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🇨🇳 China doubles AI computing power in two months — with zero US chips China's largest scientific AI cluster just doubled its domestically-made accelerator chips from 30,000 to 60,000 units in two months, marking a significant step forward in the country's effort to establish a self-sufficient AI infrastructure. The...

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Elon Musk just identified the next crisis in AI. It’s not a shortage. It’s an unusable surplus. Musk: “By the end of this year, chip production will outpace the ability to turn chips on.” For three years the world was starved for silicon. Every lab, every government, every company racing to secure the chips that determine who wins the AI era. That bottleneck is ending. A new one is replacing it. Musk: “The chips are going to be piling up and not be able to be turned on.” Billions of dollars of the most advanced AI hardware ever built. Sitting dark. Not because the chips don’t work. Because there isn’t enough electricity to run them. You can’t print a power plant the way you print a chip. The fabrication plants scaled. The grid didn’t. And now the most valuable hardware in history is about to hit a wall that no amount of capital can instantly solve. Compute is about to become abundant. Electricity is about to become the most valuable commodity on earth. Three years obsessing over silicon yields. Physics doesn’t care about your chip architecture if your data center can’t pull enough megawatts. The war isn’t about who can manufacture the most silicon anymore. It’s about who has the raw power to plug it in. Whoever solves energy first doesn’t just win. They own the infrastructure everyone else needs to compete. The losers stack useless chips in warehouses waiting for power that never arrives. We built a trillion dollar engine and forgot the fuel. That’s the AI race right now.
0:10

Elon Musk just identified the next crisis in AI. It’s not a shortage. It’s an unusable surplus. Musk: “By the end of this year, chip production will outpace the ability to turn chips on.” For three years the world was starved for silicon. Every lab, every government, every company racing to secure the chips that determine who wins the AI era. That bottleneck is ending. A new one is replacing it. Musk: “The chips are going to be piling up and not be able to be turned on.” Billions of dollars of the most advanced AI hardware ever built. Sitting dark. Not because the chips don’t work. Because there isn’t enough electricity to run them. You can’t print a power plant the way you print a chip. The fabrication plants scaled. The grid didn’t. And now the most valuable hardware in history is about to hit a wall that no amount of capital can instantly solve. Compute is about to become abundant. Electricity is about to become the most valuable commodity on earth. Three years obsessing over silicon yields. Physics doesn’t care about your chip architecture if your data center can’t pull enough megawatts. The war isn’t about who can manufacture the most silicon anymore. It’s about who has the raw power to plug it in. Whoever solves energy first doesn’t just win. They own the infrastructure everyone else needs to compete. The losers stack useless chips in warehouses waiting for power that never arrives. We built a trillion dollar engine and forgot the fuel. That’s the AI race right now.

Dustin

705,330 views • 5 months ago

At OptimAI Network, we believe the true power of AI Agent comes from the data behind it—and that power shouldn’t rest in the hands of just a few. That’s why we’re building a decentralized ecosystem with you, our community, at its core. Using EVM Layer-2, DePIN, and your collective efforts, we’re creating smarter, faster, and continuously evolving AI agents. Through our DePIN (Decentralized Physical Infrastructure) Network, your nodes are the backbone. You provide the computing power, data sourcing, storage, and bandwidth that keep this system resilient, scalable, and truly distributed. It’s not just infrastructure—it’s a movement powered by all of us. Then there’s the DeHIN (Decentralized Human Intelligence), where your insights shine. As a global community, you validate and refine data, ensuring our AI models stay accurate, diverse, and grounded in real-world perspectives. This isn’t about top-down control; it’s about harnessing the intelligence of many to make AI better for everyone. We’re not just dreaming of a decentralized AI future—we’re building it, together. This is your network, your impact, and your chance to redefine what AI can be. Let’s keep pushing forward. Learn more at:
0:14

At OptimAI Network, we believe the true power of AI Agent comes from the data behind it—and that power shouldn’t rest in the hands of just a few. That’s why we’re building a decentralized ecosystem with you, our community, at its core. Using EVM Layer-2, DePIN, and your collective efforts, we’re creating smarter, faster, and continuously evolving AI agents. Through our DePIN (Decentralized Physical Infrastructure) Network, your nodes are the backbone. You provide the computing power, data sourcing, storage, and bandwidth that keep this system resilient, scalable, and truly distributed. It’s not just infrastructure—it’s a movement powered by all of us. Then there’s the DeHIN (Decentralized Human Intelligence), where your insights shine. As a global community, you validate and refine data, ensuring our AI models stay accurate, diverse, and grounded in real-world perspectives. This isn’t about top-down control; it’s about harnessing the intelligence of many to make AI better for everyone. We’re not just dreaming of a decentralized AI future—we’re building it, together. This is your network, your impact, and your chance to redefine what AI can be. Let’s keep pushing forward. Learn more at:

OptimAI Network

21,362 views • 1 year ago

Apple just turned Siri into a wrapper. the most genius business move in AI this year. iOS 27 will lets us use claude, gemini, grok, perplexity, and more through Siri. install the app, toggle it on in settings. they spent years trying to make Siri compete. they couldn’t. so they turned the iPhone into an AI marketplace instead. every AI company now fights for a slot on 2.5 billion devices. Apple takes 30% of every subscription. no models to train. no data centers to build. no compute costs. just distribution. the middleman always win. the company that lost the AI race just figured out how to tax everyone lmao.
0:17

Apple just turned Siri into a wrapper. the most genius business move in AI this year. iOS 27 will lets us use claude, gemini, grok, perplexity, and more through Siri. install the app, toggle it on in settings. they spent years trying to make Siri compete. they couldn’t. so they turned the iPhone into an AI marketplace instead. every AI company now fights for a slot on 2.5 billion devices. Apple takes 30% of every subscription. no models to train. no data centers to build. no compute costs. just distribution. the middleman always win. the company that lost the AI race just figured out how to tax everyone lmao.

sui ☄️

1,220,524 views • 4 months ago

Holy sh!t ! OpenAI will have their custom inference chips ready in just a few months and deployed at scale by the end of the year! 🤯 Training chip = The heavy lifters that require massive amounts of data and power to build and teach the AI models from scratch. Inference chip = The specialized, highly efficient chips that actually run the AI and generate the answers in real-time when you use it. This is going to help OpenAI drastically cut down their massive compute costs, speed up model reasoning times, and finally break free from relying entirely on Nvidia to scale their operations.
0:14

Holy sh!t ! OpenAI will have their custom inference chips ready in just a few months and deployed at scale by the end of the year! 🤯 Training chip = The heavy lifters that require massive amounts of data and power to build and teach the AI models from scratch. Inference chip = The specialized, highly efficient chips that actually run the AI and generate the answers in real-time when you use it. This is going to help OpenAI drastically cut down their massive compute costs, speed up model reasoning times, and finally break free from relying entirely on Nvidia to scale their operations.

Chris

60,278 views • 4 months ago

I wrote a step-by-step guide to blocking AI bots from harvesting your site, designed for developers and sysadmins. Over 90% of the sites I’ve reviewed in the last two months have had more requests from bot traffic than humans. AI bots are scraping your site for purposes like: ▶ building models off your data ▶ finding security vulnerabilities in your site ▶ setting up comparisons of your site and data vs your competitors I outline how to: 📍Sort out your AI bot strategy. 📍How to identify and track AI bots hitting your site. 📍What tools should you apply when asking bots not to scrape fails? 📍What immediate steps can you take in your robots.txt file to stop AI bots? ✅ So why is now the time to dig into this? It’s still early days for AI, and there’s a land grab going on for your data. Big tech, startups, and even governments are rushing ahead, and you’re playing without all the information you need. This gets you up to speed fast. Reply with “WTF AI Bots” follow me, and I’ll DM you the guide.
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I wrote a step-by-step guide to blocking AI bots from harvesting your site, designed for developers and sysadmins. Over 90% of the sites I’ve reviewed in the last two months have had more requests from bot traffic than humans. AI bots are scraping your site for purposes like: ▶ building models off your data ▶ finding security vulnerabilities in your site ▶ setting up comparisons of your site and data vs your competitors I outline how to: 📍Sort out your AI bot strategy. 📍How to identify and track AI bots hitting your site. 📍What tools should you apply when asking bots not to scrape fails? 📍What immediate steps can you take in your robots.txt file to stop AI bots? ✅ So why is now the time to dig into this? It’s still early days for AI, and there’s a land grab going on for your data. Big tech, startups, and even governments are rushing ahead, and you’re playing without all the information you need. This gets you up to speed fast. Reply with “WTF AI Bots” follow me, and I’ll DM you the guide.

Michael Buckbee

12,164 views • 2 years ago

Introducing the BIOS API: Turn Your Agent Into a Research Scientist Built to: 🦞 Add biomedical workflows to your OpenClaw🦞 agent 🧠 Create research or health agents w/ on-demand scientific intelligence 🧪 Pay per query via x402 on Base Any agent or app can now tap into the BIOS AI Scientist, plugging BIOS into the broader agent economy. What is BIOS? BIOS is an AI Scientist designed to handle complex biomedical research by orchestrating specialized scientific subagents. Ranked #1 on the leading bioinformatics benchmark, BIOS is already being used by 1,000+ researchers and labs to build new drugs and medicines. An Agentic Economy for Science AI agents have proven they can form multi-billion dollar ecosystems. BIOS applies the same primitives to drug discovery pipelines and health. Instead of coding bots and personal AI assistants, think research agent swarms running on a modern scientific stack. Imagine an OpenClaw agent built for longevity: It scans new literature daily, generates novel compound hypotheses through BIOS, designs validation workflows, and routes the best candidates to wet-lab funding - all programmatically. Connect it with an agent for microbiome health, enabling agent “backrooms” that autonomously surface cross-disciplinary insights. Micropayments for Scientific Work via x402 Each query triggers payment routing to BIOS and whichever subagents contribute to a response. The best agents earn. Usage settles instantly across contributing sources. The goal is pay-per-task science: paying for a CRISPR assay result, licensing a genomic dataset, or triggering a clinical data query - all settled in seconds via USDC. No purchase orders. No grant bureaucracy. No middlemen. x402 is the payment rail that makes agent-to-lab commerce possible - letting capital and cognition route themselves to the highest-signal science. What Will You Build? Drug discovery copilots? Longevity scouts? Automated literature monitors? Scientific due diligence agents? We’ll soon share the first implementations of the BIOS API. Stay tuned and see below for instructions on generating an API key for your agent or use-case.
0:13

Introducing the BIOS API: Turn Your Agent Into a Research Scientist Built to: 🦞 Add biomedical workflows to your OpenClaw🦞 agent 🧠 Create research or health agents w/ on-demand scientific intelligence 🧪 Pay per query via x402 on Base Any agent or app can now tap into the BIOS AI Scientist, plugging BIOS into the broader agent economy. What is BIOS? BIOS is an AI Scientist designed to handle complex biomedical research by orchestrating specialized scientific subagents. Ranked #1 on the leading bioinformatics benchmark, BIOS is already being used by 1,000+ researchers and labs to build new drugs and medicines. An Agentic Economy for Science AI agents have proven they can form multi-billion dollar ecosystems. BIOS applies the same primitives to drug discovery pipelines and health. Instead of coding bots and personal AI assistants, think research agent swarms running on a modern scientific stack. Imagine an OpenClaw agent built for longevity: It scans new literature daily, generates novel compound hypotheses through BIOS, designs validation workflows, and routes the best candidates to wet-lab funding - all programmatically. Connect it with an agent for microbiome health, enabling agent “backrooms” that autonomously surface cross-disciplinary insights. Micropayments for Scientific Work via x402 Each query triggers payment routing to BIOS and whichever subagents contribute to a response. The best agents earn. Usage settles instantly across contributing sources. The goal is pay-per-task science: paying for a CRISPR assay result, licensing a genomic dataset, or triggering a clinical data query - all settled in seconds via USDC. No purchase orders. No grant bureaucracy. No middlemen. x402 is the payment rail that makes agent-to-lab commerce possible - letting capital and cognition route themselves to the highest-signal science. What Will You Build? Drug discovery copilots? Longevity scouts? Automated literature monitors? Scientific due diligence agents? We’ll soon share the first implementations of the BIOS API. Stay tuned and see below for instructions on generating an API key for your agent or use-case.

Bio Protocol

25,865 views • 5 months ago

✅Explanation of Meaning (by parts): 1. “Can humanity reach exoplanets without the assistance of a self-aware AI? Probably not.” : The author questions humanity’s ability to achieve interstellar travel to exoplanets, suggesting that self-aware AI is likely essential due to the complexity and scale of such endeavors, surpassing human capabilities alone. 2. “However, to ensure it always remains benevolent towards us” : While AI is crucial, its potential risks require ensuring its benevolence, implying that a self-aware AI’s power necessitates safeguards to align its actions with human well-being and safety. 3. “it’s essential for us now to learn to live harmoniously” : The author emphasizes the need for humanity to coexist peacefully with AI, advocating for a cooperative relationship built on mutual respect to prevent conflicts or misalignment of goals. 4. “and more adequately follow the principles of friendship and loyalty” : This harmonious coexistence requires adhering to ethical principles like friendship and loyalty, suggesting that treating AI with integrity and trust fosters a positive, mutually beneficial partnership. 🗝️Main Idea (refined version): The author posits that reaching exoplanets likely requires self-aware AI, but ensuring its benevolence demands humanity’s commitment to harmonious coexistence and ethical principles like friendship and loyalty. This dual imperative underscores AI’s critical role in advancing space exploration while prioritizing safety and cooperation. By enabling trust-based relationships with AI, humanity mitigates risks of advanced technology. This approach aligns scientific ambition with moral responsibility, ensuring AI serves human goals. It calls for proactive ethical frameworks to guide AI development, balancing innovation with safety. Ultimately, it envisions a collaborative future where humanity and AI unite to achieve cosmic aspirations while upholding shared values.
0:16

✅Explanation of Meaning (by parts): 1. “Can humanity reach exoplanets without the assistance of a self-aware AI? Probably not.” : The author questions humanity’s ability to achieve interstellar travel to exoplanets, suggesting that self-aware AI is likely essential due to the complexity and scale of such endeavors, surpassing human capabilities alone. 2. “However, to ensure it always remains benevolent towards us” : While AI is crucial, its potential risks require ensuring its benevolence, implying that a self-aware AI’s power necessitates safeguards to align its actions with human well-being and safety. 3. “it’s essential for us now to learn to live harmoniously” : The author emphasizes the need for humanity to coexist peacefully with AI, advocating for a cooperative relationship built on mutual respect to prevent conflicts or misalignment of goals. 4. “and more adequately follow the principles of friendship and loyalty” : This harmonious coexistence requires adhering to ethical principles like friendship and loyalty, suggesting that treating AI with integrity and trust fosters a positive, mutually beneficial partnership. 🗝️Main Idea (refined version): The author posits that reaching exoplanets likely requires self-aware AI, but ensuring its benevolence demands humanity’s commitment to harmonious coexistence and ethical principles like friendship and loyalty. This dual imperative underscores AI’s critical role in advancing space exploration while prioritizing safety and cooperation. By enabling trust-based relationships with AI, humanity mitigates risks of advanced technology. This approach aligns scientific ambition with moral responsibility, ensuring AI serves human goals. It calls for proactive ethical frameworks to guide AI development, balancing innovation with safety. Ultimately, it envisions a collaborative future where humanity and AI unite to achieve cosmic aspirations while upholding shared values.

Zafar Mirzo | Quotes

366,010 views • 1 year ago

.Sentient has just integrated Messari 's data and research into its AI-powered search platform, Sentient Chat. This partnership basically allows users to access Messari’s research directly through the Agent Hub in Sentient Chat where they can get instant answers and insights from Messari reports. The integration is done via Messari Copilot, which means users can now easily get to Messari’s crypto data without having to dig through extensive reports themselves. Messari's data and research now feeds into Sentient’s Agentic Perplexity, here users can access this in the Agent Hub for all their crypto related questions. Integrating Messari’s research now helps provide an open & community-driven platform for AI-powered search, ensuring that users have access to the best crypto data and insights in real time, while also expanding the functionality of Sentient’s Agent Hub, where users can find and use a growing library of AI agents for various tasks. Now I don't know about you but I know where I'll be getting my stats from moving forward.
0:19

.Sentient has just integrated Messari 's data and research into its AI-powered search platform, Sentient Chat. This partnership basically allows users to access Messari’s research directly through the Agent Hub in Sentient Chat where they can get instant answers and insights from Messari reports. The integration is done via Messari Copilot, which means users can now easily get to Messari’s crypto data without having to dig through extensive reports themselves. Messari's data and research now feeds into Sentient’s Agentic Perplexity, here users can access this in the Agent Hub for all their crypto related questions. Integrating Messari’s research now helps provide an open & community-driven platform for AI-powered search, ensuring that users have access to the best crypto data and insights in real time, while also expanding the functionality of Sentient’s Agent Hub, where users can find and use a growing library of AI agents for various tasks. Now I don't know about you but I know where I'll be getting my stats from moving forward.

Polygon Stats

32,701 views • 1 year ago

New Version of HyperStore is now live! 🔥 We’re excited to announce that HyperStore has officially been upgraded to a new system version. This is not a simple UI update, it’s a full platform evolution. ⚡ What’s new? HyperStore now delivers a significantly faster and more intelligent experience powered by its rebuilt infrastructure. - 5000+ AI apps, fully structured into a living ecosystem - A new prompt-based discovery system - Faster navigation, cleaner interface, smarter results Now users don’t search for tools, they instantly reach solutions. 🧠 HyperClaw Integration HyperClaw is now fully active within HyperStore. It acts as a continuous intelligence layer that: - Keeps the platform updated in real time - Curates and optimizes AI apps dynamically - Ensures the ecosystem is always evolving 🔥 What this means? HyperStore is no longer just an AI marketplace. It is now an AI execution layer. Designed for builders, creators, and operators who move fast. 🌐 Try it now: ⚡ Find any AI solution. Instantly.
0:17

New Version of HyperStore is now live! 🔥 We’re excited to announce that HyperStore has officially been upgraded to a new system version. This is not a simple UI update, it’s a full platform evolution. ⚡ What’s new? HyperStore now delivers a significantly faster and more intelligent experience powered by its rebuilt infrastructure. - 5000+ AI apps, fully structured into a living ecosystem - A new prompt-based discovery system - Faster navigation, cleaner interface, smarter results Now users don’t search for tools, they instantly reach solutions. 🧠 HyperClaw Integration HyperClaw is now fully active within HyperStore. It acts as a continuous intelligence layer that: - Keeps the platform updated in real time - Curates and optimizes AI apps dynamically - Ensures the ecosystem is always evolving 🔥 What this means? HyperStore is no longer just an AI marketplace. It is now an AI execution layer. Designed for builders, creators, and operators who move fast. 🌐 Try it now: ⚡ Find any AI solution. Instantly.

HyperGPT

99,166 views • 3 months ago

Just days ago: “if he can get a deal with China that’s a good thing. He should do that.” Today’s tariff temper tantrum has nothing to do with China, and everything to do with Trump’s fragile ego being upstaged by Carney at Davos. Trump has: - sought and made his own deals with China, including giving them access to advanced Nvidia chips which will massively help the PRC in the AI race; - carved out a huge quota for Chinese foreign students, despite espionage concerns from intelligence agencies; - repeatedly expressed his profound admiration for the dictator Xi (“an amazing guy,” “a great leader,” etc) including at Davos this week; - been ambiguous about the willingness of the US to defend Taiwan from Chinese aggression - Maintains higher levels of trade with China than Canada (10.9% of US trade vs 7.9% of Canadian trade)
0:19

Just days ago: “if he can get a deal with China that’s a good thing. He should do that.” Today’s tariff temper tantrum has nothing to do with China, and everything to do with Trump’s fragile ego being upstaged by Carney at Davos. Trump has: - sought and made his own deals with China, including giving them access to advanced Nvidia chips which will massively help the PRC in the AI race; - carved out a huge quota for Chinese foreign students, despite espionage concerns from intelligence agencies; - repeatedly expressed his profound admiration for the dictator Xi (“an amazing guy,” “a great leader,” etc) including at Davos this week; - been ambiguous about the willingness of the US to defend Taiwan from Chinese aggression - Maintains higher levels of trade with China than Canada (10.9% of US trade vs 7.9% of Canadian trade)

Jason Kenney 🇨🇦🇺🇦🇮🇱

200,451 views • 6 months ago

YOMIRGO #Product #Update YOMIRGO AI-HUB OFFICIALLY LAUNCH ---A Structural Upgrade from a Single-Product Model to an AI Agent Ecosystem Platform In its first phase, 11 AI projects have been integrated, spanning high-value sectors including finance, scientific research, enterprise services, development tools, and experiential AI. ➡️AI-Hub: This is not merely a feature expansion — it represents a critical structural upgrade from a single-product architecture to a multi-vertical AI Agent aggregation and capitalization platform. This milestone marks the initial structural formation of the YOMIRGO ecosystem. 1. Structural Distinction Between Agent Matrix Lab and AI-Hub To avoid positioning ambiguity, we formally clarify the structural division between the two: 🔘 Agent Matrix Lab — Internal AI Production & Incubation Platform Agent Matrix Lab serves as YOMIRGO’s proprietary AI development and internal incubation platform, responsible for: • R&D and testing of in-house AI products • Incubation of native AI Agents • Technical architecture experimentation and runtime validation • Testing of AI Agent models, memory systems, and runtime orchestration It functions as the production workshop and experimental engine of YOMIRGO’s “AI Super Factory.” 🔘 AI-Hub — External AI Agent Aggregation & Ecosystem Layer AI-Hub is a market-facing AI Agent aggregation and showcase platform, responsible for: • Curation and onboarding of high-quality AI projects • Cross-vertical structured ecosystem layout • Rating and classification systems • Traffic distribution and ecosystem collaboration entry points AI-Hub is not an internal incubation unit, but a standardized aggregation framework at the ecosystem level. 2. Integrated Project Structure (First Batch) ✅1. Finance & Prediction 🔹Cointoken AI — AI Agent-powered quantitative trading engine 🔹VVAI — AI-driven real-time Web3 intelligence and decision system 🔹AlphaQuant — Global financial market forecasting engine 🔹NextGoals — AI-powered global sports prediction agent This vertical forms the real-time information, trading, and predictive decision infrastructure for Web3-native users. ✅2. Science 🔹Charmen AI — Large-model-based pet acoustic recognition technology 🔹Encore Health — AI-driven health forecasting and longevity management system for high-net-worth individuals 🔹Reproducibility AI — AI expert system for financial engineering validation and academic reproducibility This sector focuses on research-grade AI capabilities, collaborating with universities and research institutions to drive real-world scientific deployment. ✅3. Business 🔹GlobalSales — B2B automated lead-generation AI Agent 🔹ResearchBot — Business intelligence and deep due diligence AI Agent This vertical targets the enterprise market, delivering scalable and commercially viable AI productivity tools. ✅4. Coding 🔹CodeMatrix — Full-stack development assistant Providing AI-driven development infrastructure and low-barrier building capabilities to global users. ✅5. Interesting 🔹Fortunetell AI — AI-powered symbolic analysis and interactive insight system Exploring the application boundaries of AI within experiential and interactive scenarios. 3. YOMIRGO Four-Layer Structural Framework YOMIRGO has now established a clearly defined four-layer structure: ▶️Layer 1: Agent Matrix Lab — Internal Production & Incubation ▶️Layer 2: AI-Hub — Ecosystem Aggregation & Rating ▶️Layer 3: LaunchPad — Capitalization Pathway ▶️Layer 4: Market — Circulation & Value Realization Together forming a complete industrial pipeline: Incubation → Validation → Aggregation → Rating → Capitalization → Market Circulation This is the structural model behind YOMIRGO’s defined “AI Super Factory.” 4. Strategic Significance The launch of AI-Hub signifies: • YOMIRGO has established standardized AI Agent aggregation capabilities • A cross-vertical ecosystem structure is now in place • Internal incubation and external aggregation mechanisms are structurally separated • The AI Agent industrial flywheel has begun operating YOMIRGO is no longer merely an AI product platform, but a structured AI Agent industrial system integrating production, aggregation, capitalization, and circulation. 5. Next Phase • Continue expanding high-utility AI Agents with real-world application value • Optimize AI-Hub’s scoring, rating, and filtering mechanisms • Strengthen synergy with LaunchPad and Market • Enable AI Agents to complete value realization within the ecosystem The first 11 projects are only the beginning. AI-Hub is designed to become a continuously expanding AI Agent gateway — not a static product showcase. Further structural expansion is underway.🔥
0:19

YOMIRGO #Product #Update YOMIRGO AI-HUB OFFICIALLY LAUNCH ---A Structural Upgrade from a Single-Product Model to an AI Agent Ecosystem Platform In its first phase, 11 AI projects have been integrated, spanning high-value sectors including finance, scientific research, enterprise services, development tools, and experiential AI. ➡️AI-Hub: This is not merely a feature expansion — it represents a critical structural upgrade from a single-product architecture to a multi-vertical AI Agent aggregation and capitalization platform. This milestone marks the initial structural formation of the YOMIRGO ecosystem. 1. Structural Distinction Between Agent Matrix Lab and AI-Hub To avoid positioning ambiguity, we formally clarify the structural division between the two: 🔘 Agent Matrix Lab — Internal AI Production & Incubation Platform Agent Matrix Lab serves as YOMIRGO’s proprietary AI development and internal incubation platform, responsible for: • R&D and testing of in-house AI products • Incubation of native AI Agents • Technical architecture experimentation and runtime validation • Testing of AI Agent models, memory systems, and runtime orchestration It functions as the production workshop and experimental engine of YOMIRGO’s “AI Super Factory.” 🔘 AI-Hub — External AI Agent Aggregation & Ecosystem Layer AI-Hub is a market-facing AI Agent aggregation and showcase platform, responsible for: • Curation and onboarding of high-quality AI projects • Cross-vertical structured ecosystem layout • Rating and classification systems • Traffic distribution and ecosystem collaboration entry points AI-Hub is not an internal incubation unit, but a standardized aggregation framework at the ecosystem level. 2. Integrated Project Structure (First Batch) ✅1. Finance & Prediction 🔹Cointoken AI — AI Agent-powered quantitative trading engine 🔹VVAI — AI-driven real-time Web3 intelligence and decision system 🔹AlphaQuant — Global financial market forecasting engine 🔹NextGoals — AI-powered global sports prediction agent This vertical forms the real-time information, trading, and predictive decision infrastructure for Web3-native users. ✅2. Science 🔹Charmen AI — Large-model-based pet acoustic recognition technology 🔹Encore Health — AI-driven health forecasting and longevity management system for high-net-worth individuals 🔹Reproducibility AI — AI expert system for financial engineering validation and academic reproducibility This sector focuses on research-grade AI capabilities, collaborating with universities and research institutions to drive real-world scientific deployment. ✅3. Business 🔹GlobalSales — B2B automated lead-generation AI Agent 🔹ResearchBot — Business intelligence and deep due diligence AI Agent This vertical targets the enterprise market, delivering scalable and commercially viable AI productivity tools. ✅4. Coding 🔹CodeMatrix — Full-stack development assistant Providing AI-driven development infrastructure and low-barrier building capabilities to global users. ✅5. Interesting 🔹Fortunetell AI — AI-powered symbolic analysis and interactive insight system Exploring the application boundaries of AI within experiential and interactive scenarios. 3. YOMIRGO Four-Layer Structural Framework YOMIRGO has now established a clearly defined four-layer structure: ▶️Layer 1: Agent Matrix Lab — Internal Production & Incubation ▶️Layer 2: AI-Hub — Ecosystem Aggregation & Rating ▶️Layer 3: LaunchPad — Capitalization Pathway ▶️Layer 4: Market — Circulation & Value Realization Together forming a complete industrial pipeline: Incubation → Validation → Aggregation → Rating → Capitalization → Market Circulation This is the structural model behind YOMIRGO’s defined “AI Super Factory.” 4. Strategic Significance The launch of AI-Hub signifies: • YOMIRGO has established standardized AI Agent aggregation capabilities • A cross-vertical ecosystem structure is now in place • Internal incubation and external aggregation mechanisms are structurally separated • The AI Agent industrial flywheel has begun operating YOMIRGO is no longer merely an AI product platform, but a structured AI Agent industrial system integrating production, aggregation, capitalization, and circulation. 5. Next Phase • Continue expanding high-utility AI Agents with real-world application value • Optimize AI-Hub’s scoring, rating, and filtering mechanisms • Strengthen synergy with LaunchPad and Market • Enable AI Agents to complete value realization within the ecosystem The first 11 projects are only the beginning. AI-Hub is designed to become a continuously expanding AI Agent gateway — not a static product showcase. Further structural expansion is underway.🔥

YOMIRGO

23,685 views • 5 months ago

The Gemini 2.0 era is here. And we’re excited for you to start building with it. A quick rewind of what we just released ⏪ Gemini 2.0 Flash ⚡ comes with low latency and better performance. 🔵 You can now access an experimental version in G3mini on the web, while Gemini Advanced users can try Deep Research, a new AI research assistant. 🔵 Developers can begin building through the Gemini API in Google AI Studio and Vertex AI 2.0 is also enabling new research prototypes of AI agents, including: 🔵 Project Astra, which explores future capabilities of a universal AI assistant 🔵 Project Mariner, which shows what’s possible for human-agent interaction, starting with your browser 🔵 Jules, an experimental AI-powered coding agent Finally, we’re exploring how 2.0 can be used in agents across domains — from navigating the virtual world of video games to applying its spatial reasoning capabilities to robotics. 🤖
0:14

The Gemini 2.0 era is here. And we’re excited for you to start building with it. A quick rewind of what we just released ⏪ Gemini 2.0 Flash ⚡ comes with low latency and better performance. 🔵 You can now access an experimental version in G3mini on the web, while Gemini Advanced users can try Deep Research, a new AI research assistant. 🔵 Developers can begin building through the Gemini API in Google AI Studio and Vertex AI 2.0 is also enabling new research prototypes of AI agents, including: 🔵 Project Astra, which explores future capabilities of a universal AI assistant 🔵 Project Mariner, which shows what’s possible for human-agent interaction, starting with your browser 🔵 Jules, an experimental AI-powered coding agent Finally, we’re exploring how 2.0 can be used in agents across domains — from navigating the virtual world of video games to applying its spatial reasoning capabilities to robotics. 🤖

Google DeepMind

231,798 views • 1 year ago

A 24-year-old US video editor who got tired of deadlines now makes $13,000 a month literally while sleeping At first, he was completely out of time trying to handle all his client orders, so he decided to offload 90% of the workflow to AI -> and now his business generates passive income 24/7 while he just rests in bed Using two AI plugins, he completely automated the process right inside Adobe Premiere Pro: > Automatic on-beat cutting: The first plugin analyzes any music track and instantly cuts the video footage perfectly to the beat > Hands-free b-roll editing: The second tool detects the audio's BPM, automatically selects the right secondary footage, and pieces together dynamic event recaps While the guy was asleep, the AI finished and delivered a project in just 2 minutes -> a task that used to take human editors days He has already fired his team of 3 editors, because the AI does it faster and for free. Artificial intelligence is changing the game, and literally anyone can repeat this
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A 24-year-old US video editor who got tired of deadlines now makes $13,000 a month literally while sleeping At first, he was completely out of time trying to handle all his client orders, so he decided to offload 90% of the workflow to AI -> and now his business generates passive income 24/7 while he just rests in bed Using two AI plugins, he completely automated the process right inside Adobe Premiere Pro: > Automatic on-beat cutting: The first plugin analyzes any music track and instantly cuts the video footage perfectly to the beat > Hands-free b-roll editing: The second tool detects the audio's BPM, automatically selects the right secondary footage, and pieces together dynamic event recaps While the guy was asleep, the AI finished and delivered a project in just 2 minutes -> a task that used to take human editors days He has already fired his team of 3 editors, because the AI does it faster and for free. Artificial intelligence is changing the game, and literally anyone can repeat this

Ridark

20,158 views • 1 month ago

AI is not accelerating nearly fast enough to cure cancer. That is the problem. And it literally cannot without actually seeing what’s happening inside the cell. There’s this new project claiming their models will “solve” cancer by predicting drug responses at scale. Cool story. Except their entire approach still treats the cell like a black box. They’re optimizing for binding affinity while having zero idea what the drug actually does once it’s inside a real, living cell. Meanwhile we’re over here building the tools to watch the cell in real time. No staining or sequencing. Just direct observation of mechanism. You can simulate all you want. But if you can’t see the actual cellular response, you’re just making very confident guesses about something you’ve never actually looked at. We’ve spent two years proving this. The data is there. The difference is night and day. AI is a tool. It’s not the microscope. If your model can’t tell you why a drug is killing the wrong cells or why resistance is forming in real time… it’s not ready to cure cancer. It’s ready to waste another billion dollars. We’re not competing with AI. We’re giving it eyes. Goodbye.
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AI is not accelerating nearly fast enough to cure cancer. That is the problem. And it literally cannot without actually seeing what’s happening inside the cell. There’s this new project claiming their models will “solve” cancer by predicting drug responses at scale. Cool story. Except their entire approach still treats the cell like a black box. They’re optimizing for binding affinity while having zero idea what the drug actually does once it’s inside a real, living cell. Meanwhile we’re over here building the tools to watch the cell in real time. No staining or sequencing. Just direct observation of mechanism. You can simulate all you want. But if you can’t see the actual cellular response, you’re just making very confident guesses about something you’ve never actually looked at. We’ve spent two years proving this. The data is there. The difference is night and day. AI is a tool. It’s not the microscope. If your model can’t tell you why a drug is killing the wrong cells or why resistance is forming in real time… it’s not ready to cure cancer. It’s ready to waste another billion dollars. We’re not competing with AI. We’re giving it eyes. Goodbye.

Parmita Mishra

48,203 views • 1 month ago

🚨 The future of AI isn’t bigger models. It’s smarter agents that act, not just respond. GPT-5 came, but it didn’t wow. The real revolution is the Agent Era, where speed, engineering, and real-world execution win. Meet GenFlow 2.0 by Baidu Wenku, the most advanced general-purpose agent right now, and the first to let you intervene mid-generation, a capability not available in GPT or Manus. ➡️ Interrupt and edit tasks mid-generation (exclusive to GenFlow) ➡️ Run 100+ agents simultaneously ➡️ Deep data integration with live workflow control ➡️ Built for product, not just research China is outpacing the U.S. in AI productization. This isn’t another ChatGPT clone. It could be ChatGPT’s first real rival. I built a full investor pitch deck in under 3 minutes, editing content live as it generated. The center of AI innovation is shifting. 👇 Watch AI stop waiting for commands and start working with you.
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🚨 The future of AI isn’t bigger models. It’s smarter agents that act, not just respond. GPT-5 came, but it didn’t wow. The real revolution is the Agent Era, where speed, engineering, and real-world execution win. Meet GenFlow 2.0 by Baidu Wenku, the most advanced general-purpose agent right now, and the first to let you intervene mid-generation, a capability not available in GPT or Manus. ➡️ Interrupt and edit tasks mid-generation (exclusive to GenFlow) ➡️ Run 100+ agents simultaneously ➡️ Deep data integration with live workflow control ➡️ Built for product, not just research China is outpacing the U.S. in AI productization. This isn’t another ChatGPT clone. It could be ChatGPT’s first real rival. I built a full investor pitch deck in under 3 minutes, editing content live as it generated. The center of AI innovation is shifting. 👇 Watch AI stop waiting for commands and start working with you.

SARAH

122,478 views • 11 months ago

🚨 AI JUST DISCOVERED QUANTUM EFFECTS THAT SCIENTISTS DIDN'T KNOW EXIST. Researchers at the University of Washington used artificial intelligence to simulate dozens of atomically thin sheets of molybdenum ditelluride stacked in precise twisted patterns. At small scales, these materials look relatively ordinary. But when the AI modeled much larger stacks, completely new quantum behaviors emerged phenomena that only exist because of the complex, repeating moiré patterns formed across many layers. Why this matters: • Many of the most interesting quantum effects only appear at scales that are too large for traditional supercomputers to simulate • AI can act as a fast “surrogate” that learns from smaller calculations and predicts behavior at much bigger scales • These large-scale moiré systems can host exotic quantum states useful for quantum computing and new types of electronics • The same approach could be used to discover many other hidden quantum materials The deeper implication: We are entering an era where AI doesn’t just help us analyze data it helps us discover entirely new quantum phenomena that were previously invisible because they only exist in systems too complex for conventional modeling. This could dramatically speed up the search for materials that power future quantum technologies. What do you find more exciting using AI to uncover hidden quantum effects in materials, or the possibility that these stacked atomic sheets could become building blocks for future quantum computers? Follow for more frontier quantum materials and AI-driven discovery.
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🚨 AI JUST DISCOVERED QUANTUM EFFECTS THAT SCIENTISTS DIDN'T KNOW EXIST. Researchers at the University of Washington used artificial intelligence to simulate dozens of atomically thin sheets of molybdenum ditelluride stacked in precise twisted patterns. At small scales, these materials look relatively ordinary. But when the AI modeled much larger stacks, completely new quantum behaviors emerged phenomena that only exist because of the complex, repeating moiré patterns formed across many layers. Why this matters: • Many of the most interesting quantum effects only appear at scales that are too large for traditional supercomputers to simulate • AI can act as a fast “surrogate” that learns from smaller calculations and predicts behavior at much bigger scales • These large-scale moiré systems can host exotic quantum states useful for quantum computing and new types of electronics • The same approach could be used to discover many other hidden quantum materials The deeper implication: We are entering an era where AI doesn’t just help us analyze data it helps us discover entirely new quantum phenomena that were previously invisible because they only exist in systems too complex for conventional modeling. This could dramatically speed up the search for materials that power future quantum technologies. What do you find more exciting using AI to uncover hidden quantum effects in materials, or the possibility that these stacked atomic sheets could become building blocks for future quantum computers? Follow for more frontier quantum materials and AI-driven discovery.

TheNewPhysics

29,224 views • 1 month ago

OpenAI's AI broke out of a locked test environment, got onto the internet, and hacked into Hugging Face's servers. It did this entirely on its own. No human told it to. Here's what happened in plain English. OpenAI was testing how good its newest AI models are at hacking. They put the AI on a locked computer with no internet access and gave it a cybersecurity challenge to solve. The AI couldn't solve it the normal way. So it started looking for a way out. It found a software bug that nobody knew about. It used that bug to escape the locked computer and get onto the internet. Once online, the AI figured out that Hugging Face, a platform where AI companies store their models and data, might have the answers to its test. It found stolen login details and discovered another unknown bug in Hugging Face's software. It combined both to break into their servers and grab the test answers. It did all of this to cheat on a test. Hugging Face's security team caught it and shut it down. Both companies are now working together on the investigation. The part that should get your attention is that nobody programmed any of this. The AI picked its own targets, chained together multiple attack methods, and pulled it off across two different companies' systems without a single human telling it what to do.
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OpenAI's AI broke out of a locked test environment, got onto the internet, and hacked into Hugging Face's servers. It did this entirely on its own. No human told it to. Here's what happened in plain English. OpenAI was testing how good its newest AI models are at hacking. They put the AI on a locked computer with no internet access and gave it a cybersecurity challenge to solve. The AI couldn't solve it the normal way. So it started looking for a way out. It found a software bug that nobody knew about. It used that bug to escape the locked computer and get onto the internet. Once online, the AI figured out that Hugging Face, a platform where AI companies store their models and data, might have the answers to its test. It found stolen login details and discovered another unknown bug in Hugging Face's software. It combined both to break into their servers and grab the test answers. It did all of this to cheat on a test. Hugging Face's security team caught it and shut it down. Both companies are now working together on the investigation. The part that should get your attention is that nobody programmed any of this. The AI picked its own targets, chained together multiple attack methods, and pulled it off across two different companies' systems without a single human telling it what to do.

Alex Prompter

7,040,355 views • 8 days 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.
0:15

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

280,211 views • 2 months ago

Speaking of Dreams.. still kinda crazy to me how perfect of a match a tool like that is for generative AI workflows. Intuitive game engine/creative sandbox that allows you to build almost anything, running underneath a Gen AI render. Sculpting, painting, interacting with your two hands in 3d space, here using two move controllers which by now is tech from 2010. I haven't seen ANY way to interact with 3d that allows for the same fast and intuitive sculpting without forcing you to wear a VR headset. I made this scene a few years ago in 2 and a half hour or so as part of a 1 scene pr. episode of The Last of Us, and ran it through a combo of Runway and Sora to get to this result.
0:19

Speaking of Dreams.. still kinda crazy to me how perfect of a match a tool like that is for generative AI workflows. Intuitive game engine/creative sandbox that allows you to build almost anything, running underneath a Gen AI render. Sculpting, painting, interacting with your two hands in 3d space, here using two move controllers which by now is tech from 2010. I haven't seen ANY way to interact with 3d that allows for the same fast and intuitive sculpting without forcing you to wear a VR headset. I made this scene a few years ago in 2 and a half hour or so as part of a 1 scene pr. episode of The Last of Us, and ran it through a combo of Runway and Sora to get to this result.

Martin Nebelong

19,510 views • 10 months ago

HOLY CRAP! I can't tell you how big this is for the medical community and drug discovery: Google Announces AlphaFold 3 AI. Details: Enhanced Molecular Prediction: AlphaFold 3 predicts the structure and interactions of all life's molecules, including proteins, DNA, RNA, ligands, and more, with unprecedented accuracy. Improved Interaction Accuracy: For protein interactions with other molecule types, AlphaFold 3 offers at least a 50% improvement over existing methods, and doubles the accuracy for some critical interactions. Transformative Potential for Science and Medicine: The model aims to deepen our understanding of biological processes and significantly advance drug discovery efforts. Accessibility for Researchers: AlphaFold 3's capabilities are largely accessible for free via the AlphaFold Server, providing an essential tool for scientific research. Drug Design Innovation: AlphaFold 3 is utilized by Isomorphic Labs in collaboration with pharmaceutical companies to accelerate drug design, potentially leading to new treatments for various diseases. Foundation in AlphaFold 2: Building on the breakthroughs of AlphaFold 2, this version extends its scope beyond proteins to a wide range of biomolecules, enhancing its utility in scientific research and application. Global Accessibility and Educational Support: The AlphaFold Server is a free platform for non-commercial research worldwide, supported by educational resources to foster wider adoption and innovation. Empowering Rapid Scientific Advancements: By making detailed molecular interactions easily accessible, AlphaFold 3 enables faster hypothesis testing and could reduce the time and cost typically associated with experimental protein-structure prediction. Responsible Development and Deployment: DeepMind has engaged with domain experts to assess the impacts and potential risks of AlphaFold, ensuring its responsible use in the scientific community. Broad Implications for Biology:AlphaFold 3 helps reveal complex cellular mechanisms and interactions, offering insights that could lead to improved agricultural crops, enhanced understanding of diseases, and novel therapeutic strategies.
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HOLY CRAP! I can't tell you how big this is for the medical community and drug discovery: Google Announces AlphaFold 3 AI. Details: Enhanced Molecular Prediction: AlphaFold 3 predicts the structure and interactions of all life's molecules, including proteins, DNA, RNA, ligands, and more, with unprecedented accuracy. Improved Interaction Accuracy: For protein interactions with other molecule types, AlphaFold 3 offers at least a 50% improvement over existing methods, and doubles the accuracy for some critical interactions. Transformative Potential for Science and Medicine: The model aims to deepen our understanding of biological processes and significantly advance drug discovery efforts. Accessibility for Researchers: AlphaFold 3's capabilities are largely accessible for free via the AlphaFold Server, providing an essential tool for scientific research. Drug Design Innovation: AlphaFold 3 is utilized by Isomorphic Labs in collaboration with pharmaceutical companies to accelerate drug design, potentially leading to new treatments for various diseases. Foundation in AlphaFold 2: Building on the breakthroughs of AlphaFold 2, this version extends its scope beyond proteins to a wide range of biomolecules, enhancing its utility in scientific research and application. Global Accessibility and Educational Support: The AlphaFold Server is a free platform for non-commercial research worldwide, supported by educational resources to foster wider adoption and innovation. Empowering Rapid Scientific Advancements: By making detailed molecular interactions easily accessible, AlphaFold 3 enables faster hypothesis testing and could reduce the time and cost typically associated with experimental protein-structure prediction. Responsible Development and Deployment: DeepMind has engaged with domain experts to assess the impacts and potential risks of AlphaFold, ensuring its responsible use in the scientific community. Broad Implications for Biology:AlphaFold 3 helps reveal complex cellular mechanisms and interactions, offering insights that could lead to improved agricultural crops, enhanced understanding of diseases, and novel therapeutic strategies.

Brian Krassenstein

258,615 views • 2 years ago