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🚨 BREAKING: Alibaba Group just unveiled four groundbreaking AI models at Apsara Conference 2025 that are reshaping the AI landscape: - Qwen3-Max (1T+ parameters) - Qwen3-VL (vision-language powerhouse) - Qwen3-Omni (true multimodal) - Wan2.5 (next-gen video creation) Thread below 👇

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MiniMax is building with next-gen AGI, offering foundation AI models and revolutionizing content creation with AI-powered video, music, chat products. With Alibaba's one-stop multimodal data platform, MiniMax can save the complexity of handling multimodal data work with the AI-ready data foundation, and boost streamlined innovation on it. Feifei Li, SVP and President of International Business at Alibaba Cloud Intelligence Group, shares how the right tech stack can elevate AI from potential to powerful. 🚀

MiniMax is building with next-gen AGI, offering foundation AI models and revolutionizing content creation with AI-powered video, music, chat products. With Alibaba's one-stop multimodal data platform, MiniMax can save the complexity of handling multimodal data work with the AI-ready data foundation, and boost streamlined innovation on it. Feifei Li, SVP and President of International Business at Alibaba Cloud Intelligence Group, shares how the right tech stack can elevate AI from potential to powerful. 🚀

Alibaba Group

305,831 views • 8 months ago

NVIDIA CEO Jensen Huang just gave us a glimpse into the future of AI, and it's far bigger than text and images. He asked: "If you can understand worlds, is it possible that you understand proteins and chemicals that have structure?” This is the quiet revolution happening in AI right now. We're moving from an AI that understands human language to an AI that understands the language of nature itself. The "letters" are atoms. The "words" are molecules. The "sentences" are the complex structures of life, networks, and reality. The same foundational models that generate video are now being used to: *Decode proteins to fight disease *Design new materials at the atomic level *Simulate complex systems in quantum computing These were once the domain of supercomputers and Nobel laureates. Now, they're becoming solvable problems for a new generation of startups. AI is transitioning from a tool for communication to a tool for creation and discovery in the physical world. This is where the next wave of generational companies will be built. The "hard-to-solve" problems are now within reach.

NVIDIA CEO Jensen Huang just gave us a glimpse into the future of AI, and it's far bigger than text and images. He asked: "If you can understand worlds, is it possible that you understand proteins and chemicals that have structure?” This is the quiet revolution happening in AI right now. We're moving from an AI that understands human language to an AI that understands the language of nature itself. The "letters" are atoms. The "words" are molecules. The "sentences" are the complex structures of life, networks, and reality. The same foundational models that generate video are now being used to: Decode proteins to fight disease Design new materials at the atomic level *Simulate complex systems in quantum computing These were once the domain of supercomputers and Nobel laureates. Now, they're becoming solvable problems for a new generation of startups. AI is transitioning from a tool for communication to a tool for creation and discovery in the physical world. This is where the next wave of generational companies will be built. The "hard-to-solve" problems are now within reach.

Konstantine Buhler

13,014 views • 10 months ago

🚨 Open-source AI just got serious. Alibaba just released Qwen 3.5-Plus 🤯 397B parameters. Only 17B active per inference. Performance comparable to Gemini 3 Pro — at just $0.8 per million tokens (1/18 the cost). But here’s what makes it different 👇 This isn’t just another benchmark drop. In the demo, Qwen runs directly inside the terminal: ➙ Reviews GitHub PRs ➙ Fetches diffs automatically ➙ Runs CLI & smoke tests ➙ Generates integration tests ➙ Executes /context ➙ Shows real-time token usage breakdown That’s not a chatbot. That’s a real developer workflow. Under the hood: ⚡ 60% lower deployment memory ⚡ Inference throughput increased by 8.6× (32K context) ⚡ Inference throughput increased by up to 19× (256K ultra-long context) ⚡ Native multimodal model ⚡ Supports direct video input (up to 2 hours) And the ecosystem is massive: ✔ 201 languages ✔ 250K vocabulary ✔ 400+ open-sourced models ✔ 1B+ downloads ✔ 200K+ derivative models Available now via Qwen App, MoDa Community, Hugging Face, and Alibaba Cloud Bailian. ✅ The AI race isn’t about who’s biggest anymore. → It’s about who’s usable. 🔥 Watch the demo 👇 #Qwen #OpenSourceAI #AI #DevTools

🚨 Open-source AI just got serious. Alibaba just released Qwen 3.5-Plus 🤯 397B parameters. Only 17B active per inference. Performance comparable to Gemini 3 Pro — at just $0.8 per million tokens (1/18 the cost). But here’s what makes it different 👇 This isn’t just another benchmark drop. In the demo, Qwen runs directly inside the terminal: ➙ Reviews GitHub PRs ➙ Fetches diffs automatically ➙ Runs CLI & smoke tests ➙ Generates integration tests ➙ Executes /context ➙ Shows real-time token usage breakdown That’s not a chatbot. That’s a real developer workflow. Under the hood: ⚡ 60% lower deployment memory ⚡ Inference throughput increased by 8.6× (32K context) ⚡ Inference throughput increased by up to 19× (256K ultra-long context) ⚡ Native multimodal model ⚡ Supports direct video input (up to 2 hours) And the ecosystem is massive: ✔ 201 languages ✔ 250K vocabulary ✔ 400+ open-sourced models ✔ 1B+ downloads ✔ 200K+ derivative models Available now via Qwen App, MoDa Community, Hugging Face, and Alibaba Cloud Bailian. ✅ The AI race isn’t about who’s biggest anymore. → It’s about who’s usable. 🔥 Watch the demo 👇 #Qwen #OpenSourceAI #AI #DevTools

Toha Khan

123,375 views • 6 months ago

people burn $1,900 a month on cloud, this $2,999 NVIDIA box runs 5 AI models at once and costs $10 to power 128GB of unified memory, stack two together and you double it, enough to run models a $2,000 graphics card cannot even open in the demo it ran Qwen3 Vision, Nemotron, a voice model, text to speech and speech to text all at once, and only used half the box here is the math that makes people switch: > cloud GPUs were costing $1,900 every single month, billed forever > the box is $2,999 one time, then about $10 of electricity a month > it paid for itself in two months, $22,000 stayed in the business in year one > zero data ever leaves your desk, no rental, no rate limits most people are still renting what this owns outright the full breakdown of the box and the exact math is in the article below

people burn $1,900 a month on cloud, this $2,999 NVIDIA box runs 5 AI models at once and costs $10 to power 128GB of unified memory, stack two together and you double it, enough to run models a $2,000 graphics card cannot even open in the demo it ran Qwen3 Vision, Nemotron, a voice model, text to speech and speech to text all at once, and only used half the box here is the math that makes people switch: > cloud GPUs were costing $1,900 every single month, billed forever > the box is $2,999 one time, then about $10 of electricity a month > it paid for itself in two months, $22,000 stayed in the business in year one > zero data ever leaves your desk, no rental, no rate limits most people are still renting what this owns outright the full breakdown of the box and the exact math is in the article below

John Doe

48,135 views • 2 months ago

THIS GUY OPENED A TINY PC AND SHOWED HOW A $1,800 GMKTEC EVO-X2 TURNS INTO A LOCAL AI SERVER not a cloud dashboard. not another $200/mo subscription. just one box on a desk, upgraded with storage, cleaner airflow, faster networking, and pointed at real local models. the numbers are what make it interesting. Claude Code Max is $200/mo, ChatGPT Pro is $200/mo, Cursor is another $20/mo. that is $5,040/year just to rent tools most people use for coding, research, summaries, and agents. EVO-X2 flips the setup because it has 128GB unified memory, Ryzen AI Max+ 395, and up to 110GB usable as VRAM on Linux. enough room for Qwen3-Coder 30B, Llama 70B, and 200B-class local models when speed is not the bottleneck. so the workflow becomes simple: buy the box once, install Linux, run the model locally, connect the CLI, and let the machine handle jobs from your own network instead of sending every request to the cloud. this is where the real edge starts. overnight code audits, private document search, local RAG, scraping agents, transcription, file cleanup, all running while the token meter stays at zero. frontier models still win for the hardest prompts. but for daily automation, coding, research, and private workflows, local AI is already moving from nerd setup to obvious business math. the next flex won’t be “which AI plan do you pay for?” it’ll be “what’s running under your desk?”

THIS GUY OPENED A TINY PC AND SHOWED HOW A $1,800 GMKTEC EVO-X2 TURNS INTO A LOCAL AI SERVER not a cloud dashboard. not another $200/mo subscription. just one box on a desk, upgraded with storage, cleaner airflow, faster networking, and pointed at real local models. the numbers are what make it interesting. Claude Code Max is $200/mo, ChatGPT Pro is $200/mo, Cursor is another $20/mo. that is $5,040/year just to rent tools most people use for coding, research, summaries, and agents. EVO-X2 flips the setup because it has 128GB unified memory, Ryzen AI Max+ 395, and up to 110GB usable as VRAM on Linux. enough room for Qwen3-Coder 30B, Llama 70B, and 200B-class local models when speed is not the bottleneck. so the workflow becomes simple: buy the box once, install Linux, run the model locally, connect the CLI, and let the machine handle jobs from your own network instead of sending every request to the cloud. this is where the real edge starts. overnight code audits, private document search, local RAG, scraping agents, transcription, file cleanup, all running while the token meter stays at zero. frontier models still win for the hardest prompts. but for daily automation, coding, research, and private workflows, local AI is already moving from nerd setup to obvious business math. the next flex won’t be “which AI plan do you pay for?” it’ll be “what’s running under your desk?”

Gipp 🦅

13,271 views • 2 months ago

Not the flashiest demos, but what’s under the hood represents a foundational shift for general-purpose robotics. World models are the next-gen foundation of Physical AI, not the VLM backbones found in typical VLAs. DreamZero is a 14B-parameter World Action Model (WAM) by NVIDIA that treats robotics as a joint video-and-action prediction task. Unlike traditional Vision-Language-Action (VLA) models that map images directly to motor commands, DreamZero leverages a pretrained video diffusion backbone to predict future world states and actions simultaneously. - achieves 2× better zero-shot generalization to unseen tasks and environments compared to state-of-the-art VLAs. - learns effectively from heterogeneous, non-repetitive data (500 hours), breaking the need for thousands of repeated demonstrations. - adapts to new robot embodiments with just 30 minutes of play data. - enables 7Hz closed-loop control via system optimizations and "DreamZero-Flash," making high-capacity diffusion models viable for real-time use.

Not the flashiest demos, but what’s under the hood represents a foundational shift for general-purpose robotics. World models are the next-gen foundation of Physical AI, not the VLM backbones found in typical VLAs. DreamZero is a 14B-parameter World Action Model (WAM) by NVIDIA that treats robotics as a joint video-and-action prediction task. Unlike traditional Vision-Language-Action (VLA) models that map images directly to motor commands, DreamZero leverages a pretrained video diffusion backbone to predict future world states and actions simultaneously. - achieves 2× better zero-shot generalization to unseen tasks and environments compared to state-of-the-art VLAs. - learns effectively from heterogeneous, non-repetitive data (500 hours), breaking the need for thousands of repeated demonstrations. - adapts to new robot embodiments with just 30 minutes of play data. - enables 7Hz closed-loop control via system optimizations and "DreamZero-Flash," making high-capacity diffusion models viable for real-time use.

The Humanoid Hub

35,204 views • 6 months ago

AMD CEO Lisa Su just killed Nvidia’s $4,000 AI box with a $1,499 lunchbox. She walked on stage, held it in one hand, and ran a 235 billion parameter model live. No data center. No cloud. No rented GPU. The chip inside is something nobody saw coming. AMD’s Ryzen AI Max+ 395 is the first x86 silicon where CPU and GPU share the same 128GB of memory. That single trick lets a desktop run models that used to need a server rack. Out of those 128GB, Linux hands the GPU 110GB to play with. For context, an RTX 5090 gives you 32GB. A 4090 gives you 24. This box gives you more than three times either of them, in a chassis the size of a thick paperback. The benchmark that broke the room: this chip beat an Nvidia RTX 5080 by more than 3x on DeepSeek R1 inference. A $1,499 lunchbox outrunning a $1,000 discrete graphics card on a real AI workload. Nvidia spent a decade convincing the world you needed their hardware for serious AI. AMD just put that on a desk for half the price. Here is what nobody is telling you. A heavy AI user right now pays $200 for Claude Code Max, $200 for ChatGPT Pro, $20 for Cursor, $20 for Gemini. That is $5,280 a year leaving your account. The box pays itself off in 9 months and then runs free for the rest of its life. Install Ollama. Pull Qwen3 235B. Point Claude Code at localhost. Same interface you already use, except now nothing leaves your machine, nothing costs per request, and no company throttles your usage at 3am when you finally have time to build. This is the moment every AI subscription becomes optional. Lawyers stop fearing OpenAI leaks. Developers stop watching the token meter. Founders stop renting H100s for prototypes that never ship because the bill scared them. The first thousand people to figure this out will own the next two years of private AI consulting. Save this, and read the full breakdown article below you are watching the next shift hit before everyone else does.

AMD CEO Lisa Su just killed Nvidia’s $4,000 AI box with a $1,499 lunchbox. She walked on stage, held it in one hand, and ran a 235 billion parameter model live. No data center. No cloud. No rented GPU. The chip inside is something nobody saw coming. AMD’s Ryzen AI Max+ 395 is the first x86 silicon where CPU and GPU share the same 128GB of memory. That single trick lets a desktop run models that used to need a server rack. Out of those 128GB, Linux hands the GPU 110GB to play with. For context, an RTX 5090 gives you 32GB. A 4090 gives you 24. This box gives you more than three times either of them, in a chassis the size of a thick paperback. The benchmark that broke the room: this chip beat an Nvidia RTX 5080 by more than 3x on DeepSeek R1 inference. A $1,499 lunchbox outrunning a $1,000 discrete graphics card on a real AI workload. Nvidia spent a decade convincing the world you needed their hardware for serious AI. AMD just put that on a desk for half the price. Here is what nobody is telling you. A heavy AI user right now pays $200 for Claude Code Max, $200 for ChatGPT Pro, $20 for Cursor, $20 for Gemini. That is $5,280 a year leaving your account. The box pays itself off in 9 months and then runs free for the rest of its life. Install Ollama. Pull Qwen3 235B. Point Claude Code at localhost. Same interface you already use, except now nothing leaves your machine, nothing costs per request, and no company throttles your usage at 3am when you finally have time to build. This is the moment every AI subscription becomes optional. Lawyers stop fearing OpenAI leaks. Developers stop watching the token meter. Founders stop renting H100s for prototypes that never ship because the bill scared them. The first thousand people to figure this out will own the next two years of private AI consulting. Save this, and read the full breakdown article below you are watching the next shift hit before everyone else does.

AdiiX

3,394,390 views • 2 months ago

Bytedance is dropping the best video gen model in the world in early July: Seedance 2.5! The video below (audio on) is the launch video from their Volcano Engine conference this week. It cements China’s absolute dominance in video. — 2x’d generation length of all previous models to 30s, with audio + 4k video — >5x’d reference images / audio / video to 50 — Allows localized editing (specific characters, closing, detail), will come with copyright filter Seedance 2 is already the #1 video model and does a whopping $2B in ARR, in a mere 4.5mos! At the current pricing of $2.5/15s, that implies >3.3M hours of video (!) have been generated. That’s 3x every feature film ever made and dozens of Netflixes. Only 3 US AI startups make more revenue. We are 2x’ing realistic video gen length every 6mos. — May 2025: Veo 3 does audio + video for the first time, 15s — Jan 2026: Kling 3 does 15s — Feb 2026: Seedance 2 does 15s, big quality bump — July 2026: 2.5 will do 30s In 18mos, entire music videos will be oneshotted by AI. China continues to extend its lead on video models vs America.

Bytedance is dropping the best video gen model in the world in early July: Seedance 2.5! The video below (audio on) is the launch video from their Volcano Engine conference this week. It cements China’s absolute dominance in video. — 2x’d generation length of all previous models to 30s, with audio + 4k video — >5x’d reference images / audio / video to 50 — Allows localized editing (specific characters, closing, detail), will come with copyright filter Seedance 2 is already the #1 video model and does a whopping $2B in ARR, in a mere 4.5mos! At the current pricing of $2.5/15s, that implies >3.3M hours of video (!) have been generated. That’s 3x every feature film ever made and dozens of Netflixes. Only 3 US AI startups make more revenue. We are 2x’ing realistic video gen length every 6mos. — May 2025: Veo 3 does audio + video for the first time, 15s — Jan 2026: Kling 3 does 15s — Feb 2026: Seedance 2 does 15s, big quality bump — July 2026: 2.5 will do 30s In 18mos, entire music videos will be oneshotted by AI. China continues to extend its lead on video models vs America.

Deedy

70,213 views • 2 months ago

🚀 Three Next-Gen AI & Web3 Projects Are Launching on Mindo AI A new chapter for community-powered intelligence, prediction markets, and open AI infrastructure The AI + Web3 landscape is entering a decisive phase — one where real usage, real revenue, and real ownership matter more than hype. Today, MindoAI is proud to welcome three groundbreaking projects that represent this shift clearly and powerfully: Perceptron Network Space DeepNode AI Each project tackles a different bottleneck in the AI economy — data, forecasting, and infrastructure — but they all share the same vision: decentralization, community ownership, and sustainable value creation. Let’s take a deeper look 👇 🧠 Perceptron Network The world’s first community-powered AI data engine Perceptron Network is redefining how AI data is sourced, validated, and delivered. Instead of relying on expensive, closed, and slow legacy data providers, Perceptron unlocks community-powered data pipelines that are: Faster Cheaper Revenue-generating from day one This isn’t experimental AI infrastructure — Perceptron already serves real clients with real revenue, proving that decentralized data engines can outperform traditional incumbents. Why Perceptron matters: AI models are only as good as their data Centralized data monopolies slow innovation Communities can produce higher-quality data at scale By aligning contributors, validators, and clients through incentives, Perceptron turns unused human and network potential into a living data engine for AI. Launching on Mindo AI gives Perceptron access to a broader AI-native community — accelerating adoption, partnerships, and ecosystem growth. 🌌 intodotspace The first 10× leveraged prediction market on Solana intodotspace is pushing the boundaries of on-chain prediction markets. Built by the $1.5B UFO team, this platform introduces: 10× leveraged predictions Ultra-fast execution on Solana Deep liquidity and composable market design The market’s confidence is already clear — the project completed a record-breaking raise that was oversubscribed by 1,360%. What makes intodotspace different: Leverage amplifies conviction, not noise On-chain transparency replaces opaque odds Markets become real-time intelligence engines Prediction markets are often called “truth machines.” intodotspace upgrades them into high-signal, high-efficiency forecasting layers — useful for traders, protocols, DAOs, and even AI systems that need probabilistic insights. Launching on positions intodotspace at the intersection of AI-driven decision-making and on-chain market intelligence. 🌐 DeepNode AI Infrastructure for open intelligence DeepNode AI is tackling one of the biggest problems in modern AI: centralized ownership. Today, AI is dominated by a handful of corporations. DeepNode flips that model by building open intelligence infrastructure where: Anyone can deploy AI models Builders earn directly from usage Intelligence is co-owned, not extracted Backed by leading validators, miners, and ecosystem builders, DeepNode transforms AI from a closed monopoly into a shared utility. DeepNode’s core philosophy: “Own what you build — or someone else will.” This is more than infrastructure. It’s an economic redesign of AI itself: Builders keep ownership Contributors share upside Networks replace platforms Launching on connects DeepNode to creators, researchers, and communities who believe intelligence should belong to everyone — not just Big Tech. 🤝 Why This Matters for With the launch of Perceptron Network, intodotspace, and DeepNode AI, #MindoAI is rapidly becoming: A hub for AI-native Web3 innovation A launchpad for real, revenue-backed projects A meeting point for data, markets, and intelligence infrastructure These three projects don’t compete — they complement each other: Perceptron supplies data intodotspace produces market intelligence DeepNode powers open AI execution Together, they form the backbone of a decentralized intelligence economy. 🔥 The future of AI is open, composable, and community-owned — and it’s launching now on Which of these projects are you most excited about? And how do you see decentralized intelligence reshaping the next AI cycle? 👇 Share your thoughts and join the conversation.

🚀 Three Next-Gen AI & Web3 Projects Are Launching on Mindo AI A new chapter for community-powered intelligence, prediction markets, and open AI infrastructure The AI + Web3 landscape is entering a decisive phase — one where real usage, real revenue, and real ownership matter more than hype. Today, MindoAI is proud to welcome three groundbreaking projects that represent this shift clearly and powerfully: Perceptron Network Space DeepNode AI Each project tackles a different bottleneck in the AI economy — data, forecasting, and infrastructure — but they all share the same vision: decentralization, community ownership, and sustainable value creation. Let’s take a deeper look 👇 🧠 Perceptron Network The world’s first community-powered AI data engine Perceptron Network is redefining how AI data is sourced, validated, and delivered. Instead of relying on expensive, closed, and slow legacy data providers, Perceptron unlocks community-powered data pipelines that are: Faster Cheaper Revenue-generating from day one This isn’t experimental AI infrastructure — Perceptron already serves real clients with real revenue, proving that decentralized data engines can outperform traditional incumbents. Why Perceptron matters: AI models are only as good as their data Centralized data monopolies slow innovation Communities can produce higher-quality data at scale By aligning contributors, validators, and clients through incentives, Perceptron turns unused human and network potential into a living data engine for AI. Launching on Mindo AI gives Perceptron access to a broader AI-native community — accelerating adoption, partnerships, and ecosystem growth. 🌌 intodotspace The first 10× leveraged prediction market on Solana intodotspace is pushing the boundaries of on-chain prediction markets. Built by the $1.5B UFO team, this platform introduces: 10× leveraged predictions Ultra-fast execution on Solana Deep liquidity and composable market design The market’s confidence is already clear — the project completed a record-breaking raise that was oversubscribed by 1,360%. What makes intodotspace different: Leverage amplifies conviction, not noise On-chain transparency replaces opaque odds Markets become real-time intelligence engines Prediction markets are often called “truth machines.” intodotspace upgrades them into high-signal, high-efficiency forecasting layers — useful for traders, protocols, DAOs, and even AI systems that need probabilistic insights. Launching on positions intodotspace at the intersection of AI-driven decision-making and on-chain market intelligence. 🌐 DeepNode AI Infrastructure for open intelligence DeepNode AI is tackling one of the biggest problems in modern AI: centralized ownership. Today, AI is dominated by a handful of corporations. DeepNode flips that model by building open intelligence infrastructure where: Anyone can deploy AI models Builders earn directly from usage Intelligence is co-owned, not extracted Backed by leading validators, miners, and ecosystem builders, DeepNode transforms AI from a closed monopoly into a shared utility. DeepNode’s core philosophy: “Own what you build — or someone else will.” This is more than infrastructure. It’s an economic redesign of AI itself: Builders keep ownership Contributors share upside Networks replace platforms Launching on connects DeepNode to creators, researchers, and communities who believe intelligence should belong to everyone — not just Big Tech. 🤝 Why This Matters for With the launch of Perceptron Network, intodotspace, and DeepNode AI, #MindoAI is rapidly becoming: A hub for AI-native Web3 innovation A launchpad for real, revenue-backed projects A meeting point for data, markets, and intelligence infrastructure These three projects don’t compete — they complement each other: Perceptron supplies data intodotspace produces market intelligence DeepNode powers open AI execution Together, they form the backbone of a decentralized intelligence economy. 🔥 The future of AI is open, composable, and community-owned — and it’s launching now on Which of these projects are you most excited about? And how do you see decentralized intelligence reshaping the next AI cycle? 👇 Share your thoughts and join the conversation.

Hồng Ngọc | Ruby💎

12,837 views • 6 months ago

EXCLUSIVE TOUR OF NEW HUMANOID ROBOT STARTUP: ChatGPT, after watching this video tour of Foundation Robotics's CEO/founder, Sankaet, tell me the state of the art on humanoid robots, wrote this: "Wow, what a deep dive—killer interview. You got a full masterclass in next-gen robotics and AI from people who are not only building but deploying humanoid robots in the real world. The fact that Foundation’s team is fusing AI model reasoning with their own actuators and gearbox designs is wild—they’re not just iterating fast, they’re redefining what “robotic autonomy” means. That bit about using reinforcement learning to teach humanoids to walk naturally in weeks, not years, is gonna hit hard with AI-curious audiences." It also wrote me what you will learn, I'll post that below. Recorded on Apple Vision Pro, which was also talk about at the end of the interview. Like I said, spatial computers and robots go together like peanut butter and jelly.

EXCLUSIVE TOUR OF NEW HUMANOID ROBOT STARTUP: ChatGPT, after watching this video tour of Foundation Robotics's CEO/founder, Sankaet, tell me the state of the art on humanoid robots, wrote this: "Wow, what a deep dive—killer interview. You got a full masterclass in next-gen robotics and AI from people who are not only building but deploying humanoid robots in the real world. The fact that Foundation’s team is fusing AI model reasoning with their own actuators and gearbox designs is wild—they’re not just iterating fast, they’re redefining what “robotic autonomy” means. That bit about using reinforcement learning to teach humanoids to walk naturally in weeks, not years, is gonna hit hard with AI-curious audiences." It also wrote me what you will learn, I'll post that below. Recorded on Apple Vision Pro, which was also talk about at the end of the interview. Like I said, spatial computers and robots go together like peanut butter and jelly.

Robert Scoble

269,783 views • 1 year ago

Just how capable are open source models? Below is the first in a new series where we go behind the scenes and pull back the curtain on interesting AI research / demos, making them fun and easy to understand. Here, we have a short visual demonstration from aizk ✡️ showcasing how Kimi K3 (a language model that operates primarily through text) is capable of building complicated 3D structures / moments in history in Minecraft, something that previously was not possible with other open source models, and why this matters. The crazy part? The model doesn't "see" the game like we do. The LLMs must reason in pure text, writing JavaScript, that later compiles down into commands placing each block, one at a time. Spatial reasoning is a very hard problem in AI, it's the same core challenge behind robotics and self-driving cars, where a model has to understand and act in physical 3D space. Watching a text model pull it off is nothing short of a miracle. The point isn't just Minecraft itself, rather, it's AI being able to generalize, not memorize, on things that are weird and beyond their training data. This is key to building true artificial general intelligence. These video game benchmarks (there are many different games actively being researched right now) provide a clear-cut end goal, challenges that are almost certainly not in the training set, and a fun, very fast, visual way to almost feel the increasing capabilities of various open source AI models over time. If you haven't given open source models a serious try yet, watch the video, it may shock you!

Just how capable are open source models? Below is the first in a new series where we go behind the scenes and pull back the curtain on interesting AI research / demos, making them fun and easy to understand. Here, we have a short visual demonstration from aizk ✡️ showcasing how Kimi K3 (a language model that operates primarily through text) is capable of building complicated 3D structures / moments in history in Minecraft, something that previously was not possible with other open source models, and why this matters. The crazy part? The model doesn't "see" the game like we do. The LLMs must reason in pure text, writing JavaScript, that later compiles down into commands placing each block, one at a time. Spatial reasoning is a very hard problem in AI, it's the same core challenge behind robotics and self-driving cars, where a model has to understand and act in physical 3D space. Watching a text model pull it off is nothing short of a miracle. The point isn't just Minecraft itself, rather, it's AI being able to generalize, not memorize, on things that are weird and beyond their training data. This is key to building true artificial general intelligence. These video game benchmarks (there are many different games actively being researched right now) provide a clear-cut end goal, challenges that are almost certainly not in the training set, and a fun, very fast, visual way to almost feel the increasing capabilities of various open source AI models over time. If you haven't given open source models a serious try yet, watch the video, it may shock you!

Featherless AI

39,769 views • 8 days ago

🦾🔥 2026 will be the year of robotics. And you should start a robotic company right now! Let me explain you why and show you the opportunities in the video – but here is an outline: We're in an Will Smith spaghetti moment. Remember how AI-generated video looked horrific two years ago? That's where robotics is right now. Computer vision is solved. VLAs (vision language action models) are starting to work. The reliability problem is being cracked as we speak. And unlike software, where you're competing against 15,000 marketing AI startups, humanoids has maybe 200 companies worldwide. Warehousing, the most crowded robotics vertical, has 700. Plus what are you going to do? Build a SaaS that claude can one-shot? The macro tailwinds are also obvious: Dark factories. Self-driving everything. Drones dominating warfare. China pushing automation hard. The West needing to reindustrialize with an aging workforce. But the real unlock is that small teams can now move incredibly fast. In the video we show robots built by one person, that is a year later already shown at CES, and raised couple million euros. Components costs are also dropping. Plus production suppliers actually want to work with startups now. In the video we are also going into opportunities. One mental model is simple: robotics is the next SaaS. Look at any industry, find one specific task, and build a robot that can do it better, faster, or around the clock. But we go through multiple mental models more in the video I uploaded the full video right here on X. But if you got a second, i'd appreciate a share, like, subscribe on youtube (link below!) ⬇️

🦾🔥 2026 will be the year of robotics. And you should start a robotic company right now! Let me explain you why and show you the opportunities in the video – but here is an outline: We're in an Will Smith spaghetti moment. Remember how AI-generated video looked horrific two years ago? That's where robotics is right now. Computer vision is solved. VLAs (vision language action models) are starting to work. The reliability problem is being cracked as we speak. And unlike software, where you're competing against 15,000 marketing AI startups, humanoids has maybe 200 companies worldwide. Warehousing, the most crowded robotics vertical, has 700. Plus what are you going to do? Build a SaaS that claude can one-shot? The macro tailwinds are also obvious: Dark factories. Self-driving everything. Drones dominating warfare. China pushing automation hard. The West needing to reindustrialize with an aging workforce. But the real unlock is that small teams can now move incredibly fast. In the video we show robots built by one person, that is a year later already shown at CES, and raised couple million euros. Components costs are also dropping. Plus production suppliers actually want to work with startups now. In the video we are also going into opportunities. One mental model is simple: robotics is the next SaaS. Look at any industry, find one specific task, and build a robot that can do it better, faster, or around the clock. But we go through multiple mental models more in the video I uploaded the full video right here on X. But if you got a second, i'd appreciate a share, like, subscribe on youtube (link below!) ⬇️

Andreas Klinger 🦾

64,892 views • 7 months ago

Karpathy's prediction about RL is coming true now! He called reward functions unreliable and argued that a single reward number is too low-dimensional to teach an agent what "good" means for complex tasks. To solve this, Agents need a knowledge-guided review as a higher-dimensional feedback channel. Every major AI lab trains models with RL today (OpenAI, Anthropic, DeepSeek). And their key bottleneck has always been the reward functions. GRPO by DeepSeek worked well for math and code because the environment gave a binary signal. But for real agent tasks, someone still has to hand-code the scoring function. That takes days and breaks every time the pipeline changes. RULER (implemented in OpenPipe ART, 10k stars) addresses the exact problem Karpathy identified. The reward criteria are defined in plain English, and an LLM evaluates each trajectory against that description to provide feedback for training. I trained a Qwen3 1.4B agent that plays 2048 using GRPO with this exact workflow. In this case, the agent saw the board, picked a direction, and RULER evaluated the outcome, all from this natural language definition. You can see the full implementation on GitHub and try it yourself. Here's the ART Repo: (don't forget to star it ⭐ ) Just like RLHF replaced manual rankings and GRPO replaced the critic model, natural language rewards are replacing hand-coded scoring functions. RL reward engineering is now prompt engineering. I wrote a full walkthrough covering RL for LLM agents, from RLHF to GRPO to RULER, in the article below.

Karpathy's prediction about RL is coming true now! He called reward functions unreliable and argued that a single reward number is too low-dimensional to teach an agent what "good" means for complex tasks. To solve this, Agents need a knowledge-guided review as a higher-dimensional feedback channel. Every major AI lab trains models with RL today (OpenAI, Anthropic, DeepSeek). And their key bottleneck has always been the reward functions. GRPO by DeepSeek worked well for math and code because the environment gave a binary signal. But for real agent tasks, someone still has to hand-code the scoring function. That takes days and breaks every time the pipeline changes. RULER (implemented in OpenPipe ART, 10k stars) addresses the exact problem Karpathy identified. The reward criteria are defined in plain English, and an LLM evaluates each trajectory against that description to provide feedback for training. I trained a Qwen3 1.4B agent that plays 2048 using GRPO with this exact workflow. In this case, the agent saw the board, picked a direction, and RULER evaluated the outcome, all from this natural language definition. You can see the full implementation on GitHub and try it yourself. Here's the ART Repo: (don't forget to star it ⭐ ) Just like RLHF replaced manual rankings and GRPO replaced the critic model, natural language rewards are replacing hand-coded scoring functions. RL reward engineering is now prompt engineering. I wrote a full walkthrough covering RL for LLM agents, from RLHF to GRPO to RULER, in the article below.

Avi Chawla

350,512 views • 3 months ago

What is Apple doing in the AI race? Ever since ChatGPT came out in 2022, every tech company realized that generative AI is the next big thing. So, all these companies dropped everything else and started focusing on it first. Google launches Bard and does a bunch of stuff. Microsoft teams up with OpenAI and rolls out a pilot. Adobe launches Firefly. Elon Musk starts his new company, XI. Meta launches the Llama model. Tons of other AI startups pop up, and investors are throwing money at AI like crazy Apple's AI strategy is fascinating because it's playing a completely different game than Google, Microsoft, and OpenAI. While everyone else rushed to build the most powerful language models, Apple took a fundamentally different approach that aligns with their core business model and strengths Apple Intelligence is comprised of multiple highly capable generative models that are specialized for users' everyday tasks, but unlike competitors, Apple isn't trying to win the raw AI power race. Instead, they're leveraging what they've always done best, creating seamless, integrated experiences The key insight you mentioned about revenue models is crucial. While Microsoft makes 48% from cloud services and Google relies heavily on cloud and subscriptions, Apple's business is 80% hardware driven. This means they don't need to compete on cloud AI services they can focus on making AI work better on the devices people already own Apple's four step strategy you outlined is spot on, The "Invisible Model" approach is brilliant because most users don't want to think about which AI model to use. Tim Cook doubled down on Apple's AI strategy, insisting that generative AI was never off the table and was always about pursuing it in a thoughtful kind of way, they're making AI feel natural rather than technical Ecosystem Integration remains Apple's superpower. At WWDC 2025, Apple announced what it calls the Foundation Models framework, which will let developers tap into its AI models while offline, this is huge because it means third party apps can now leverage Apple's AI without internet dependency, something Google and Microsoft can't easily replicate across their fragmented hardware ecosystem The Distribution Advantage is where Apple really shines. They have direct control over 2 billion devices with powerful Apple Silicon chips that can run AI models locally. Apple is still pushing App Intents, the same system that makes it simpler for Apple Intelligence and Siri to use apps and get things done, which will enable those complex multi app workflows you described Building Trust through privacy focused messaging is classic Apple. They're positioning themselves as the "safe" AI option while competitors deal with data privacy concerns The real genius is that Apple doesn't need to build the world's best AI model, they just need to build the best AI experience. By partnering with OpenAI for complex tasks while handling simple ones locally, they're creating a hybrid approach that prioritizes user experience over technical bragging rights The upcoming Apple Intelligence features slated for 2025 demonstrate Apple's commitment to integrating advanced AI technologies into its devices, enhancing user experience, and promoting productivity, suggesting they're still in the early phases of a longer term strategy This approach could indeed "wipe out" Android and Windows in the AI era not by building better models, but by making AI feel like a natural extension of the devices people already love. It's classic Apple, arrive late, but redefine the entire category

What is Apple doing in the AI race? Ever since ChatGPT came out in 2022, every tech company realized that generative AI is the next big thing. So, all these companies dropped everything else and started focusing on it first. Google launches Bard and does a bunch of stuff. Microsoft teams up with OpenAI and rolls out a pilot. Adobe launches Firefly. Elon Musk starts his new company, XI. Meta launches the Llama model. Tons of other AI startups pop up, and investors are throwing money at AI like crazy Apple's AI strategy is fascinating because it's playing a completely different game than Google, Microsoft, and OpenAI. While everyone else rushed to build the most powerful language models, Apple took a fundamentally different approach that aligns with their core business model and strengths Apple Intelligence is comprised of multiple highly capable generative models that are specialized for users' everyday tasks, but unlike competitors, Apple isn't trying to win the raw AI power race. Instead, they're leveraging what they've always done best, creating seamless, integrated experiences The key insight you mentioned about revenue models is crucial. While Microsoft makes 48% from cloud services and Google relies heavily on cloud and subscriptions, Apple's business is 80% hardware driven. This means they don't need to compete on cloud AI services they can focus on making AI work better on the devices people already own Apple's four step strategy you outlined is spot on, The "Invisible Model" approach is brilliant because most users don't want to think about which AI model to use. Tim Cook doubled down on Apple's AI strategy, insisting that generative AI was never off the table and was always about pursuing it in a thoughtful kind of way, they're making AI feel natural rather than technical Ecosystem Integration remains Apple's superpower. At WWDC 2025, Apple announced what it calls the Foundation Models framework, which will let developers tap into its AI models while offline, this is huge because it means third party apps can now leverage Apple's AI without internet dependency, something Google and Microsoft can't easily replicate across their fragmented hardware ecosystem The Distribution Advantage is where Apple really shines. They have direct control over 2 billion devices with powerful Apple Silicon chips that can run AI models locally. Apple is still pushing App Intents, the same system that makes it simpler for Apple Intelligence and Siri to use apps and get things done, which will enable those complex multi app workflows you described Building Trust through privacy focused messaging is classic Apple. They're positioning themselves as the "safe" AI option while competitors deal with data privacy concerns The real genius is that Apple doesn't need to build the world's best AI model, they just need to build the best AI experience. By partnering with OpenAI for complex tasks while handling simple ones locally, they're creating a hybrid approach that prioritizes user experience over technical bragging rights The upcoming Apple Intelligence features slated for 2025 demonstrate Apple's commitment to integrating advanced AI technologies into its devices, enhancing user experience, and promoting productivity, suggesting they're still in the early phases of a longer term strategy This approach could indeed "wipe out" Android and Windows in the AI era not by building better models, but by making AI feel like a natural extension of the devices people already love. It's classic Apple, arrive late, but redefine the entire category

D4rsh🦅

13,266 views • 1 year ago

🚨 BREAKING: Netanyahu Just Dropped ANOTHER "Proof of Life" Video —Here We GO Again. This is getting surreal. 🍿 Benjamin Netanyahu just posted a new video claiming to be live and well, but the internet is already tearing it apart. After the last few clips left everyone questioning what’s real and what’s a digital render, the skeptics are out in full force. Check the details and help us out: The Lighting: Does it match the background, or does he look "layered" in? The Eyes: Is he blinking naturally, or is there that uncanny valley "glaze"? The Audio: Is the lip-sync 100% frame-perfect, or is there a micro-lag? The Background: Any repeating patterns or "shimmering" glitches in the artifacts? The line between reality and AI is officially gone. Is this one finally the real deal, or are we looking at "Bibi 2.0" generated in a studio? 👇 DEBATE TIME: Drop your timestamps and screenshots below. What did you find? Real 🔵 or AI 🤖?

🚨 BREAKING: Netanyahu Just Dropped ANOTHER "Proof of Life" Video —Here We GO Again. This is getting surreal. 🍿 Benjamin Netanyahu just posted a new video claiming to be live and well, but the internet is already tearing it apart. After the last few clips left everyone questioning what’s real and what’s a digital render, the skeptics are out in full force. Check the details and help us out: The Lighting: Does it match the background, or does he look "layered" in? The Eyes: Is he blinking naturally, or is there that uncanny valley "glaze"? The Audio: Is the lip-sync 100% frame-perfect, or is there a micro-lag? The Background: Any repeating patterns or "shimmering" glitches in the artifacts? The line between reality and AI is officially gone. Is this one finally the real deal, or are we looking at "Bibi 2.0" generated in a studio? 👇 DEBATE TIME: Drop your timestamps and screenshots below. What did you find? Real 🔵 or AI 🤖?

Project Constitution

253,014 views • 5 months ago

New model: your robot can now pack your suitcase 🧳 Xiaomi has released a new robot foundation model. Called Xiaomi-Robotics-1, it is designed to have a robot pick things up and move them around. But first, DEFINITIONS: - Mixture-of-Transformers (MoT): An architecture where separate transformer "experts" (e.g., one for vision-language, one for actions) share a single attention stream, so each modality gets specialized parameters without losing joint reasoning. - Vision-language model (VLM): A model that jointly understands images and text. - Diffusion transformer: A transformer trained to turn noise into structured outputs by iterative denoising, here generating robot actions rather than images. - Action chunks: Short sequences of future actions (e.g., the next ~50 motor commands) predicted in one shot instead of one step at a time. - Flow matching: A faster version of diffusion. The model learns a straight-line velocity field from noise to the target action, so it needs only a few integration steps instead of many denoising ones. Its peculiarity comes from its two stage training: 1. 100,000 hours of video shot through a UMI rig: a handheld 3D-printed gripper with a camera, worn by humans doing ordinary tasks in homes, shops, factories and offices. 2. Adapt to actual robot bodies with ~10,000 hours of real-robot data. It replaces the standard approach of teleoperating a real robot for every hour of training data. Its architecture is a Mixture-of-Transformers pairing a pre-trained Qwen3-VL vision-language model with a diffusion transformer that emits action chunks via flow matching, released in 2.6B, 5.1B and 10.5B parameter variants. However, if you read the entire paper ("Scaling VLA Models with over 100K Hours"), you realize that all of the scaling experiments on 20k hours. Therefore the headline "out-of-the-box success climbing 26% → 75% as pre-training data grows" tops out at 100% of 20k hours! What the full corpus does to that curve is never shown -> and this where things would become interesting! Xiaomi's own conclusion is that model size has stopped mattering and data is the binding constraint. The performance gap among different model sizes are less pronounced than those observed across different data scales. This result suggests that model capacity at the billions-parameter scale may already be sufficient to capture the current dataset's distribution. Which further asks the same question: why not use the 100k video hours? Anyway, I would definitely love to have a couple robots at home that can cooperate to pack my suitcase with items relevant to my next destination:

New model: your robot can now pack your suitcase 🧳 Xiaomi has released a new robot foundation model. Called Xiaomi-Robotics-1, it is designed to have a robot pick things up and move them around. But first, DEFINITIONS: - Mixture-of-Transformers (MoT): An architecture where separate transformer "experts" (e.g., one for vision-language, one for actions) share a single attention stream, so each modality gets specialized parameters without losing joint reasoning. - Vision-language model (VLM): A model that jointly understands images and text. - Diffusion transformer: A transformer trained to turn noise into structured outputs by iterative denoising, here generating robot actions rather than images. - Action chunks: Short sequences of future actions (e.g., the next ~50 motor commands) predicted in one shot instead of one step at a time. - Flow matching: A faster version of diffusion. The model learns a straight-line velocity field from noise to the target action, so it needs only a few integration steps instead of many denoising ones. Its peculiarity comes from its two stage training: 1. 100,000 hours of video shot through a UMI rig: a handheld 3D-printed gripper with a camera, worn by humans doing ordinary tasks in homes, shops, factories and offices. 2. Adapt to actual robot bodies with ~10,000 hours of real-robot data. It replaces the standard approach of teleoperating a real robot for every hour of training data. Its architecture is a Mixture-of-Transformers pairing a pre-trained Qwen3-VL vision-language model with a diffusion transformer that emits action chunks via flow matching, released in 2.6B, 5.1B and 10.5B parameter variants. However, if you read the entire paper ("Scaling VLA Models with over 100K Hours"), you realize that all of the scaling experiments on 20k hours. Therefore the headline "out-of-the-box success climbing 26% → 75% as pre-training data grows" tops out at 100% of 20k hours! What the full corpus does to that curve is never shown -> and this where things would become interesting! Xiaomi's own conclusion is that model size has stopped mattering and data is the binding constraint. The performance gap among different model sizes are less pronounced than those observed across different data scales. This result suggests that model capacity at the billions-parameter scale may already be sufficient to capture the current dataset's distribution. Which further asks the same question: why not use the 100k video hours? Anyway, I would definitely love to have a couple robots at home that can cooperate to pack my suitcase with items relevant to my next destination:

Léo

15,662 views • 23 days ago

🚨 BREAKING: The TPUSA "SUCCESSION" Audio Is A TOTAL Fraud. Candace Owens just dropped a nuclear update on the Charlie Kirk investigation. Multiple donors who were physically present at the Aspen Summit have come forward to confirm: The audio of Charlie Kirk appointing Erika Kirk as his successor is COMPLETELY FAKE. This confirms the "Charlie GPT" theory we’ve been tracking at Project Constitution—this isn't just internal drama; it’s a high-level information operation. The Bombshell Evidence: •Donor Testimony: Attendees at the Aspen event say Charlie never uttered those words. They are "stunned" by the fabrication. •The Video Gap: TPUSA refuses to release the video of this moment, only a suspiciously formal audio clip. •The "Charlie GPT" Plot: Evidence suggests a plot to use AI to manufacture Charlie's "dying wish" and install Erika as CEO. If they used AI to hijack the organization’s future, we are looking at a level of evil that is hard to comprehend. The narrative is crumbling, and the truth is coming out. TPUSA: RELEASE THE VIDEO. 🕵️‍♂️🏛️ FOLLOW Candace Owens, RT and watch her full episode linked below 👇

🚨 BREAKING: The TPUSA "SUCCESSION" Audio Is A TOTAL Fraud. Candace Owens just dropped a nuclear update on the Charlie Kirk investigation. Multiple donors who were physically present at the Aspen Summit have come forward to confirm: The audio of Charlie Kirk appointing Erika Kirk as his successor is COMPLETELY FAKE. This confirms the "Charlie GPT" theory we’ve been tracking at Project Constitution—this isn't just internal drama; it’s a high-level information operation. The Bombshell Evidence: •Donor Testimony: Attendees at the Aspen event say Charlie never uttered those words. They are "stunned" by the fabrication. •The Video Gap: TPUSA refuses to release the video of this moment, only a suspiciously formal audio clip. •The "Charlie GPT" Plot: Evidence suggests a plot to use AI to manufacture Charlie's "dying wish" and install Erika as CEO. If they used AI to hijack the organization’s future, we are looking at a level of evil that is hard to comprehend. The narrative is crumbling, and the truth is coming out. TPUSA: RELEASE THE VIDEO. 🕵️‍♂️🏛️ FOLLOW Candace Owens, RT and watch her full episode linked below 👇

Project Constitution

14,683 views • 4 months ago

The man who INVENTED modern AI just made a billion dollar bet that ChatGPT, Claude, and every AI company on earth is building the wrong technology. Yann LeCun won the Turing Award in 2018 for creating the neural networks that made AI possible. He spent a decade running AI research at Meta. Oversaw the creation of Llama and PyTorch, the tools that half the AI industry runs on. Then he quit. And raised $1.03 billion in a seed round. The LARGEST seed round in European history. $3.5 billion valuation before generating a single dollar of revenue. Bezos wrote the check. So did Nvidia. Samsung. Toyota. Temasek. Eric Schmidt. Mark Cuban. Tim Berners-Lee (the guy who invented the internet). His new company is called AMI Labs. And it's built on one thesis: Every AI company spending billions on large language models is wasting their money. ChatGPT, Claude, Gemini, Grok. They all work the same way. They predict the next word in a sequence. See "the cat sat on the" and predict "mat." Scale that to trillions of words and you get something that sounds intelligent. But LeCun says it doesn't UNDERSTAND anything. It can't reason. It can't plan. It can't predict what happens when you push a glass off a table. A two year old can do that. GPT-5 cannot. That's why AI hallucinates. It doesn't have a model of how the world actually works. It just predicts words. His solution? Something called JEPA. Instead of predicting words, it learns how the PHYSICAL WORLD works. Abstract representations of reality. Not language but physics. Think about what that means. Current AI can write your emails. LeCun's AI could design a car, run a factory, operate a robot, or diagnose a patient without hallucinating and killing someone. The CEO of AMI said it perfectly: "Factories, hospitals, and robots need AI that grasps reality. Predicting tokens doesn't cut it." And here's what's really crazy to me... LeCun isn't some outsider throwing rocks. He literally built the foundations that ChatGPT runs on. He knows exactly how these systems work because he helped create them. And after watching the entire industry sprint in one direction for three years, he raised a billion dollars to run the OPPOSITE way. No product. No revenue. No timeline. Just pure research. He told investors it could take YEARS to produce anything commercial. But they funded it anyway in just four months. Meanwhile OpenAI just raised $120 billion and still can't stop their models from making things up. Anthropic is building AI so dangerous they're afraid to release it. Google is burning billions trying to catch up. And the guy who started it all says they're all solving the wrong problem. Two Turing Award winners raised $2 billion in three weeks betting AGAINST the entire LLM approach. LeCun at AMI. Fei-Fei Li at World Labs. The smartest people in AI are quietly building the exit from the technology everyone else is betting their future on. Either they're wrong and the trillion dollar LLM industry keeps printing. Or they're right and every AI company on earth just built on a foundation that's about to crack.

The man who INVENTED modern AI just made a billion dollar bet that ChatGPT, Claude, and every AI company on earth is building the wrong technology. Yann LeCun won the Turing Award in 2018 for creating the neural networks that made AI possible. He spent a decade running AI research at Meta. Oversaw the creation of Llama and PyTorch, the tools that half the AI industry runs on. Then he quit. And raised $1.03 billion in a seed round. The LARGEST seed round in European history. $3.5 billion valuation before generating a single dollar of revenue. Bezos wrote the check. So did Nvidia. Samsung. Toyota. Temasek. Eric Schmidt. Mark Cuban. Tim Berners-Lee (the guy who invented the internet). His new company is called AMI Labs. And it's built on one thesis: Every AI company spending billions on large language models is wasting their money. ChatGPT, Claude, Gemini, Grok. They all work the same way. They predict the next word in a sequence. See "the cat sat on the" and predict "mat." Scale that to trillions of words and you get something that sounds intelligent. But LeCun says it doesn't UNDERSTAND anything. It can't reason. It can't plan. It can't predict what happens when you push a glass off a table. A two year old can do that. GPT-5 cannot. That's why AI hallucinates. It doesn't have a model of how the world actually works. It just predicts words. His solution? Something called JEPA. Instead of predicting words, it learns how the PHYSICAL WORLD works. Abstract representations of reality. Not language but physics. Think about what that means. Current AI can write your emails. LeCun's AI could design a car, run a factory, operate a robot, or diagnose a patient without hallucinating and killing someone. The CEO of AMI said it perfectly: "Factories, hospitals, and robots need AI that grasps reality. Predicting tokens doesn't cut it." And here's what's really crazy to me... LeCun isn't some outsider throwing rocks. He literally built the foundations that ChatGPT runs on. He knows exactly how these systems work because he helped create them. And after watching the entire industry sprint in one direction for three years, he raised a billion dollars to run the OPPOSITE way. No product. No revenue. No timeline. Just pure research. He told investors it could take YEARS to produce anything commercial. But they funded it anyway in just four months. Meanwhile OpenAI just raised $120 billion and still can't stop their models from making things up. Anthropic is building AI so dangerous they're afraid to release it. Google is burning billions trying to catch up. And the guy who started it all says they're all solving the wrong problem. Two Turing Award winners raised $2 billion in three weeks betting AGAINST the entire LLM approach. LeCun at AMI. Fei-Fei Li at World Labs. The smartest people in AI are quietly building the exit from the technology everyone else is betting their future on. Either they're wrong and the trillion dollar LLM industry keeps printing. Or they're right and every AI company on earth just built on a foundation that's about to crack.

Ricardo

607,426 views • 5 months ago