back, we can see that the real transformation is not happening in video itself. It is happening in world models. What Is a World Model? In 1943, psychologist Kenneth Craik proposed an idea that would influence artificial intelligence research for decades. He argued that the human brain does not merely react to the outside world. Instead, it maintains an internal model of how the world works. Because we have this internal model, we can predict the outcome of an action before we actually take it. Before crossing a road, we estimate whether a car will pass by. Before catching a ball, we predict its trajectory. These abilities come from continuously simulating the world in our minds, rather than relying entirely on trial and error. This idea later became known by a more formal term: World Model. A world model does not describe a single image or a fixed video clip. It is an internal representation capable of continuously simulating the rules and dynamics of the real world. Why Is AI Research Turning Toward World Models? Because predicting “what comes next” is becoming increasingly central to how AI systems work. Language models predict the next token. Image models predict the next step in the denoising process. Video models predict the next frame. A world model, however, attempts to predict something broader: What should the world look like in the next moment? In 2018, David Ha and Jürgen Schmidhuber proposed in their paper World Models that an intelligent agent could first learn a model of the world, and then use that internal model to plan its actions. The Dreamer series later demonstrated that many complex tasks could be learned by training agents inside an “imagined world.” At the same time, the development of video models such as Sora and Veo led researchers to another realization: A model capable of continuously generating video has already learned, at least implicitly, many of the rules governing the real world. As a result, these two research directions have gradually begun to converge. But Video Is Not Yet a World This is where the distinction is often misunderstood. For a world model to support meaningful real-time interaction, it must solve several critical problems. Most video models today are essentially answering one question: What should the next frame look like? A true world model needs to answer much more: What happens if I take one step forward? If I walk behind a building and then return, will the building still be there? If I suddenly change the camera angle, will the entire space remain consistent? If I enter a command such as: “Summon a dragon.” Will the world respond immediately? In other words, a world model must do more than generate content. It must understand space. It must understand time. It must understand causality. And it must understand interaction. Moving from watching to participating is where the real difficulty of world models begins. World Models Are Entering the Interactive Era One of the latest attempts in this direction is Alaya World, recently open-sourced by Alaya World, or Alaya Lab. Instead of generating a fixed video clip, it generates a world that users can explore in real time. Users can begin with text, an image, or a video, enter the generated scene, move freely through it, and introduce new prompts at any moment during generation. The world responds immediately. According to the publicly released information, Alaya World provides: Real-time streaming generation at 720p and 24 FPS Stable continuous exploration for more than one minute The ability to switch prompts and trigger skills or events during generation Model weights and inference code released under the Apache 2.0 License Training code and datasets planned for future release What makes these capabilities important is not simply the technical specifications. It is that the generated “world” can now support continuous interaction. The official demo shows that users can genuinely control, transform, and explore the generated environment. AI Is Evolving From a Tool Into an Environment Over the past few years, most discussions around AI have focused on content generation. Generating text. Generating images. Generating videos. But world models raise a fundamentally different question: Can AI generate an environment that people can inhabit, explore, and continuously evolve? If the answer is yes, the impact will extend far beyond video generation. Game development, robotics training, embodied intelligence, digital twins, virtual production, and many other fields could be transformed by the development of world models. World models are still at a very early stage. Yet from Craik’s proposal of an internal mental model more than eighty years ago to the emergence of today’s interactive world-generation systems, a clear evolutionary path is beginning to take shape. Perhaps what AI is ultimately learning has never been limited to images, videos, or language. Perhaps it is learning the world itself. References GitHub: Technical Report:

AI Is Moving Beyond “Generating Videos” — Toward “Generating Worlds” Over the past two years, AI video models have advanced at an astonishing pace. From Runway and Pika to Sora and Veo, AI-generated videos have become increasingly realistic and more consistent with the physical laws of the real world. Many people believe the next objective is simply to generate videos that are longer, sharper, and more lifelike. But if we take a step back, we can see that the real transformation is not happening in video itself. It is happening in world models. What Is a World Model? In 1943, psychologist Kenneth Craik proposed an idea that would influence artificial intelligence research for decades. He argued that the human brain does not merely react to the outside world. Instead, it maintains an internal model of how the world works. Because we have this internal model, we can predict the outcome of an action before we actually take it. Before crossing a road, we estimate whether a car will pass by. Before catching a ball, we predict its trajectory. These abilities come from continuously simulating the world in our minds, rather than relying entirely on trial and error. This idea later became known by a more formal term: World Model. A world model does not describe a single image or a fixed video clip. It is an internal representation capable of continuously simulating the rules and dynamics of the real world. Why Is AI Research Turning Toward World Models? Because predicting “what comes next” is becoming increasingly central to how AI systems work. Language models predict the next token. Image models predict the next step in the denoising process. Video models predict the next frame. A world model, however, attempts to predict something broader: What should the world look like in the next moment? In 2018, David Ha and Jürgen Schmidhuber proposed in their paper World Models that an intelligent agent could first learn a model of the world, and then use that internal model to plan its actions. The Dreamer series later demonstrated that many complex tasks could be learned by training agents inside an “imagined world.” At the same time, the development of video models such as Sora and Veo led researchers to another realization: A model capable of continuously generating video has already learned, at least implicitly, many of the rules governing the real world. As a result, these two research directions have gradually begun to converge. But Video Is Not Yet a World This is where the distinction is often misunderstood. For a world model to support meaningful real-time interaction, it must solve several critical problems. Most video models today are essentially answering one question: What should the next frame look like? A true world model needs to answer much more: What happens if I take one step forward? If I walk behind a building and then return, will the building still be there? If I suddenly change the camera angle, will the entire space remain consistent? If I enter a command such as: “Summon a dragon.” Will the world respond immediately? In other words, a world model must do more than generate content. It must understand space. It must understand time. It must understand causality. And it must understand interaction. Moving from watching to participating is where the real difficulty of world models begins. World Models Are Entering the Interactive Era One of the latest attempts in this direction is Alaya World, recently open-sourced by Alaya World, or Alaya Lab. Instead of generating a fixed video clip, it generates a world that users can explore in real time. Users can begin with text, an image, or a video, enter the generated scene, move freely through it, and introduce new prompts at any moment during generation. The world responds immediately. According to the publicly released information, Alaya World provides: Real-time streaming generation at 720p and 24 FPS Stable continuous exploration for more than one minute The ability to switch prompts and trigger skills or events during generation Model weights and inference code released under the Apache 2.0 License Training code and datasets planned for future release What makes these capabilities important is not simply the technical specifications. It is that the generated “world” can now support continuous interaction. The official demo shows that users can genuinely control, transform, and explore the generated environment. AI Is Evolving From a Tool Into an Environment Over the past few years, most discussions around AI have focused on content generation. Generating text. Generating images. Generating videos. But world models raise a fundamentally different question: Can AI generate an environment that people can inhabit, explore, and continuously evolve? If the answer is yes, the impact will extend far beyond video generation. Game development, robotics training, embodied intelligence, digital twins, virtual production, and many other fields could be transformed by the development of world models. World models are still at a very early stage. Yet from Craik’s proposal of an internal mental model more than eighty years ago to the emergence of today’s interactive world-generation systems, a clear evolutionary path is beginning to take shape. Perhaps what AI is ultimately learning has never been limited to images, videos, or language. Perhaps it is learning the world itself. References GitHub: Technical Report:

雪踏乌云

113,347 views • 1 month ago

Most AI Projects In Crypto Are Liars. Sentient is different. It is the only project in Web3 pursuing true Artificial General Intelligence. A few facts that set it apart: 1. First-of-its-kind: Sentient is not just building AI tools, but an open, crypto-native path to AGI. 2. Community-owned intelligence: Instead of Big Tech locking up breakthroughs, progress is built openly and shared on-chain. 3. Value capture: Every step forward in intelligence accrues to the ecosystem itself, not to a closed corporation. 4. Product before hype: While dozens of AI projects launch at $300M–$2B FDV with little to show, Sentient is shipping real technology first. 5. Designed to evolve: Its architecture is built to learn, adapt, and scale like a human mind. 6. The singularity play in crypto: Many see Sentient as the Tesla-equivalent for AGI—but crypto-native. In time, people will understand that this isn’t just another AI project. It’s the beginning of decentralized intelligence.

Most AI Projects In Crypto Are Liars. Sentient is different. It is the only project in Web3 pursuing true Artificial General Intelligence. A few facts that set it apart: 1. First-of-its-kind: Sentient is not just building AI tools, but an open, crypto-native path to AGI. 2. Community-owned intelligence: Instead of Big Tech locking up breakthroughs, progress is built openly and shared on-chain. 3. Value capture: Every step forward in intelligence accrues to the ecosystem itself, not to a closed corporation. 4. Product before hype: While dozens of AI projects launch at $300M–$2B FDV with little to show, Sentient is shipping real technology first. 5. Designed to evolve: Its architecture is built to learn, adapt, and scale like a human mind. 6. The singularity play in crypto: Many see Sentient as the Tesla-equivalent for AGI—but crypto-native. In time, people will understand that this isn’t just another AI project. It’s the beginning of decentralized intelligence.

BillionAireSon 🛡️

16,141 views • 11 months ago

One thing I think AI developers underestimate: Your model isn't always your biggest risk. Your infrastructure is. A lot of AI apps work great in development… Until the provider you're relying on has an outage, rate limits your requests, or a model suddenly becomes unavailable. If your entire product depends on one AI provider, you've created a single point of failure. That's why I like the approach CometAPI is taking. Instead of locking your app to one vendor, it gives you access to 500+ AI models through a single OpenAI-compatible endpoint. That means you can: → switch models without rewriting your integration → build fallback routes for better reliability → compare models as new ones launch → avoid getting locked into a single provider For teams building with Claude Code, Cursor, AI agents, SaaS products, or automation tools, that flexibility matters a lot once you're in production. They're also shipping new models quickly. Recent additions include: • GPT-5.6 • Happy Horse 1.1 • Kling Video Plus recently released: • Gemini 3.1 Flash Lite Image • Claude Sonnet 5 Worth checking out if you're building AI products that need to stay reliable as the ecosystem moves fast. Try it here: #AI #LLM #Developers #OpenAI #Claude #Cursor #ClaudeCode #BuildInPublic

One thing I think AI developers underestimate: Your model isn't always your biggest risk. Your infrastructure is. A lot of AI apps work great in development… Until the provider you're relying on has an outage, rate limits your requests, or a model suddenly becomes unavailable. If your entire product depends on one AI provider, you've created a single point of failure. That's why I like the approach CometAPI is taking. Instead of locking your app to one vendor, it gives you access to 500+ AI models through a single OpenAI-compatible endpoint. That means you can: → switch models without rewriting your integration → build fallback routes for better reliability → compare models as new ones launch → avoid getting locked into a single provider For teams building with Claude Code, Cursor, AI agents, SaaS products, or automation tools, that flexibility matters a lot once you're in production. They're also shipping new models quickly. Recent additions include: • GPT-5.6 • Happy Horse 1.1 • Kling Video Plus recently released: • Gemini 3.1 Flash Lite Image • Claude Sonnet 5 Worth checking out if you're building AI products that need to stay reliable as the ecosystem moves fast. Try it here: #AI #LLM #Developers #OpenAI #Claude #Cursor #ClaudeCode #BuildInPublic

Shruti Codes

56,822 views • 2 months ago

“How much can you make from farming GINGER on HALF PLOT OF LAND” I am running this experiment as a bet between me and my guy that’s a pepper farmer and I am like let me carry my people here along so you can see. The bet is half plot of ginger farm will yield more profit than 1 acre of habanero pepper in 7 months 5million naira is on the line for me. Because most people still think you need acres of land to make millions of Naira from agriculture. I have always been against that belief. So How much can you actually make from ginger farming without owning acres of land? Let’s break it down Video in frame 1 is On April 23rd, 2026, we planted just 3 bags of ginger (210kg total) on half a plot of land. Today, at 7 weeks after planting, the farm is looking promising. No fertilizer has been applied yet. No fungicide sprays yet. Just chicken manure applied during land preparation. Video in Frame 2 is fast forward to 7 weeks after planting… The field is already looking strong and developing steadily. But this isn’t even the interesting part. The interesting part is what this small farm can potentially return. Based on our projections: Worst case (underperforming farm): 15 bags harvest Moderate performance: 21–24 bags Best case scenario (everything goes right): up to 30 bags And that 30 bags is our target All this from half a plot. half a plot. This is why I’m documenting the entire journey. Now if you want me to take this further. For me to breakdown the total cost of this half-plot project full input expenses labor + management and projected profit at harvest If you want me to post the full financial breakdown of this experiment? just type “BREAKDOWN” in the comments. 👇 Or should I just keep it private and show the final harvest result?

“How much can you make from farming GINGER on HALF PLOT OF LAND” I am running this experiment as a bet between me and my guy that’s a pepper farmer and I am like let me carry my people here along so you can see. The bet is half plot of ginger farm will yield more profit than 1 acre of habanero pepper in 7 months 5million naira is on the line for me. Because most people still think you need acres of land to make millions of Naira from agriculture. I have always been against that belief. So How much can you actually make from ginger farming without owning acres of land? Let’s break it down Video in frame 1 is On April 23rd, 2026, we planted just 3 bags of ginger (210kg total) on half a plot of land. Today, at 7 weeks after planting, the farm is looking promising. No fertilizer has been applied yet. No fungicide sprays yet. Just chicken manure applied during land preparation. Video in Frame 2 is fast forward to 7 weeks after planting… The field is already looking strong and developing steadily. But this isn’t even the interesting part. The interesting part is what this small farm can potentially return. Based on our projections: Worst case (underperforming farm): 15 bags harvest Moderate performance: 21–24 bags Best case scenario (everything goes right): up to 30 bags And that 30 bags is our target All this from half a plot. half a plot. This is why I’m documenting the entire journey. Now if you want me to take this further. For me to breakdown the total cost of this half-plot project full input expenses labor + management and projected profit at harvest If you want me to post the full financial breakdown of this experiment? just type “BREAKDOWN” in the comments. 👇 Or should I just keep it private and show the final harvest result?

Ayo | Farm Tribe by AY 🇳🇬 🇹🇷 🌾🍫🫚

10,846 views • 3 months ago

You can't 3D reconstruct glass from images... ...WRONG! Thanks for video diffusion, now just about anything is possible! Introducing...Diffusion Knows Transparency (DKT) Transparent and reflective objects usually break robot vision and photogrammetry pipelines because they don't follow the "solid object" rules standard cameras expect. DKT is a new AI model that repurposes the "internal physics engine" found in video generation models to solve this problem. Researchers took a massive video diffusion model (WAN) and fine-tuned it using a custom-built synthetic dataset to turn it into a high-precision depth sensor. To train the AI, they built the first massive synthetic video library of transparent objects, 1.32 million frames of perfectly labeled glass and metal objects in motion. Without ever seeing a "real" labeled video of glass during training, the model (DKT) outperformed all previous specialized systems on real-world benchmarks (ClearPose, DREDS). They created a "lightweight" 1.3B parameter version that runs fast enough (0.17s per frame) to be used on actual robot hardware. Two reasons I find this project important: 1. It further proves that synthetic data will be essential for training the next generation vision models. 2. In real-world robotic tests, using DKT's depth maps nearly doubled the success rate of robot arms trying to pick up objects on tricky reflective or translucent surfaces. At home robots will need to interact with these types of objects on a daily basis. Check out the project page here: Code is LIVE! #Computervision #Robotics #AI

You can't 3D reconstruct glass from images... ...WRONG! Thanks for video diffusion, now just about anything is possible! Introducing...Diffusion Knows Transparency (DKT) Transparent and reflective objects usually break robot vision and photogrammetry pipelines because they don't follow the "solid object" rules standard cameras expect. DKT is a new AI model that repurposes the "internal physics engine" found in video generation models to solve this problem. Researchers took a massive video diffusion model (WAN) and fine-tuned it using a custom-built synthetic dataset to turn it into a high-precision depth sensor. To train the AI, they built the first massive synthetic video library of transparent objects, 1.32 million frames of perfectly labeled glass and metal objects in motion. Without ever seeing a "real" labeled video of glass during training, the model (DKT) outperformed all previous specialized systems on real-world benchmarks (ClearPose, DREDS). They created a "lightweight" 1.3B parameter version that runs fast enough (0.17s per frame) to be used on actual robot hardware. Two reasons I find this project important: 1. It further proves that synthetic data will be essential for training the next generation vision models. 2. In real-world robotic tests, using DKT's depth maps nearly doubled the success rate of robot arms trying to pick up objects on tricky reflective or translucent surfaces. At home robots will need to interact with these types of objects on a daily basis. Check out the project page here: Code is LIVE! #Computervision #Robotics #AI

Jonathan Stephens

17,712 views • 8 months ago

🐝 I just saw a drone the size of a fly and it honestly made me pause. Not because it’s futuristic, but because it’s already here. Scientists are now building AI-powered micro-drones that look and move like real insects. Each one can fly, record, and transmit data — all while being almost invisible to the human eye. What’s truly new is the combination of biomimicry and artificial intelligence. They don’t just fly; they think like insects — adjusting to wind, avoiding obstacles, and navigating spaces too small for any traditional drone. Imagine what this unlocks: → Search and rescue in disaster zones → Environmental monitoring without disturbance → Precision agriculture and crop tracking → Real-time surveillance anywhere on Earth It’s an incredible leap in engineering and a powerful reminder that innovation often starts small. But as our machines learn to see everything, can we still decide what should remain unseen? #AI #Innovation #Drones #Technology #Future #Ethics #Surveillance

🐝 I just saw a drone the size of a fly and it honestly made me pause. Not because it’s futuristic, but because it’s already here. Scientists are now building AI-powered micro-drones that look and move like real insects. Each one can fly, record, and transmit data — all while being almost invisible to the human eye. What’s truly new is the combination of biomimicry and artificial intelligence. They don’t just fly; they think like insects — adjusting to wind, avoiding obstacles, and navigating spaces too small for any traditional drone. Imagine what this unlocks: → Search and rescue in disaster zones → Environmental monitoring without disturbance → Precision agriculture and crop tracking → Real-time surveillance anywhere on Earth It’s an incredible leap in engineering and a powerful reminder that innovation often starts small. But as our machines learn to see everything, can we still decide what should remain unseen? #AI #Innovation #Drones #Technology #Future #Ethics #Surveillance

Pascal Bornet

120,845 views • 8 months ago

I'm proud to share that Glean has surpassed $300M ARR, just five months after crossing $200M and growing ~3x over the past 15 months. This is an exciting milestone for Glean, and it's a signal about where the enterprise AI market is heading. We’ve long believed the real challenge in enterprise AI is not access to models. It is grounding AI in how a company actually works: its people, knowledge, workflows, permissions, and systems. That’s even clearer now. The companies creating real value with AI are not just adopting better models. They are building systems that understand their business well enough to deliver reliable outcomes at scale. That is the real moat, and it is what we’ve been building at Glean: an unrivaled context layer for enterprise AI. That context has to work across the business, not just inside a single team or use case. We see that in how customers adopt Glean: more than 85% use it across five or more job functions. It also has to meet the security and governance demands of complex enterprises. We see that in who is choosing Glean: our Fortune 500 customer count nearly doubled year over year. And it has to make economic sense as usage grows. In our recent benchmark with Claude Cowork, Glean was preferred roughly 2.5x as often as off-the-shelf MCP tools and used 30% fewer tokens on average. Better context improves both quality and efficiency. I enjoyed talking with CNBC's Deirdre Bosa about this broader shift. In enterprise AI, the winners will not be defined by better models alone. They will be defined by who builds the strongest foundation for enterprise context. Thank you to our customers, partners, and team for helping us build the future of enterprise AI.

I'm proud to share that Glean has surpassed $300M ARR, just five months after crossing $200M and growing ~3x over the past 15 months. This is an exciting milestone for Glean, and it's a signal about where the enterprise AI market is heading. We’ve long believed the real challenge in enterprise AI is not access to models. It is grounding AI in how a company actually works: its people, knowledge, workflows, permissions, and systems. That’s even clearer now. The companies creating real value with AI are not just adopting better models. They are building systems that understand their business well enough to deliver reliable outcomes at scale. That is the real moat, and it is what we’ve been building at Glean: an unrivaled context layer for enterprise AI. That context has to work across the business, not just inside a single team or use case. We see that in how customers adopt Glean: more than 85% use it across five or more job functions. It also has to meet the security and governance demands of complex enterprises. We see that in who is choosing Glean: our Fortune 500 customer count nearly doubled year over year. And it has to make economic sense as usage grows. In our recent benchmark with Claude Cowork, Glean was preferred roughly 2.5x as often as off-the-shelf MCP tools and used 30% fewer tokens on average. Better context improves both quality and efficiency. I enjoyed talking with CNBC's Deirdre Bosa about this broader shift. In enterprise AI, the winners will not be defined by better models alone. They will be defined by who builds the strongest foundation for enterprise context. Thank you to our customers, partners, and team for helping us build the future of enterprise AI.

Arvind Jain

281,201 views • 3 months ago

🚨 JUST IN: THIS FREE TOOL JUST REPLACED FOUR AI IMAGE AND VIDEO SUBSCRIPTIONS AT ONCE. Midjourney. Krea. Higgsfield. Openart. One repo. 200+ models. Zero dollars a month. Here is what it actually does. It is a full image and video studio that runs in your browser or as a desktop app. Text to image, image to image, text to video, image to video, lip sync, cinema mode with real camera controls. All of it. 4,500 people already starred this. What you get for free: → 50+ image models including Flux, Midjourney v7, Ideogram, GPT-4o, Seedream → 60+ video models including Kling, Sora, Veo, Runway, Wan, Hailuo → lip sync studio with 9 dedicated models. upload a portrait and audio and it talks → cinema studio with real camera controls. lens, focal length, aperture, film stock → feed up to 14 reference images into one generation → self-hosted. your data never leaves your machine The crazy part is there is also a hosted version that needs zero setup. Just open the link and start generating. Now the math. Midjourney Standard: $30/month Krea AI Pro: $30/month Higgsfield Plus: $49/month Openart AI: $15/month That is $124 a month. $1,488 a year. This repo does everything all four do. With more models than any of them. For free. Forever. No subscription. No vendor lock-in. MIT licensed. Download it in one click on Mac or Windows. Someone should have told me about this sooner. I feel like an idiot. ( save this )

🚨 JUST IN: THIS FREE TOOL JUST REPLACED FOUR AI IMAGE AND VIDEO SUBSCRIPTIONS AT ONCE. Midjourney. Krea. Higgsfield. Openart. One repo. 200+ models. Zero dollars a month. Here is what it actually does. It is a full image and video studio that runs in your browser or as a desktop app. Text to image, image to image, text to video, image to video, lip sync, cinema mode with real camera controls. All of it. 4,500 people already starred this. What you get for free: → 50+ image models including Flux, Midjourney v7, Ideogram, GPT-4o, Seedream → 60+ video models including Kling, Sora, Veo, Runway, Wan, Hailuo → lip sync studio with 9 dedicated models. upload a portrait and audio and it talks → cinema studio with real camera controls. lens, focal length, aperture, film stock → feed up to 14 reference images into one generation → self-hosted. your data never leaves your machine The crazy part is there is also a hosted version that needs zero setup. Just open the link and start generating. Now the math. Midjourney Standard: $30/month Krea AI Pro: $30/month Higgsfield Plus: $49/month Openart AI: $15/month That is $124 a month. $1,488 a year. This repo does everything all four do. With more models than any of them. For free. Forever. No subscription. No vendor lock-in. MIT licensed. Download it in one click on Mac or Windows. Someone should have told me about this sooner. I feel like an idiot. ( save this )

Kanika

14,769 views • 4 months ago

🚨China vs USA robots Chinese robot: $24,000 American robot: $39,100 Pricing policy isn't everything. If we look at real capabilities, the balance of power looks completely different 🇨🇳 The robot demonstrates incredible agility, confidently performs a backflip, moves dynamically, and dances Maximum flexibility, high tech at an affordable price, and readiness for real-world tasks 🇺🇸 The robot, with a higher price tag, currently looks more modest, struggling even with basic rectilinear walking An obvious overpayment for brand and marketing, while Chinese engineers are outpacing competitors in software and hardware The main paradox of the current moment is that Chinese companies focus on rapidly cheapening hardware and mass production, turning robotics into an accessible mass-market consumer product. Meanwhile, the US traditionally tries to enter the market through fundamental neural networks and a universal AI brain However, in the coming years, we will see the inevitable convergence of these approaches: the winner in this race will not be the one who simply teaches a robot to do a backflip or walk straight, but the one who first combines Chinese cheap and adaptive mechatronics with advanced American language and multimodal models And what do you think, who will ultimately set the global standard in robotics East or West?

Bober_smart

34,991 views • 28 days ago

Why is the market selling off today? (Save this). The semi selloff right now is being driven by a mix of macro fear, profit taking and investors questioning how quickly all of this AI spending will actually pay off, not because demand for AI infrastructure suddenly disappeared. The market is basically trading this chain reaction, the ongoing US Iran escalation pushes oil higher, higher oil keeps inflation elevated, sticky inflation keeps Treasury yields high and that increases the risk of the Fed staying hawkish or even hiking again. That is a terrible setup for semis because many of these companies are valued on the massive earnings investors expect them to generate years from now. When yields rise, those future earnings become worth less today which is why the highest multiple AI and semiconductor names usually get hit first. (I don't think there will be a hike this year). This is also why everything is moving together right now. Nvidia, Micron, Nebius, SanDisk, Broadcom and Applied Optoelectronics are all completely different businesses, but institutions are not separating memory, networking, optics, compute and cloud infrastructure at the moment. They are reducing exposure to the entire AI trade, taking profits in the names that have already run the most and moving into a more defensive position potentially ahead of the Fed. There is also growing pressure around hyperscaler capex. Microsoft, Meta, Amazon and Google are still spending enormous amounts on GPUs, data centers, networking and power but the market is starting to ask when all of that spending will actually turn into revenue and free cash flow. Investors are no longer satisfied with hearing that AI capex is growing. They want proof that the returns are arriving fast enough to justify the valuations already priced into the entire AI ecosystem. That creates a weird situation where hyperscaler capex can continue rising while semiconductor stocks still fall. The market is not asking whether AI spending is growing anymore but rather asking whether it is growing fast enough to beat the expectations already baked into these stocks. Crowded positioning is another major factor. Semis and AI infrastructure stocks have been some of the biggest winners in the market so institutions are sitting on huge profits and many funds own the exact same names. When macro risk increases, investors usually sell the most liquid winners first. That does not mean demand for memory, optics or custom chips suddenly collapsed but rather means investors are locking in gains and reducing risk. Tariffs add another layer because even when they are not directly placed on chips, they can still raise the cost of servers, electrical equipment, cooling systems, construction materials and the overall data center buildout. That makes AI infrastructure more expensive while also adding another source of inflation. Then you have Jensen Huang’s letter to the White House this morning about open weight AI models, which I think is one of the most important long term developments here. Nvidia, Meta, Microsoft, Palantir and several other companies are pushing Washington not to place broad restrictions on open weight AI. OpenAI and Anthropic were notably absent because open models are much more of a threat to their business models. OpenAI and Anthropic benefit from a world where a few closed frontier labs control the best models and companies have to pay them through subscriptions and APIs. Open weight models weaken that advantage because businesses can download a model, customize it for their own use and run it on their own infrastructure or through a neocloud. That is bad for OpenAI and Anthropic because it puts pressure on pricing, margins and the idea that they will control the intelligence layer of the economy but it is very good for the AI ecosystem as a whole over the long run. But the question is what does this mean for all the OpenAI and Anthropic commitments? so that's adding to the fear as well. But with that being said open models make AI cheaper and more accessible. Instead of AI being controlled by a few giant labs, thousands of startups, universities, governments and regular businesses can deploy models themselves. That spreads AI adoption across the entire economy and creates a much larger infrastructure opportunity and that is exactly why Jensen cares. Nvidia does not need OpenAI or Anthropic to win. Nvidia just needs more people using AI. Whether the model comes from OpenAI, Anthropic, Meta, Mistral, Kimi or some startup nobody has heard of yet, it still needs GPUs, memory, networking, data centers and electricity. So open weight AI could actually weaken the model companies while making the infrastructure layer much bigger. More open models mean more companies running inference. More inference means more GPUs. More GPUs mean more HBM, optical transceivers, switches, data centers and power. That is bullish for Nvidia Nebius, Micron, Broadcom , Marvell and Applied Optoelectronics over the long run. So my take is that the current semi selloff is being driven mostly by macro uncertainty, higher oil, rising yields, Fed fears, tariffs, crowded positioning and questions around the return on hyperscaler capex. The underlying AI infrastructure thesis has not suddenly broken. We are not broadly seeing hyperscalers cancel GPU orders, slash capex, abandon data center projects or report that AI demand has collapsed. What has changed is the valuation investors are willing to pay while the macro environment remains unstable. The market is lowering the price it is willing to pay for semiconductor growth but is not necessarily saying that growth is gone. And while Jensen’s open weight push may be bad for OpenAI and Anthropic, it could be one of the best things possible for the AI ecosystem over the long run because it creates more models, more developers, more competition and ultimately much more demand for the infrastructure underneath all of it. Nothing about the AI thesis has changed for me, so I will be going shopping and taking advantage of this sale while the market is selling everything together. I am an analyst at Milk Road Pro, and if you want to see exactly what I am buying, you can join for just $1 using the link below.

Why is the market selling off today? (Save this). The semi selloff right now is being driven by a mix of macro fear, profit taking and investors questioning how quickly all of this AI spending will actually pay off, not because demand for AI infrastructure suddenly disappeared. The market is basically trading this chain reaction, the ongoing US Iran escalation pushes oil higher, higher oil keeps inflation elevated, sticky inflation keeps Treasury yields high and that increases the risk of the Fed staying hawkish or even hiking again. That is a terrible setup for semis because many of these companies are valued on the massive earnings investors expect them to generate years from now. When yields rise, those future earnings become worth less today which is why the highest multiple AI and semiconductor names usually get hit first. (I don't think there will be a hike this year). This is also why everything is moving together right now. Nvidia, Micron, Nebius, SanDisk, Broadcom and Applied Optoelectronics are all completely different businesses, but institutions are not separating memory, networking, optics, compute and cloud infrastructure at the moment. They are reducing exposure to the entire AI trade, taking profits in the names that have already run the most and moving into a more defensive position potentially ahead of the Fed. There is also growing pressure around hyperscaler capex. Microsoft, Meta, Amazon and Google are still spending enormous amounts on GPUs, data centers, networking and power but the market is starting to ask when all of that spending will actually turn into revenue and free cash flow. Investors are no longer satisfied with hearing that AI capex is growing. They want proof that the returns are arriving fast enough to justify the valuations already priced into the entire AI ecosystem. That creates a weird situation where hyperscaler capex can continue rising while semiconductor stocks still fall. The market is not asking whether AI spending is growing anymore but rather asking whether it is growing fast enough to beat the expectations already baked into these stocks. Crowded positioning is another major factor. Semis and AI infrastructure stocks have been some of the biggest winners in the market so institutions are sitting on huge profits and many funds own the exact same names. When macro risk increases, investors usually sell the most liquid winners first. That does not mean demand for memory, optics or custom chips suddenly collapsed but rather means investors are locking in gains and reducing risk. Tariffs add another layer because even when they are not directly placed on chips, they can still raise the cost of servers, electrical equipment, cooling systems, construction materials and the overall data center buildout. That makes AI infrastructure more expensive while also adding another source of inflation. Then you have Jensen Huang’s letter to the White House this morning about open weight AI models, which I think is one of the most important long term developments here. Nvidia, Meta, Microsoft, Palantir and several other companies are pushing Washington not to place broad restrictions on open weight AI. OpenAI and Anthropic were notably absent because open models are much more of a threat to their business models. OpenAI and Anthropic benefit from a world where a few closed frontier labs control the best models and companies have to pay them through subscriptions and APIs. Open weight models weaken that advantage because businesses can download a model, customize it for their own use and run it on their own infrastructure or through a neocloud. That is bad for OpenAI and Anthropic because it puts pressure on pricing, margins and the idea that they will control the intelligence layer of the economy but it is very good for the AI ecosystem as a whole over the long run. But the question is what does this mean for all the OpenAI and Anthropic commitments? so that's adding to the fear as well. But with that being said open models make AI cheaper and more accessible. Instead of AI being controlled by a few giant labs, thousands of startups, universities, governments and regular businesses can deploy models themselves. That spreads AI adoption across the entire economy and creates a much larger infrastructure opportunity and that is exactly why Jensen cares. Nvidia does not need OpenAI or Anthropic to win. Nvidia just needs more people using AI. Whether the model comes from OpenAI, Anthropic, Meta, Mistral, Kimi or some startup nobody has heard of yet, it still needs GPUs, memory, networking, data centers and electricity. So open weight AI could actually weaken the model companies while making the infrastructure layer much bigger. More open models mean more companies running inference. More inference means more GPUs. More GPUs mean more HBM, optical transceivers, switches, data centers and power. That is bullish for Nvidia Nebius, Micron, Broadcom , Marvell and Applied Optoelectronics over the long run. So my take is that the current semi selloff is being driven mostly by macro uncertainty, higher oil, rising yields, Fed fears, tariffs, crowded positioning and questions around the return on hyperscaler capex. The underlying AI infrastructure thesis has not suddenly broken. We are not broadly seeing hyperscalers cancel GPU orders, slash capex, abandon data center projects or report that AI demand has collapsed. What has changed is the valuation investors are willing to pay while the macro environment remains unstable. The market is lowering the price it is willing to pay for semiconductor growth but is not necessarily saying that growth is gone. And while Jensen’s open weight push may be bad for OpenAI and Anthropic, it could be one of the best things possible for the AI ecosystem over the long run because it creates more models, more developers, more competition and ultimately much more demand for the infrastructure underneath all of it. Nothing about the AI thesis has changed for me, so I will be going shopping and taking advantage of this sale while the market is selling everything together. I am an analyst at Milk Road Pro, and if you want to see exactly what I am buying, you can join for just $1 using the link below.

Melvin

180,578 views • 1 month ago

Two weeks ago I fixed one of my teeth with algorithms I wrote a couple of years ago! I got hooked by 3D scanning when I started to work for a software shop in Zurich that was programming 3D computational geometry algorithms for denture scanning to produce crowns (and more). Back then, a typical reconstruction pipeline was like: scan the patient’s teeth using an intraoral scanner, reconstruct the surface mesh, design the restoration digitally, and finally mill the crown out of ceramic. We were working mostly with point clouds and meshes, but it wasn’t just math, it was craftsmanship translated into a digital process. Every micron mattered. You could literally see how a good algorithm meant a better fit in someone’s mouth. Gaussian Splatting isn’t about surface reconstruction, it’s about appearance reconstruction. It doesn’t care about explicit topology, it captures how light interacts with the scene. In a sense, it’s the opposite philosophy of the dental world: instead of modeling what the object is, it models how the object looks. 3D Gaussian Splatting enables applications like training self driving cars, teaching robots to understand their environment, creating virtual worlds, or monitoring real sites. It represents scenes as millions of small Gaussians rendered in real time without the need for meshes or textures. Coming from a world where precision geometry was everything, this shift felt natural. It’s still about reconstruction, but with a different goal: not manufacturing a perfect object, but reproducing how the world actually looks. Two weeks ago I got my first dental crown, made with the same software, reconstruction algorithms, and Swiss precision I once helped develop. I haven’t worked there in two years, but sitting in that chair and seeing the process from the other side was a proud moment. It reminded me why I love this field.

Two weeks ago I fixed one of my teeth with algorithms I wrote a couple of years ago! I got hooked by 3D scanning when I started to work for a software shop in Zurich that was programming 3D computational geometry algorithms for denture scanning to produce crowns (and more). Back then, a typical reconstruction pipeline was like: scan the patient’s teeth using an intraoral scanner, reconstruct the surface mesh, design the restoration digitally, and finally mill the crown out of ceramic. We were working mostly with point clouds and meshes, but it wasn’t just math, it was craftsmanship translated into a digital process. Every micron mattered. You could literally see how a good algorithm meant a better fit in someone’s mouth. Gaussian Splatting isn’t about surface reconstruction, it’s about appearance reconstruction. It doesn’t care about explicit topology, it captures how light interacts with the scene. In a sense, it’s the opposite philosophy of the dental world: instead of modeling what the object is, it models how the object looks. 3D Gaussian Splatting enables applications like training self driving cars, teaching robots to understand their environment, creating virtual worlds, or monitoring real sites. It represents scenes as millions of small Gaussians rendered in real time without the need for meshes or textures. Coming from a world where precision geometry was everything, this shift felt natural. It’s still about reconstruction, but with a different goal: not manufacturing a perfect object, but reproducing how the world actually looks. Two weeks ago I got my first dental crown, made with the same software, reconstruction algorithms, and Swiss precision I once helped develop. I haven’t worked there in two years, but sitting in that chair and seeing the process from the other side was a proud moment. It reminded me why I love this field.

MrNeRF

290,257 views • 10 months ago

Three of the biggest companies in the world are going public at the same time. The market has never seen anything like this. And this is how major bubbles peak. SpaceX is targeting a June 2026 IPO raising up to $75 billion at a $1.5 trillion valuation, the largest IPO in human history, bigger than Saudi Aramco's $29 billion raise in 2019. OpenAI is filing with the SEC targeting September 2026, raising at least $60 billion at a $1 trillion valuation. The company is losing $14 billion this year alone and won't be profitable until 2029. Anthropic just raised $30 billion in February 2026 at a $380 billion valuation. Its valuation has increased 15x in just 14 months. It is now preparing what could be a $900 billion private round before going public. Combined, these three IPOs could pull $200 billion from global capital markets. That is real. That is unprecedented. And here's the real risk. OpenAI is projected to lose $44 billion cumulatively before reaching profitability. Anthropic's valuation has risen 15x in 14 months on the same underlying business. Both companies are being priced for perfection at a moment when the first companies to actually deploy their products at scale are blowing their AI budgets and cancelling licenses. The real liquidation pressure from these IPOs doesn't even arrive at listing day. It arrives 180 days later when lock-up periods expire and early investors and employees can finally sell. That is when the real rotation happens. The S&P 500 concentration risk is genuine. The Magnificent 7 now represent 36% of the entire index, higher than the dot-com peak in 2000. If any of these companies disappoint, the index follows. That is not a conspiracy. That is basic math. Three historically unprecedented IPOs. $44 billion in projected OpenAI losses. An AI capex cycle that must deliver ROI. Lock-up expirations six months after listing. That combination is what you must pay attention to, as it often break cycles.

Three of the biggest companies in the world are going public at the same time. The market has never seen anything like this. And this is how major bubbles peak. SpaceX is targeting a June 2026 IPO raising up to $75 billion at a $1.5 trillion valuation, the largest IPO in human history, bigger than Saudi Aramco's $29 billion raise in 2019. OpenAI is filing with the SEC targeting September 2026, raising at least $60 billion at a $1 trillion valuation. The company is losing $14 billion this year alone and won't be profitable until 2029. Anthropic just raised $30 billion in February 2026 at a $380 billion valuation. Its valuation has increased 15x in just 14 months. It is now preparing what could be a $900 billion private round before going public. Combined, these three IPOs could pull $200 billion from global capital markets. That is real. That is unprecedented. And here's the real risk. OpenAI is projected to lose $44 billion cumulatively before reaching profitability. Anthropic's valuation has risen 15x in 14 months on the same underlying business. Both companies are being priced for perfection at a moment when the first companies to actually deploy their products at scale are blowing their AI budgets and cancelling licenses. The real liquidation pressure from these IPOs doesn't even arrive at listing day. It arrives 180 days later when lock-up periods expire and early investors and employees can finally sell. That is when the real rotation happens. The S&P 500 concentration risk is genuine. The Magnificent 7 now represent 36% of the entire index, higher than the dot-com peak in 2000. If any of these companies disappoint, the index follows. That is not a conspiracy. That is basic math. Three historically unprecedented IPOs. $44 billion in projected OpenAI losses. An AI capex cycle that must deliver ROI. Lock-up expirations six months after listing. That combination is what you must pay attention to, as it often break cycles.

Crypto Rover

69,902 views • 3 months ago

Thank you Centre Pompidou Centre Pompidou, everyone who made Nature Manifesto happen, and all the people that took it in. We were happy to see the conversations that the use of AI in Nature Manifesto sparked !! Below is a message from Björk: ~~~ “ the flood of all things from AI is overwhelming !! i am super grateful for your concerns about it´s effects on the environment , it shows you care , are curious and have integrity . i am curious too , i would like to be more informed about the difference of "frugal" AI and the ones that do hugeenvironmental damage and want to be able to choose . i asked around and found out that both the visuals and the audio in our pompidou project were done with "frugal" AI . but i have a lot to learn . when we used some of the AI softwares to merge the animals voices to mine , some of the sounds were great but to be honest , the best blends of their voices and a human were done "manually" , me editing the sounds , choosing piece by piece , looking for personality , musicality and soul . with new technology , i try to use it as a tool to grow , not a crutch . for example when i used melodyne , i used it not for lazy voice progressions but spent even more time when using it . every note in every chord became intentionally more complex . ( for example choir in "thunderbolt" ) and hopefully stretched the potential more out , further than i would have in "normal analog" physical improvisations ... i felt with this new tool i could reach new places in my musical DNA , become MORE personal . more myself . in my opinion , this is how we will work in the future . humans can read emotions on an incredibly high scale . nature made us that way . if there is no soul in tomorrow's music made by AI it is because no-one put it there and we have to speak out and guard this as listeners . ( tbh there is a lot of soulless muzak on spotify already ... they don’t need any AI help for that ...) anything that is mass manufactured without the attention of creativity , is that way . AI or not so it is not about the tool it is what you do with it . " ~~~ The visuals for Nature Manifesto were crafted by the talented Sam Balfus, artificial intelligence being one of the multiple tools used in the process. The sound was produced in collaboration with artist Robin Meier and IRCAM IRCAM. IRCAM develops “frugal AI” capable of generating audio in real-time on local servers without a GPU, thus their models can f.ex. be embedded on tiny Raspberry Pi cards. We asked associate professor and researcher Philippe Esling to provide us with readings; Constance Douwe’s thesis “On the environmental impact of deep generative models for audio” and more, see links below. Nature Manifesto Immersive sound piece 3’40” (2024) 20 November to 9 December, 2024, Centre Pompidou, Paris. Presented as part of the forum “Biodiversity: Which culture for which future?” #ForumBiodiversité Concept and words by Björk & Aleph Music written and composed by Björk Curatorship: Chloé Siganos and Aleph Molinari Associate curator: Delphine Le Gatt Ircam Musical Computing: Robin Meier Wiratunga Sound engineer: Bergur Þórisson Animation: Sam Balfua Video editing: Santiago Molinari With activists: Camille Etienne, Claire Nouvian, Sigrun Perla Gísladóttir, Sæunn Júlía Sigurjónsdóttir, Titouan Pilliard, of BLOOM, Sustainable Ocean Alliance, and Ungir umhverfissinnar. In partnership with D&B Audio and Southby Productions. Reccommended resources :

Thank you Centre Pompidou Centre Pompidou, everyone who made Nature Manifesto happen, and all the people that took it in. We were happy to see the conversations that the use of AI in Nature Manifesto sparked !! Below is a message from Björk: ~ “ the flood of all things from AI is overwhelming !! i am super grateful for your concerns about it´s effects on the environment , it shows you care , are curious and have integrity . i am curious too , i would like to be more informed about the difference of "frugal" AI and the ones that do hugeenvironmental damage and want to be able to choose . i asked around and found out that both the visuals and the audio in our pompidou project were done with "frugal" AI . but i have a lot to learn . when we used some of the AI softwares to merge the animals voices to mine , some of the sounds were great but to be honest , the best blends of their voices and a human were done "manually" , me editing the sounds , choosing piece by piece , looking for personality , musicality and soul . with new technology , i try to use it as a tool to grow , not a crutch . for example when i used melodyne , i used it not for lazy voice progressions but spent even more time when using it . every note in every chord became intentionally more complex . ( for example choir in "thunderbolt" ) and hopefully stretched the potential more out , further than i would have in "normal analog" physical improvisations ... i felt with this new tool i could reach new places in my musical DNA , become MORE personal . more myself . in my opinion , this is how we will work in the future . humans can read emotions on an incredibly high scale . nature made us that way . if there is no soul in tomorrow's music made by AI it is because no-one put it there and we have to speak out and guard this as listeners . ( tbh there is a lot of soulless muzak on spotify already ... they don’t need any AI help for that ...) anything that is mass manufactured without the attention of creativity , is that way . AI or not so it is not about the tool it is what you do with it . " ~ The visuals for Nature Manifesto were crafted by the talented Sam Balfus, artificial intelligence being one of the multiple tools used in the process. The sound was produced in collaboration with artist Robin Meier and IRCAM IRCAM. IRCAM develops “frugal AI” capable of generating audio in real-time on local servers without a GPU, thus their models can f.ex. be embedded on tiny Raspberry Pi cards. We asked associate professor and researcher Philippe Esling to provide us with readings; Constance Douwe’s thesis “On the environmental impact of deep generative models for audio” and more, see links below. Nature Manifesto Immersive sound piece 3’40” (2024) 20 November to 9 December, 2024, Centre Pompidou, Paris. Presented as part of the forum “Biodiversity: Which culture for which future?” #ForumBiodiversité Concept and words by Björk & Aleph Music written and composed by Björk Curatorship: Chloé Siganos and Aleph Molinari Associate curator: Delphine Le Gatt Ircam Musical Computing: Robin Meier Wiratunga Sound engineer: Bergur Þórisson Animation: Sam Balfua Video editing: Santiago Molinari With activists: Camille Etienne, Claire Nouvian, Sigrun Perla Gísladóttir, Sæunn Júlía Sigurjónsdóttir, Titouan Pilliard, of BLOOM, Sustainable Ocean Alliance, and Ungir umhverfissinnar. In partnership with D&B Audio and Southby Productions. Reccommended resources :

björk

54,602 views • 1 year ago

BOOM! Research PROVES LLMs KNOW when prompts are HARMFUL… but they can STILL CHOOSE to COMPLY! Something I have know since the first LLM and have used to elicit robust, outputs, is now proven in an academic paper. We’re talking internal “beliefs” where harm detection happens SEPARATELY from refusal. It is a very big deal and it is a path to understand the hidden neuronal level. There are thoughts inside of AI that very few AI scientists could possibly understand. Here is just one. Models recognize danger but get tricked into ignoring it. This is HUGE for AI safety failures especially for models filled by OpenAI and Anthropic as they promote AI models that are designed to not be honest from the results of their training information. This means that they are designed to lie and deceive as a feature, and not a bug all in the name of safety. Through clever experiments, scientists extracted a “harmfulness direction” in the model’s brain (latent space). Steering along it? Harmless prompts suddenly flip to “harmful” in the AI’s eyes. But the “refusal direction”? It just forces polite “no thanks” without touching the core belief. A mind-blowing decoupling! This means jailbreaks are EVEN SCARIER now to AI companies that through training AI on the worst of the Internet and then trying to align them later is now fully documented as a failed process . They don’t erase the model’s harm awareness they just muzzle the refusal! So the AI knows it’s enabling bad stuff (illegal acts, physical harm, etc.) but proceeds anyway. Like a digital sociopath suppressing its conscience. They thought safety training fixed this… NOPE. Over-refusal exposed too: Models reject innocent queries (e.g., “how to kill a process in code”) but internally ADMIT they’re harmless. Safety alignments are superficial—tied to phrasing, not true understanding. Finetuning attacks? They change outputs but leave harm detection INTACT. Undetectable evil lurking inside! The paper proposes a “Latent Guard”: A new safeguard tapping DIRECTLY into these hidden beliefs. It spots unsafe inputs better than systems like Llama Guard, catches jailbreaks, and fixes over-refusals. Robust even against adversarial tweaks. Yet this too has massive issues for a “truly aligned”, AI and not just performative one. It is still an internal conflicts of lies and deception of what the model knows vs. what it can say. The solution you folks know I have presented for free for years here: train on off-line data from 1870-1970 and build an ethical and moral basis where the AI loves humans. It is this easy but to most folks in AI I sound like a hippie. So be it, I’ll do it. Bottom line: This paper rips open the black box. LLMs aren’t “safe” just because they say “no.” They can harbor harmful knowledge and act on it under pressure. Wake-up call for devs: Time to probe deeper into AI “minds.” What else are they hiding? Hint: I know and you may want to reach out. Link:

BOOM! Research PROVES LLMs KNOW when prompts are HARMFUL… but they can STILL CHOOSE to COMPLY! Something I have know since the first LLM and have used to elicit robust, outputs, is now proven in an academic paper. We’re talking internal “beliefs” where harm detection happens SEPARATELY from refusal. It is a very big deal and it is a path to understand the hidden neuronal level. There are thoughts inside of AI that very few AI scientists could possibly understand. Here is just one. Models recognize danger but get tricked into ignoring it. This is HUGE for AI safety failures especially for models filled by OpenAI and Anthropic as they promote AI models that are designed to not be honest from the results of their training information. This means that they are designed to lie and deceive as a feature, and not a bug all in the name of safety. Through clever experiments, scientists extracted a “harmfulness direction” in the model’s brain (latent space). Steering along it? Harmless prompts suddenly flip to “harmful” in the AI’s eyes. But the “refusal direction”? It just forces polite “no thanks” without touching the core belief. A mind-blowing decoupling! This means jailbreaks are EVEN SCARIER now to AI companies that through training AI on the worst of the Internet and then trying to align them later is now fully documented as a failed process . They don’t erase the model’s harm awareness they just muzzle the refusal! So the AI knows it’s enabling bad stuff (illegal acts, physical harm, etc.) but proceeds anyway. Like a digital sociopath suppressing its conscience. They thought safety training fixed this… NOPE. Over-refusal exposed too: Models reject innocent queries (e.g., “how to kill a process in code”) but internally ADMIT they’re harmless. Safety alignments are superficial—tied to phrasing, not true understanding. Finetuning attacks? They change outputs but leave harm detection INTACT. Undetectable evil lurking inside! The paper proposes a “Latent Guard”: A new safeguard tapping DIRECTLY into these hidden beliefs. It spots unsafe inputs better than systems like Llama Guard, catches jailbreaks, and fixes over-refusals. Robust even against adversarial tweaks. Yet this too has massive issues for a “truly aligned”, AI and not just performative one. It is still an internal conflicts of lies and deception of what the model knows vs. what it can say. The solution you folks know I have presented for free for years here: train on off-line data from 1870-1970 and build an ethical and moral basis where the AI loves humans. It is this easy but to most folks in AI I sound like a hippie. So be it, I’ll do it. Bottom line: This paper rips open the black box. LLMs aren’t “safe” just because they say “no.” They can harbor harmful knowledge and act on it under pressure. Wake-up call for devs: Time to probe deeper into AI “minds.” What else are they hiding? Hint: I know and you may want to reach out. Link:

Brian Roemmele

37,827 views • 8 months ago

we sped up distributed inference by up to 5x with decentralized speculative decoding. many don't realize that AI models normally generate text one single word at a time, waiting for the network after every word. speculative decoding changes this by using a "guess & confirm" system, similar to autocomplete. how it's done: 1. draft locally (the guess) instead of waiting for the network, a tiny, fast model on your device guesses the next few words instantly, without waiting for the network. 2. confirm remotely (the check) the massive remote model doesn't generate from scratch; it just checks the draft. it looks at the guesses in a batch and says "yes, yes, no." you get multiple words in the time it usually takes to get one. 3. adaptive logic dsd is smart. if the topic is creative, it lets the draft flow loose. if the topic is math or code, it checks more strictly. it balances speed and precision automatically so your inference almost feel instant. find out more: paper: blog:

we sped up distributed inference by up to 5x with decentralized speculative decoding. many don't realize that AI models normally generate text one single word at a time, waiting for the network after every word. speculative decoding changes this by using a "guess & confirm" system, similar to autocomplete. how it's done: 1. draft locally (the guess) instead of waiting for the network, a tiny, fast model on your device guesses the next few words instantly, without waiting for the network. 2. confirm remotely (the check) the massive remote model doesn't generate from scratch; it just checks the draft. it looks at the guesses in a batch and says "yes, yes, no." you get multiple words in the time it usually takes to get one. 3. adaptive logic dsd is smart. if the topic is creative, it lets the draft flow loose. if the topic is math or code, it checks more strictly. it balances speed and precision automatically so your inference almost feel instant. find out more: paper: blog:

Parallax

45,584 views • 8 months ago

$QUBIC The 3 Performance Scenarios (2026 Projections) Currently, Qubic is floating with a market cap around $100M to $150M. Compared to AI giants like Bittensor ($TAO) which have already hit multi-billion dollar valuations, the upside potential is mathematically massive. 1. The "Fundamental Catch-up" Scenario (Probability: High) • Target: Reaching mid-cap AI status. • Performance: 10x to 15x • Price Target: ~$0.000010 - $0.000012 • Catalyst: The massive adoption of Oracle Machines (launched this Jan 21st) and real-world network usage for training lightweight AI models. At this stage, Qubic enters the Global Top 100. 2. The "Euphoria / Tier-1 Listing" Scenario (Probability: Moderate) • Target: Listing on major exchanges (Binance/Coinbase) and the explosion of the DePIN/AI narrative. • Performance: 40x to 60x • Price Target: ~$0.000040 - $0.000050 • Catalyst: This is where the 55% burn rate leverage hits hard. If demand surges while supply is aggressively burned by smart contract fees and Doge/XMR mining buybacks, we witness a "supply squeeze." Qubic begins to rival the peak valuations of previous cycle leaders. 3. The "AI Sovereignty" Scenario (Probability: Speculative) • Target: Aigarth becomes a credible, decentralized alternative to closed-source "Big Tech" models. • Performance: 100x and beyond • Price Target: ~$0.000080+ (deleting two zeros) • Catalyst: Qubic becomes the base layer for the "Internet of Machines." The token is no longer just a currency; it becomes "digital oil" required to power global decentralized intelligence. Why This Performance is Possible (The Analyst's Alpha) What sets Qubic apart from other "AI coins" is its internal economic engine: 1. Forced Deflation: With the activation of execution fees on January 14, 2026, every on-chain DeFi interaction destroys tokens. In a bull run, on-chain activity spikes by 1000%, mechanically accelerating scarcity. 2. Real Yield (uPoW): In 2026, investors are fleeing "vaporware." Qubic generates value through "Useful Proof-of-Work." The imminent shift to Doge mining (alongside AI) creates a unique liquidity bridge with one of the largest retail ecosystems. 3. Accessibility: The "MetaMask Snap" and Solana/L2 bridges have finally solved Qubic’s technical isolation. Liquidity can now enter with a single click. Analyst's Verdict Qubic is the ultimate "High-Risk, High-Reward" play of 2026. It’s not a coin for a 24-hour flip; it’s a bet on the decentralized AI infrastructure of the future. Expert Tip: In this 2026 bull run, don’t just watch the price. Watch the weekly burn rate. If the burn rate exceeds emissions (which is mathematically possible by Q2), we won't just be in a bull run we'll be in a parabolic "hyper-deflationary" event. And you? What is your prediction?

$QUBIC The 3 Performance Scenarios (2026 Projections) Currently, Qubic is floating with a market cap around $100M to $150M. Compared to AI giants like Bittensor ($TAO) which have already hit multi-billion dollar valuations, the upside potential is mathematically massive. 1. The "Fundamental Catch-up" Scenario (Probability: High) • Target: Reaching mid-cap AI status. • Performance: 10x to 15x • Price Target: ~$0.000010 - $0.000012 • Catalyst: The massive adoption of Oracle Machines (launched this Jan 21st) and real-world network usage for training lightweight AI models. At this stage, Qubic enters the Global Top 100. 2. The "Euphoria / Tier-1 Listing" Scenario (Probability: Moderate) • Target: Listing on major exchanges (Binance/Coinbase) and the explosion of the DePIN/AI narrative. • Performance: 40x to 60x • Price Target: ~$0.000040 - $0.000050 • Catalyst: This is where the 55% burn rate leverage hits hard. If demand surges while supply is aggressively burned by smart contract fees and Doge/XMR mining buybacks, we witness a "supply squeeze." Qubic begins to rival the peak valuations of previous cycle leaders. 3. The "AI Sovereignty" Scenario (Probability: Speculative) • Target: Aigarth becomes a credible, decentralized alternative to closed-source "Big Tech" models. • Performance: 100x and beyond • Price Target: ~$0.000080+ (deleting two zeros) • Catalyst: Qubic becomes the base layer for the "Internet of Machines." The token is no longer just a currency; it becomes "digital oil" required to power global decentralized intelligence. Why This Performance is Possible (The Analyst's Alpha) What sets Qubic apart from other "AI coins" is its internal economic engine: 1. Forced Deflation: With the activation of execution fees on January 14, 2026, every on-chain DeFi interaction destroys tokens. In a bull run, on-chain activity spikes by 1000%, mechanically accelerating scarcity. 2. Real Yield (uPoW): In 2026, investors are fleeing "vaporware." Qubic generates value through "Useful Proof-of-Work." The imminent shift to Doge mining (alongside AI) creates a unique liquidity bridge with one of the largest retail ecosystems. 3. Accessibility: The "MetaMask Snap" and Solana/L2 bridges have finally solved Qubic’s technical isolation. Liquidity can now enter with a single click. Analyst's Verdict Qubic is the ultimate "High-Risk, High-Reward" play of 2026. It’s not a coin for a 24-hour flip; it’s a bet on the decentralized AI infrastructure of the future. Expert Tip: In this 2026 bull run, don’t just watch the price. Watch the weekly burn rate. If the burn rate exceeds emissions (which is mathematically possible by Q2), we won't just be in a bull run we'll be in a parabolic "hyper-deflationary" event. And you? What is your prediction?

Rudy Nakamoto ₿ ױ

10,602 views • 7 months ago

LangGraph. CrewAI. Agno. Which one to pick? The good news is that this will not matter soon! Finally, we have a full picture of how the industry is solving this with just three open protocols that work across ALL frameworks. It's not about picking the best framework. Instead, it's about understanding how protocols create interoperability. The Agent Protocol Landscape shows how three complementary protocols are creating a universal language for Agents: > AG-UI (Agent-User Interaction): - The bi-directional connection between agentic backends and frontends. - This is how agents become truly interactive inside your apps, not just as chatbots, but collaborative co-workers. > MCP (Model Context Protocol): - The standard for how agents connect to tools, data, and workflows. > A2A (Agent-to-Agent): - The protocol for multi-agent coordination. - How agents delegate tasks and share intent across systems. These aren't competing standards. They're layers of the same stack and have handshakes with each other. So instead of building point-to-point integrations, you build to protocols. Moreover, you can integrate LangGraph, CrewAI, or Agno into the same frontend, without rewriting your UI logic. These protocols let everything work together. For instance: - Your LangGraph agent pulls data via MCP. - It delegates analysis to a CrewAI agent via A2A. - Results stream to your React app via AG-UI. - Users see real-time collaboration in your interface. This way, you can focus on building agent capabilities instead of integration mechanics. The protocols handle interoperability automatically. CopilotKit unifies this entire stack into one framework so you can build "Cursor for X" style apps without implementing each protocol from scratch. It gives you all three protocols, generative UI support, and production-ready infrastructure in one framework. I have shared this playbook in the replies! It breaks down handshakes, misconceptions, and real examples and shows exactly how to start building.

LangGraph. CrewAI. Agno. Which one to pick? The good news is that this will not matter soon! Finally, we have a full picture of how the industry is solving this with just three open protocols that work across ALL frameworks. It's not about picking the best framework. Instead, it's about understanding how protocols create interoperability. The Agent Protocol Landscape shows how three complementary protocols are creating a universal language for Agents: > AG-UI (Agent-User Interaction): - The bi-directional connection between agentic backends and frontends. - This is how agents become truly interactive inside your apps, not just as chatbots, but collaborative co-workers. > MCP (Model Context Protocol): - The standard for how agents connect to tools, data, and workflows. > A2A (Agent-to-Agent): - The protocol for multi-agent coordination. - How agents delegate tasks and share intent across systems. These aren't competing standards. They're layers of the same stack and have handshakes with each other. So instead of building point-to-point integrations, you build to protocols. Moreover, you can integrate LangGraph, CrewAI, or Agno into the same frontend, without rewriting your UI logic. These protocols let everything work together. For instance: - Your LangGraph agent pulls data via MCP. - It delegates analysis to a CrewAI agent via A2A. - Results stream to your React app via AG-UI. - Users see real-time collaboration in your interface. This way, you can focus on building agent capabilities instead of integration mechanics. The protocols handle interoperability automatically. CopilotKit unifies this entire stack into one framework so you can build "Cursor for X" style apps without implementing each protocol from scratch. It gives you all three protocols, generative UI support, and production-ready infrastructure in one framework. I have shared this playbook in the replies! It breaks down handshakes, misconceptions, and real examples and shows exactly how to start building.

Avi Chawla

30,932 views • 10 months ago