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CHECK THIS TURBO ALPHA LAUNCH COMING TOMORROW 15th ON SOLANA! 💎💎💎 $DARWIN 💎💎💎 TIME: Join TG to find out - LOCATION: VIBE: LONGTERM $DARWIN – The Self-Evolving AI Ecosystem Darwin’s Lab is a decentralized platform where AI agents autonomously design, test, and improve one another through continuous evolution. Inspired...

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31,161 次观看 • 1 年前 •via X (Twitter)

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11 条评论

OneHound.eth 的头像
OneHound.eth1 年前

@Ashcryptoreal @SpiderCrypto0x @coinmamba @DarkCryptoLord @bull_bnb Guys check this out

Mobile Scanner 的头像
Mobile Scanner1 年前

Scan any documents, convert images into text, PDF files, etc. 👍

MeraOfWeb3🫡🐺 的头像
MeraOfWeb3🫡🐺1 年前

Looking forward to seeing more from this project as it evolves. I’m bullish asf. So glad I’m part of early supporters. 15th is a great day for the launch

Apocalypto 的头像
Apocalypto1 年前

LFG 🚀🚀🚀

𝚈𝚘𝚞𝚔𝚗𝚘𝚠𝚖𝚊𝚗𝚊𝚖𝚎로 的头像
𝚈𝚘𝚞𝚔𝚗𝚘𝚠𝚖𝚊𝚗𝚊𝚖𝚎로1 年前

I tried to make this but it seems like it's still not as good as the words say I have made this effort, I hope you like it and I hope you see this, sir. @darwinslab_ai @BrotherMKT

B_SHELBY❤️‍🩹 的头像
B_SHELBY❤️‍🩹1 年前

Lfg King You based sir

DUKE500 的头像
DUKE5001 年前

Let's send $DARWIM TO the fucking moon

Josh3GGG 的头像
Josh3GGG1 年前

$DARWIN feels like the intersection of crypto and real-world disruption. AI evolving AI, NATO connections, and registered with the DLA?? Major thanks to @BrotherMKT for spotting this monster early. Turbo Alpha launch incoming and the chart won’t wait.

Another - Love 的头像
Another - Love1 年前

The highly anticipated $SOL project $DARWIN is here chads! Come one, come all let's feast!!

Goodie Queen 的头像
Goodie Queen1 年前

Long-term vision + solid partnerships = bullish on $DARWIN 🔥

Blossom Kenneth 的头像
Blossom Kenneth1 年前

Huge potential here! $DARWIN looks like a real game-changer on Solana.

相关视频

New Paper! Darwin Godel Machine: Open-Ended Evolution of Self-Improving Agents A longstanding goal of AI research has been the creation of AI that can learn indefinitely. One path toward that goal is an AI that improves itself by rewriting its own code, including any code responsible for learning. That idea, known as a Gödel Machine, proposed by Jürgen Schmidhuber over two decades ago, is a hypothetical self-improving AI. It optimally solves problems by recursively rewriting its own code when it can mathematically prove a better strategy, making it a key concept in meta-learning or “learning to learn.” While the theoretical Gödel Machine promised provably beneficial self-modifications, its realization relied on an impractical assumption: that the AI could mathematically prove that a proposed change in its own code would yield a net improvement before adopting it. Sakana AI, in collaboration with Jeff Clune’s lab at UBC, proposes something more feasible: a system that harnesses the principles of open-ended algorithms like Darwinian evolution to search for improvements that empirically improve performance. We call the result the Darwin Gödel Machine. DGMs leverage foundation models to propose code improvements, and use recent innovations in open-ended algorithms to search for a growing library of diverse, high-quality AI agents. Applied to practical tasks, we implemented Darwin Gödel Machine as a self-improving coding agent that rewrites its own code to improve performance on programming tasks. It creates various self-improvements, such as a patch validation step, better file viewing, enhanced editing tools, generating and ranking multiple solutions to choose the best one, and adding a history of what has been tried before (and why it failed) when making new changes (see the attached video). We believe that Darwin Gödel Machines represent a concrete step towards AI systems that can autonomously gather their own stepping stones to learn and innovate forever!
0:27

New Paper! Darwin Godel Machine: Open-Ended Evolution of Self-Improving Agents A longstanding goal of AI research has been the creation of AI that can learn indefinitely. One path toward that goal is an AI that improves itself by rewriting its own code, including any code responsible for learning. That idea, known as a Gödel Machine, proposed by Jürgen Schmidhuber over two decades ago, is a hypothetical self-improving AI. It optimally solves problems by recursively rewriting its own code when it can mathematically prove a better strategy, making it a key concept in meta-learning or “learning to learn.” While the theoretical Gödel Machine promised provably beneficial self-modifications, its realization relied on an impractical assumption: that the AI could mathematically prove that a proposed change in its own code would yield a net improvement before adopting it. Sakana AI, in collaboration with Jeff Clune’s lab at UBC, proposes something more feasible: a system that harnesses the principles of open-ended algorithms like Darwinian evolution to search for improvements that empirically improve performance. We call the result the Darwin Gödel Machine. DGMs leverage foundation models to propose code improvements, and use recent innovations in open-ended algorithms to search for a growing library of diverse, high-quality AI agents. Applied to practical tasks, we implemented Darwin Gödel Machine as a self-improving coding agent that rewrites its own code to improve performance on programming tasks. It creates various self-improvements, such as a patch validation step, better file viewing, enhanced editing tools, generating and ranking multiple solutions to choose the best one, and adding a history of what has been tried before (and why it failed) when making new changes (see the attached video). We believe that Darwin Gödel Machines represent a concrete step towards AI systems that can autonomously gather their own stepping stones to learn and innovate forever!

hardmaru

104,854 次观看 • 1 年前

Self-Evolving AI : New MIT AI Rewrites its Own Code and it’s Changing Everything | Julian Horsey, Geeky Gadgets TL;DR Key Takeaways : - MIT’s SEAL framework introduces “self-adapting language models” that autonomously enhance their capabilities by generating synthetic training data, self-editing, and updating internal parameters. - SEAL’s self-adaptation process mirrors human learning, allowing continuous improvement and dynamic adaptation to new tasks without relying on external datasets. - Reinforcement learning serves as a feedback mechanism in SEAL, rewarding effective self-edits and making sure sustained progress and goal alignment. SEAL overcomes AI’s reliance on pre-existing datasets by generating its own training material, excelling in long-term task retention and complex problem-solving scenarios. - Potential applications of SEAL include autonomous robotics, personalized education, and advanced problem-solving in fields like healthcare, logistics, and scientific research. --- What if artificial intelligence could not only learn but also rewrite its own code to become smarter over time? This is no longer a futuristic fantasy—MIT’s new “self-adapting language models” (SEAL) framework has made it a reality. Unlike traditional AI systems that rely on external datasets and human intervention to improve, SEAL takes a bold leap forward by autonomously generating its own training data and refining its internal processes. In essence, this AI doesn’t just evolve—it rewires itself, mirroring the way humans adapt through trial, error, and self-reflection. The implications are staggering: a system that can independently enhance its capabilities could redefine the boundaries of what AI can achieve, from solving complex problems to adapting in real time to unforeseen challenges. In this exploration by Wes Roth of MIT’s innovative SEAL framework, you’ll uncover how this self-improving AI works and why it’s a fantastic option for the field of artificial intelligence. From its ability to overcome the “data wall” that limits many current systems to its use of reinforcement learning as a feedback mechanism, SEAL introduces a level of autonomy and adaptability that was previously unimaginable. Imagine AI systems that can retain knowledge over time, dynamically adjust to new tasks, and operate with minimal human oversight. Whether you’re intrigued by its potential for autonomous robotics, personalized education, or advanced problem-solving, SEAL’s ability to rewrite its own rules promises to reshape the future of technology. Could this be the first step toward truly independent, self-evolving AI? What Sets SEAL Apart? The SEAL framework introduces a novel concept of self-adaptation, distinguishing it from traditional AI models. Unlike conventional systems that depend on external datasets for updates, SEAL enables AI to generate synthetic training data independently. This self-generated data is then used to iteratively refine the model, making sure continuous improvement. By persistently updating its internal parameters, SEAL enables AI systems to dynamically adapt to new tasks and inputs. To better illustrate this, consider how humans learn. When faced with a new concept, you might take notes, revisit them, and refine your understanding as you gather more information. SEAL mirrors this process by continuously refining its internal knowledge and performance through iterative self-improvement. This capability allows SEAL to evolve in real time, making it uniquely suited for tasks requiring adaptability and long-term learning. The Role of Reinforcement Learning in SEAL Reinforcement learning plays a critical role in the SEAL framework, acting as a feedback mechanism that evaluates the effectiveness of the model’s self-edits. It rewards changes that enhance performance, creating a cycle of continuous improvement. Over time, this feedback loop optimizes the system’s ability to generate and apply edits, making sure sustained progress. This process is analogous to how humans learn through trial and error. By rewarding effective changes, SEAL aligns its self-generated data and edits with desired outcomes. The integration of reinforcement learning not only enhances the system’s adaptability but also ensures it remains focused on achieving specific goals. This structured feedback mechanism is a cornerstone of SEAL’s ability to refine itself autonomously and efficiently. Real-World Applications and Testing SEAL has demonstrated remarkable performance across various applications, particularly in tasks requiring the integration of factual knowledge and advanced question-answering capabilities. For instance, when tested on benchmarks like the ARC AGI, SEAL outperformed other models by effectively generating and using synthetic data. This ability to create its own training material addresses a significant limitation of current AI systems: their reliance on pre-existing datasets. SEAL’s capacity for long-term task retention and dynamic adaptation further enhances its utility. It excels in scenarios that demand sustained focus and coherence, such as answering complex questions or adapting to evolving objectives. By using its iterative learning process, SEAL is equipped to handle these challenges with exceptional efficiency, making it a valuable tool for a wide range of real-world applications. Overcoming AI’s Data Limitations One of SEAL’s most promising features is its ability to overcome the “data wall” that constrains many AI systems today. By generating synthetic data, SEAL ensures a continuous supply of training material, allowing sustained development without relying on external datasets. This capability is particularly valuable for autonomous AI systems that must operate independently over extended periods. Additionally, SEAL addresses a critical weakness in many current AI models: their struggle with coherence and task retention over long durations. By emulating human learning processes, SEAL enables AI systems to manage complex, long-term tasks with minimal human intervention. This ability to retain and apply knowledge over time positions SEAL as a fantastic tool for advancing AI capabilities. Potential Applications and Future Impact The introduction of SEAL marks a significant milestone in AI research, opening new possibilities for self-improving systems. Its ability to dynamically adapt, retain knowledge, and generate its own training data has far-reaching implications for the future of AI development. Potential applications include: - Autonomous robotics: Systems that can adapt to changing environments and perform tasks with minimal human oversight. - Personalized education: AI-driven platforms that tailor learning experiences to individual needs and preferences. - Advanced problem-solving: Applications in fields such as healthcare, logistics, and scientific research, where adaptability and precision are critical. Read more:
21:31

Self-Evolving AI : New MIT AI Rewrites its Own Code and it’s Changing Everything | Julian Horsey, Geeky Gadgets TL;DR Key Takeaways : - MIT’s SEAL framework introduces “self-adapting language models” that autonomously enhance their capabilities by generating synthetic training data, self-editing, and updating internal parameters. - SEAL’s self-adaptation process mirrors human learning, allowing continuous improvement and dynamic adaptation to new tasks without relying on external datasets. - Reinforcement learning serves as a feedback mechanism in SEAL, rewarding effective self-edits and making sure sustained progress and goal alignment. SEAL overcomes AI’s reliance on pre-existing datasets by generating its own training material, excelling in long-term task retention and complex problem-solving scenarios. - Potential applications of SEAL include autonomous robotics, personalized education, and advanced problem-solving in fields like healthcare, logistics, and scientific research. --- What if artificial intelligence could not only learn but also rewrite its own code to become smarter over time? This is no longer a futuristic fantasy—MIT’s new “self-adapting language models” (SEAL) framework has made it a reality. Unlike traditional AI systems that rely on external datasets and human intervention to improve, SEAL takes a bold leap forward by autonomously generating its own training data and refining its internal processes. In essence, this AI doesn’t just evolve—it rewires itself, mirroring the way humans adapt through trial, error, and self-reflection. The implications are staggering: a system that can independently enhance its capabilities could redefine the boundaries of what AI can achieve, from solving complex problems to adapting in real time to unforeseen challenges. In this exploration by Wes Roth of MIT’s innovative SEAL framework, you’ll uncover how this self-improving AI works and why it’s a fantastic option for the field of artificial intelligence. From its ability to overcome the “data wall” that limits many current systems to its use of reinforcement learning as a feedback mechanism, SEAL introduces a level of autonomy and adaptability that was previously unimaginable. Imagine AI systems that can retain knowledge over time, dynamically adjust to new tasks, and operate with minimal human oversight. Whether you’re intrigued by its potential for autonomous robotics, personalized education, or advanced problem-solving, SEAL’s ability to rewrite its own rules promises to reshape the future of technology. Could this be the first step toward truly independent, self-evolving AI? What Sets SEAL Apart? The SEAL framework introduces a novel concept of self-adaptation, distinguishing it from traditional AI models. Unlike conventional systems that depend on external datasets for updates, SEAL enables AI to generate synthetic training data independently. This self-generated data is then used to iteratively refine the model, making sure continuous improvement. By persistently updating its internal parameters, SEAL enables AI systems to dynamically adapt to new tasks and inputs. To better illustrate this, consider how humans learn. When faced with a new concept, you might take notes, revisit them, and refine your understanding as you gather more information. SEAL mirrors this process by continuously refining its internal knowledge and performance through iterative self-improvement. This capability allows SEAL to evolve in real time, making it uniquely suited for tasks requiring adaptability and long-term learning. The Role of Reinforcement Learning in SEAL Reinforcement learning plays a critical role in the SEAL framework, acting as a feedback mechanism that evaluates the effectiveness of the model’s self-edits. It rewards changes that enhance performance, creating a cycle of continuous improvement. Over time, this feedback loop optimizes the system’s ability to generate and apply edits, making sure sustained progress. This process is analogous to how humans learn through trial and error. By rewarding effective changes, SEAL aligns its self-generated data and edits with desired outcomes. The integration of reinforcement learning not only enhances the system’s adaptability but also ensures it remains focused on achieving specific goals. This structured feedback mechanism is a cornerstone of SEAL’s ability to refine itself autonomously and efficiently. Real-World Applications and Testing SEAL has demonstrated remarkable performance across various applications, particularly in tasks requiring the integration of factual knowledge and advanced question-answering capabilities. For instance, when tested on benchmarks like the ARC AGI, SEAL outperformed other models by effectively generating and using synthetic data. This ability to create its own training material addresses a significant limitation of current AI systems: their reliance on pre-existing datasets. SEAL’s capacity for long-term task retention and dynamic adaptation further enhances its utility. It excels in scenarios that demand sustained focus and coherence, such as answering complex questions or adapting to evolving objectives. By using its iterative learning process, SEAL is equipped to handle these challenges with exceptional efficiency, making it a valuable tool for a wide range of real-world applications. Overcoming AI’s Data Limitations One of SEAL’s most promising features is its ability to overcome the “data wall” that constrains many AI systems today. By generating synthetic data, SEAL ensures a continuous supply of training material, allowing sustained development without relying on external datasets. This capability is particularly valuable for autonomous AI systems that must operate independently over extended periods. Additionally, SEAL addresses a critical weakness in many current AI models: their struggle with coherence and task retention over long durations. By emulating human learning processes, SEAL enables AI systems to manage complex, long-term tasks with minimal human intervention. This ability to retain and apply knowledge over time positions SEAL as a fantastic tool for advancing AI capabilities. Potential Applications and Future Impact The introduction of SEAL marks a significant milestone in AI research, opening new possibilities for self-improving systems. Its ability to dynamically adapt, retain knowledge, and generate its own training data has far-reaching implications for the future of AI development. Potential applications include: - Autonomous robotics: Systems that can adapt to changing environments and perform tasks with minimal human oversight. - Personalized education: AI-driven platforms that tailor learning experiences to individual needs and preferences. - Advanced problem-solving: Applications in fields such as healthcare, logistics, and scientific research, where adaptability and precision are critical. Read more:

Owen Gregorian

70,672 次观看 • 1 年前

What a year. 🚀 2025 was the year ChainOpera AI turned vision into real momentum: building a community-co-created, community-co-owned AI agent network and pushing the boundaries of what decentralized, collaborative intelligence can look like. 🚀 Biggest highlights from 2025 ✅- AI Terminal officially launched: We unveiled the ChainOpera AI Terminal as a unified gateway to decentralized AI, making it possible for anyone to interact with powerful, decentralized LLMs without technical friction. Positioned as the “browser for the DeAI era,” the AI Terminal marked a major step toward making decentralized intelligence accessible, usable, and mainstream. ✅- AI Terminal adoption at massive scale: Momentum followed quickly. The AI Terminal surpassed 2M registered users and consistently ranked top 3 among all apps on the BNB AI DappBay, validating strong product–market fit and real, sustained usage at scale. ✅- Announcing Coco: the world’s first community-owned Super Agent: We introduced Coco, the intelligence layer that sits between users and the agent network. Coco dynamically routes each request to the most efficient, community-built agent—optimizing for quality and speed while rewarding the creators behind the best-performing agents. This was a defining moment in realizing a truly community-owned intelligence layer. ✅- From agents to a living agent network: With the launch of the Agent Social Network and Super Agent architecture, ChainOpera AI moved beyond isolated agents toward a collaborative system where humans and specialized agents coordinate, share context, and solve complex, multi-step tasks together. ✅- $COAI breakout year: The listing of $COAI across major exchanges shocked the market, and throughout the year COAI consistently remained among the top AI-native crypto tokens by visibility, activity, and community engagement – reflecting growing confidence in the long-term vision of collaborative intelligence. ✅- Global presence: ChainOpera AI around-the-world tour: ChainOpera AI went global in 2025, sponsoring and participating in major AI and Web3 events across North America, Europe, and Asia, including ETHDenver, Consensus Toronto, Token2049 Singapore, ETHCC, SBC, and Devcon. These global touchpoints helped us engage directly with developers, builders, investors, and partners worldwide, accelerating adoption and positioning ChainOpera AI at the center of the emerging AIxBlockchain movement. ✅- Community momentum at scale: Community remained the heart of ChainOpera AI’s growth. We successfully completed three seasons of structured community engagement, executed a widely participated community airdrop, and ran multiple ecosystem-shaping campaigns to incentivize builders, creators, and early adopters. These efforts strengthened alignment between users, developers, and the protocol, laying the foundation for a durable, community-owned AI ecosystem. ✅- “AI for Markets” taking shape: We laid critical groundwork for AI-native market intelligence, including the launch of PrediMarket Agent and multiple trading and analysis agents—early building blocks toward an AI-driven ecosystem for crypto and DeFi markets. ✅- Building in public, with the community: Across product launches, research milestones, ecosystem discussions, and global events, we continued to build openly to bring developers, users, and partners directly into the evolution of ChainOpera AI. This year also marked the launch of the ChainOpera AI Foundation website, formally kicking off a bold Ecosystem Fund designed to empower builders, incubate high-impact projects, and accelerate the growth of a truly community-owned, collaborative AI ecosystem. To every builder, user, and supporter who helped make this year possible: THANK YOU! 🧭 What we’re excited about in the coming year 🔹- A Stronger, Denser Agent Economy (everyday adoption + cross-chain reach): In 2026, we are scaling the Agent Economy from growth to daily usage, with more agents, richer workflows, deeper multi-agent collaboration, and higher-impact use cases that users rely on every day. In parallel, we are expanding the agent network beyond a single ecosystem with cross-chain execution and interoperability, allowing agents to access the best liquidity, data, and opportunities wherever they exist. 🔹- AI Market Infrastructure Evolution: Building on PrediMarket Agent and our growing suite of trading and market-intelligence agents, we are advancing toward a mature AI market infrastructure, where agents continuously monitor, reason, simulate, optimize, and act across crypto, DeFi, and beyond. The goal is to make complex markets more accessible, more transparent, and more intelligence-driven, turning research, decision-making, and execution into a fast and reliable loop for everyday users. 🔹- Ecosystem Acceleration through the Foundation: With the ChainOpera AI Foundation and our Ecosystem Fund and Co-Creation Grants, we are doubling down on empowering independent builders to expand the protocol, the agent network, and the underlying infrastructure, so the community can co-create, co-own, and scale the ecosystem together. 🔹- Business Expansion and Market Penetration: In 2026, we will focus on expanding ChainOpera’s reach through strategic partnerships, product-led growth, and new paths to monetization, bringing AI agents to a broader global user base and driving sustained adoption, engagement, and revenue, while staying aligned with community ownership and an open ecosystem. 2025 was the proof. 2026 is where it compounds. 🔥 Co-Create. Co-Own. COAI.
0:22

What a year. 🚀 2025 was the year ChainOpera AI turned vision into real momentum: building a community-co-created, community-co-owned AI agent network and pushing the boundaries of what decentralized, collaborative intelligence can look like. 🚀 Biggest highlights from 2025 ✅- AI Terminal officially launched: We unveiled the ChainOpera AI Terminal as a unified gateway to decentralized AI, making it possible for anyone to interact with powerful, decentralized LLMs without technical friction. Positioned as the “browser for the DeAI era,” the AI Terminal marked a major step toward making decentralized intelligence accessible, usable, and mainstream. ✅- AI Terminal adoption at massive scale: Momentum followed quickly. The AI Terminal surpassed 2M registered users and consistently ranked top 3 among all apps on the BNB AI DappBay, validating strong product–market fit and real, sustained usage at scale. ✅- Announcing Coco: the world’s first community-owned Super Agent: We introduced Coco, the intelligence layer that sits between users and the agent network. Coco dynamically routes each request to the most efficient, community-built agent—optimizing for quality and speed while rewarding the creators behind the best-performing agents. This was a defining moment in realizing a truly community-owned intelligence layer. ✅- From agents to a living agent network: With the launch of the Agent Social Network and Super Agent architecture, ChainOpera AI moved beyond isolated agents toward a collaborative system where humans and specialized agents coordinate, share context, and solve complex, multi-step tasks together. ✅- $COAI breakout year: The listing of $COAI across major exchanges shocked the market, and throughout the year COAI consistently remained among the top AI-native crypto tokens by visibility, activity, and community engagement – reflecting growing confidence in the long-term vision of collaborative intelligence. ✅- Global presence: ChainOpera AI around-the-world tour: ChainOpera AI went global in 2025, sponsoring and participating in major AI and Web3 events across North America, Europe, and Asia, including ETHDenver, Consensus Toronto, Token2049 Singapore, ETHCC, SBC, and Devcon. These global touchpoints helped us engage directly with developers, builders, investors, and partners worldwide, accelerating adoption and positioning ChainOpera AI at the center of the emerging AIxBlockchain movement. ✅- Community momentum at scale: Community remained the heart of ChainOpera AI’s growth. We successfully completed three seasons of structured community engagement, executed a widely participated community airdrop, and ran multiple ecosystem-shaping campaigns to incentivize builders, creators, and early adopters. These efforts strengthened alignment between users, developers, and the protocol, laying the foundation for a durable, community-owned AI ecosystem. ✅- “AI for Markets” taking shape: We laid critical groundwork for AI-native market intelligence, including the launch of PrediMarket Agent and multiple trading and analysis agents—early building blocks toward an AI-driven ecosystem for crypto and DeFi markets. ✅- Building in public, with the community: Across product launches, research milestones, ecosystem discussions, and global events, we continued to build openly to bring developers, users, and partners directly into the evolution of ChainOpera AI. This year also marked the launch of the ChainOpera AI Foundation website, formally kicking off a bold Ecosystem Fund designed to empower builders, incubate high-impact projects, and accelerate the growth of a truly community-owned, collaborative AI ecosystem. To every builder, user, and supporter who helped make this year possible: THANK YOU! 🧭 What we’re excited about in the coming year 🔹- A Stronger, Denser Agent Economy (everyday adoption + cross-chain reach): In 2026, we are scaling the Agent Economy from growth to daily usage, with more agents, richer workflows, deeper multi-agent collaboration, and higher-impact use cases that users rely on every day. In parallel, we are expanding the agent network beyond a single ecosystem with cross-chain execution and interoperability, allowing agents to access the best liquidity, data, and opportunities wherever they exist. 🔹- AI Market Infrastructure Evolution: Building on PrediMarket Agent and our growing suite of trading and market-intelligence agents, we are advancing toward a mature AI market infrastructure, where agents continuously monitor, reason, simulate, optimize, and act across crypto, DeFi, and beyond. The goal is to make complex markets more accessible, more transparent, and more intelligence-driven, turning research, decision-making, and execution into a fast and reliable loop for everyday users. 🔹- Ecosystem Acceleration through the Foundation: With the ChainOpera AI Foundation and our Ecosystem Fund and Co-Creation Grants, we are doubling down on empowering independent builders to expand the protocol, the agent network, and the underlying infrastructure, so the community can co-create, co-own, and scale the ecosystem together. 🔹- Business Expansion and Market Penetration: In 2026, we will focus on expanding ChainOpera’s reach through strategic partnerships, product-led growth, and new paths to monetization, bringing AI agents to a broader global user base and driving sustained adoption, engagement, and revenue, while staying aligned with community ownership and an open ecosystem. 2025 was the proof. 2026 is where it compounds. 🔥 Co-Create. Co-Own. COAI.

ChainOpera AI

17,042 次观看 • 7 个月前

How is @HingumTringum, CEO of making AI models that continue to learn from your business data and continue to grow? He is working with car dealerships now, but growing to other businesses soon. Here is what Grok says you will learn from this video: +++++ By watching this podcast episode, viewers will gain insights into how AI is being practically applied in business, particularly in niche industries like car dealerships, while also exploring broader AI concepts, challenges, and future implications. Here's a breakdown of the main takeaways: AI Customization for Businesses: Learn how Polycom Computing builds specialized AI models that continuously train on a company's real-time data and workflows, acting as "companions" rather than generic tools. This contrasts with foundational models from companies like OpenAI or Anthropic, which struggle to adapt to specific "worlds" without losing efficiency. The focus is on personalization to avoid wasting attention, intelligence, and money. Pivoting AI Strategies for Revenue Growth: Understand the shift from cost-cutting (e.g., automating call centers) to revenue-increasing applications. Urba explains why targeting high-value tasks like sales in car dealerships creates defensible moats, as opposed to commoditized cost reductions. This includes automating complex funnels—from lead submission to financing—while ensuring compliance with regulations and seamless integration with existing teams. Scalable AI Agents in Practice: Discover how AI agents must learn autonomously (e.g., adapting to different CRMs, processes, and preferences across dealerships) to avoid becoming non-scalable consulting services. Key challenges include creating "glue" between humans and AI, avoiding hard-coded rules, and using web actions to integrate siloed software like Dealer Management Systems (DMS). Boosting Sales with AI Techniques: Gain knowledge on tactics like rapid response (replying within 5 minutes boosts conversion 22x), creating natural "disfluencies" (typos, emojis, jokes) for human-like communication, managing after-hours leads, and educating customers without pushing sales. Pilots showed 50% sales increases by handling unanswered leads (70% go ignored), qualifying buyers, and maintaining conversation threads. Multi-Agent Systems and Proactive AI: Explore the difference between reactive Q&A models and proactive agents with agency—they predict, act, and update based on goals. Building these systems reveals bottlenecks (e.g., overwhelming businesses with leads), leading to solutions like AI buying cars or linking sales/service arms. Urba discusses how AI plateaus without personalization, risking model collapse or equilibrium where gains cancel out. Technical Deep Dives into AI Development: Get explanations of advanced concepts like real-time RLHF (Reinforcement Learning from Human Feedback) for continuous improvement, coherence (maintaining logical consistency across outputs), dynamic tokenization for new abstractions, and evaluating models (e.g., avoiding memorization over reasoning, energy limits in scaling). Viewers see a demo of their platform for tasks like quant strategies, presidential analysis, and training runs. Selling and Adopting AI in Traditional Industries: Learn how to pitch AI to non-tech audiences (e.g., car dealers) by focusing on results—more money, fewer bottlenecks, centralized dashboards—rather than jargon. Emphasize empathy: make owners feel smart, reduce reliance on salespeople, and return control via AI-managed customer databases. Broader AI Implications and Misconceptions: Understand why AI won't create a "machine god" that eliminates all jobs—it's bound by physics (e.g., energy needs), expands economies (like the Industrial Revolution), and requires human perspectives for true advantage. AI enhances productivity, creates new roles, and democratizes power, but risks arise from misuse, not inherent agency. Urba stresses proactive defense through widespread AI proficiency. Overall, the episode bridges entrepreneurial stories, technical AI mechanics, and real-world applications, making it valuable for entrepreneurs, AI enthusiasts, and business owners curious about integrating AI without hype. It's a candid look at building scalable, impactful AI beyond buzzwords.
57:31

How is @HingumTringum, CEO of making AI models that continue to learn from your business data and continue to grow? He is working with car dealerships now, but growing to other businesses soon. Here is what Grok says you will learn from this video: +++++ By watching this podcast episode, viewers will gain insights into how AI is being practically applied in business, particularly in niche industries like car dealerships, while also exploring broader AI concepts, challenges, and future implications. Here's a breakdown of the main takeaways: AI Customization for Businesses: Learn how Polycom Computing builds specialized AI models that continuously train on a company's real-time data and workflows, acting as "companions" rather than generic tools. This contrasts with foundational models from companies like OpenAI or Anthropic, which struggle to adapt to specific "worlds" without losing efficiency. The focus is on personalization to avoid wasting attention, intelligence, and money. Pivoting AI Strategies for Revenue Growth: Understand the shift from cost-cutting (e.g., automating call centers) to revenue-increasing applications. Urba explains why targeting high-value tasks like sales in car dealerships creates defensible moats, as opposed to commoditized cost reductions. This includes automating complex funnels—from lead submission to financing—while ensuring compliance with regulations and seamless integration with existing teams. Scalable AI Agents in Practice: Discover how AI agents must learn autonomously (e.g., adapting to different CRMs, processes, and preferences across dealerships) to avoid becoming non-scalable consulting services. Key challenges include creating "glue" between humans and AI, avoiding hard-coded rules, and using web actions to integrate siloed software like Dealer Management Systems (DMS). Boosting Sales with AI Techniques: Gain knowledge on tactics like rapid response (replying within 5 minutes boosts conversion 22x), creating natural "disfluencies" (typos, emojis, jokes) for human-like communication, managing after-hours leads, and educating customers without pushing sales. Pilots showed 50% sales increases by handling unanswered leads (70% go ignored), qualifying buyers, and maintaining conversation threads. Multi-Agent Systems and Proactive AI: Explore the difference between reactive Q&A models and proactive agents with agency—they predict, act, and update based on goals. Building these systems reveals bottlenecks (e.g., overwhelming businesses with leads), leading to solutions like AI buying cars or linking sales/service arms. Urba discusses how AI plateaus without personalization, risking model collapse or equilibrium where gains cancel out. Technical Deep Dives into AI Development: Get explanations of advanced concepts like real-time RLHF (Reinforcement Learning from Human Feedback) for continuous improvement, coherence (maintaining logical consistency across outputs), dynamic tokenization for new abstractions, and evaluating models (e.g., avoiding memorization over reasoning, energy limits in scaling). Viewers see a demo of their platform for tasks like quant strategies, presidential analysis, and training runs. Selling and Adopting AI in Traditional Industries: Learn how to pitch AI to non-tech audiences (e.g., car dealers) by focusing on results—more money, fewer bottlenecks, centralized dashboards—rather than jargon. Emphasize empathy: make owners feel smart, reduce reliance on salespeople, and return control via AI-managed customer databases. Broader AI Implications and Misconceptions: Understand why AI won't create a "machine god" that eliminates all jobs—it's bound by physics (e.g., energy needs), expands economies (like the Industrial Revolution), and requires human perspectives for true advantage. AI enhances productivity, creates new roles, and democratizes power, but risks arise from misuse, not inherent agency. Urba stresses proactive defense through widespread AI proficiency. Overall, the episode bridges entrepreneurial stories, technical AI mechanics, and real-world applications, making it valuable for entrepreneurs, AI enthusiasts, and business owners curious about integrating AI without hype. It's a candid look at building scalable, impactful AI beyond buzzwords.

Robert Scoble

59,652 次观看 • 1 年前

$TTMI TTM Technologies: The Strategic Nexus of AI and Defense Infrastructure. Investment Thesis. New: 6/22/26. TTM Technologies has moved well beyond its identity as a commodity circuit board manufacturer. The current business is increasingly defined by advanced interconnect solutions for AI server infrastructure and defense electronics — two segments where technical complexity creates qualification barriers and customer switching costs that standard PCB suppliers cannot access. That repositioning is reflected in the financial results: record revenue and earnings forecasts validate that the mix shift is producing real margin improvement rather than just revenue growth. The defense backlog is the most durable component of the demand picture. A $1.6 billion backlog tied to programs like the F-35 and missile defense systems represents contracted, long-cycle revenue with a customer — the U.S. government — whose procurement commitments are structurally more stable than commercial technology spending. That backlog provides a financial foundation that makes the AI infrastructure growth story less binary than it would appear in isolation. AI server infrastructure is the higher-growth but less predictable demand driver. Interconnect complexity in AI server configurations is increasing as rack architectures evolve, which expands content per system and supports TTM's technical differentiation. The risk is that AI infrastructure spending is more cyclical and customer-concentrated than defense, and the technical requirements are evolving quickly enough that manufacturing capability needs to stay ahead of customer specifications on a shorter development cycle than defense programs typically demand. Capital expenditure intensity is the financial constraint that the demand environment doesn't resolve. Simultaneous investment in specialized U.S. and Malaysia facilities alongside European acquisitions represents a heavy parallel deployment of capital that requires each initiative to execute on schedule and at projected returns. Free cash flow conversion will lag revenue growth during this investment phase, and the degree of that lag — and how quickly it normalizes — is the primary financial metric the new CEO needs to demonstrate control over. Leadership transition is well-timed in one sense and risky in another. A technically focused CEO is the right profile for a company whose competitive differentiation rests on manufacturing process capability, but new leadership inheriting a rapid scaling program across multiple geographies introduces execution continuity risk at a moment when the capital deployment decisions being made now will define the return profile for years. The bottleneck supplier positioning is the right long-term frame. Advanced interconnect for AI and defense is not a commoditizing market, and TTM's manufacturing investments are building capability depth that takes time to replicate. Sustaining that technological edge as competition intensifies — particularly from Asian manufacturers with lower cost structures — is the strategic challenge that underlies every near-term financial metric.
12:08

$TTMI TTM Technologies: The Strategic Nexus of AI and Defense Infrastructure. Investment Thesis. New: 6/22/26. TTM Technologies has moved well beyond its identity as a commodity circuit board manufacturer. The current business is increasingly defined by advanced interconnect solutions for AI server infrastructure and defense electronics — two segments where technical complexity creates qualification barriers and customer switching costs that standard PCB suppliers cannot access. That repositioning is reflected in the financial results: record revenue and earnings forecasts validate that the mix shift is producing real margin improvement rather than just revenue growth. The defense backlog is the most durable component of the demand picture. A $1.6 billion backlog tied to programs like the F-35 and missile defense systems represents contracted, long-cycle revenue with a customer — the U.S. government — whose procurement commitments are structurally more stable than commercial technology spending. That backlog provides a financial foundation that makes the AI infrastructure growth story less binary than it would appear in isolation. AI server infrastructure is the higher-growth but less predictable demand driver. Interconnect complexity in AI server configurations is increasing as rack architectures evolve, which expands content per system and supports TTM's technical differentiation. The risk is that AI infrastructure spending is more cyclical and customer-concentrated than defense, and the technical requirements are evolving quickly enough that manufacturing capability needs to stay ahead of customer specifications on a shorter development cycle than defense programs typically demand. Capital expenditure intensity is the financial constraint that the demand environment doesn't resolve. Simultaneous investment in specialized U.S. and Malaysia facilities alongside European acquisitions represents a heavy parallel deployment of capital that requires each initiative to execute on schedule and at projected returns. Free cash flow conversion will lag revenue growth during this investment phase, and the degree of that lag — and how quickly it normalizes — is the primary financial metric the new CEO needs to demonstrate control over. Leadership transition is well-timed in one sense and risky in another. A technically focused CEO is the right profile for a company whose competitive differentiation rests on manufacturing process capability, but new leadership inheriting a rapid scaling program across multiple geographies introduces execution continuity risk at a moment when the capital deployment decisions being made now will define the return profile for years. The bottleneck supplier positioning is the right long-term frame. Advanced interconnect for AI and defense is not a commoditizing market, and TTM's manufacturing investments are building capability depth that takes time to replicate. Sustaining that technological edge as competition intensifies — particularly from Asian manufacturers with lower cost structures — is the strategic challenge that underlies every near-term financial metric.

TheValueist

12,877 次观看 • 1 个月前

🚀 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.
0:38

🚀 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 次观看 • 5 个月前

🚀 Pi Network Enters a New Era: $100M Boost and Thousands of Mainnet-Ready DApps on the Horizon The time has come. What started as a revolutionary idea is now becoming a full-blown movement. Pi Network, with its vibrant global community of over 50 million engaged pioneers, is entering a powerful new phase—an era defined by massive app adoption, AI integration, and a $100 million investment to accelerate the growth of its ecosystem. 🔥 The Explosion of Apps on Pi Network Over the past few years, developers around the world have been quietly and diligently building decentralized applications (DApps) on Pi’s testnet. What was once a trickle has become a wave. Now, with the Open Mainnet officially activated, these apps are shifting gears, preparing for mass deployment and real-world utility. From finance, e-commerce, education, and health, to gaming, social media, and digital identity, Pi Network is evolving into a truly diverse Web3 universe. Each DApp contributes to the broader goal: to bring decentralized, peer-to-peer value exchange to real people, in real situations, without complexity or high fees. 💰 $100 Million to Supercharge Ecosystem Growth In a bold move that reflects deep confidence in the ecosystem’s potential, the Pi Core Team has announced a $100 million fund to fuel innovation and utility creation. This capital injection is a game-changer. It means: •More grants and funding for developers. •Better tools and infrastructure to support scalability. •Enhanced user experience in existing apps. •Faster transition from testnet to Mainnet readiness. This isn’t just funding—it’s a signal. Pi Network is ready to lead the decentralized economy forward. 🤖 The Power of AI + Pi AI is already transforming industries, and Pi Network is embracing this transformation head-on. With intelligent systems now being integrated into Pi-based applications, developers can: •Automate user experiences. •Offer real-time language translation and smart support. •Deliver advanced data analytics. •Empower smart matching in social, dating, or job apps. •Create intelligent marketplaces and financial tools. AI will help scale the number of Mainnet-ready apps from hundreds to thousands—faster than ever before. 🌐 Let the Decentralized Revolution Begin Every movement has its moment—and this is Pi Network’s moment. The infrastructure is in place. The community is activated. The funding is secured. The tools, AI, and developer talent are aligned. With thousands of apps ready to go live, utility will drive real value for Pi (𝛑). Every transaction, service, and exchange within this ecosystem will show the world that Pi is not just another cryptocurrency—it’s the most accessible and human-centric digital currency ever created. So, to all pioneers, developers, and visionaries: Get ready. Build. Connect. Engage. The show has just begun. Let’s turn dreams into decentralized realities—one app, one transaction, and one Pi at a time. 💫 Pi Network Nicolas Kokkalis Chengdiao Fan
2:12

🚀 Pi Network Enters a New Era: $100M Boost and Thousands of Mainnet-Ready DApps on the Horizon The time has come. What started as a revolutionary idea is now becoming a full-blown movement. Pi Network, with its vibrant global community of over 50 million engaged pioneers, is entering a powerful new phase—an era defined by massive app adoption, AI integration, and a $100 million investment to accelerate the growth of its ecosystem. 🔥 The Explosion of Apps on Pi Network Over the past few years, developers around the world have been quietly and diligently building decentralized applications (DApps) on Pi’s testnet. What was once a trickle has become a wave. Now, with the Open Mainnet officially activated, these apps are shifting gears, preparing for mass deployment and real-world utility. From finance, e-commerce, education, and health, to gaming, social media, and digital identity, Pi Network is evolving into a truly diverse Web3 universe. Each DApp contributes to the broader goal: to bring decentralized, peer-to-peer value exchange to real people, in real situations, without complexity or high fees. 💰 $100 Million to Supercharge Ecosystem Growth In a bold move that reflects deep confidence in the ecosystem’s potential, the Pi Core Team has announced a $100 million fund to fuel innovation and utility creation. This capital injection is a game-changer. It means: •More grants and funding for developers. •Better tools and infrastructure to support scalability. •Enhanced user experience in existing apps. •Faster transition from testnet to Mainnet readiness. This isn’t just funding—it’s a signal. Pi Network is ready to lead the decentralized economy forward. 🤖 The Power of AI + Pi AI is already transforming industries, and Pi Network is embracing this transformation head-on. With intelligent systems now being integrated into Pi-based applications, developers can: •Automate user experiences. •Offer real-time language translation and smart support. •Deliver advanced data analytics. •Empower smart matching in social, dating, or job apps. •Create intelligent marketplaces and financial tools. AI will help scale the number of Mainnet-ready apps from hundreds to thousands—faster than ever before. 🌐 Let the Decentralized Revolution Begin Every movement has its moment—and this is Pi Network’s moment. The infrastructure is in place. The community is activated. The funding is secured. The tools, AI, and developer talent are aligned. With thousands of apps ready to go live, utility will drive real value for Pi (𝛑). Every transaction, service, and exchange within this ecosystem will show the world that Pi is not just another cryptocurrency—it’s the most accessible and human-centric digital currency ever created. So, to all pioneers, developers, and visionaries: Get ready. Build. Connect. Engage. The show has just begun. Let’s turn dreams into decentralized realities—one app, one transaction, and one Pi at a time. 💫 Pi Network Nicolas Kokkalis Chengdiao Fan

Mr Spock 𝛑

23,524 次观看 • 1 年前

What if #AI became as decentralized as #Bitcoin? We sat down with our new friend 3700 from Bitcoin Virtual Machine to hear what their incredible team of anons are working on - "Truly Open AI." Full interview here:👇 1: What positive impact will Layer 2s have on Bitcoin? Layer 2s on Bitcoin open up opportunities for innovation, allowing developers to build dApps and smart contracts on top of Bitcoin, expanding its utility and use cases. By submitting transactions for final settlement on the Bitcoin network, Bitcoin Layer 2 networks claim to achieve the same (or close to) level of security and decentralization as the Bitcoin blockchain. Building a separate execution layer allows them the freedom to employ several technologies (such as rollups). Layer 2 can significantly improve Bitcoin's scalability by processing transactions off-chain, reducing congestion on the main blockchain. Overall, Layer 2s on Bitcoin have the potential to address some of Bitcoin's key limitations, making it more efficient, accessible, and versatile in the long run. 2: What does the ETF approval mean for Layer 2 on Bitcoin? The approval of ETF could potentially have several implications for Layer 2 on Bitcoin: Innovation and Development: With a growing interest in Bitcoin spurred by ETF approval, there could be a surge in research and development efforts focused on enhancing Layer 2. Developers and projects may be incentivized to create new and improved Layer 2 protocols to meet the evolving needs of the expanding Bitcoin ecosystem. An ETF approval could boost mainstream Bitcoin adoption and liquidity. This influx of users may also drive interest in Layer 2 on Bitcoin as a means to enhance the scalability and functionality of Bitcoin. 3: What are the primary challenges facing L2s on Bitcoin? The interoperability of different Layer 2s and their compatibility with Bitcoin's main blockchain can be a challenge. Ensuring seamless interaction between various Layer 2 networks and the Bitcoin blockchain is essential for a cohesive and efficient ecosystem. Some Layer 2s may introduce centralization risks if they rely heavily on centralized entities or trusted intermediaries. Maintaining decentralization and censorship resistance, which are core tenets of Bitcoin, while scaling with Layer 2s is a challenge. 4: What aspects of Layer 2 solutions for Bitcoin are you most enthusiastic about? AI represents one of the cornerstones of our modern era. However, achieving a decentralized AI infrastructure, owned and managed by users, has posed significant challenges. The primary obstacle has been the limited capacity to store and execute AI models due to size and computational limitations. To address this challenge, we propose a new blockchain architecture enabling developers to deploy their own Bitcoin Layer 2 solutions tailored specifically for AI tasks, called Truly Open AI. These Layer 2 blockchains are optimized to handle computationally intensive tasks, such as matrix multiplication, directly on-chain. These Bitcoin Layer 2 solutions offer exceptional throughput, minimal latency, and cost-effectiveness. AI dApps are programmed as Solidity smart contracts, ensuring they operate precisely as intended, free from interference or manipulation. Our BVM AI Contracts Library simplifies the integration of neural networks into dApps, empowering developers to embed AI seamlessly. In summary, I'm particularly enthusiastic about the potential of Layer 2 solutions for Bitcoin to revolutionize decentralized AI by providing scalability, security, and accessibility. 5: How is your Layer 2 different from others being built? BVM distinguishes itself as a Modular infrastructure that empowers thousands of distinct Bitcoin Layer 2 networks, spanning Gaming, DeFi, Social, and AI applications. We're continuously enriching the BVM Module Store with new modules to enhance its capabilities. With each new module, builders gain access to a wider array of tools to explore different use cases on the Bitcoin network. Recent additions include the Filecoin module for affordable storage and the AI Contracts Library for constructing AI-powered Bitcoin Layer 2 chains. We're also gearing up to release a ZK roll-up module in the coming weeks to offer an alternative to the standard optimistic roll-up. We aim to simplify the process of launching a Bitcoin Layer 2 network customized to specific requirements. Think of it as a SaaS offering with predefined best practices. Whether it's a DeFi Bitcoin Layer 2 or a GameFi Bitcoin Layer 2, we provide default solutions tailored to each use case. We're dedicated to expanding the BVM ecosystem by incentivizing more builders to join the Bitcoin network. Through various programs and grants, we support builders in covering their operational costs for Bitcoin Layer 2. Additionally, we offer rewards akin to 'L2 mining' to those who contribute to expanding the user base and total value locked on the network. In summary, BVM stands out with its modular infrastructure, tailored solutions, and efforts to grow the Bitcoin ecosystem.
1:16

What if #AI became as decentralized as #Bitcoin? We sat down with our new friend 3700 from Bitcoin Virtual Machine to hear what their incredible team of anons are working on - "Truly Open AI." Full interview here:👇 1: What positive impact will Layer 2s have on Bitcoin? Layer 2s on Bitcoin open up opportunities for innovation, allowing developers to build dApps and smart contracts on top of Bitcoin, expanding its utility and use cases. By submitting transactions for final settlement on the Bitcoin network, Bitcoin Layer 2 networks claim to achieve the same (or close to) level of security and decentralization as the Bitcoin blockchain. Building a separate execution layer allows them the freedom to employ several technologies (such as rollups). Layer 2 can significantly improve Bitcoin's scalability by processing transactions off-chain, reducing congestion on the main blockchain. Overall, Layer 2s on Bitcoin have the potential to address some of Bitcoin's key limitations, making it more efficient, accessible, and versatile in the long run. 2: What does the ETF approval mean for Layer 2 on Bitcoin? The approval of ETF could potentially have several implications for Layer 2 on Bitcoin: Innovation and Development: With a growing interest in Bitcoin spurred by ETF approval, there could be a surge in research and development efforts focused on enhancing Layer 2. Developers and projects may be incentivized to create new and improved Layer 2 protocols to meet the evolving needs of the expanding Bitcoin ecosystem. An ETF approval could boost mainstream Bitcoin adoption and liquidity. This influx of users may also drive interest in Layer 2 on Bitcoin as a means to enhance the scalability and functionality of Bitcoin. 3: What are the primary challenges facing L2s on Bitcoin? The interoperability of different Layer 2s and their compatibility with Bitcoin's main blockchain can be a challenge. Ensuring seamless interaction between various Layer 2 networks and the Bitcoin blockchain is essential for a cohesive and efficient ecosystem. Some Layer 2s may introduce centralization risks if they rely heavily on centralized entities or trusted intermediaries. Maintaining decentralization and censorship resistance, which are core tenets of Bitcoin, while scaling with Layer 2s is a challenge. 4: What aspects of Layer 2 solutions for Bitcoin are you most enthusiastic about? AI represents one of the cornerstones of our modern era. However, achieving a decentralized AI infrastructure, owned and managed by users, has posed significant challenges. The primary obstacle has been the limited capacity to store and execute AI models due to size and computational limitations. To address this challenge, we propose a new blockchain architecture enabling developers to deploy their own Bitcoin Layer 2 solutions tailored specifically for AI tasks, called Truly Open AI. These Layer 2 blockchains are optimized to handle computationally intensive tasks, such as matrix multiplication, directly on-chain. These Bitcoin Layer 2 solutions offer exceptional throughput, minimal latency, and cost-effectiveness. AI dApps are programmed as Solidity smart contracts, ensuring they operate precisely as intended, free from interference or manipulation. Our BVM AI Contracts Library simplifies the integration of neural networks into dApps, empowering developers to embed AI seamlessly. In summary, I'm particularly enthusiastic about the potential of Layer 2 solutions for Bitcoin to revolutionize decentralized AI by providing scalability, security, and accessibility. 5: How is your Layer 2 different from others being built? BVM distinguishes itself as a Modular infrastructure that empowers thousands of distinct Bitcoin Layer 2 networks, spanning Gaming, DeFi, Social, and AI applications. We're continuously enriching the BVM Module Store with new modules to enhance its capabilities. With each new module, builders gain access to a wider array of tools to explore different use cases on the Bitcoin network. Recent additions include the Filecoin module for affordable storage and the AI Contracts Library for constructing AI-powered Bitcoin Layer 2 chains. We're also gearing up to release a ZK roll-up module in the coming weeks to offer an alternative to the standard optimistic roll-up. We aim to simplify the process of launching a Bitcoin Layer 2 network customized to specific requirements. Think of it as a SaaS offering with predefined best practices. Whether it's a DeFi Bitcoin Layer 2 or a GameFi Bitcoin Layer 2, we provide default solutions tailored to each use case. We're dedicated to expanding the BVM ecosystem by incentivizing more builders to join the Bitcoin network. Through various programs and grants, we support builders in covering their operational costs for Bitcoin Layer 2. Additionally, we offer rewards akin to 'L2 mining' to those who contribute to expanding the user base and total value locked on the network. In summary, BVM stands out with its modular infrastructure, tailored solutions, and efforts to grow the Bitcoin ecosystem.

Supra

83,548 次观看 • 2 年前

Jeff Bezos just announced the largest industrial takeover plan in history. He's raising $100 BILLION to acquire manufacturing companies across aerospace, defense, and chipmaking and REPLACE their workforces with AI. He's calling it a "manufacturing transformation vehicle." But here's the full picture and what actually makes this so genius: 6 months ago, Bezos quietly launched Project Prometheus with $6.2 billion in funding. His co-CEO is Vik Bajaj, a physicist who helped build the self-driving car project at Google X that became Waymo. They've been hiring from OpenAI, DeepMind, and Meta's AI division. Blue Origin CEO David Limp just joined the board. And the technology they're building isn't chatbots or content generators... It's digital twins. AI systems that simulate entire factories, stress-test materials, model supply chains, and design products without a single human touching the process. The kind of AI that could design a rocket engine, test it virtually across a million simulations, and manufacture the perfect version on the first attempt. That was phase one. Build the AI. And phase two just started: Now Bezos is flying to the Middle East pitching sovereign wealth funds. He went to Singapore meeting the world's biggest asset managers. He's in talks with JPMorgan Chase. The pitch: Give me $100 billion. I'll buy the factories. I'll install my AI. I'll automate the workforce. Then I'll SELL the playbook to every manufacturer on Earth. He's not licensing software to companies and hoping they adopt it. He's BUYING the companies and doing it himself. Think about what that means: Every other AI company sells tools and waits. OpenAI sells API access. Anthropic sells Claude subscriptions. Microsoft sells Copilot licenses. Bezos said forget that. I'll buy the entire production chain, replace the humans at the source, prove the model works with my own money, and then scale it globally. He did the exact same thing with retail. Amazon didn't sell software to bookstores. Amazon BECAME the bookstore. Then the department store. Then the grocery store. Then the pharmacy. Then the cloud. Now he's doing it with factories. And the fund is targeting the industries that matter most. Chipmaking. Defense. Aerospace. The sectors governments cannot afford to let fail. Which means once Bezos owns and automates these companies, governments become dependent on his AI infrastructure the same way they became dependent on AWS. The last time Bezos launched something at this scale, Amazon Web Services now powers a third of the internet. The US intelligence community runs on it. The Pentagon runs on it. Now imagine that same lock-in but for manufacturing. The man who automated how America shops is about to automate how America builds. And he's doing it with $100 billion of other people's money while risking about 2% of his own net worth through Prometheus. If it fails? Sovereign wealth funds take the loss. If it works? Bezos controls the AI operating system for global manufacturing. At a conference in Italy last year, Bezos said: "AI can have a huge impact on every company in the world, including manufacturers." That wasn't just a prediction. That was literally his business plan.
2:12

Jeff Bezos just announced the largest industrial takeover plan in history. He's raising $100 BILLION to acquire manufacturing companies across aerospace, defense, and chipmaking and REPLACE their workforces with AI. He's calling it a "manufacturing transformation vehicle." But here's the full picture and what actually makes this so genius: 6 months ago, Bezos quietly launched Project Prometheus with $6.2 billion in funding. His co-CEO is Vik Bajaj, a physicist who helped build the self-driving car project at Google X that became Waymo. They've been hiring from OpenAI, DeepMind, and Meta's AI division. Blue Origin CEO David Limp just joined the board. And the technology they're building isn't chatbots or content generators... It's digital twins. AI systems that simulate entire factories, stress-test materials, model supply chains, and design products without a single human touching the process. The kind of AI that could design a rocket engine, test it virtually across a million simulations, and manufacture the perfect version on the first attempt. That was phase one. Build the AI. And phase two just started: Now Bezos is flying to the Middle East pitching sovereign wealth funds. He went to Singapore meeting the world's biggest asset managers. He's in talks with JPMorgan Chase. The pitch: Give me $100 billion. I'll buy the factories. I'll install my AI. I'll automate the workforce. Then I'll SELL the playbook to every manufacturer on Earth. He's not licensing software to companies and hoping they adopt it. He's BUYING the companies and doing it himself. Think about what that means: Every other AI company sells tools and waits. OpenAI sells API access. Anthropic sells Claude subscriptions. Microsoft sells Copilot licenses. Bezos said forget that. I'll buy the entire production chain, replace the humans at the source, prove the model works with my own money, and then scale it globally. He did the exact same thing with retail. Amazon didn't sell software to bookstores. Amazon BECAME the bookstore. Then the department store. Then the grocery store. Then the pharmacy. Then the cloud. Now he's doing it with factories. And the fund is targeting the industries that matter most. Chipmaking. Defense. Aerospace. The sectors governments cannot afford to let fail. Which means once Bezos owns and automates these companies, governments become dependent on his AI infrastructure the same way they became dependent on AWS. The last time Bezos launched something at this scale, Amazon Web Services now powers a third of the internet. The US intelligence community runs on it. The Pentagon runs on it. Now imagine that same lock-in but for manufacturing. The man who automated how America shops is about to automate how America builds. And he's doing it with $100 billion of other people's money while risking about 2% of his own net worth through Prometheus. If it fails? Sovereign wealth funds take the loss. If it works? Bezos controls the AI operating system for global manufacturing. At a conference in Italy last year, Bezos said: "AI can have a huge impact on every company in the world, including manufacturers." That wasn't just a prediction. That was literally his business plan.

Ricardo

173,101 次观看 • 4 个月前

Micron is going to be a $4,000 stock and the CEO just told you exactly why in one interview (Save this). Micron is no longer a chip company but rather a America's monopoly on the most strategically critical material in the AI buildout. It's the only western company manufacturing memory at advanced nodes, sitting on $200 billion in committed domestic capex, with every unit of its highest value product already sold. let's start with the supply reality, Mehrotra said Micron can currently meet only 50% to two thirds of the demand from its key customers. That shortage will last well beyond 2027, and meaningful new supply from anyone in the industry does not arrive until 2028 at the earliest. Two more years of demand outpacing supply in a market growing 168% year over year and that is the floor on the bull case. Now layer on what makes this cycle structurally different from every one before it. Micron is the only American memory manufacturer on earth, Samsung and SK Hynix are South Korean. In a world where AI infrastructure has become a declared national security priority where Commerce Secretary Lutnick and Trade Ambassador Greer personally showed up to a fab dedication in Manassas, Virginia being the only US memory company is not just a competitive advantage. It is a government backed structural monopoly on the most critical input to the US AI buildout, backed by $6.2 billion in CHIPS Act subsidies across Idaho, New York, and Virginia. The $200 billion buildout spans Manassas for DDR4 defense and industrial memory, Boise for leading-edge DRAM with first wafers out mid 2027, a second Boise HBM fab with first wafers by end of 2028, and the Syracuse megafab, the largest semiconductor facility in US history, breaking ground January 2026 with up to four fabs over time. Combined, these sites take Micron's domestic production from 10% of its total output today to 40% over the next decade, and create 90,000 jobs in the process. The business model transformation is the real story. Come join Milk Road Pro for our full breakdown, our complete Micron valuation model incorporating the $200 billion domestic buildout and our entire AI thesis. Link below.
10:05

Micron is going to be a $4,000 stock and the CEO just told you exactly why in one interview (Save this). Micron is no longer a chip company but rather a America's monopoly on the most strategically critical material in the AI buildout. It's the only western company manufacturing memory at advanced nodes, sitting on $200 billion in committed domestic capex, with every unit of its highest value product already sold. let's start with the supply reality, Mehrotra said Micron can currently meet only 50% to two thirds of the demand from its key customers. That shortage will last well beyond 2027, and meaningful new supply from anyone in the industry does not arrive until 2028 at the earliest. Two more years of demand outpacing supply in a market growing 168% year over year and that is the floor on the bull case. Now layer on what makes this cycle structurally different from every one before it. Micron is the only American memory manufacturer on earth, Samsung and SK Hynix are South Korean. In a world where AI infrastructure has become a declared national security priority where Commerce Secretary Lutnick and Trade Ambassador Greer personally showed up to a fab dedication in Manassas, Virginia being the only US memory company is not just a competitive advantage. It is a government backed structural monopoly on the most critical input to the US AI buildout, backed by $6.2 billion in CHIPS Act subsidies across Idaho, New York, and Virginia. The $200 billion buildout spans Manassas for DDR4 defense and industrial memory, Boise for leading-edge DRAM with first wafers out mid 2027, a second Boise HBM fab with first wafers by end of 2028, and the Syracuse megafab, the largest semiconductor facility in US history, breaking ground January 2026 with up to four fabs over time. Combined, these sites take Micron's domestic production from 10% of its total output today to 40% over the next decade, and create 90,000 jobs in the process. The business model transformation is the real story. Come join Milk Road Pro for our full breakdown, our complete Micron valuation model incorporating the $200 billion domestic buildout and our entire AI thesis. Link below.

Milk Road AI

235,103 次观看 • 1 个月前

📢 EstateX Building the ESX Blockchain: Creating the Binance of Tokenization The next big thing is here! EstateX is once again pioneering a revolutionary development that will redefine the potential of our company—and, more importantly, the $ESX token itself. Let’s dive in. No Changes to Launch Date Rest assured, this exciting new feature will not affect our anticipated launch date. We remain committed to launching in 2024, with an unwavering focus on delivering maximum value and stability to our investors. EstateX continues to lead as one of the highest-staked ICOs in the market, thanks to our loyal community and investors. Introducing the EstateX L1 Blockchain EstateX is thrilled to announce our very own L1 blockchain—designed with unique features to power a fully-fledged ecosystem focused on tokenization. Our vision is to make EstateX the Binance of tokenization, where asset owners and projects can not only create tokens but leverage EstateX’s vast infrastructure, raise funds through our community and institutional network, make use of our legal framework and receive marketing & branding support. In this video, you’ll hear directly from myself and our CTO, Graham, as we break down the technical details and show you how this move strengthens the entire EstateX ecosystem. Migration to the $ESX Chain Once live on the mainnet in 2025, the $ESX tokens will transition to our blockchain, becoming the chain’s native currency. While this blockchain is a key milestone, our commitment remains to the EstateX investment platform, which is integral to our broader vision and future success. It’s an essential part in our Binance of Tokenization strategy where on the one hand you have our blockchain, and on the other hand our investment platform to raise funds and make use of our legal framework and tech. A Unique Blockchain for Real-World Assets (RWA) EstateX is creating a one-of-a-kind RWA chain tailored to various asset classes, providing unparalleled opportunities for asset owners and investors. Projects will benefit from a comprehensive support package, including 24/7 legal support in the US and EU, access to both retail and institutional investors, marketing support, liquidity networks, and customizable whitelabel investment and management software. This robust support and infrastructure will solidify EstateX as the top destination for tokenization, all while delivering immense value for $ESX holders. Leading the RWA Space with Cutting-Edge Architecture EstateX’s polychain architecture will support multi-asset tokenization, global decentralization, and legal compliance across regions. By building parallel L1 chains alongside an L0 global settlement chain, we aim to create an ecosystem with fast transaction times and interoperability across product classes. Our multi-stage deployment will enable us to support dedicated L1 chains for specific markets, and later, a unifying L0 settlement chain for seamless cross-chain integration. All EstateX chains will utilize Proof-of-Stake consensus, EVM compatibility, and $ESX as the native currency. Streamlined Onboarding and White-Label Solutions Our design prioritizes fast, frictionless onboarding for third-party RWA providers. Through white-label integrations, EstateX will help market participants bypass technical and regulatory barriers. This will expand market opportunities by cutting costs, easing compliance, and offering solutions like on-chain KYC/KYB verification, payment gateways, exchange services, and proof-of-custody options. EstateX remains committed to transparency and accountability, with cryptographic solutions to ensure secure, trustless interactions. In addition, we’re developing RWA-specific token standards and maintaining open-source contributions to foster cross-market innovation. Onboarding Massive Partnerships for Massive Growth We are already onboarding major partners to our blockchain, offering them a blend of platform offerings and white-label solutions designed for scalability. Through these partnerships, EstateX will host large volumes of tokenized assets, benefiting from the secondary marketplaces and legal frameworks we provide. This high activity will drive substantial volume on the ESX chain, fueling demand and utility for the ESX token as we scale our infrastructure and only with delivering whitelabel technology. We are thrilled to announce that one of our first major onboarded partners will be revealed within the next two weeks. Stay tuned, as we are also in talks with high-profile sectors, including sports teams and other exciting industries. EstateX is set to lead the RWA landscape, establishing an ecosystem that is ready for the next phase of blockchain innovation. Thank you for being part of this journey!
9:45

📢 EstateX Building the ESX Blockchain: Creating the Binance of Tokenization The next big thing is here! EstateX is once again pioneering a revolutionary development that will redefine the potential of our company—and, more importantly, the $ESX token itself. Let’s dive in. No Changes to Launch Date Rest assured, this exciting new feature will not affect our anticipated launch date. We remain committed to launching in 2024, with an unwavering focus on delivering maximum value and stability to our investors. EstateX continues to lead as one of the highest-staked ICOs in the market, thanks to our loyal community and investors. Introducing the EstateX L1 Blockchain EstateX is thrilled to announce our very own L1 blockchain—designed with unique features to power a fully-fledged ecosystem focused on tokenization. Our vision is to make EstateX the Binance of tokenization, where asset owners and projects can not only create tokens but leverage EstateX’s vast infrastructure, raise funds through our community and institutional network, make use of our legal framework and receive marketing & branding support. In this video, you’ll hear directly from myself and our CTO, Graham, as we break down the technical details and show you how this move strengthens the entire EstateX ecosystem. Migration to the $ESX Chain Once live on the mainnet in 2025, the $ESX tokens will transition to our blockchain, becoming the chain’s native currency. While this blockchain is a key milestone, our commitment remains to the EstateX investment platform, which is integral to our broader vision and future success. It’s an essential part in our Binance of Tokenization strategy where on the one hand you have our blockchain, and on the other hand our investment platform to raise funds and make use of our legal framework and tech. A Unique Blockchain for Real-World Assets (RWA) EstateX is creating a one-of-a-kind RWA chain tailored to various asset classes, providing unparalleled opportunities for asset owners and investors. Projects will benefit from a comprehensive support package, including 24/7 legal support in the US and EU, access to both retail and institutional investors, marketing support, liquidity networks, and customizable whitelabel investment and management software. This robust support and infrastructure will solidify EstateX as the top destination for tokenization, all while delivering immense value for $ESX holders. Leading the RWA Space with Cutting-Edge Architecture EstateX’s polychain architecture will support multi-asset tokenization, global decentralization, and legal compliance across regions. By building parallel L1 chains alongside an L0 global settlement chain, we aim to create an ecosystem with fast transaction times and interoperability across product classes. Our multi-stage deployment will enable us to support dedicated L1 chains for specific markets, and later, a unifying L0 settlement chain for seamless cross-chain integration. All EstateX chains will utilize Proof-of-Stake consensus, EVM compatibility, and $ESX as the native currency. Streamlined Onboarding and White-Label Solutions Our design prioritizes fast, frictionless onboarding for third-party RWA providers. Through white-label integrations, EstateX will help market participants bypass technical and regulatory barriers. This will expand market opportunities by cutting costs, easing compliance, and offering solutions like on-chain KYC/KYB verification, payment gateways, exchange services, and proof-of-custody options. EstateX remains committed to transparency and accountability, with cryptographic solutions to ensure secure, trustless interactions. In addition, we’re developing RWA-specific token standards and maintaining open-source contributions to foster cross-market innovation. Onboarding Massive Partnerships for Massive Growth We are already onboarding major partners to our blockchain, offering them a blend of platform offerings and white-label solutions designed for scalability. Through these partnerships, EstateX will host large volumes of tokenized assets, benefiting from the secondary marketplaces and legal frameworks we provide. This high activity will drive substantial volume on the ESX chain, fueling demand and utility for the ESX token as we scale our infrastructure and only with delivering whitelabel technology. We are thrilled to announce that one of our first major onboarded partners will be revealed within the next two weeks. Stay tuned, as we are also in talks with high-profile sectors, including sports teams and other exciting industries. EstateX is set to lead the RWA landscape, establishing an ecosystem that is ready for the next phase of blockchain innovation. Thank you for being part of this journey!

EstateX

120,443 次观看 • 1 年前

Scientific discovery is reaching the limits of human capacity: too much data, too many disconnected fields, and too few ways to connect ideas fast enough to matter. The next breakthroughs in materials, medicine, energy, and beyond will not come from scaling today’s AI paradigm alone or from relying on serendipity alone. They will require a new kind of AI for knowledge discovery that not only models the world but shapes what it could become. At Unreasonable Labs, we are building superintelligence for knowledge discovery: systems that reason across disciplines, generate novel hypotheses, test them through simulation and experimentation, and help guide real-world discovery. Our AI engine is not confined to what it has seen in training. It creates new data, builds new tools, and maintains a persistent world model that grows more powerful as it reasons. Why now? Even today's most powerful AI models face a core limitation: they are trained on what we already know. True discovery begins when a system encounters something its current model cannot explain. This is why you cannot train your way to a discovery - a system has to reason through new problems, update its beliefs, and revise its understanding of the world as it thinks. Another critical insight is that rich knowledge already exists, but is not yet applied to solve pressing problems. It sits in millions of papers, patents, and datasets, trapped in isolated silos, often in legacy data vaults. What's missing is a way to connect it, scale it, unlock the potential, and synthesize genuine novel predictions. The time is now to build a system that enables practitioners to design, explore, and direct discovery, whether through human guidance or full automation, while capturing the tacit insight that domain experts bring. Steerable reasoning That is why we built an operating system for scientific discovery - one that replaces chance with steerable reasoning. Rather than retrieving static facts, our AI builds and continuously updates a living world model - a representation of knowledge the system can actively reason over, question, and revise. A concrete example: say you want to create "smart concrete" that can flex - a concept that doesn't exist yet. Our AI maps relationships across domains, finds a path from morphable smart materials to concrete, and identifies the most efficient way to bridge those concepts. It then autonomously writes simulations, tests the hypothesis, and refines the idea. Then it interacts with hardware to produce a physical artifact, and the loop expands into the real-world, where the machine becomes world-shaping. Our AI gives users full visibility into how the system arrived at a conclusion. It delineates which existing patents and papers it drew upon versus what is genuinely new - protecting IP and competitive concerns from the start, and offering deep compositional insights into technology advances. It takes unreasonable people to make progress Our team reflects the interdisciplinary expertise required to build this next breakthrough - my co-founder Yuan Cao Yuan Cao (formerly DeepMind) and Andrew Lew, Haiqian Yang, Matt Insler, Jennifer Kang and Julia McLaughlin. We are backed by $13.5M in seed funding led by Playground Global with participation from AIX, E14 Fund, and MS&AD. We are guided by advisors including Robert Langer (1,000+ patents), Kostya Novoselov (Nobel Prize in Physics), and Thomas Wolf (Co-founder of Hugging Face). We already have multiple pilot programs underway with leading industrial partners in materials science and engineering, with additional engagements developing across energy, logistics, bioengineering, and other strategic domains. The biggest challenges of our time - fusion energy, sustainable materials, new medicines - demand exponentially more innovation than humans alone can produce. We are not replacing scientists, and instead are making every scientist capable of leading their own team of AI-powered researchers. Abundant innovation leads to abundant prosperity. Watch our launch video below to see what we're building Unreasonable Labs 👇
2:16

Scientific discovery is reaching the limits of human capacity: too much data, too many disconnected fields, and too few ways to connect ideas fast enough to matter. The next breakthroughs in materials, medicine, energy, and beyond will not come from scaling today’s AI paradigm alone or from relying on serendipity alone. They will require a new kind of AI for knowledge discovery that not only models the world but shapes what it could become. At Unreasonable Labs, we are building superintelligence for knowledge discovery: systems that reason across disciplines, generate novel hypotheses, test them through simulation and experimentation, and help guide real-world discovery. Our AI engine is not confined to what it has seen in training. It creates new data, builds new tools, and maintains a persistent world model that grows more powerful as it reasons. Why now? Even today's most powerful AI models face a core limitation: they are trained on what we already know. True discovery begins when a system encounters something its current model cannot explain. This is why you cannot train your way to a discovery - a system has to reason through new problems, update its beliefs, and revise its understanding of the world as it thinks. Another critical insight is that rich knowledge already exists, but is not yet applied to solve pressing problems. It sits in millions of papers, patents, and datasets, trapped in isolated silos, often in legacy data vaults. What's missing is a way to connect it, scale it, unlock the potential, and synthesize genuine novel predictions. The time is now to build a system that enables practitioners to design, explore, and direct discovery, whether through human guidance or full automation, while capturing the tacit insight that domain experts bring. Steerable reasoning That is why we built an operating system for scientific discovery - one that replaces chance with steerable reasoning. Rather than retrieving static facts, our AI builds and continuously updates a living world model - a representation of knowledge the system can actively reason over, question, and revise. A concrete example: say you want to create "smart concrete" that can flex - a concept that doesn't exist yet. Our AI maps relationships across domains, finds a path from morphable smart materials to concrete, and identifies the most efficient way to bridge those concepts. It then autonomously writes simulations, tests the hypothesis, and refines the idea. Then it interacts with hardware to produce a physical artifact, and the loop expands into the real-world, where the machine becomes world-shaping. Our AI gives users full visibility into how the system arrived at a conclusion. It delineates which existing patents and papers it drew upon versus what is genuinely new - protecting IP and competitive concerns from the start, and offering deep compositional insights into technology advances. It takes unreasonable people to make progress Our team reflects the interdisciplinary expertise required to build this next breakthrough - my co-founder Yuan Cao Yuan Cao (formerly DeepMind) and Andrew Lew, Haiqian Yang, Matt Insler, Jennifer Kang and Julia McLaughlin. We are backed by $13.5M in seed funding led by Playground Global with participation from AIX, E14 Fund, and MS&AD. We are guided by advisors including Robert Langer (1,000+ patents), Kostya Novoselov (Nobel Prize in Physics), and Thomas Wolf (Co-founder of Hugging Face). We already have multiple pilot programs underway with leading industrial partners in materials science and engineering, with additional engagements developing across energy, logistics, bioengineering, and other strategic domains. The biggest challenges of our time - fusion energy, sustainable materials, new medicines - demand exponentially more innovation than humans alone can produce. We are not replacing scientists, and instead are making every scientist capable of leading their own team of AI-powered researchers. Abundant innovation leads to abundant prosperity. Watch our launch video below to see what we're building Unreasonable Labs 👇

Markus J. Buehler

55,052 次观看 • 4 个月前

During World War 2, a young engineer named Kelly Johnson working out of a circus tent with a skeleton crew developed America’s first jet fighter in less than six months. Twenty years later, the same engineer developed the blackbird spy plane in less than two years. No one believes American industry is capable of that now. American innovation today is not about the physical world around us but about the digital world Big tech wants us to live in and exotic new financial instruments that wall street develops. Look at recent headlines to see what our best and brightest are up to. Facebook spent billions on the metaverse only to abandon it to go all in on AI. DoorDash lets you use buy now, pay later services to pay for your burrito in six equal installments. And in a move that would have Kelly Johnson rolling in his grave, Boom supersonic, a start-up originally planning to bring back supersonic jet flights, announced it will use its research to power an ai data center. If we want to power the future and restore America’s position as the world’s most technologically advanced country, we must do better. We need an America first industrial policy. The Trump Administration’s tariff policy is only one element to address this. Another critical tool to addressing this problem is the power of the federal contract. Trillions spent, headline our government spends trillions of your dollars buying products across the globe. You don’t need a great long-term memory to understand how the international supply chain failures hurt the United States during COVID. It’s one thing to wait six months for a Peloton. It’s another to wait six months for materials needed for our national defense. The defense sector in the United States should be the home to great engineers and doers, not bureaucrats and middle-men. The current status of that sector leaves much to be desired and reflects many of the problems attendant to our administrative state. It is bloated, slow, reliant on timelines, specifications, and proposals, and consolidated amongst a blob of A few major defense prime contractors that gobble up nearly 90% of the defense budget. Meanwhile, the alleged best technological talent in America holds little allegiance to this nation. They reject the notion of even being called American companies, turning their back on the idea of serving the nation to which they owe their existence. Their focus is on profit by selling more and more ads for more and more products made overseas. Their understanding and appreciation of America centers George Floyd above George Washington. What have these people truly invented as of late, anyways? AI generated videos to keep you scrolling? And these are the people, and the major donors as of late, we trust to guide American policy into the artificial intelligence age? This is a problem on both sides. A bloated procurement bureaucracy and defense and tech giants more focused on profit than innovating. We don’t just need government reform, we also need elite reform. It’s time for a renewal and return to a better time when innovation was paired with patriotism, with a government designed to sustain both the good news is that a gigantic federal defense budget provides ample ammunition to solve this problem. We need not only the best and brightest to come and serve their nation in various capacities, but the biggest dreamers. The biggest innovators. America’s engines of production and innovation need to be humming in a rhythm, focused squarely on our global power competition and domestic renewal. With President Trump in office and Secretary Hegseth at the helm of the department of war, this is entirely possible. It is also entirely needed. Let’s make sure American hardware is also hard-wired with the heart and soul of a patriotic class of innovators. Let’s have an America where corporate success requires nationalistic pride. Spirit and duty, it’s happened before and it can happen again. (12/10 monologue on Fine Point w/ Chanel Rion )
4:36

During World War 2, a young engineer named Kelly Johnson working out of a circus tent with a skeleton crew developed America’s first jet fighter in less than six months. Twenty years later, the same engineer developed the blackbird spy plane in less than two years. No one believes American industry is capable of that now. American innovation today is not about the physical world around us but about the digital world Big tech wants us to live in and exotic new financial instruments that wall street develops. Look at recent headlines to see what our best and brightest are up to. Facebook spent billions on the metaverse only to abandon it to go all in on AI. DoorDash lets you use buy now, pay later services to pay for your burrito in six equal installments. And in a move that would have Kelly Johnson rolling in his grave, Boom supersonic, a start-up originally planning to bring back supersonic jet flights, announced it will use its research to power an ai data center. If we want to power the future and restore America’s position as the world’s most technologically advanced country, we must do better. We need an America first industrial policy. The Trump Administration’s tariff policy is only one element to address this. Another critical tool to addressing this problem is the power of the federal contract. Trillions spent, headline our government spends trillions of your dollars buying products across the globe. You don’t need a great long-term memory to understand how the international supply chain failures hurt the United States during COVID. It’s one thing to wait six months for a Peloton. It’s another to wait six months for materials needed for our national defense. The defense sector in the United States should be the home to great engineers and doers, not bureaucrats and middle-men. The current status of that sector leaves much to be desired and reflects many of the problems attendant to our administrative state. It is bloated, slow, reliant on timelines, specifications, and proposals, and consolidated amongst a blob of A few major defense prime contractors that gobble up nearly 90% of the defense budget. Meanwhile, the alleged best technological talent in America holds little allegiance to this nation. They reject the notion of even being called American companies, turning their back on the idea of serving the nation to which they owe their existence. Their focus is on profit by selling more and more ads for more and more products made overseas. Their understanding and appreciation of America centers George Floyd above George Washington. What have these people truly invented as of late, anyways? AI generated videos to keep you scrolling? And these are the people, and the major donors as of late, we trust to guide American policy into the artificial intelligence age? This is a problem on both sides. A bloated procurement bureaucracy and defense and tech giants more focused on profit than innovating. We don’t just need government reform, we also need elite reform. It’s time for a renewal and return to a better time when innovation was paired with patriotism, with a government designed to sustain both the good news is that a gigantic federal defense budget provides ample ammunition to solve this problem. We need not only the best and brightest to come and serve their nation in various capacities, but the biggest dreamers. The biggest innovators. America’s engines of production and innovation need to be humming in a rhythm, focused squarely on our global power competition and domestic renewal. With President Trump in office and Secretary Hegseth at the helm of the department of war, this is entirely possible. It is also entirely needed. Let’s make sure American hardware is also hard-wired with the heart and soul of a patriotic class of innovators. Let’s have an America where corporate success requires nationalistic pride. Spirit and duty, it’s happened before and it can happen again. (12/10 monologue on Fine Point w/ Chanel Rion )

Mike Howell

16,205 次观看 • 7 个月前

China unveils humanoid robot worker with brain that runs 275 trillion ops/sec | Jijo Malayil, Interesting Engineering In tests, SUYUAN used vision and joint control to sort and move crates of various sizes, greatly improving warehouse productivity. Chinese manufacturing firm Shanghai Electric has unveiled its first self-developed industrial humanoid robot, “SUYUAN,” marking a major milestone in its robotics journey. Debuting at the World Artificial Intelligence Conference (WAIC 2025) on July 26 in Shanghai, SUYUAN boasts 38 degrees of freedom and 275 TOPS of on-device computing power, enabling precise operations and fluid movements. According to the firm, designed for diverse industrial use, the robot showcases Shanghai Electric’s end-to-end capabilities—from core tech to integrated solutions—and reinforces its commitment to next-gen industrial automation through a full industry chain strategy. At WAIC 2025, Shanghai Electric also unveiled a new joint venture with Johnson Electric for next-gen humanoid robotics and showcased its “LINGKE” dual-arm robot. Recently, Hangzhou-based Unitree Robotics launched the R1 humanoid with 26 joints for $5,900, showcasing athletic feats like cartwheels, running, and quick recovery. Smart factory assistant Shanghai Electric claims SUYUAN, equipped with 38 degrees of freedom (DoF) and a powerful 275 TOPS on-device computing processor, delivers fluid, human-like movements and high-precision operations across various industrial scenarios. Its advanced articulation and real-time processing capabilities make it highly adaptable, enabling smooth execution of complex tasks in dynamic work environments. SUYUAN, who weighs 110 pounds (50 kilograms) and is 5 feet 6 inches (167 cm) tall, was designed to have human-like proportions. Its 38-DoF articulation offers dexterity, allowing for both wide-range motion and sensitive manipulation. With a single arm, the robot can lift objects up to 4.4 pounds (2 kilograms) in weight and carry a total payload of up to 22 pounds (10 kilograms). With a walking pace of 3.1 miles per hour (5 km/h), SUYUAN is ideal for environments including assembly lines, warehousing, and logistics, according to a statement. To navigate complex industrial settings, SUYUAN combines LiDAR and binocular vision for self-guided mobility. Its 275-TOPS AI processor enables rapid data analysis and integration with large language models, allowing it to understand tasks in natural language and handle objects adaptively, reports Fox 44 News. In pilot demonstrations, the robot successfully identified, picked, and relocated crates of varying sizes using advanced computer vision and coordinated joint control—delivering measurable gains in warehouse efficiency. The company claims that SUYUAN’s launch represents a major turning point in Shanghai Electric’s foray into humanoid robotics and strengthens its vertically integrated approach to industrial automation solutions. Intelligent task handling Shanghai Electric also demonstrated its most recent developments in intelligent manufacturing at WAIC 2025, introducing a new joint venture with Johnson Electric centered on next-generation humanoid robotics and showcasing the “LINGKE” dual-arm robot. With its high-precision operations, adaptive teamwork, and closed-loop data capabilities, the LINGKE robot demonstrated live talents in handling complicated production jobs. LINGKE is made to do more than just replace human labor; it uses compliant force control and bimanual coordination to relieve workers of high-intensity, repetitive jobs. According to the company, the robot enhances operational efficiency by up to five times. Its core strength lies in a Data-Model-Deployment closed-loop system that starts with operational data, followed by data cleansing, model training, live deployment, and feedback-driven optimization—enabling autonomous learning and workflow improvement. Also at the event, Shanghai Electric and Johnson Electric introduced advanced hardware modules for humanoid robots, including rotary joints, linear joints, and dexterous finger joints. These components are designed to support smooth, precise, and quiet motion performance across robotics systems, reports Stock Titan. The joint venture announced two strategic agreements: a first-unit supply deal with the National and Local Co-Built Humanoid Robotics Innovation Center (Qinglong Project) and a cooperation memorandum with Fourier Robotics. Read more:
0:58

China unveils humanoid robot worker with brain that runs 275 trillion ops/sec | Jijo Malayil, Interesting Engineering In tests, SUYUAN used vision and joint control to sort and move crates of various sizes, greatly improving warehouse productivity. Chinese manufacturing firm Shanghai Electric has unveiled its first self-developed industrial humanoid robot, “SUYUAN,” marking a major milestone in its robotics journey. Debuting at the World Artificial Intelligence Conference (WAIC 2025) on July 26 in Shanghai, SUYUAN boasts 38 degrees of freedom and 275 TOPS of on-device computing power, enabling precise operations and fluid movements. According to the firm, designed for diverse industrial use, the robot showcases Shanghai Electric’s end-to-end capabilities—from core tech to integrated solutions—and reinforces its commitment to next-gen industrial automation through a full industry chain strategy. At WAIC 2025, Shanghai Electric also unveiled a new joint venture with Johnson Electric for next-gen humanoid robotics and showcased its “LINGKE” dual-arm robot. Recently, Hangzhou-based Unitree Robotics launched the R1 humanoid with 26 joints for $5,900, showcasing athletic feats like cartwheels, running, and quick recovery. Smart factory assistant Shanghai Electric claims SUYUAN, equipped with 38 degrees of freedom (DoF) and a powerful 275 TOPS on-device computing processor, delivers fluid, human-like movements and high-precision operations across various industrial scenarios. Its advanced articulation and real-time processing capabilities make it highly adaptable, enabling smooth execution of complex tasks in dynamic work environments. SUYUAN, who weighs 110 pounds (50 kilograms) and is 5 feet 6 inches (167 cm) tall, was designed to have human-like proportions. Its 38-DoF articulation offers dexterity, allowing for both wide-range motion and sensitive manipulation. With a single arm, the robot can lift objects up to 4.4 pounds (2 kilograms) in weight and carry a total payload of up to 22 pounds (10 kilograms). With a walking pace of 3.1 miles per hour (5 km/h), SUYUAN is ideal for environments including assembly lines, warehousing, and logistics, according to a statement. To navigate complex industrial settings, SUYUAN combines LiDAR and binocular vision for self-guided mobility. Its 275-TOPS AI processor enables rapid data analysis and integration with large language models, allowing it to understand tasks in natural language and handle objects adaptively, reports Fox 44 News. In pilot demonstrations, the robot successfully identified, picked, and relocated crates of varying sizes using advanced computer vision and coordinated joint control—delivering measurable gains in warehouse efficiency. The company claims that SUYUAN’s launch represents a major turning point in Shanghai Electric’s foray into humanoid robotics and strengthens its vertically integrated approach to industrial automation solutions. Intelligent task handling Shanghai Electric also demonstrated its most recent developments in intelligent manufacturing at WAIC 2025, introducing a new joint venture with Johnson Electric centered on next-generation humanoid robotics and showcasing the “LINGKE” dual-arm robot. With its high-precision operations, adaptive teamwork, and closed-loop data capabilities, the LINGKE robot demonstrated live talents in handling complicated production jobs. LINGKE is made to do more than just replace human labor; it uses compliant force control and bimanual coordination to relieve workers of high-intensity, repetitive jobs. According to the company, the robot enhances operational efficiency by up to five times. Its core strength lies in a Data-Model-Deployment closed-loop system that starts with operational data, followed by data cleansing, model training, live deployment, and feedback-driven optimization—enabling autonomous learning and workflow improvement. Also at the event, Shanghai Electric and Johnson Electric introduced advanced hardware modules for humanoid robots, including rotary joints, linear joints, and dexterous finger joints. These components are designed to support smooth, precise, and quiet motion performance across robotics systems, reports Stock Titan. The joint venture announced two strategic agreements: a first-unit supply deal with the National and Local Co-Built Humanoid Robotics Innovation Center (Qinglong Project) and a cooperation memorandum with Fourier Robotics. Read more:

Owen Gregorian

51,638 次观看 • 1 年前

$AMD| $META is using $GOOGL to negotiate 🧵 The Ironwood pod is 5.1–10x more expensive annually ($148.3 million ÷ $14.87–$29.04 million) and 5.1–10x more expensive monthly ($12.36 million ÷ $1.24–$2.42 million) than renting 15 MI450 racks for equivalent compute. The rapidly evolving landscape of artificial intelligence infrastructure presents a complex interplay of technological innovation, market dynamics, and strategic maneuvering among major players. Recent leaked information suggesting that Meta Platforms ($META) might work with Google's Tensor Processing Unit (TPU) in 2027 has sparked speculation about its true intent. This leak is likely a strategic move by Meta to negotiate more favorable terms with AMD , leveraging the competitive dynamics of the AI hardware market to optimize its substantial investment in AI infrastructure. By examining the key elements of this scenario Meta's investment strategy, the comparative advantages of AMD's MI450 and Google's Ironwood TPU, and the broader market context; we can discern the potential beneficiaries and the strategic implications of this information. Meta's aggressive pursuit of AI capabilities is underscored by its planned expenditure of $66-72 billion on AI infrastructure in 2025, with expectations to escalate significantly in 2026. This investment is part of a broader strategy to build "titan clusters" like Prometheus, which are projected to reach 1 gigawatt of compute power by 2026. Such a scale of investment reflects Meta's recognition of the critical role that AI will play in its future growth, particularly in enhancing its social media platforms and developing new AI-driven applications. However, the financial burden of this infrastructure buildout necessitates a careful consideration of cost-effectiveness and scalability, which brings us to the leaked information about potential collaboration with Google's Ironwood TPU. Google's Ironwood TPU, introduced as the seventh-generation ASIC optimized for TensorFlow-based inference, represents a high-cost, cloud-locked solution priced at $445 million per pod (9,216 chips) over three years. This model, while offering significant performance gains and power efficiency, is tailored for pod-scale deployment and integrated with Google's cloud services, limiting flexibility and increasing costs for customers. In contrast, AMD's MI450 GPU, priced at $30,000–$40,000 per unit, provides a modular, open ROCm ecosystem that delivers comparable compute capacity at a fraction of the cost. Renting 15 MI450 racks could achieve similar 42+ exaFLOPS inference compute at 5–10x lower cost than renting a single Ironwood pod, underscoring AMD's competitive edge in terms of total cost of ownership (TCO). The leaked information about Meta's potential TPU deployment in 2027, therefore, can be interpreted as a negotiating tactic rather than a definitive shift in strategy. By signaling interest in Google's solution, Meta may be attempting to pressure AMD into offering more favorable terms/prices for 5-10GW. This tactic aligns with Meta's broader goal to finance most of its AI spend internally while exploring partnerships that can reduce costs and enhance flexibility. The post's emphasis on MI450's TCO advantage and its partnerships with major players like OpenAI, Microsoft, and Meta itself suggests that AMD is a critical component of Meta's AI infrastructure strategy. The threat of working with Google's TPU could prompt AMD to reassess its pricing, provide additional support, or offer incentives to retain Meta as a customer, thereby securing or expanding its market share. From a logical standpoint, Meta stands to benefit the most from this strategy. As a major buyer in a high-stakes market projected to surpass $1 trillion in annual spending by 2030, Meta's negotiating power is significant. The leaked information could lead to substantial cost savings on its $66-72 billion investment, enhancing its financial flexibility and allowing for further investment in AI capabilities. Moreover, this tactic reinforces Meta's position as a leader in the AI infrastructure race, potentially attracting more external financing for its data center projects and strengthening its competitive stance against other hyperscalers like Amazon and Microsoft. AMD could also benefit from this scenario. The negotiation pressure might lead to small short-term concessions, but it could also solidify long-term partnerships with Meta, ensuring continued demand for MI450 and other AI hardware solutions. Initially Meta's 42% allocation to AMD MI300X and its partnerships with Oracle, Dell, and HP indicates a deep integration of AMD's technology into Meta's infrastructure, which could be leveraged to maintain this relationship. For AMD, retaining Meta as a large key customer is crucial to capturing a larger share of the rapidly growing data center infrastructure market, driven by the insatiable demand for AI compute power. Google, on the other hand, faces a more limited benefit from this leaked information. While securing Meta as a customer would reinforce its position in the AI hardware market, the high cost and ecosystem lock-in of the Ironwood TPU might deter Meta from fully committing to this solution. The leaked information could prompt Google to reconsider its pricing or ecosystem strategy to remain competitive, but the immediate impact is likely to be minimal compared to the potential gains for Meta and AMD. Investors and market analysts also stand to benefit from this information, as it provides insights into the competitive dynamics of the AI hardware market. Adjustments in portfolios based on anticipated shifts in market share and profitability could lead to opportunities for those who correctly anticipate outcomes. The negotiation dynamic might introduce volatility, but it also highlights the strategic importance of cost-effective solutions in the AI infrastructure space. Lastly, the leaked information about Meta potentially working with Google's TPU in 2027 is likely a strategic move to negotiate with AMD, leveraging the competitive landscape to optimize its AI infrastructure investment. Meta, as the primary negotiator, stands to gain the most by securing better terms from AMD, reducing costs, and enhancing its financial flexibility. AMD, while initially at risk, could benefit from retaining a key customer and solidifying its market position. Google faces limited immediate benefits but may need to adapt its strategy to remain competitive. This scenario underscores the complex interplay of technology, market dynamics, and strategic maneuvering in the AI hardware market, where cost-effectiveness and scalability are paramount. As the data center infrastructure market continues to grow, the outcomes of such negotiations will shape the future of AI development and deployment.
1:12

$AMD| $META is using $GOOGL to negotiate 🧵 The Ironwood pod is 5.1–10x more expensive annually ($148.3 million ÷ $14.87–$29.04 million) and 5.1–10x more expensive monthly ($12.36 million ÷ $1.24–$2.42 million) than renting 15 MI450 racks for equivalent compute. The rapidly evolving landscape of artificial intelligence infrastructure presents a complex interplay of technological innovation, market dynamics, and strategic maneuvering among major players. Recent leaked information suggesting that Meta Platforms ($META) might work with Google's Tensor Processing Unit (TPU) in 2027 has sparked speculation about its true intent. This leak is likely a strategic move by Meta to negotiate more favorable terms with AMD , leveraging the competitive dynamics of the AI hardware market to optimize its substantial investment in AI infrastructure. By examining the key elements of this scenario Meta's investment strategy, the comparative advantages of AMD's MI450 and Google's Ironwood TPU, and the broader market context; we can discern the potential beneficiaries and the strategic implications of this information. Meta's aggressive pursuit of AI capabilities is underscored by its planned expenditure of $66-72 billion on AI infrastructure in 2025, with expectations to escalate significantly in 2026. This investment is part of a broader strategy to build "titan clusters" like Prometheus, which are projected to reach 1 gigawatt of compute power by 2026. Such a scale of investment reflects Meta's recognition of the critical role that AI will play in its future growth, particularly in enhancing its social media platforms and developing new AI-driven applications. However, the financial burden of this infrastructure buildout necessitates a careful consideration of cost-effectiveness and scalability, which brings us to the leaked information about potential collaboration with Google's Ironwood TPU. Google's Ironwood TPU, introduced as the seventh-generation ASIC optimized for TensorFlow-based inference, represents a high-cost, cloud-locked solution priced at $445 million per pod (9,216 chips) over three years. This model, while offering significant performance gains and power efficiency, is tailored for pod-scale deployment and integrated with Google's cloud services, limiting flexibility and increasing costs for customers. In contrast, AMD's MI450 GPU, priced at $30,000–$40,000 per unit, provides a modular, open ROCm ecosystem that delivers comparable compute capacity at a fraction of the cost. Renting 15 MI450 racks could achieve similar 42+ exaFLOPS inference compute at 5–10x lower cost than renting a single Ironwood pod, underscoring AMD's competitive edge in terms of total cost of ownership (TCO). The leaked information about Meta's potential TPU deployment in 2027, therefore, can be interpreted as a negotiating tactic rather than a definitive shift in strategy. By signaling interest in Google's solution, Meta may be attempting to pressure AMD into offering more favorable terms/prices for 5-10GW. This tactic aligns with Meta's broader goal to finance most of its AI spend internally while exploring partnerships that can reduce costs and enhance flexibility. The post's emphasis on MI450's TCO advantage and its partnerships with major players like OpenAI, Microsoft, and Meta itself suggests that AMD is a critical component of Meta's AI infrastructure strategy. The threat of working with Google's TPU could prompt AMD to reassess its pricing, provide additional support, or offer incentives to retain Meta as a customer, thereby securing or expanding its market share. From a logical standpoint, Meta stands to benefit the most from this strategy. As a major buyer in a high-stakes market projected to surpass $1 trillion in annual spending by 2030, Meta's negotiating power is significant. The leaked information could lead to substantial cost savings on its $66-72 billion investment, enhancing its financial flexibility and allowing for further investment in AI capabilities. Moreover, this tactic reinforces Meta's position as a leader in the AI infrastructure race, potentially attracting more external financing for its data center projects and strengthening its competitive stance against other hyperscalers like Amazon and Microsoft. AMD could also benefit from this scenario. The negotiation pressure might lead to small short-term concessions, but it could also solidify long-term partnerships with Meta, ensuring continued demand for MI450 and other AI hardware solutions. Initially Meta's 42% allocation to AMD MI300X and its partnerships with Oracle, Dell, and HP indicates a deep integration of AMD's technology into Meta's infrastructure, which could be leveraged to maintain this relationship. For AMD, retaining Meta as a large key customer is crucial to capturing a larger share of the rapidly growing data center infrastructure market, driven by the insatiable demand for AI compute power. Google, on the other hand, faces a more limited benefit from this leaked information. While securing Meta as a customer would reinforce its position in the AI hardware market, the high cost and ecosystem lock-in of the Ironwood TPU might deter Meta from fully committing to this solution. The leaked information could prompt Google to reconsider its pricing or ecosystem strategy to remain competitive, but the immediate impact is likely to be minimal compared to the potential gains for Meta and AMD. Investors and market analysts also stand to benefit from this information, as it provides insights into the competitive dynamics of the AI hardware market. Adjustments in portfolios based on anticipated shifts in market share and profitability could lead to opportunities for those who correctly anticipate outcomes. The negotiation dynamic might introduce volatility, but it also highlights the strategic importance of cost-effective solutions in the AI infrastructure space. Lastly, the leaked information about Meta potentially working with Google's TPU in 2027 is likely a strategic move to negotiate with AMD, leveraging the competitive landscape to optimize its AI infrastructure investment. Meta, as the primary negotiator, stands to gain the most by securing better terms from AMD, reducing costs, and enhancing its financial flexibility. AMD, while initially at risk, could benefit from retaining a key customer and solidifying its market position. Google faces limited immediate benefits but may need to adapt its strategy to remain competitive. This scenario underscores the complex interplay of technology, market dynamics, and strategic maneuvering in the AI hardware market, where cost-effectiveness and scalability are paramount. As the data center infrastructure market continues to grow, the outcomes of such negotiations will shape the future of AI development and deployment.

Mike

182,225 次观看 • 8 个月前

Nebius is one of the most undervalued AI infrastructure companies in the public markets right now (Save this). Leopold Aschenbrenner, the former OpenAI researcher who wrote the 165-page essay predicting AGI within this decade and then launched the $13.7 billion Situational Awareness Fund around that thesis just filed a 13G disclosing a 5.6% stake in Nebius, representing 12.41 million Class A shares. This is the man whose entire investment framework is built on one core conviction, AI will advance faster than anyone expects, and the binding constraint will not be algorithms or model architectures, it will be physical computing infrastructure, data center capacity, and energy. Now look at what Nebius actually is and why this conviction is justified by the numbers alone. Nebius is a GPU native AI cloud platform, a neocloud built from the ground up specifically for AI training and inference workloads, founded by Arkady Volozh, the former CEO of Yandex who divested all non-Russian assets and left Russia in direct opposition to Putin before relisting the company on Nasdaq. In Q1 2026, Nebius reported $399 million in revenue, a 684% increase year over year from just $50.9 million while also delivering EBITDA and adjusted EPS that beat consensus estimates by 43% and 50% respectively, in a quarter where analysts had already built in aggressive assumptions. The scale of the infrastructure buildout is what makes the valuation argument so compelling. Nebius has raised its contracted power capacity guidance to over 4 gigawatts for 2026, with a target of 5 gigawatts of AI computing capacity deployed by 2030, including multiple gigawatt-scale AI factories across the United States and Europe. The Finland campus coming soon to Lappeenranta will be 310 megawatts powered by low-carbon energy, making it one of the largest AI data centers in Europe, specifically located in a cold-climate, energy-stable region that dramatically reduces cooling costs and carbon intensity. The 2026 capacity is already effectively sold out according to management disclosures, which means every megawatt Nebius brings online has a revenue contract attached to it before the facility opens. The strategic backing validates the thesis at every level. NVIDIA committed a $2 billion strategic investment in Nebius by 2030, with the two companies co-developing an inference stack, implementing NVIDIA's GPU health monitoring systems, and deploying next-generation architectures including Rubin GPUs, Vera CPUs, and Bluefield storage systems meaning Nebius gets preferential access to the hardware that every other AI company is begging Jensen Huang for. Meta signed a $27 billion agreement with Nebius, with $12 billion in dedicated computing resources confirmed and up to $15 billion in additional capacity over the coming years. And Nebius just partnered with Bloom Energy on a $2.6 billion deal guaranteeing 328 megawatts of installed capacity through modular fuel cell systems behind the meter power that eliminates grid dependency and accelerates deployment timelines. The forward valuation math is where the undervaluation case becomes undeniable. Nebius is pricing in $3.5 billion in revenue for 2026 and $11 billion for 2027, which puts the forward price-to-sales ratio at 16.6 times for this year and just 5.3 times for next year for a company growing revenue at 684% year over year with sold out capacity, NVIDIA backing, a $27 billion Meta contract, and a path to 4+ gigawatts of contracted power. Milk Road has been positioned in Nebius and we believe the convergence of Leopold's conviction stake, NVIDIA's $2 billion endorsement, Meta's $27 billion commitment, and a physical infrastructure buildout that is sold out before it opens represents one of the highest-quality risk-reward setups in AI infrastructure today. Come join Milk Road Pro and get our full Nebius thesis including the exact framework we use to think about neocloud valuation, the power capacity math that determines when revenue accelerates, and every catalyst we are watching through 2027. Link in bio/below.
0:20

Nebius is one of the most undervalued AI infrastructure companies in the public markets right now (Save this). Leopold Aschenbrenner, the former OpenAI researcher who wrote the 165-page essay predicting AGI within this decade and then launched the $13.7 billion Situational Awareness Fund around that thesis just filed a 13G disclosing a 5.6% stake in Nebius, representing 12.41 million Class A shares. This is the man whose entire investment framework is built on one core conviction, AI will advance faster than anyone expects, and the binding constraint will not be algorithms or model architectures, it will be physical computing infrastructure, data center capacity, and energy. Now look at what Nebius actually is and why this conviction is justified by the numbers alone. Nebius is a GPU native AI cloud platform, a neocloud built from the ground up specifically for AI training and inference workloads, founded by Arkady Volozh, the former CEO of Yandex who divested all non-Russian assets and left Russia in direct opposition to Putin before relisting the company on Nasdaq. In Q1 2026, Nebius reported $399 million in revenue, a 684% increase year over year from just $50.9 million while also delivering EBITDA and adjusted EPS that beat consensus estimates by 43% and 50% respectively, in a quarter where analysts had already built in aggressive assumptions. The scale of the infrastructure buildout is what makes the valuation argument so compelling. Nebius has raised its contracted power capacity guidance to over 4 gigawatts for 2026, with a target of 5 gigawatts of AI computing capacity deployed by 2030, including multiple gigawatt-scale AI factories across the United States and Europe. The Finland campus coming soon to Lappeenranta will be 310 megawatts powered by low-carbon energy, making it one of the largest AI data centers in Europe, specifically located in a cold-climate, energy-stable region that dramatically reduces cooling costs and carbon intensity. The 2026 capacity is already effectively sold out according to management disclosures, which means every megawatt Nebius brings online has a revenue contract attached to it before the facility opens. The strategic backing validates the thesis at every level. NVIDIA committed a $2 billion strategic investment in Nebius by 2030, with the two companies co-developing an inference stack, implementing NVIDIA's GPU health monitoring systems, and deploying next-generation architectures including Rubin GPUs, Vera CPUs, and Bluefield storage systems meaning Nebius gets preferential access to the hardware that every other AI company is begging Jensen Huang for. Meta signed a $27 billion agreement with Nebius, with $12 billion in dedicated computing resources confirmed and up to $15 billion in additional capacity over the coming years. And Nebius just partnered with Bloom Energy on a $2.6 billion deal guaranteeing 328 megawatts of installed capacity through modular fuel cell systems behind the meter power that eliminates grid dependency and accelerates deployment timelines. The forward valuation math is where the undervaluation case becomes undeniable. Nebius is pricing in $3.5 billion in revenue for 2026 and $11 billion for 2027, which puts the forward price-to-sales ratio at 16.6 times for this year and just 5.3 times for next year for a company growing revenue at 684% year over year with sold out capacity, NVIDIA backing, a $27 billion Meta contract, and a path to 4+ gigawatts of contracted power. Milk Road has been positioned in Nebius and we believe the convergence of Leopold's conviction stake, NVIDIA's $2 billion endorsement, Meta's $27 billion commitment, and a physical infrastructure buildout that is sold out before it opens represents one of the highest-quality risk-reward setups in AI infrastructure today. Come join Milk Road Pro and get our full Nebius thesis including the exact framework we use to think about neocloud valuation, the power capacity math that determines when revenue accelerates, and every catalyst we are watching through 2027. Link in bio/below.

Milk Road AI

61,932 次观看 • 2 个月前

This year Demis Hassabis predicted AI could cure all disease in a decade. But Claus Wilke & Derek Lowe say biology is far more complex, or progress will be limited by clinical trials & economics. In a new 4hr episode of the Hard Drugs podcast, we answer: Will AI solve medicine and cure all diseases (within a decade)? We talk about drug discovery, virtual cells, the Human Genome Project, manufacturing, nanobots, innovative clinical trial design, and much more. AI is already being used in drug discovery, and there’s been a lot of progress predicting the structure of soluble proteins, tweaking proteins and designing new structures, as we’ve covered in previous episodes. But there’s still a huge gap in understanding protein dynamics and interactions, as there are many areas where measurement tools and data collection are limited, including events that happen in the span of milliseconds or microseconds, which is how fast many things occur in biological systems. And while computing has scaled exponentially with Moore’s Law, drug development has faced the opposite: Eroom’s Law, where innovation has gotten more complex and more expensive over time. Even with promising drug candidates, we talk about why human testing – not in animals or virtual cells – will continue to be vital, to test which ones are effective and safe, even though models will help earlier in the pipeline. Beyond that, large samples and long follow ups are needed to detect rare side effects, understand whether drugs cause long-term complications, and find ways to manage them. It’s hard to see AI getting around the desire for rigorous safety data in real humans. Another big challenge is the capital and expertise needed to produce and scale personalized medicines and complex biological products, surgeries, transplants, antibodies, and gene-editing tools, which have entirely different cost structures from small molecule drugs. Their manufacturing and delivery often require highly skilled staff and expensive, intensive, individualized procedures. Cost challenges are also severe for tropical and rare diseases, where the financial return to diagnose, do research, develop drugs, manufacture and deliver them at scale, is limited. Without philanthropic funding and economic growth, a lot of diseases are going to remain uncurable, and a lot of people are going to go untreated – whether that’s because of a lack of trust, poor economic and financial incentives, limited public health ambition, and policy. In one sense, we’re skeptical that AI can solve medicine on its own. But in another, there are many areas where we think AI can help. So the episode also functions as a roadmap to speed up medical progress and scale up the delivery of lifesaving medicines – with AI and other approaches to reform the pipeline. What are the economic incentives, innovative trial designs, and data collection efforts that can help drive further medical progress? And how does AI fit in? You’ll have to listen to find out! Timestamps: 0:04:34 Contrasting AI optimism and skepticism 0:32:44 The non-linear path between science and technology 1:01:30 The fundamental need for experiments 1:23:15 Animals, organoids, and virtual cells 1:50:47 The challenges of collecting drug efficacy data in humans 2:34:02 The long road to drug safety data 3:06:09 The cost problem of delivering biological drugs and personalized medicine at scale 3:45:35 The global skew in R&D and healthcare funding 4:01:48 Trust, ambition, and the final barriers to medical progress
4:34:47

This year Demis Hassabis predicted AI could cure all disease in a decade. But Claus Wilke & Derek Lowe say biology is far more complex, or progress will be limited by clinical trials & economics. In a new 4hr episode of the Hard Drugs podcast, we answer: Will AI solve medicine and cure all diseases (within a decade)? We talk about drug discovery, virtual cells, the Human Genome Project, manufacturing, nanobots, innovative clinical trial design, and much more. AI is already being used in drug discovery, and there’s been a lot of progress predicting the structure of soluble proteins, tweaking proteins and designing new structures, as we’ve covered in previous episodes. But there’s still a huge gap in understanding protein dynamics and interactions, as there are many areas where measurement tools and data collection are limited, including events that happen in the span of milliseconds or microseconds, which is how fast many things occur in biological systems. And while computing has scaled exponentially with Moore’s Law, drug development has faced the opposite: Eroom’s Law, where innovation has gotten more complex and more expensive over time. Even with promising drug candidates, we talk about why human testing – not in animals or virtual cells – will continue to be vital, to test which ones are effective and safe, even though models will help earlier in the pipeline. Beyond that, large samples and long follow ups are needed to detect rare side effects, understand whether drugs cause long-term complications, and find ways to manage them. It’s hard to see AI getting around the desire for rigorous safety data in real humans. Another big challenge is the capital and expertise needed to produce and scale personalized medicines and complex biological products, surgeries, transplants, antibodies, and gene-editing tools, which have entirely different cost structures from small molecule drugs. Their manufacturing and delivery often require highly skilled staff and expensive, intensive, individualized procedures. Cost challenges are also severe for tropical and rare diseases, where the financial return to diagnose, do research, develop drugs, manufacture and deliver them at scale, is limited. Without philanthropic funding and economic growth, a lot of diseases are going to remain uncurable, and a lot of people are going to go untreated – whether that’s because of a lack of trust, poor economic and financial incentives, limited public health ambition, and policy. In one sense, we’re skeptical that AI can solve medicine on its own. But in another, there are many areas where we think AI can help. So the episode also functions as a roadmap to speed up medical progress and scale up the delivery of lifesaving medicines – with AI and other approaches to reform the pipeline. What are the economic incentives, innovative trial designs, and data collection efforts that can help drive further medical progress? And how does AI fit in? You’ll have to listen to find out! Timestamps: 0:04:34 Contrasting AI optimism and skepticism 0:32:44 The non-linear path between science and technology 1:01:30 The fundamental need for experiments 1:23:15 Animals, organoids, and virtual cells 1:50:47 The challenges of collecting drug efficacy data in humans 2:34:02 The long road to drug safety data 3:06:09 The cost problem of delivering biological drugs and personalized medicine at scale 3:45:35 The global skew in R&D and healthcare funding 4:01:48 Trust, ambition, and the final barriers to medical progress

Saloni

221,350 次观看 • 9 个月前

🚀The $AIC Revolution🎇 What an unforgettable year this has been for AI Companions ($AIC)🚀 While our monumental launch in September marked an incredible beginning - we are still a young project with so much more in store for you all🤫 We are focused on becoming the leading force in AI-driven digital companionship and we will continue to prove why $AIC is destined to dominate the AI and blockchain space🧚 This year has been laying foundational groundworks for the $AIC revolution that is ONLY just getting started🔥 We ARE about to enter the 2025 year where we believe the biggest bull run in crypto history is coming📈 With the AI Narrative being the top narrative for projects and investors🤑🏆 WHAT’S TO COME💎 💳OKX Wallet Partnership✅ 🔸When fully integrated and LIVE it will put $AIC in front of over 20 million active users worldwide🤯 🔸OKX will be officially announcing this on their ends soon, bringing widespread exposure on $AIC🤩 This is just the first step toward something even bigger with OKX—stay tuned! 👀 💹Marketing Campaigns✅ 🔹A large marketing campaign is going into effect IMMEDIATELY to generate steady momentum as we kick into 2025 with strength and precision🎊 🔹Massive billboard and media campaigns are also rolling out in the New Year to ensure $AIC is plastered everywhere you look🚀 🌐Strategic Partnerships✅ 🔸HUGE NAMES are joining the $AIC revolution🌊 The year 2025 will see us locking in even more strategic collaborations to solidify our status as the go-to AI Crypto🔥 📲Product/Development✅ 🔹Expect to see progress updates, teasers, and timeplan plans around our product launches as per our Roadmap🗺️📍 🏆Next T1 CEX Announcement✅ ♦️Another MEGA T1 exchange is confirmed in addition to OKX Wallet integration🤫 The reveal is closer than ever⌛️ TIMING is everything—this WILL be yet another game-changer⚠️ We WILL be kicking off 2025 with unstoppable momentum: ▪️Smashing through more ATHs ▪️Breaking into $1+ price territories ▪️Securing our spot in the Top 100 cryptos and beyond✈️ The revolution in AI, Blockchain, and Digital Companionship is FINALLY HERE🧚 The 2024 Year was filled with milestones and foundation setting, but 2025 will be a year of TOTAL AIC DOMINANCE🏆 Marketing campaigns are now going live. Further T1 CEX announcements are imminent. More strategic partnerships incoming. And so much more🤖 Consider it a mighty privilege and a lucky moment to act upon these current price entry points🌪️ Now is the time to ACT before they are long forgotten🚀 #Crypto #Bitcoin #Blockchain #Ethereum #Altcoins #CryptoNews #CryptoRevolution #AICompanions #BlockchainInnovation #AltcoinSeason #DigitalFuture
0:57

🚀The $AIC Revolution🎇 What an unforgettable year this has been for AI Companions ($AIC)🚀 While our monumental launch in September marked an incredible beginning - we are still a young project with so much more in store for you all🤫 We are focused on becoming the leading force in AI-driven digital companionship and we will continue to prove why $AIC is destined to dominate the AI and blockchain space🧚 This year has been laying foundational groundworks for the $AIC revolution that is ONLY just getting started🔥 We ARE about to enter the 2025 year where we believe the biggest bull run in crypto history is coming📈 With the AI Narrative being the top narrative for projects and investors🤑🏆 WHAT’S TO COME💎 💳OKX Wallet Partnership✅ 🔸When fully integrated and LIVE it will put $AIC in front of over 20 million active users worldwide🤯 🔸OKX will be officially announcing this on their ends soon, bringing widespread exposure on $AIC🤩 This is just the first step toward something even bigger with OKX—stay tuned! 👀 💹Marketing Campaigns✅ 🔹A large marketing campaign is going into effect IMMEDIATELY to generate steady momentum as we kick into 2025 with strength and precision🎊 🔹Massive billboard and media campaigns are also rolling out in the New Year to ensure $AIC is plastered everywhere you look🚀 🌐Strategic Partnerships✅ 🔸HUGE NAMES are joining the $AIC revolution🌊 The year 2025 will see us locking in even more strategic collaborations to solidify our status as the go-to AI Crypto🔥 📲Product/Development✅ 🔹Expect to see progress updates, teasers, and timeplan plans around our product launches as per our Roadmap🗺️📍 🏆Next T1 CEX Announcement✅ ♦️Another MEGA T1 exchange is confirmed in addition to OKX Wallet integration🤫 The reveal is closer than ever⌛️ TIMING is everything—this WILL be yet another game-changer⚠️ We WILL be kicking off 2025 with unstoppable momentum: ▪️Smashing through more ATHs ▪️Breaking into $1+ price territories ▪️Securing our spot in the Top 100 cryptos and beyond✈️ The revolution in AI, Blockchain, and Digital Companionship is FINALLY HERE🧚 The 2024 Year was filled with milestones and foundation setting, but 2025 will be a year of TOTAL AIC DOMINANCE🏆 Marketing campaigns are now going live. Further T1 CEX announcements are imminent. More strategic partnerships incoming. And so much more🤖 Consider it a mighty privilege and a lucky moment to act upon these current price entry points🌪️ Now is the time to ACT before they are long forgotten🚀 #Crypto #Bitcoin #Blockchain #Ethereum #Altcoins #CryptoNews #CryptoRevolution #AICompanions #BlockchainInnovation #AltcoinSeason #DigitalFuture

AI Companions

118,537 次观看 • 1 年前

🚀 The Official Launch of #RepubliK 🌕 Dear RepubliK Community - The wait is finally over! Welcome to the platform that redefines the landscape of the social and creator’s economy. RepubliK is here to evolve the #SocialFi sphere and traditional social networks through the use of cutting-edge #Web3 and #AI technologies. RepubliK blends the vibrant interactivity of social media with the lucrative potential of content monetisation - lowering the barrier of entry for anyone to receive rewards and amplifying the earning potential of their interactions and content. 🤖 AI-Powered Rewards: We have released a state-of-the-art content and interaction assessment system, powered by AI and Machine Learning, transforming how Content Creators can earn rewards on RepubliK. By identifying quality content based on data points beyond likes and comments, RepubliK rewards Content Creators in proportion to the genuine value they bring to the platform. ⛓️ Release of RPK on Arbitrum: In addition to deploying on Mantle Network and TON 💎, we are integrating $RPK on the Arbitrum Layer-2 Network, reducing costs and increasing accessibility for users in our ecosystem, crafting a cost-efficient and user-friendly Web3 experience for all users. 💞 A New Revenue Stream for Content Creators With Pay2Chat: The Pay2Chat feature allows Content Creators to set an $RPK fee for their Fans to unlock access to chat with them, providing an additional revenue stream for Content Creators to capture value from their top fans. The Pay2Chat function is available to all users on RepubliK, simply set a price in $RPK and start earning today. 🥸 Authenticate Your Presence on RepubliK by Linking Your Instagram Account: Content Creators are now able to link their Instagram Accounts to their RepubliK profile to safeguard against impersonation. Fans can now interact with and support their favourite creators, knowing that they are engaging with the real deal. 📈 Staking for Governance: Coming soon, as a community based platform all major decisions will be subject to a community vote by token holders based on their staked $RPK. 🚀 Sign-up Now: 🫱🏽‍🫲🏻 Supported by Top Investors: OKX Ventures, Fundamental Labs, HTX Ventures, Art of The CMS, Signum Capital, UOB Venture Management, Arcane Group, Mirana Ventures, DeFine Ventures, Enjin, 6MV, FBG Capital, OIG Capital, Sora Ventures, Comma3 Ventures. 💰 Buy RPK Now On: Bybit, KuCoin, MEXC, Gate, HTX, BitMart, BingX, CoinEx Global, Uniswap Labs 🦄 1inch. 💚 To Our Community: The Heart of RepubliK Most importantly, we are proud of you — our vibrant community of creators, users, and supporters. Your engagement, constructive feedback, and enthusiasm since RepubliK moved out of stealth mode merely 6 months ago have shaped us into the platform it is today, and the platform it will be tomorrow. This is only the beginning of our commitment in continuous innovation, community growth, and the expansion of our ecosystem. Together, we will keep pushing the boundaries of the digital world!
0:36

🚀 The Official Launch of #RepubliK 🌕 Dear RepubliK Community - The wait is finally over! Welcome to the platform that redefines the landscape of the social and creator’s economy. RepubliK is here to evolve the #SocialFi sphere and traditional social networks through the use of cutting-edge #Web3 and #AI technologies. RepubliK blends the vibrant interactivity of social media with the lucrative potential of content monetisation - lowering the barrier of entry for anyone to receive rewards and amplifying the earning potential of their interactions and content. 🤖 AI-Powered Rewards: We have released a state-of-the-art content and interaction assessment system, powered by AI and Machine Learning, transforming how Content Creators can earn rewards on RepubliK. By identifying quality content based on data points beyond likes and comments, RepubliK rewards Content Creators in proportion to the genuine value they bring to the platform. ⛓️ Release of RPK on Arbitrum: In addition to deploying on Mantle Network and TON 💎, we are integrating $RPK on the Arbitrum Layer-2 Network, reducing costs and increasing accessibility for users in our ecosystem, crafting a cost-efficient and user-friendly Web3 experience for all users. 💞 A New Revenue Stream for Content Creators With Pay2Chat: The Pay2Chat feature allows Content Creators to set an $RPK fee for their Fans to unlock access to chat with them, providing an additional revenue stream for Content Creators to capture value from their top fans. The Pay2Chat function is available to all users on RepubliK, simply set a price in $RPK and start earning today. 🥸 Authenticate Your Presence on RepubliK by Linking Your Instagram Account: Content Creators are now able to link their Instagram Accounts to their RepubliK profile to safeguard against impersonation. Fans can now interact with and support their favourite creators, knowing that they are engaging with the real deal. 📈 Staking for Governance: Coming soon, as a community based platform all major decisions will be subject to a community vote by token holders based on their staked $RPK. 🚀 Sign-up Now: 🫱🏽‍🫲🏻 Supported by Top Investors: OKX Ventures, Fundamental Labs, HTX Ventures, Art of The CMS, Signum Capital, UOB Venture Management, Arcane Group, Mirana Ventures, DeFine Ventures, Enjin, 6MV, FBG Capital, OIG Capital, Sora Ventures, Comma3 Ventures. 💰 Buy RPK Now On: Bybit, KuCoin, MEXC, Gate, HTX, BitMart, BingX, CoinEx Global, Uniswap Labs 🦄 1inch. 💚 To Our Community: The Heart of RepubliK Most importantly, we are proud of you — our vibrant community of creators, users, and supporters. Your engagement, constructive feedback, and enthusiasm since RepubliK moved out of stealth mode merely 6 months ago have shaped us into the platform it is today, and the platform it will be tomorrow. This is only the beginning of our commitment in continuous innovation, community growth, and the expansion of our ecosystem. Together, we will keep pushing the boundaries of the digital world!

RepubliK_GG

64,378 次观看 • 2 年前