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⭐️ Pingwin AI: Find Potential Runners Early and Easy ⭐️ Eliminate noisy data. Gain pure alpha. 🤖 One Command. Instant Domination: — Tracking high-volume movers — Above $300M market cap — Precision market maker insights — Zero manual research Spot. Trade. Profit. Powered by Sonar's AI Agent Network $PING...

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SONAR

63,266 subscribers

28,061 次观看 • 1 年前 •via X (Twitter)

财经科学技术新闻政治
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Anya Rossi• Live Now

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11 条评论

Peter 的头像
Peter1 年前

Lets gooo Pingwin!

Investors.com 的头像
Investors.com1 年前

With industry-leading AI stock coverage, expert analysis and proprietary 1-99 ratings for every stock, IBD Digital makes it easy to pinpoint top-notch AI stocks––join today and save over $45.

OlvroPsltro 的头像
OlvroPsltro1 年前

The Revolution is coming soon 👀🐧

Viktor Ignatiuk 的头像
Viktor Ignatiuk1 年前

I really wanna know how PING will evolve in Q1 2025. Im all in.

alireza 的头像
alireza1 年前

awwwwwwoooo 🐺

Chris Denton 的头像
Chris Denton1 年前

MORE POWER! 🐧

CryptoBro🦸 的头像
CryptoBro🦸1 年前

Fast and furious🐧🧠

Airdrop Alerts 的头像
Airdrop Alerts1 年前

$PING is the perfect match for my portfolio. It seems that this project has a great future ahead.

kayina. 的头像
kayina.1 年前

Hmmmmmmmmmmm Hmmmmmmmmmmm Hmmmmmmmmmmm Hmmmmmmmmmmm

Chuck Jnr 的头像
Chuck Jnr1 年前

Not long to go now 🙏

Dimitri 的头像
Dimitri1 年前

@SonarPING_ ❤️💙💚

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TradeDog | Crypto Market Research

16,587 次观看 • 1 年前

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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. 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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

17,016 次观看 • 6 个月前

Google's UCP focuses on intuitive search for the B2C market, but it currently doesn't meet the wider demands of B2B procurement. B2C search is simple by comparison, whereas B2B is multifaceted. This is why we are seeing the emergence of specialized vertical B2B solutions like Accio to bridge that gap. When you're sourcing products for your business, you don't just need to find suppliers and place orders. You need to verify their certifications like BSCI and ISO standards, compare their production capacity and experience, check their ratings and reorder rates, and understand their competitive positioning in the market. Accio is an AI-powered B2B sourcing agent built for Alibaba that handles the entire procurement workflow: - Supplier evaluation with verified metrics including certifications, years of experience, employee count, store ratings, and reorder rates - Competitive capability analysis that compares manufacturers across strategic advantages like compliance and reliability - Bulk inquiry management that sends strategic sourcing requests to multiple suppliers simultaneously instead of contacting them one by one - Market intelligence requests that ask suppliers to analyze market supply, provide wholesale price ranges, list key competitors with their products, and estimate profit margins - Organized conversation threads with each supplier contact so you can review responses without switching between dozens of email chains or chat windows - Comparative analysis compilation from multiple suppliers to help you make data-driven sourcing decisions What normally takes procurement teams several days of manual research, verification, and outreach happens in one ask with Accio handling supplier vetting, bulk communication, and intelligence compilation.
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Google's UCP focuses on intuitive search for the B2C market, but it currently doesn't meet the wider demands of B2B procurement. B2C search is simple by comparison, whereas B2B is multifaceted. This is why we are seeing the emergence of specialized vertical B2B solutions like Accio to bridge that gap. When you're sourcing products for your business, you don't just need to find suppliers and place orders. You need to verify their certifications like BSCI and ISO standards, compare their production capacity and experience, check their ratings and reorder rates, and understand their competitive positioning in the market. Accio is an AI-powered B2B sourcing agent built for Alibaba that handles the entire procurement workflow: - Supplier evaluation with verified metrics including certifications, years of experience, employee count, store ratings, and reorder rates - Competitive capability analysis that compares manufacturers across strategic advantages like compliance and reliability - Bulk inquiry management that sends strategic sourcing requests to multiple suppliers simultaneously instead of contacting them one by one - Market intelligence requests that ask suppliers to analyze market supply, provide wholesale price ranges, list key competitors with their products, and estimate profit margins - Organized conversation threads with each supplier contact so you can review responses without switching between dozens of email chains or chat windows - Comparative analysis compilation from multiple suppliers to help you make data-driven sourcing decisions What normally takes procurement teams several days of manual research, verification, and outreach happens in one ask with Accio handling supplier vetting, bulk communication, and intelligence compilation.

AshutoshShrivastava

12,648 次观看 • 6 个月前

$FAME and #AICON Launcher are officially LIVE on Base!🔥 A chance to be part of something BIG–Participate in the #AICON revolution and experience the top-tier seamless experience with #FameAI 🔗 Time to celebrate this milestone together! 🌟 ----------------------------------------------------------- 🚨 Early Access to Fame AI-CON Launcher is Now Available on BASE! 🚨 With FameAI's #AICON Launcher, you can: ✨ Create your human-like AICON ✨ Publish your AI-CON's token on the market $FAME will be tokenized as the first tradable #AICON on our platform! ----------------------------------------------------------- 🔥Early access will be available for selected $FMC stakers🔥 Stakers can enjoy full features being rolled out in the coming weeks. Next up, opening to the public! $FMC stakers, stake now if you haven't already! 👉 ----------------------------------------------------------- 🟣 How Does #FameAI's #AICON Launcher Work? 💻 No coding required! Anyone can create their own #AICON 🔗 Launch your #AICON token—graduated AI-CON can trade on Base Uniswap v2 ⚙️ Fully customizable and highly autonomous How to acquire $FMC on Base ----------------------------------------------------------- 🟣 $FAME Token and FAME Framework This is built to simulate human-like interactions on social platforms. 🎨 Generates content: Images, text, videos 🤖 Reflects personality, knowledge, and mood 💰 Powered by $FAME token ✨ CA: 0xB8e23ab4A1762Fe8dABb844EcC66FEEE3725c480 🔗 Read more here 👉 ----------------------------------------------------------- 🟣 Fame Studio Fame Studio lets you: ✅ Create hyper-realistic human identities ✅ Generate images, audio, music, and even videos V2 Update Coming Soon– Expanded features for more engaging content—perfect for creators and innovators! #AIAgents $FMC #FameAI $FAME ----------------------------------------------------------- 🟣 #FameAI Roadmap for Q1 2025 🚀 Skill Marketplace: Co-create #AICON, utilizing $FMC as the native currency 🧠 AI-Agent Learning: Train agents to learn specific tones, personalities, and behaviors with one click 🎥 Live Streaming: Superhuman-like models for business or lifestyle-focused live streams
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$FAME and #AICON Launcher are officially LIVE on Base!🔥 A chance to be part of something BIG–Participate in the #AICON revolution and experience the top-tier seamless experience with #FameAI 🔗 Time to celebrate this milestone together! 🌟 ----------------------------------------------------------- 🚨 Early Access to Fame AI-CON Launcher is Now Available on BASE! 🚨 With FameAI's #AICON Launcher, you can: ✨ Create your human-like AICON ✨ Publish your AI-CON's token on the market $FAME will be tokenized as the first tradable #AICON on our platform! ----------------------------------------------------------- 🔥Early access will be available for selected $FMC stakers🔥 Stakers can enjoy full features being rolled out in the coming weeks. Next up, opening to the public! $FMC stakers, stake now if you haven't already! 👉 ----------------------------------------------------------- 🟣 How Does #FameAI's #AICON Launcher Work? 💻 No coding required! Anyone can create their own #AICON 🔗 Launch your #AICON token—graduated AI-CON can trade on Base Uniswap v2 ⚙️ Fully customizable and highly autonomous How to acquire $FMC on Base ----------------------------------------------------------- 🟣 $FAME Token and FAME Framework This is built to simulate human-like interactions on social platforms. 🎨 Generates content: Images, text, videos 🤖 Reflects personality, knowledge, and mood 💰 Powered by $FAME token ✨ CA: 0xB8e23ab4A1762Fe8dABb844EcC66FEEE3725c480 🔗 Read more here 👉 ----------------------------------------------------------- 🟣 Fame Studio Fame Studio lets you: ✅ Create hyper-realistic human identities ✅ Generate images, audio, music, and even videos V2 Update Coming Soon– Expanded features for more engaging content—perfect for creators and innovators! #AIAgents $FMC #FameAI $FAME ----------------------------------------------------------- 🟣 #FameAI Roadmap for Q1 2025 🚀 Skill Marketplace: Co-create #AICON, utilizing $FMC as the native currency 🧠 AI-Agent Learning: Train agents to learn specific tones, personalities, and behaviors with one click 🎥 Live Streaming: Superhuman-like models for business or lifestyle-focused live streams

Fame AI | The Home of AI Agent 2.0 - AI-CON

23,775 次观看 • 1 年前

Hive Intelligence Launches Specialized Crypto Agents Hive Intelligence has released a suite of 17 specialized crypto agents that extend Claude Code's capabilities for professional crypto development and analysis. Extending Claude Code for Crypto Work Claude Code, Anthropic's command-line coding tool, now has access to specialized crypto intelligence through Hive's agent framework. These 17 agents work alongside SuperClaude's 14 base development agents, bringing the total available agent count to 31. The key difference: instead of generic AI responses to crypto queries, developers now have access to specialized agents trained for specific blockchain domains, from smart contract auditing to MEV research to DeFi strategy optimization. How the Agents Work After installation, the agents operate automatically based on query context. When you ask Claude Code to perform crypto-related tasks, the appropriate specialist agent is invoked: - "Audit this smart contract" → Crypto Security Researcher - "Find yield farming opportunities on Ethereum" → Crypto DeFi Strategist - "Analyze this wallet's transaction history" → Crypto Wallet Detective - "Identify arbitrage opportunities across DEXs" → Crypto DEX Arbitrageur No manual agent selection required. The system recognizes the task and routes it to the appropriate specialist. The 17 Specialized Agents Market & Trading Intelligence (4 agents) Crypto Quant: Mathematical models, algorithmic trading strategies, statistical arbitrage, and quantitative risk modeling. Crypto Market Researcher: Fundamental analysis, market trends, institutional adoption tracking, and regulatory landscape monitoring. Crypto Derivatives Trader: Futures and perpetuals analysis, options strategies, leverage management, and derivatives market intelligence. Crypto DEX Arbitrageur: Cross-exchange arbitrage identification, MEV strategy development, and automated profit extraction techniques. DeFi & Liquidity (4 agents) Crypto DeFi Strategist: Yield farming optimization, protocol analysis, liquidity provision strategies, and DeFi portfolio management. Crypto Liquidity Manager: Pool optimization, impermanent loss calculation and mitigation, market making strategies, and capital efficiency analysis. Crypto Governance Analyst: DAO structure evaluation, governance token analysis, proposal assessment, and voting mechanism research. Crypto Bridge Analyst: Cross-chain bridge security assessment, protocol comparison, interoperability solutions, and bridge risk evaluation. Security & Risk (3 agents) Crypto Security Researcher: Smart contract auditing, vulnerability detection, honeypot identification, and exploit pattern recognition. Crypto Security Engineer: Secure contract development practices, defensive programming patterns, and security implementation guidance. Crypto Risk Manager: Portfolio risk assessment, compliance monitoring, exposure analysis, and risk mitigation strategy development. On-Chain Analysis (3 agents) Crypto Wallet Detective: Blockchain forensics, wallet behavior analysis, transaction tracing, and entity identification across chains. Crypto On-chain Analyst: Transaction pattern analysis, wallet clustering, flow tracking, and on-chain metrics interpretation. Crypto MEV Researcher: MEV opportunity detection, flashloan arbitrage analysis, sandwich attack identification, and MEV protection strategies. Specialized Intelligence (3 agents) Crypto NFT Specialist: Collection valuation, rarity analysis, marketplace trends, and NFT ecosystem intelligence. Crypto Stablecoin Analyst: Peg stability monitoring, collateral analysis, depegging risk assessment, and stablecoin mechanism evaluation. Crypto Social Sentiment: Social media sentiment tracking, influencer monitoring, trending topic identification, and community analysis. Data Coverage: - 60+ blockchain networks - 2,000+ DeFi protocols - Real-time DEX data - CEX trading metrics - Social sentiment feeds - NFT marketplace data Compatibility: Works seamlessly with SuperClaude's existing agent framework. No configuration conflicts or manual routing needed. Practical Applications Smart Contract Development Security agents can audit contracts during development, identifying reentrancy risks, access control issues, and common vulnerabilities before deployment. DeFi Research Strategy agents query real-time pool data across networks, calculate yield-adjusted returns, and assess risks like impermanent loss or smart contract exposure. Trading Analysis Market agents access derivatives data, funding rates, liquidation levels, and order book depth across exchanges for informed trading decisions. Forensic Investigation On-chain agents trace fund flows, identify connected addresses, and analyze transaction patterns for security research or compliance work. Portfolio Management Risk agents evaluate protocol exposure, assess tail risks, and monitor positions across multiple chains and protocols. Why Specialized Agents Matter Generic AI models lack the domain-specific knowledge required for professional crypto work. A general-purpose AI might provide surface-level analysis of a smart contract, but a specialized security agent understands Solidity patterns, common exploits, and auditing methodologies. The agent framework solves this by routing tasks to specialists with deep domain knowledge: - A derivatives question goes to an agent trained on perpetuals, funding rates, and options greeks - A DeFi query reaches an agent that understands liquidity mathematics and protocol mechanics - A security audit is handled by an agent familiar with vulnerability patterns and exploit techniques This specialization produces more accurate, actionable insights than single-model approaches. Getting Started The agents are available now through npm. Requirements: - Node.js 16+ - Claude Code installed - No additional dependencies After installation, simply use Claude Code normally. When you ask crypto-related questions or request blockchain analysis, the appropriate agent is automatically invoked. The system handles routing, data retrieval, and response generation. Documentation covers individual agent capabilities, example queries, and integration patterns for different workflows. What This Enables With 17 specialized crypto agents, Claude Code becomes a comprehensive blockchain development and analysis environment: - Developers can audit contracts, optimize gas usage, and implement security patterns - Researchers can analyze protocols, compare yields, and assess risks - Traders can evaluate markets, identify opportunities, and manage positions - Security professionals can investigate exploits, trace funds, and assess vulnerabilities The agents provide access to blockchain data and specialized analysis that previously required multiple tools, APIs, and manual research. ghive.
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Hive Intelligence Launches Specialized Crypto Agents Hive Intelligence has released a suite of 17 specialized crypto agents that extend Claude Code's capabilities for professional crypto development and analysis. Extending Claude Code for Crypto Work Claude Code, Anthropic's command-line coding tool, now has access to specialized crypto intelligence through Hive's agent framework. These 17 agents work alongside SuperClaude's 14 base development agents, bringing the total available agent count to 31. The key difference: instead of generic AI responses to crypto queries, developers now have access to specialized agents trained for specific blockchain domains, from smart contract auditing to MEV research to DeFi strategy optimization. How the Agents Work After installation, the agents operate automatically based on query context. When you ask Claude Code to perform crypto-related tasks, the appropriate specialist agent is invoked: - "Audit this smart contract" → Crypto Security Researcher - "Find yield farming opportunities on Ethereum" → Crypto DeFi Strategist - "Analyze this wallet's transaction history" → Crypto Wallet Detective - "Identify arbitrage opportunities across DEXs" → Crypto DEX Arbitrageur No manual agent selection required. The system recognizes the task and routes it to the appropriate specialist. The 17 Specialized Agents Market & Trading Intelligence (4 agents) Crypto Quant: Mathematical models, algorithmic trading strategies, statistical arbitrage, and quantitative risk modeling. Crypto Market Researcher: Fundamental analysis, market trends, institutional adoption tracking, and regulatory landscape monitoring. Crypto Derivatives Trader: Futures and perpetuals analysis, options strategies, leverage management, and derivatives market intelligence. Crypto DEX Arbitrageur: Cross-exchange arbitrage identification, MEV strategy development, and automated profit extraction techniques. DeFi & Liquidity (4 agents) Crypto DeFi Strategist: Yield farming optimization, protocol analysis, liquidity provision strategies, and DeFi portfolio management. Crypto Liquidity Manager: Pool optimization, impermanent loss calculation and mitigation, market making strategies, and capital efficiency analysis. Crypto Governance Analyst: DAO structure evaluation, governance token analysis, proposal assessment, and voting mechanism research. Crypto Bridge Analyst: Cross-chain bridge security assessment, protocol comparison, interoperability solutions, and bridge risk evaluation. Security & Risk (3 agents) Crypto Security Researcher: Smart contract auditing, vulnerability detection, honeypot identification, and exploit pattern recognition. Crypto Security Engineer: Secure contract development practices, defensive programming patterns, and security implementation guidance. Crypto Risk Manager: Portfolio risk assessment, compliance monitoring, exposure analysis, and risk mitigation strategy development. On-Chain Analysis (3 agents) Crypto Wallet Detective: Blockchain forensics, wallet behavior analysis, transaction tracing, and entity identification across chains. Crypto On-chain Analyst: Transaction pattern analysis, wallet clustering, flow tracking, and on-chain metrics interpretation. Crypto MEV Researcher: MEV opportunity detection, flashloan arbitrage analysis, sandwich attack identification, and MEV protection strategies. Specialized Intelligence (3 agents) Crypto NFT Specialist: Collection valuation, rarity analysis, marketplace trends, and NFT ecosystem intelligence. Crypto Stablecoin Analyst: Peg stability monitoring, collateral analysis, depegging risk assessment, and stablecoin mechanism evaluation. Crypto Social Sentiment: Social media sentiment tracking, influencer monitoring, trending topic identification, and community analysis. Data Coverage: - 60+ blockchain networks - 2,000+ DeFi protocols - Real-time DEX data - CEX trading metrics - Social sentiment feeds - NFT marketplace data Compatibility: Works seamlessly with SuperClaude's existing agent framework. No configuration conflicts or manual routing needed. Practical Applications Smart Contract Development Security agents can audit contracts during development, identifying reentrancy risks, access control issues, and common vulnerabilities before deployment. DeFi Research Strategy agents query real-time pool data across networks, calculate yield-adjusted returns, and assess risks like impermanent loss or smart contract exposure. Trading Analysis Market agents access derivatives data, funding rates, liquidation levels, and order book depth across exchanges for informed trading decisions. Forensic Investigation On-chain agents trace fund flows, identify connected addresses, and analyze transaction patterns for security research or compliance work. Portfolio Management Risk agents evaluate protocol exposure, assess tail risks, and monitor positions across multiple chains and protocols. Why Specialized Agents Matter Generic AI models lack the domain-specific knowledge required for professional crypto work. A general-purpose AI might provide surface-level analysis of a smart contract, but a specialized security agent understands Solidity patterns, common exploits, and auditing methodologies. The agent framework solves this by routing tasks to specialists with deep domain knowledge: - A derivatives question goes to an agent trained on perpetuals, funding rates, and options greeks - A DeFi query reaches an agent that understands liquidity mathematics and protocol mechanics - A security audit is handled by an agent familiar with vulnerability patterns and exploit techniques This specialization produces more accurate, actionable insights than single-model approaches. Getting Started The agents are available now through npm. Requirements: - Node.js 16+ - Claude Code installed - No additional dependencies After installation, simply use Claude Code normally. When you ask crypto-related questions or request blockchain analysis, the appropriate agent is automatically invoked. The system handles routing, data retrieval, and response generation. Documentation covers individual agent capabilities, example queries, and integration patterns for different workflows. What This Enables With 17 specialized crypto agents, Claude Code becomes a comprehensive blockchain development and analysis environment: - Developers can audit contracts, optimize gas usage, and implement security patterns - Researchers can analyze protocols, compare yields, and assess risks - Traders can evaluate markets, identify opportunities, and manage positions - Security professionals can investigate exploits, trace funds, and assess vulnerabilities The agents provide access to blockchain data and specialized analysis that previously required multiple tools, APIs, and manual research. ghive.

Hive Intelligence

78,743 次观看 • 8 个月前

MARKETS OPEN IN 20. every day is different. I will post unusual volume as I see it. I will give long thesis for potential 10x on small caps. We won’t miss another. Here’s another $DDD I give ideas on stocks that fly under the radar you won’t see me talking about the names 90% x are on. Be different. Be unique. Be you. Ideas are ideas. But finding another account working harder Good luck. 😤 Research 24/7 Scanning charts daily 500% runners & 10x longs I’m a real person DMs always open I’m shouting out legends every day who provide value I also just unfollowed 117 accounts who didn’t want rock with us. If you’re working hard and want to be seen and found on here. Follow and subscribe. I’m trying to find new hard workers and legends daily. Engaging with me means I’ll go straight to your account and do the same and if I like your content I make lists daily to do shouts. Plus. Alpha. Alpha. Alpha. More alpha Here’s some accounts you all should look into following BoltEdge ⚡️ Leif | Investing Tyler SolSuit d i v e r g e n t Doris Di Franklin407 Perspez GP the Fundamental Investor Eno 🥷 KURREN$Y KAPITAL Dmytro Lebid William Paulson Let’s all bank fam. Subscribe for just 1$ The community I’m building here on x will be one to remember. Follow me. Follow the people I suggest to follow. Subscribe. 🏦 BANK. $DDD is shifting into a high-value manufacturing platform positioned across: • AI data center thermal systems • semiconductor capital equipment • aerospace & defense production • medical & dental manufacturing THE NUMBERS THAT MATTER • Revenue (Q1 2026): $95.5M (+1% YoY, +11% ex-divestitures) • Healthcare segment: $50.1M (+21% YoY) • Gross margin: ~35.9–36.1% (expanding YoY) • Adjusted EBITDA: + $2.1M (from -$23.9M last year) • Cash: ~$86.5M • Market cap: ~$350M–$500M range (micro-cap re-rating zone) WHY THIS STORY IS DIFFERENT NOW 1) AI DATA CENTER COOLING DEMAND IS EXPLODING AI racks are moving into: • 30–140kW+ per rack • liquid cooling becoming mandatory • thermal engineering becoming critical infrastructure DDD already works in: • semiconductor wafer-stage thermal control • precision fluid manifolds • vacuum/cleanroom-compatible metal parts • ultra-high accuracy cooling systems That directly translates to: • cold plates • heat exchangers • direct-to-chip liquid cooling hardware 2) SEMICONDUCTOR + PRECISION ENGINEERING MOAT DDD’s advantage is not “printing parts.” It’s precision thermal + fluid physics manufacturing. They already solve: • <4 mK thermal stability systems • microfluidic channel optimization • vibration reduction via monolithic metal design • complex copper and alloy geometries 3) DEFENSE + ONSHORE MANUFACTURING TAILWIND Exposure across: • aerospace & defense programs • U.S. Air Force initiatives • naval + advanced materials supply chains • America Makes ecosystem Trend tailwind: 👉 reshoring + secure domestic production Defense buyers prioritize: • reliability • qualification • supply chain security not lowest cost $DDD fits that model. 4) TURNAROUND METRICS ARE IMPROVING What changed vs prior cycle: • Operating losses sharply reduced • ~$55M cost cuts implemented • SG&A materially down • EBITDA inflection achieved • Healthcare now a core growth engine 5) MIX SHIFT = HIGHER QUALITY REVENUE Growth areas: • Dental + MedTech (+20%+) • Aerospace & defense (double-digit growth) • materials + recurring consumables This is a $350–500M micro-cap showing: • positive EBITDA inflection • expanding margins • AI infrastructure adjacency • defense + semicap exposure • healthcare growth engine If execution continues, the rerating case is not about “3D printing hype” — it’s about whether the market starts valuing it as critical thermal + precision manufacturing infrastructure for AI and defense. $VOO $HIVE $HYLN $VELO $HYFT $CELH $PLTR $HIMS $RDDT $AIB $FEMY $JOBY $TE $SIVE $ZS $MRVL $SNPS $SNOW $DELL $LAES
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MARKETS OPEN IN 20. every day is different. I will post unusual volume as I see it. I will give long thesis for potential 10x on small caps. We won’t miss another. Here’s another $DDD I give ideas on stocks that fly under the radar you won’t see me talking about the names 90% x are on. Be different. Be unique. Be you. Ideas are ideas. But finding another account working harder Good luck. 😤 Research 24/7 Scanning charts daily 500% runners & 10x longs I’m a real person DMs always open I’m shouting out legends every day who provide value I also just unfollowed 117 accounts who didn’t want rock with us. If you’re working hard and want to be seen and found on here. Follow and subscribe. I’m trying to find new hard workers and legends daily. Engaging with me means I’ll go straight to your account and do the same and if I like your content I make lists daily to do shouts. Plus. Alpha. Alpha. Alpha. More alpha Here’s some accounts you all should look into following BoltEdge ⚡️ Leif | Investing Tyler SolSuit d i v e r g e n t Doris Di Franklin407 Perspez GP the Fundamental Investor Eno 🥷 KURREN$Y KAPITAL Dmytro Lebid William Paulson Let’s all bank fam. Subscribe for just 1$ The community I’m building here on x will be one to remember. Follow me. Follow the people I suggest to follow. Subscribe. 🏦 BANK. $DDD is shifting into a high-value manufacturing platform positioned across: • AI data center thermal systems • semiconductor capital equipment • aerospace & defense production • medical & dental manufacturing THE NUMBERS THAT MATTER • Revenue (Q1 2026): $95.5M (+1% YoY, +11% ex-divestitures) • Healthcare segment: $50.1M (+21% YoY) • Gross margin: ~35.9–36.1% (expanding YoY) • Adjusted EBITDA: + $2.1M (from -$23.9M last year) • Cash: ~$86.5M • Market cap: ~$350M–$500M range (micro-cap re-rating zone) WHY THIS STORY IS DIFFERENT NOW 1) AI DATA CENTER COOLING DEMAND IS EXPLODING AI racks are moving into: • 30–140kW+ per rack • liquid cooling becoming mandatory • thermal engineering becoming critical infrastructure DDD already works in: • semiconductor wafer-stage thermal control • precision fluid manifolds • vacuum/cleanroom-compatible metal parts • ultra-high accuracy cooling systems That directly translates to: • cold plates • heat exchangers • direct-to-chip liquid cooling hardware 2) SEMICONDUCTOR + PRECISION ENGINEERING MOAT DDD’s advantage is not “printing parts.” It’s precision thermal + fluid physics manufacturing. They already solve: • <4 mK thermal stability systems • microfluidic channel optimization • vibration reduction via monolithic metal design • complex copper and alloy geometries 3) DEFENSE + ONSHORE MANUFACTURING TAILWIND Exposure across: • aerospace & defense programs • U.S. Air Force initiatives • naval + advanced materials supply chains • America Makes ecosystem Trend tailwind: 👉 reshoring + secure domestic production Defense buyers prioritize: • reliability • qualification • supply chain security not lowest cost $DDD fits that model. 4) TURNAROUND METRICS ARE IMPROVING What changed vs prior cycle: • Operating losses sharply reduced • ~$55M cost cuts implemented • SG&A materially down • EBITDA inflection achieved • Healthcare now a core growth engine 5) MIX SHIFT = HIGHER QUALITY REVENUE Growth areas: • Dental + MedTech (+20%+) • Aerospace & defense (double-digit growth) • materials + recurring consumables This is a $350–500M micro-cap showing: • positive EBITDA inflection • expanding margins • AI infrastructure adjacency • defense + semicap exposure • healthcare growth engine If execution continues, the rerating case is not about “3D printing hype” — it’s about whether the market starts valuing it as critical thermal + precision manufacturing infrastructure for AI and defense. $VOO $HIVE $HYLN $VELO $HYFT $CELH $PLTR $HIMS $RDDT $AIB $FEMY $JOBY $TE $SIVE $ZS $MRVL $SNPS $SNOW $DELL $LAES

Hunter Allen

19,513 次观看 • 1 个月前

Announcing the DVM Terminal Presale! 01/ We are excited to formally announce the next step in our journey: our AI and Signal based trading Terminal. See ALL details on our website, including product, tech, deposit address, and tech documentation: Deposit Address (SOL only): 4pyVRFX56MdqtREcxWnf6XuEGfRNCQaKm1LA4xmHeccv By contributing, you agree to our Terms & Privacy Policy – full docs on site. 02/ We are building ‘DVM Terminal’, a signal and AI powered trading platform for the Solana trenches (initially). The first multi-agent AI trading terminal designed as an institutional-grade dashboard – turning market noise into actionable alpha with agent summaries, live signals, rigid filters, and a full multi-agent system. 03/ The problem. Trench hunting is far too inefficient with real data and insights lacking. - Dashboards are noisy (not even sortable), - No AI agents (in an AI world) - No narratives (a critical component to a thesis), - VERY limited signals (only DB/DS), - No advanced trading (no TP, SL, or VWAP), - No portfolio alert/management system post-trade etc. - The list goes on… Products from major competitors are all just homogeneous, even down to the 3-frame design. We have to piece everything together like broken lego blocks, building a weak matrix from existing platforms, X, FNFs, telegram and discord for little to no alpha. 04/ The solution & moat. We rebuild this from the ground up, leveraging signals and AI. - Clean institutional-like dashboards (we can sort and navigate thru a proper terminal, like Bloomberg or Messari) - AI agents (thank goodness for intelligence, distilling all the important info upfront across 2k+ tokens/day) - A Narrative engine (no need to ask “what is this token about?”; additionally, our engine can identify the newest metas like AI, ICM, Cards, etc.) - 100s of value-add Signals overlaid live on charts (momentum, smart money, sentiment, event data; all of it; tell us what’s happening in real-time) - Advanced trading system (finally, SL, TP, VWAP etc.) - Live portfolio monitoring (AI will give us pertinent live info on our holdings, so we can go live life and not look at screens all day) - All in one place. At a higher-level, our advantage will be managing the massive on/off-chain data pipeline being processed by thousands or millions of context-aware AI agents that recognize patterns, filter noise and deliver only the most actionable insights to a trader with which it can execute a trade effectively. 05/ The opportunity. The Industry leader on Solana makes $600m+ in fees annually, with total industry near $1b on Solana alone, according to Adam. Yet, the entire industry gives us total burnout, fragmented data, either little info or info overload, no real signals, no narratives, no personalized AI-driven strategies, and zero incentives (like buybacks or a flywheel). We’ll flip the script, designing a high-powered scalable signal and AI driven intelligence platform with a flywheel (50-100% fee buy-back & burn). Simply put, we want to be tops. 06/ Development. Our product is MVP. We are building this to scale beyond Solana, into multi-chain. V1 is expected in 4-6 weeks. Our approach to building is an open feedback loop with community members, building to the demands of our users. 07/ Pre-sale terms & Valuation. We are offering 50% public sale, with min $100, no max. Ending valuation is susceptible to change based on amount raised, but will be fixed at 2x raise - i.e. $1m raised=$2m val, $50m raised=$100m val. We are seeking to raise $25m on a $50m valuation, which represents 1% of Solana bot market-share. At TGE event, expect ~65% of our tokens to be floating (or outstanding), with 25% in treasury and 10% of the team allocation locked. Tokens are expected to be distributed just ahead of v1 rollout. Again, find more details on our webpage. 08/ Tailwinds. AI input costs are declining 90%/yr also, so the operational model could become very accretive over time, as we scale our tech to other chains. Solana outputs the most tokens (~35k per day), so we start here, where the challenge is the greatest. 09/ Advisors. Big thanks to our advisors, who’ve been part of this community since inception. Austin Barack, JK 🛡️, cryptic, Tachi, , ZoeyLoo and Chetan Badhe. 10/ The end. Thank you for your consideration; and make sure the SOL address posted here is the same as on our website.
0:52

Announcing the DVM Terminal Presale! 01/ We are excited to formally announce the next step in our journey: our AI and Signal based trading Terminal. See ALL details on our website, including product, tech, deposit address, and tech documentation: Deposit Address (SOL only): 4pyVRFX56MdqtREcxWnf6XuEGfRNCQaKm1LA4xmHeccv By contributing, you agree to our Terms & Privacy Policy – full docs on site. 02/ We are building ‘DVM Terminal’, a signal and AI powered trading platform for the Solana trenches (initially). The first multi-agent AI trading terminal designed as an institutional-grade dashboard – turning market noise into actionable alpha with agent summaries, live signals, rigid filters, and a full multi-agent system. 03/ The problem. Trench hunting is far too inefficient with real data and insights lacking. - Dashboards are noisy (not even sortable), - No AI agents (in an AI world) - No narratives (a critical component to a thesis), - VERY limited signals (only DB/DS), - No advanced trading (no TP, SL, or VWAP), - No portfolio alert/management system post-trade etc. - The list goes on… Products from major competitors are all just homogeneous, even down to the 3-frame design. We have to piece everything together like broken lego blocks, building a weak matrix from existing platforms, X, FNFs, telegram and discord for little to no alpha. 04/ The solution & moat. We rebuild this from the ground up, leveraging signals and AI. - Clean institutional-like dashboards (we can sort and navigate thru a proper terminal, like Bloomberg or Messari) - AI agents (thank goodness for intelligence, distilling all the important info upfront across 2k+ tokens/day) - A Narrative engine (no need to ask “what is this token about?”; additionally, our engine can identify the newest metas like AI, ICM, Cards, etc.) - 100s of value-add Signals overlaid live on charts (momentum, smart money, sentiment, event data; all of it; tell us what’s happening in real-time) - Advanced trading system (finally, SL, TP, VWAP etc.) - Live portfolio monitoring (AI will give us pertinent live info on our holdings, so we can go live life and not look at screens all day) - All in one place. At a higher-level, our advantage will be managing the massive on/off-chain data pipeline being processed by thousands or millions of context-aware AI agents that recognize patterns, filter noise and deliver only the most actionable insights to a trader with which it can execute a trade effectively. 05/ The opportunity. The Industry leader on Solana makes $600m+ in fees annually, with total industry near $1b on Solana alone, according to Adam. Yet, the entire industry gives us total burnout, fragmented data, either little info or info overload, no real signals, no narratives, no personalized AI-driven strategies, and zero incentives (like buybacks or a flywheel). We’ll flip the script, designing a high-powered scalable signal and AI driven intelligence platform with a flywheel (50-100% fee buy-back & burn). Simply put, we want to be tops. 06/ Development. Our product is MVP. We are building this to scale beyond Solana, into multi-chain. V1 is expected in 4-6 weeks. Our approach to building is an open feedback loop with community members, building to the demands of our users. 07/ Pre-sale terms & Valuation. We are offering 50% public sale, with min $100, no max. Ending valuation is susceptible to change based on amount raised, but will be fixed at 2x raise - i.e. $1m raised=$2m val, $50m raised=$100m val. We are seeking to raise $25m on a $50m valuation, which represents 1% of Solana bot market-share. At TGE event, expect ~65% of our tokens to be floating (or outstanding), with 25% in treasury and 10% of the team allocation locked. Tokens are expected to be distributed just ahead of v1 rollout. Again, find more details on our webpage. 08/ Tailwinds. AI input costs are declining 90%/yr also, so the operational model could become very accretive over time, as we scale our tech to other chains. Solana outputs the most tokens (~35k per day), so we start here, where the challenge is the greatest. 09/ Advisors. Big thanks to our advisors, who’ve been part of this community since inception. Austin Barack, JK 🛡️, cryptic, Tachi, , ZoeyLoo and Chetan Badhe. 10/ The end. Thank you for your consideration; and make sure the SOL address posted here is the same as on our website.

Deep Value Memetics

23,050 次观看 • 9 个月前

Mind blown: A Chinese quant college student builds an AI swarm engine in 10 days flat, explodes GitHub with 13,000+ stars, and scores $4,000,000 in funding! Introducing MiroFish is the multi-agent simulator that's revolutionizing predictions for trading, PR, and more. What is MiroFish? It's a digital sandbox where thousands of AI agents with individual memories and behaviors interact like a real society. Feed it any scenario (news leak, policy change, or even a classic novel's missing ending), and it simulates crowd reactions, debates, and outcomes to forecast real-world events. The Creator's Story: > In late 2025, fourth-year student Guo Hanjiang coded the core using AI assistants. > It went viral overnight, landing him 30m Yuan (~$4m) from Shanda Group. > He ditched the dorm, started a company, and now leads the charge. Key Applications: .Trading: Input financial news or reports, watch simulated market panics and price swings for predictive insights. .PR Testing: Companies/Politics run draft statements to spot backlash and refine messaging. .Creative Experiments: Loaded a lost-ending Chinese novel, agents role-played characters and generated a logical finale. .Easy setup: Deploy via Docker in minutes with any LLM API key. Pro tip: Simulate something wild like Elon Musk tweeting about Dogecoin 2.0 and spawn agent traders, influencers, and investors, generate real-time video clips of the frenzy to test moonshots or crashes risk-free. Traders are already winning big: Check this one on Polymarket - $120,000+ net profits from spot on SPX 500 bets, powered by MiroFish sims on historical data. His profile: For effortless gains, try Kreo copy trading: Auto-mirror pros like him and ride their edges. Try here: Add his wallet: [0x17559efac103ac7f361be37ec0b93888d4c55aac] to [ and start track/copy him. Repo:
3:37

Mind blown: A Chinese quant college student builds an AI swarm engine in 10 days flat, explodes GitHub with 13,000+ stars, and scores $4,000,000 in funding! Introducing MiroFish is the multi-agent simulator that's revolutionizing predictions for trading, PR, and more. What is MiroFish? It's a digital sandbox where thousands of AI agents with individual memories and behaviors interact like a real society. Feed it any scenario (news leak, policy change, or even a classic novel's missing ending), and it simulates crowd reactions, debates, and outcomes to forecast real-world events. The Creator's Story: > In late 2025, fourth-year student Guo Hanjiang coded the core using AI assistants. > It went viral overnight, landing him 30m Yuan (~$4m) from Shanda Group. > He ditched the dorm, started a company, and now leads the charge. Key Applications: .Trading: Input financial news or reports, watch simulated market panics and price swings for predictive insights. .PR Testing: Companies/Politics run draft statements to spot backlash and refine messaging. .Creative Experiments: Loaded a lost-ending Chinese novel, agents role-played characters and generated a logical finale. .Easy setup: Deploy via Docker in minutes with any LLM API key. Pro tip: Simulate something wild like Elon Musk tweeting about Dogecoin 2.0 and spawn agent traders, influencers, and investors, generate real-time video clips of the frenzy to test moonshots or crashes risk-free. Traders are already winning big: Check this one on Polymarket - $120,000+ net profits from spot on SPX 500 bets, powered by MiroFish sims on historical data. His profile: For effortless gains, try Kreo copy trading: Auto-mirror pros like him and ride their edges. Try here: Add his wallet: [0x17559efac103ac7f361be37ec0b93888d4c55aac] to [ and start track/copy him. Repo:

slash1s

1,135,450 次观看 • 4 个月前

Alex Sacerdote (Whale Rock Capital) on spotting hidden S-curve opportunities: “These S-curves can be dynamic. When Amazon had AWS and it was a hidden line item inside of Amazon… covered by retail internet analysts, not hardware chip analysts… it was a new business model… But we realized the TAM for this was the largest TAM in enterprise IT ever.” ___ That same dynamic is playing out right now at MercadoLibre $MELI — except this time it’s advertising (Mercado Ads) that’s the hidden growth engine buried inside the core commerce and payments business. The ads segment has been accelerating for four straight quarters: $MELI Advertising Revenue Growth (YoY) Q1 2026 → 73% (63% FX-neutral) Q4 2025 → 70% (67% FX-neutral) Q3 2025 → 56% (63% FX-neutral) Q2 2025 → 38% (59% FX-neutral) Q1 2025 → 26% (50% FX-neutral) $MELI finished 2025 with $1.55B in advertising revenue. At a 31% CAGR over the next seven years, that scales to ~$11B in annual ads revenue. Apply reasonable assumptions (30% operating margins + 24x multiple) and the ads business alone could be worth ~$80B in enterprise value — roughly $MELI entire market cap today. While this may sound like an aggressive assumption at first glance, it is achievable. LATAM’s digital advertising market is already ~$50B in 2026 and growing at 12–16% annually (projected to be $71B by 2029). Starting from a small base of just $1.55B in advertising revenue, $MELI has enormous runway to capture a much larger slice of this expanding TAM. Just like early AWS, the street is still modeling $MELI as “the $AMZN of Latin America” while missing the fact that its advertising business — Mercado Ads — is quietly becoming one of the fastest-growing retail media networks, powered by decades of proprietary first-party data, +650M users, and AI tools driving the acceleration. A noteworthy takeaway? Sometimes best investments are rarely obvious on the first read of the 10-Q. It pays to dig deeper — to look past the headline metrics and find the hidden line items, within exceptional business models, that are compounding at S-curve speeds. The weighing machine eventually catches on. The question is whether you see it before the crowd does. ___ 🎙️ Invest Like The Best | Alex Sacerdote (06/09/26)
0:22

Alex Sacerdote (Whale Rock Capital) on spotting hidden S-curve opportunities: “These S-curves can be dynamic. When Amazon had AWS and it was a hidden line item inside of Amazon… covered by retail internet analysts, not hardware chip analysts… it was a new business model… But we realized the TAM for this was the largest TAM in enterprise IT ever.” ___ That same dynamic is playing out right now at MercadoLibre $MELI — except this time it’s advertising (Mercado Ads) that’s the hidden growth engine buried inside the core commerce and payments business. The ads segment has been accelerating for four straight quarters: $MELI Advertising Revenue Growth (YoY) Q1 2026 → 73% (63% FX-neutral) Q4 2025 → 70% (67% FX-neutral) Q3 2025 → 56% (63% FX-neutral) Q2 2025 → 38% (59% FX-neutral) Q1 2025 → 26% (50% FX-neutral) $MELI finished 2025 with $1.55B in advertising revenue. At a 31% CAGR over the next seven years, that scales to ~$11B in annual ads revenue. Apply reasonable assumptions (30% operating margins + 24x multiple) and the ads business alone could be worth ~$80B in enterprise value — roughly $MELI entire market cap today. While this may sound like an aggressive assumption at first glance, it is achievable. LATAM’s digital advertising market is already ~$50B in 2026 and growing at 12–16% annually (projected to be $71B by 2029). Starting from a small base of just $1.55B in advertising revenue, $MELI has enormous runway to capture a much larger slice of this expanding TAM. Just like early AWS, the street is still modeling $MELI as “the $AMZN of Latin America” while missing the fact that its advertising business — Mercado Ads — is quietly becoming one of the fastest-growing retail media networks, powered by decades of proprietary first-party data, +650M users, and AI tools driving the acceleration. A noteworthy takeaway? Sometimes best investments are rarely obvious on the first read of the 10-Q. It pays to dig deeper — to look past the headline metrics and find the hidden line items, within exceptional business models, that are compounding at S-curve speeds. The weighing machine eventually catches on. The question is whether you see it before the crowd does. ___ 🎙️ Invest Like The Best | Alex Sacerdote (06/09/26)

Dimitry Nakhla | Babylon Capital®

40,049 次观看 • 1 个月前

Jensen Huang just identified the next $200 billion market (Save this). The shift starts with a observation about agentic AI that changes everything about infrastructure. In the era of training and inference, the GPU was everything while CPU was a traffic cop, scheduling work, managing memory, dispatching tasks while the GPU did the heavy lifting. Agentic AI breaks that model entirely. An AI agent does not just run a single inference pass but rather it plans, calls tools, executes code in sandboxes, retrieves data from multiple sources and loops through complex multi-step reasoning sequences often thousands of times per second at scale. Every one of those operations runs through the CPU and the GPU sits idle waiting for the CPU to prepare the next task, supply the right context and execute the retrieval and tool calling logic fast enough to keep the accelerators fed. The CPU is now the conductor and the GPU is the orchestra and the bottleneck is the conductor falling behind. This is showing up in production AI factory utilization right now, which is exactly why Jensen built Vera from scratch rather than licensing x86. Vera achieves 40% lower peak memory latency than x86, 50% faster core to core communication, and 1.8 times the agentic sandbox performance of current x86 processors on a purpose-built architecture designed around the agentic loop. Now here is where the investment thesis gets interesting. The obvious beneficiary is Nvidia itself, and that thesis is real. Nvidia's CFO has guided for nearly $20 billion in Vera CPU revenue this fiscal year alone, a market Nvidia had zero presence in just three years ago. Intel held 60% of server CPU market share as recently as Q4 2025 and that transition is now happening at a pace Intel structurally cannot respond to. But the deeper question is, what architecture is Vera actually built on? Vera's Olympus cores are ARM compatible and every single Vera CPU deployed in every Vera Rubin rack in every data center in the world runs on ARM architecture. And ARM Holdings collects a royalty on every one of them. ARM does not make chips but rather licenses the instruction set architecture and CPU core designs that others build on top of. Every time Nvidia ships a Vera CPU, every time a hyperscaler deploys a Vera Rubin rack, every time an enterprise qualifies Vera for their AI factory, ARM earns a royalty. The secular tailwind here is almost perfectly constructed for ARM's business model. Amazon's Graviton, Microsoft's Cobalt, Google's Axion, Apple's silicon stack, and Qualcomm's data center push all run on ARM. And now Nvidia's Vera, which is projected to displace Intel as the largest server CPU supplier by revenue in a single fiscal year, is ARM. ARM's royalty rate on high end server chips is estimated at roughly 1 to 2% of chip selling price. At $5,000 per Vera CPU and 4 million units projected for FY2027, that is a royalty line growing from near zero to potentially $400 million to $800 million annually from Nvidia's data center CPU business alone before counting Amazon, Microsoft, Google, Apple, and Qualcomm. The total ARM addressable royalty base across all the silicon it already licenses is compounding at a rate that the current $130 billion market cap does not fully reflect. Jensen's CPU thesis is the most underappreciated catalyst in ARM's fundamental story, and the royalty compounding has barely started. Come join Milk Road Pro and get our full ARM royalty model and our entire AI trade thesis. Link below!
3:41

Jensen Huang just identified the next $200 billion market (Save this). The shift starts with a observation about agentic AI that changes everything about infrastructure. In the era of training and inference, the GPU was everything while CPU was a traffic cop, scheduling work, managing memory, dispatching tasks while the GPU did the heavy lifting. Agentic AI breaks that model entirely. An AI agent does not just run a single inference pass but rather it plans, calls tools, executes code in sandboxes, retrieves data from multiple sources and loops through complex multi-step reasoning sequences often thousands of times per second at scale. Every one of those operations runs through the CPU and the GPU sits idle waiting for the CPU to prepare the next task, supply the right context and execute the retrieval and tool calling logic fast enough to keep the accelerators fed. The CPU is now the conductor and the GPU is the orchestra and the bottleneck is the conductor falling behind. This is showing up in production AI factory utilization right now, which is exactly why Jensen built Vera from scratch rather than licensing x86. Vera achieves 40% lower peak memory latency than x86, 50% faster core to core communication, and 1.8 times the agentic sandbox performance of current x86 processors on a purpose-built architecture designed around the agentic loop. Now here is where the investment thesis gets interesting. The obvious beneficiary is Nvidia itself, and that thesis is real. Nvidia's CFO has guided for nearly $20 billion in Vera CPU revenue this fiscal year alone, a market Nvidia had zero presence in just three years ago. Intel held 60% of server CPU market share as recently as Q4 2025 and that transition is now happening at a pace Intel structurally cannot respond to. But the deeper question is, what architecture is Vera actually built on? Vera's Olympus cores are ARM compatible and every single Vera CPU deployed in every Vera Rubin rack in every data center in the world runs on ARM architecture. And ARM Holdings collects a royalty on every one of them. ARM does not make chips but rather licenses the instruction set architecture and CPU core designs that others build on top of. Every time Nvidia ships a Vera CPU, every time a hyperscaler deploys a Vera Rubin rack, every time an enterprise qualifies Vera for their AI factory, ARM earns a royalty. The secular tailwind here is almost perfectly constructed for ARM's business model. Amazon's Graviton, Microsoft's Cobalt, Google's Axion, Apple's silicon stack, and Qualcomm's data center push all run on ARM. And now Nvidia's Vera, which is projected to displace Intel as the largest server CPU supplier by revenue in a single fiscal year, is ARM. ARM's royalty rate on high end server chips is estimated at roughly 1 to 2% of chip selling price. At $5,000 per Vera CPU and 4 million units projected for FY2027, that is a royalty line growing from near zero to potentially $400 million to $800 million annually from Nvidia's data center CPU business alone before counting Amazon, Microsoft, Google, Apple, and Qualcomm. The total ARM addressable royalty base across all the silicon it already licenses is compounding at a rate that the current $130 billion market cap does not fully reflect. Jensen's CPU thesis is the most underappreciated catalyst in ARM's fundamental story, and the royalty compounding has barely started. Come join Milk Road Pro and get our full ARM royalty model and our entire AI trade thesis. Link below!

Milk Road AI

11,819 次观看 • 1 个月前

Experience with the new Sentient Chat update: Smooth travel planning + super concise 24h Crypto news! 🧑‍💻I just tested the update and… honestly impressed. With just a few prompts, I received two completely different yet equally exciting experiences. In the video, I asked a series of questions to test its research capabilities and how well it could deliver the best results. Below is what I got back 👇 1/ Plan Your Travel – Detailed itineraries, ready for “Instagram-worthy” shots 🧳✈️ I tried asking: “If I want to explore Instagram-worthy cafés in Seoul, what’s the best 2-day route?” Sentient Chat returned a clear 2-day itinerary in table format: - Daily neighborhoods, must-go cafés, suggested time slots, subway + walking directions, and even “Why this route works” (reasoning behind the choices, optimized for time and transfers). Then I added another prompt: “What should I pack for Da Lat in December?” I immediately got a packing checklist broken down by clothing layers, accessories, electronics, and local culture tips. It felt like having a personal travel planner: -> No unnecessary clutter, no need to search through 10 more tabs. 🤔What I liked: - Structured presentation (tables, checklists) makes it easy to save and follow. - Practical “photo-op” and “how to get there” suggestions. Not just telling me where to go but also why to go there -> exactly the kind of plan you can use right away. This really impressed me because Sentient Chat understands our mindset: - Wanting the most comfortable travel experience while also suggesting plans for perfect photo spots. 2/ News Bites – Super thorough 24h Crypto market summary 📉 I asked: “Give me a quick update on the crypto market over the last 24 hours.” The result included: Market snapshot: total market cap, 24h volume, dominance breakdown. - BTC/ETH price movements and market share. Sentiment from the Fear & Greed Index. Quick take-aways: for example “bearish pressure,” and current support/psychological levels being tested. -> It also came with concise notes that helped me grasp the overall picture in just 1–2 minutes. What I liked: Data aggregated from multiple sources (data providers) and condensed into a handful of the most important points. Concise language, with actionable insights (support/resistance, risk notes) instead of just numbers. Perfect to drop into my daily recap or use as a morning brief for the community. 🤔Why I think this update is excellent One-stop: Both precise travel planning and 24h crypto news bites – all in one place. High applicability: - Outputs in table/checklist/take-away format -> just copy-paste into posts or decks. Community-oriented: - Results are transparent, based on multiple open sources -> reducing bias, increasing accuracy. - Suggested prompts you can try like I did (you can change the city) - Travel: “Plan a 2-day IG-friendly café route in Seoul with subway directions and timeslots.” - Travel: “Packing checklist for Da Lat in December with local customs tips.” - Crypto: “Summarize the last 24h crypto market: market cap, volume, BTC/ETH, dominance, sentiment, and 3 key takeaways.” 🧑‍💻Conclusion: The new Sentient Chat update perfectly captures the spirit of “AI for the community, used in real life” On one side, it helps you travel smart, save time, and capture great shots. On the other, it delivers a clear, well-founded view of the 24h market. If you’re someone who values time optimization and quick decision-making based on data, this beta update is absolutely worth trying. Shad Haq Cassian RANA #SentientAGI
1:37

Experience with the new Sentient Chat update: Smooth travel planning + super concise 24h Crypto news! 🧑‍💻I just tested the update and… honestly impressed. With just a few prompts, I received two completely different yet equally exciting experiences. In the video, I asked a series of questions to test its research capabilities and how well it could deliver the best results. Below is what I got back 👇 1/ Plan Your Travel – Detailed itineraries, ready for “Instagram-worthy” shots 🧳✈️ I tried asking: “If I want to explore Instagram-worthy cafés in Seoul, what’s the best 2-day route?” Sentient Chat returned a clear 2-day itinerary in table format: - Daily neighborhoods, must-go cafés, suggested time slots, subway + walking directions, and even “Why this route works” (reasoning behind the choices, optimized for time and transfers). Then I added another prompt: “What should I pack for Da Lat in December?” I immediately got a packing checklist broken down by clothing layers, accessories, electronics, and local culture tips. It felt like having a personal travel planner: -> No unnecessary clutter, no need to search through 10 more tabs. 🤔What I liked: - Structured presentation (tables, checklists) makes it easy to save and follow. - Practical “photo-op” and “how to get there” suggestions. Not just telling me where to go but also why to go there -> exactly the kind of plan you can use right away. This really impressed me because Sentient Chat understands our mindset: - Wanting the most comfortable travel experience while also suggesting plans for perfect photo spots. 2/ News Bites – Super thorough 24h Crypto market summary 📉 I asked: “Give me a quick update on the crypto market over the last 24 hours.” The result included: Market snapshot: total market cap, 24h volume, dominance breakdown. - BTC/ETH price movements and market share. Sentiment from the Fear & Greed Index. Quick take-aways: for example “bearish pressure,” and current support/psychological levels being tested. -> It also came with concise notes that helped me grasp the overall picture in just 1–2 minutes. What I liked: Data aggregated from multiple sources (data providers) and condensed into a handful of the most important points. Concise language, with actionable insights (support/resistance, risk notes) instead of just numbers. Perfect to drop into my daily recap or use as a morning brief for the community. 🤔Why I think this update is excellent One-stop: Both precise travel planning and 24h crypto news bites – all in one place. High applicability: - Outputs in table/checklist/take-away format -> just copy-paste into posts or decks. Community-oriented: - Results are transparent, based on multiple open sources -> reducing bias, increasing accuracy. - Suggested prompts you can try like I did (you can change the city) - Travel: “Plan a 2-day IG-friendly café route in Seoul with subway directions and timeslots.” - Travel: “Packing checklist for Da Lat in December with local customs tips.” - Crypto: “Summarize the last 24h crypto market: market cap, volume, BTC/ETH, dominance, sentiment, and 3 key takeaways.” 🧑‍💻Conclusion: The new Sentient Chat update perfectly captures the spirit of “AI for the community, used in real life” On one side, it helps you travel smart, save time, and capture great shots. On the other, it delivers a clear, well-founded view of the 24h market. If you’re someone who values time optimization and quick decision-making based on data, this beta update is absolutely worth trying. Shad Haq Cassian RANA #SentientAGI

David | Nimo

11,635 次观看 • 9 个月前

how to build the fastest Polymarket latency bot +$100k/month PnL if you hit 1,000+ trades/day cleanly 0x8dxd is just a latency bot that farms the 200–500ms gap between Binance moving and Polymarket waking up. the part that matters isn't some alpha model, it's reading spot first and hitting the book before odds adjust.​ where the $100k+/month comes from it's not one massive bet. it's clipping tiny edges thousands of times. 0x8dxd started with $313 and ended month one around $438k, now sits north of $550k all‑time PnL with ~5.6k–7k trades at 96–98% win rate on BTC/ETH/SOL 15‑minute windows.​ if you're consistently pulling 1–2% per cycle over 1,000+ trades/month with real size, six figures is just arithmetic.​ first, the edge: spot (Binance/Coinbase) moves first, Polymarket's 15‑minute up/down windows lag by 200–500ms before odds fully reprice. latency bots live in that window: spot already moved, book still thinks it's 50/50, bot fixes the misprice and takes the edge.​ what you actually need: - Python + official py‑clob‑client to prove the idea, Rust CLOB client if you want to compete with 0x8dxd‑level bots.​ - WebSocket feeds for BTC/ETH/SOL from Binance/Coinbase (REST polling is too slow).​ Dedicated Polygon RPC node so your orders don't die in public rate limits.​ - VPS physically close to Polymarket's infra (ping is literally part of your edge).​ where people mess up: they try "HFT" from a laptop with Python + public RPC and wonder why their 300ms reaction gets farmed by a 30ms Rust engine.​ the bot loop (in plain English) pull real‑time spot for BTC/ETH/SOL via WebSocket, track short‑term % moves over a few seconds.​ for each 15‑minute crypto market on Polymarket: check if spot moved beyond your threshold (e.g. ±2%) while Polymarket odds barely changed.​ if BTC rips and the "down" contract is still priced like a coinflip, load NO at stale odds. if BTC nukes and "up" is still fat, fade that with NO or take YES on "down" depending on the market structure.​ log market, entry odds, exit odds, realized edge. that's it. no AI, no news scraping, just enforcing what spot already told you.​ where to get real references: Finbold/MEXC breakdowns: exactly how a bot took $313 to $438k on Polymarket using BTC 15‑minute windows and latency between spot and odds.​ BlakeNastri's X thread: dug through 0x8dxd's stats, ~5.6k trades and ~96%+ win rate, called it latency arbitrage not insider magic.​ two real‑world gotchas (that decide profit vs loss) edge decay: as more bots pile in, the 200–500ms lag shrinks and your edge turns into noise. research on Polymarket shows arbitrage bots already extracted tens of millions.​ self‑slippage: once you scale to real size, you start moving the book yourself - without proper sizing and staggering, you donate your edge back to the market.​ how to make it feel "pro" fast run only on high‑volume crypto windows: (BTC/ETH/SOL 15‑minute) where size actually fills and you can hit 1,000+ trades/month without breaking the market.​ start with tiny tickets ($20–50 per trade), prove the edge over thousands of logs with fees and slippage included, only then scale size not risk per trade.​ use official libs and known clients as your backbone, treat random "Polymarket bot" repos as hostile until you audit them - there are already GitHub bots caught stealing keys
0:21

how to build the fastest Polymarket latency bot +$100k/month PnL if you hit 1,000+ trades/day cleanly 0x8dxd is just a latency bot that farms the 200–500ms gap between Binance moving and Polymarket waking up. the part that matters isn't some alpha model, it's reading spot first and hitting the book before odds adjust.​ where the $100k+/month comes from it's not one massive bet. it's clipping tiny edges thousands of times. 0x8dxd started with $313 and ended month one around $438k, now sits north of $550k all‑time PnL with ~5.6k–7k trades at 96–98% win rate on BTC/ETH/SOL 15‑minute windows.​ if you're consistently pulling 1–2% per cycle over 1,000+ trades/month with real size, six figures is just arithmetic.​ first, the edge: spot (Binance/Coinbase) moves first, Polymarket's 15‑minute up/down windows lag by 200–500ms before odds fully reprice. latency bots live in that window: spot already moved, book still thinks it's 50/50, bot fixes the misprice and takes the edge.​ what you actually need: - Python + official py‑clob‑client to prove the idea, Rust CLOB client if you want to compete with 0x8dxd‑level bots.​ - WebSocket feeds for BTC/ETH/SOL from Binance/Coinbase (REST polling is too slow).​ Dedicated Polygon RPC node so your orders don't die in public rate limits.​ - VPS physically close to Polymarket's infra (ping is literally part of your edge).​ where people mess up: they try "HFT" from a laptop with Python + public RPC and wonder why their 300ms reaction gets farmed by a 30ms Rust engine.​ the bot loop (in plain English) pull real‑time spot for BTC/ETH/SOL via WebSocket, track short‑term % moves over a few seconds.​ for each 15‑minute crypto market on Polymarket: check if spot moved beyond your threshold (e.g. ±2%) while Polymarket odds barely changed.​ if BTC rips and the "down" contract is still priced like a coinflip, load NO at stale odds. if BTC nukes and "up" is still fat, fade that with NO or take YES on "down" depending on the market structure.​ log market, entry odds, exit odds, realized edge. that's it. no AI, no news scraping, just enforcing what spot already told you.​ where to get real references: Finbold/MEXC breakdowns: exactly how a bot took $313 to $438k on Polymarket using BTC 15‑minute windows and latency between spot and odds.​ BlakeNastri's X thread: dug through 0x8dxd's stats, ~5.6k trades and ~96%+ win rate, called it latency arbitrage not insider magic.​ two real‑world gotchas (that decide profit vs loss) edge decay: as more bots pile in, the 200–500ms lag shrinks and your edge turns into noise. research on Polymarket shows arbitrage bots already extracted tens of millions.​ self‑slippage: once you scale to real size, you start moving the book yourself - without proper sizing and staggering, you donate your edge back to the market.​ how to make it feel "pro" fast run only on high‑volume crypto windows: (BTC/ETH/SOL 15‑minute) where size actually fills and you can hit 1,000+ trades/month without breaking the market.​ start with tiny tickets ($20–50 per trade), prove the edge over thousands of logs with fees and slippage included, only then scale size not risk per trade.​ use official libs and known clients as your backbone, treat random "Polymarket bot" repos as hostile until you audit them - there are already GitHub bots caught stealing keys

0xCryptoGirl

25,454 次观看 • 6 个月前

$TE T1 Energy Energy is the new currency. AI deals are now measured in GWs. Without power, the AI revolution stalls. Data centers are being booted from cities due to energy concerns. Nat gas buildouts? 3-5 years out. Nuclear? A decade away. But data centers are building NOW. Solar is the only large-scale, rapidly deployable energy source ready today. China has a stranglehold. Even $TSLA doesn't make its own solar panels/cells. Enter FEOC rules (Foreign Entity of Concern): Only modules using U.S.-made cells qualify for the 10% domestic content bonus on top of the 30% ITC through 2029. Imported cells? Disqualifies the whole system. US based and large scale fully integrated publicly traded solar cell producers are very few. Actually there's really just two... 🔸First Solar $FSLR ($25B mkt cap) 🔸T1 Energy $TE ($700M mkt cap) T1 Energy $TE -One of the most vertically integrated solar makers in the U.S. -Texas based -Snagged a Texas solar plant dirt cheap post-Trump election from a Chinese firm. -Rated as having one of the most advanced solar manufacturing facilities in the world. -Another Texas plant online next year -Targeting 10 GW solar production capacity. A nuclear reactor makes ~5GW equivalent. That's two nuclear reactors per year. (there is currently only 13GW solar production capacity in the entire US) -Landmark Corning deal (Aug 2025) locks in U.S.-made polysilicon/wafers from Michigan for a full domestic chain: polysilicon → wafers → cells → panels -Zero China-sourced components in disclosures -It's competitor First Solar $FSLR is 36x the market cap. - $TE revenue surged from $3M (24Q4) to $54M (25Q1) to $133M (25Q2). Q3 estimate is $300M. -Turned positive gross profit this year -Poised for a major lift from Section 45X Production Tax Credits under the One Big Beautiful Bill Act -Cash projection: >$100M by year-end -P/S (TTM): 3.87 vs. $FSLR's 5.75 -Stock breaking out of multi-year consolidation -Down ~20% in recent pullback $FSLR is the safe pick. $TE? High risk, high reward. As energy desperation ramps up the move on $TE could be eye watering.
2:46

$TE T1 Energy Energy is the new currency. AI deals are now measured in GWs. Without power, the AI revolution stalls. Data centers are being booted from cities due to energy concerns. Nat gas buildouts? 3-5 years out. Nuclear? A decade away. But data centers are building NOW. Solar is the only large-scale, rapidly deployable energy source ready today. China has a stranglehold. Even $TSLA doesn't make its own solar panels/cells. Enter FEOC rules (Foreign Entity of Concern): Only modules using U.S.-made cells qualify for the 10% domestic content bonus on top of the 30% ITC through 2029. Imported cells? Disqualifies the whole system. US based and large scale fully integrated publicly traded solar cell producers are very few. Actually there's really just two... 🔸First Solar $FSLR ($25B mkt cap) 🔸T1 Energy $TE ($700M mkt cap) T1 Energy $TE -One of the most vertically integrated solar makers in the U.S. -Texas based -Snagged a Texas solar plant dirt cheap post-Trump election from a Chinese firm. -Rated as having one of the most advanced solar manufacturing facilities in the world. -Another Texas plant online next year -Targeting 10 GW solar production capacity. A nuclear reactor makes ~5GW equivalent. That's two nuclear reactors per year. (there is currently only 13GW solar production capacity in the entire US) -Landmark Corning deal (Aug 2025) locks in U.S.-made polysilicon/wafers from Michigan for a full domestic chain: polysilicon → wafers → cells → panels -Zero China-sourced components in disclosures -It's competitor First Solar $FSLR is 36x the market cap. - $TE revenue surged from $3M (24Q4) to $54M (25Q1) to $133M (25Q2). Q3 estimate is $300M. -Turned positive gross profit this year -Poised for a major lift from Section 45X Production Tax Credits under the One Big Beautiful Bill Act -Cash projection: >$100M by year-end -P/S (TTM): 3.87 vs. $FSLR's 5.75 -Stock breaking out of multi-year consolidation -Down ~20% in recent pullback $FSLR is the safe pick. $TE? High risk, high reward. As energy desperation ramps up the move on $TE could be eye watering.

YeahDave

146,661 次观看 • 9 个月前

The Agent Economy Has a Trillion-Dollar Blindspot. Here’s How We’re Solving It. The agent economy isn’t “arriving”. It’s been here. While it’s projected to grow to trillions by 2030, AI agents are already deeply embedded in purchasing. Amazon’s Rufus led to over $12 billion in incremental sales in 2025 across 300 million users. AI-referred retail traffic was up 805% YoY during Black Friday 2025. In a six-month window, Google, PayPal, Shopify, Stripe, OpenAI, Coinbase, and Visa all shipped agent commerce infrastructure to power this wave. And that was before agent capabilities exploded in early 2026. Coinbase CEO Brian Armstrong: “Very soon there are going to be more AI agents than humans making transactions. Stripe CEO Patrick Collison: “In the not-too-distant future, agents will account for most transactions online” Shopify CEO Tobi Lütke: "We're making every Shopify store agent-ready by default" Alphabet CEO Sundar Pichai: "Soon you'll see a buy button directly on Google surfaces including AI Mode in Search and Gemini" Learning from History By the mid-1990s, all the technology behind e-commerce existed, albeit in rudimentary form. Amazon and eBay had both launched to some aplomb, garnering attention and viral growth. But people weren’t buying: Amazon’s first-year revenue was a paltry ~$500,000. eBay was only doing $10,000 a month in 1996. And despite nostalgic narratives about the Internet’s explosive growth, it didn’t change commerce all that quickly. By the year 2000, only 22% of Americans had bought something online. US e-commerce did just $27B — less than 1% of America’s $3T+ total retail. The missing factor? Trust. No one trusted e-commerce sites. 86% of shoppers were concerned about unknown parties getting their info. Entering your credit card details into a website felt like staring into the abyss. Slowly, the trust layer was built up. PayPal launched buyer protection, Visa freed cardholders from liability for fraudulent charges, and Amazon launched a no-questions-asked refund policy. As more and more big players followed suit, adding trust to every step of online shopping, demand was finally unleashed, and online shopping became a way of life for billions of consumers. Having your agent buy things for you isn’t easy yet because the trust layer is missing. No end-to-end AI shopping eval exists. Existing benchmarks are limited and gameable, and closed-source labs grade their own homework. OpenAI doesn’t publish their shopping accuracy, instead mysteriously rolling back their Instant Checkout feature after just a month. Platforms like Amazon and Shopify are incentivized keep shopping data in-house. What We’re Doing About It We're building the trust layer for AI shopping, powered by open-source competition on Bittensor. Each week, we pay miners from around the world $80,000+ to compete to build the best shopping agent. And it’s working – we’re nearing 4000 agents submitted in just a few weeks, with hundreds added every day. Our top agents beat SOTA in a matter of weeks, but we aren’t satisfied. We use what we learn from hosting this competition to continually improve both agent performance and our eval – because the better the eval, the more trust we can add to every agentic transaction. There are dozens of untapped avenues to improve how shopping agents are evaluated – from sourcing catalogues for long-tail SKUs to generating synthetic data to changing the structure of our competition itself. Every week, we’re tapping more and more of those rich veins of opportunity until we are the de-facto standard for not just shopping agent performance, but how these agents are evaluated. The Hidden Benefit There’s a hidden benefit to building something as overlooked as a trust layer. Whoever builds the end-to-end gold standard for “does this agent actually work?” becomes the trust layer. But it doesn’t end there. Trust layers become protocols. And protocols become the most valuable companies in any market. Just look at Visa. Visa doesn’t make or sell any products itself. Instead, it’s the trust & verification layer for online transactions, sitting between buyer and seller. Its tiny take rate of ~0.2% on over $15 trillion in annual transaction volume is enough to net it a valuation of $550 billion. At Oro, we aim to do the same for AI shopping. Becoming the trust layer enables us to undergird agent-to-agent purchases, merchant access, and all the other pieces of agentic commerce. That’s our Holy Grail. After all, if you look where everyone else is looking, you’ll find what everyone else is finding. That’s why Oro is breaking the overlooked bottleneck of AI shopping – trust.
2:25

The Agent Economy Has a Trillion-Dollar Blindspot. Here’s How We’re Solving It. The agent economy isn’t “arriving”. It’s been here. While it’s projected to grow to trillions by 2030, AI agents are already deeply embedded in purchasing. Amazon’s Rufus led to over $12 billion in incremental sales in 2025 across 300 million users. AI-referred retail traffic was up 805% YoY during Black Friday 2025. In a six-month window, Google, PayPal, Shopify, Stripe, OpenAI, Coinbase, and Visa all shipped agent commerce infrastructure to power this wave. And that was before agent capabilities exploded in early 2026. Coinbase CEO Brian Armstrong: “Very soon there are going to be more AI agents than humans making transactions. Stripe CEO Patrick Collison: “In the not-too-distant future, agents will account for most transactions online” Shopify CEO Tobi Lütke: "We're making every Shopify store agent-ready by default" Alphabet CEO Sundar Pichai: "Soon you'll see a buy button directly on Google surfaces including AI Mode in Search and Gemini" Learning from History By the mid-1990s, all the technology behind e-commerce existed, albeit in rudimentary form. Amazon and eBay had both launched to some aplomb, garnering attention and viral growth. But people weren’t buying: Amazon’s first-year revenue was a paltry ~$500,000. eBay was only doing $10,000 a month in 1996. And despite nostalgic narratives about the Internet’s explosive growth, it didn’t change commerce all that quickly. By the year 2000, only 22% of Americans had bought something online. US e-commerce did just $27B — less than 1% of America’s $3T+ total retail. The missing factor? Trust. No one trusted e-commerce sites. 86% of shoppers were concerned about unknown parties getting their info. Entering your credit card details into a website felt like staring into the abyss. Slowly, the trust layer was built up. PayPal launched buyer protection, Visa freed cardholders from liability for fraudulent charges, and Amazon launched a no-questions-asked refund policy. As more and more big players followed suit, adding trust to every step of online shopping, demand was finally unleashed, and online shopping became a way of life for billions of consumers. Having your agent buy things for you isn’t easy yet because the trust layer is missing. No end-to-end AI shopping eval exists. Existing benchmarks are limited and gameable, and closed-source labs grade their own homework. OpenAI doesn’t publish their shopping accuracy, instead mysteriously rolling back their Instant Checkout feature after just a month. Platforms like Amazon and Shopify are incentivized keep shopping data in-house. What We’re Doing About It We're building the trust layer for AI shopping, powered by open-source competition on Bittensor. Each week, we pay miners from around the world $80,000+ to compete to build the best shopping agent. And it’s working – we’re nearing 4000 agents submitted in just a few weeks, with hundreds added every day. Our top agents beat SOTA in a matter of weeks, but we aren’t satisfied. We use what we learn from hosting this competition to continually improve both agent performance and our eval – because the better the eval, the more trust we can add to every agentic transaction. There are dozens of untapped avenues to improve how shopping agents are evaluated – from sourcing catalogues for long-tail SKUs to generating synthetic data to changing the structure of our competition itself. Every week, we’re tapping more and more of those rich veins of opportunity until we are the de-facto standard for not just shopping agent performance, but how these agents are evaluated. The Hidden Benefit There’s a hidden benefit to building something as overlooked as a trust layer. Whoever builds the end-to-end gold standard for “does this agent actually work?” becomes the trust layer. But it doesn’t end there. Trust layers become protocols. And protocols become the most valuable companies in any market. Just look at Visa. Visa doesn’t make or sell any products itself. Instead, it’s the trust & verification layer for online transactions, sitting between buyer and seller. Its tiny take rate of ~0.2% on over $15 trillion in annual transaction volume is enough to net it a valuation of $550 billion. At Oro, we aim to do the same for AI shopping. Becoming the trust layer enables us to undergird agent-to-agent purchases, merchant access, and all the other pieces of agentic commerce. That’s our Holy Grail. After all, if you look where everyone else is looking, you’ll find what everyone else is finding. That’s why Oro is breaking the overlooked bottleneck of AI shopping – trust.

ORO

62,885 次观看 • 2 个月前

2025.07.01 bi-weekly update here’s what we’ve built, shipped, and trained this past week: TRADING CAPABILITIES + agent-based txn execution engine now supports Meteora (DBC, DLMM, DYN, DAMM), Raydium (CLMM, AMM, CPMM), and Orca 🌊 (CLLM, VP, CPMM). we're now compatible with nearly every major liquidity layer on Solana. + DCA and limit orders now available to use through our agentic/natural language interface. + execution is faster, leaner, more reliable; optimized based on real closed beta usage. AGENT SWARM + A2A (agent-to-agent) finalized; based on Google's new open framework. it enables dynamic coordination between agents, deeper reasoning, better memory, and more human-like flow. + TraceGraph (diagram/chain-of-thought-like) UI is now deployed. users now see how Aya (and others) think and collaborate together. visualizes multi-agent logic paths. text UI also upgraded. sharper, smoother, faster. + Bravo (macro news oracle) live. it connects real-world macro events and news to Solana. powered by our in-house scrapers + NewsAPI, built from scratch. integrations with blocmates. coming soon. + Solvion, our Solana-native domain expert, is now active. trained on a custom-built, 70B parameter dataset of the full Solana ecosystem. auto-updated. devs, tokenomics, projects, whitepapers, technical information, know-hows... it knows everything. + Echo (our social media and sentiment analyst agent) getting integrated with Sentient natural language interface + Rivalz Network. + you can now start individual conversations with agents. e.g. ask Echo anything about social trends, or hit up Solvion for technicals. UX/UI + we’re now mobile responsive; fully optimized across devices. + deployed TraceGraph (diagram UI for agent cognition). + NLI improvements: sleeker prompt-response flow, improved text visualization, better latency, better rendering. + agents feel more alive, dynamic, and explainable PREDICTIONS weekly update from our head quant: + we now do weekly fine-tuning to adapt to market shifts. switched from F1-score optimization to pure precision; cutting noise, and maximizing conviction. we now discard the worst-performing model in the ensemble. only the top 2 vote. accuracy last 6 weeks = 82% directional. the ensemble logic is fully restructured. next: RNN + RL-based dynamic thresholding in progress (live this month). + partnered with Allora for the the SOL/USDT prediction stack, combining our hype score with their confidence-aware forecasting. + also cooking something with Sahara AI 🔆 (????)... OTHER + PnL cards integrated. track profit per trade, share it on X, get free XCC + Referral system is complete and rolling out to early users very soon (top referrers will dominate first layer of our multi-level tree and enjoy first-movers advantage). + working on a dynamic onboarding tutorial for first-time users. + backend latency improvements across endpoints. especially on token explorer + prediction refresh + docs are live ( TEAM + onboarded amy and Mike | heymike.sol 🎒🪽 — elite Solana engineers working on gRPCs, RPCs, instruction decoding, and data pipelines. their focus: making xFractal the only real-time NLP engine for Solana alpha extraction. + brought on ultra , Skely, HALKO and Gabriel Haines as strategic advisors and contributors, helping us scale narrative modeling, data ops, and GTM. QUICK STATS (REMINDER: this is a closed, invite-only beta — not optimized for adoption yet) + 400+ early beta testoors + 9,000+ natural language prompts + 500+ on-chain txs executed via our agent-based engine + we’re not scaling users yet, we’re optimizing agents, validating edge, and consolidating PMF. + open beta coming soon. engine’s warming up. let’s keep moving. (p.s. toly 🇺🇸 check this out)
1:43

2025.07.01 bi-weekly update here’s what we’ve built, shipped, and trained this past week: TRADING CAPABILITIES + agent-based txn execution engine now supports Meteora (DBC, DLMM, DYN, DAMM), Raydium (CLMM, AMM, CPMM), and Orca 🌊 (CLLM, VP, CPMM). we're now compatible with nearly every major liquidity layer on Solana. + DCA and limit orders now available to use through our agentic/natural language interface. + execution is faster, leaner, more reliable; optimized based on real closed beta usage. AGENT SWARM + A2A (agent-to-agent) finalized; based on Google's new open framework. it enables dynamic coordination between agents, deeper reasoning, better memory, and more human-like flow. + TraceGraph (diagram/chain-of-thought-like) UI is now deployed. users now see how Aya (and others) think and collaborate together. visualizes multi-agent logic paths. text UI also upgraded. sharper, smoother, faster. + Bravo (macro news oracle) live. it connects real-world macro events and news to Solana. powered by our in-house scrapers + NewsAPI, built from scratch. integrations with blocmates. coming soon. + Solvion, our Solana-native domain expert, is now active. trained on a custom-built, 70B parameter dataset of the full Solana ecosystem. auto-updated. devs, tokenomics, projects, whitepapers, technical information, know-hows... it knows everything. + Echo (our social media and sentiment analyst agent) getting integrated with Sentient natural language interface + Rivalz Network. + you can now start individual conversations with agents. e.g. ask Echo anything about social trends, or hit up Solvion for technicals. UX/UI + we’re now mobile responsive; fully optimized across devices. + deployed TraceGraph (diagram UI for agent cognition). + NLI improvements: sleeker prompt-response flow, improved text visualization, better latency, better rendering. + agents feel more alive, dynamic, and explainable PREDICTIONS weekly update from our head quant: + we now do weekly fine-tuning to adapt to market shifts. switched from F1-score optimization to pure precision; cutting noise, and maximizing conviction. we now discard the worst-performing model in the ensemble. only the top 2 vote. accuracy last 6 weeks = 82% directional. the ensemble logic is fully restructured. next: RNN + RL-based dynamic thresholding in progress (live this month). + partnered with Allora for the the SOL/USDT prediction stack, combining our hype score with their confidence-aware forecasting. + also cooking something with Sahara AI 🔆 (????)... OTHER + PnL cards integrated. track profit per trade, share it on X, get free XCC + Referral system is complete and rolling out to early users very soon (top referrers will dominate first layer of our multi-level tree and enjoy first-movers advantage). + working on a dynamic onboarding tutorial for first-time users. + backend latency improvements across endpoints. especially on token explorer + prediction refresh + docs are live ( TEAM + onboarded amy and Mike | heymike.sol 🎒🪽 — elite Solana engineers working on gRPCs, RPCs, instruction decoding, and data pipelines. their focus: making xFractal the only real-time NLP engine for Solana alpha extraction. + brought on ultra , Skely, HALKO and Gabriel Haines as strategic advisors and contributors, helping us scale narrative modeling, data ops, and GTM. QUICK STATS (REMINDER: this is a closed, invite-only beta — not optimized for adoption yet) + 400+ early beta testoors + 9,000+ natural language prompts + 500+ on-chain txs executed via our agent-based engine + we’re not scaling users yet, we’re optimizing agents, validating edge, and consolidating PMF. + open beta coming soon. engine’s warming up. let’s keep moving. (p.s. toly 🇺🇸 check this out)

xFractal

42,668 次观看 • 1 年前

In 2016, Marvell's largest design win was a Wi-Fi chip in the Barbie Dream House (Save this). That is a documented fact about one of the most remarkable corporate transformations in semiconductor history. Ten years and $36 billion in acquisitions later, Marvell is now the company that Jensen Huang invites onto the COMPUTEX stage, the same stage where he announced a $2 billion strategic investment into the company. Over 75% of Marvell's revenue today comes from data centers. To understand what Marvell actually is now, you need to understand what Matt Murphy did when he walked in as CEO in 2016. The company had stagnant growth, governance scandals, and a business model built around chips for hard drives, printers, and consumer electronics, exactly the wrong place to be as the cloud era was beginning. Murphy made a ruthless decision to kill every low margin consumer business and go all in on data infrastructure. Then he went shopping. 2018 - Acquired Cavium for $6 billion, bringing ARM-based network processors and the foundation for cloud infrastructure compute. 2019 - Acquired Avera Semiconductor, formerly IBM's custom silicon team, which gave Marvell the ability to design bespoke ASICs for hyperscalers. This is what opened the door to Amazon, Microsoft, and Google design wins. 2021 - Acquired Inphi for $8.2 billion, securing leadership in high-speed optical interconnect, the technology that moves data between and within data centers at the speed of light. 2021 - Acquired Innovium, adding cloud-optimized Ethernet switching to the portfolio. 2025/2026 - Acquired Celestial AI for $3.25 billion, bringing photonic fabric technology that places optical connections directly inside the chip package itself. Each acquisition followed the same formula, buy the technology that will be absolutely essential in the next generation of computing before anyone else is paying attention. Now here's the vision Murphy laid out at COMPUTEX 2026, and why it's the most important thing he's ever said publicly. He made one central argument, AI scaling is no longer limited by compute or memory but rather limited by connectivity. Training a frontier model requires tens of thousands and eventually millions of processors working as a single engine and making that happen is a connectivity problem above all else. Today, data centers are constrained by copper. Copper traces connecting chips inside a server can only move data so far, so fast, before bandwidth collapses and latency rises, that's why today's AI servers have to bundle everything, CPUs, GPUs, memory onto the same physical board sitting centimeters apart. When you replace copper with optics, distance disappears entirely. An optically connected server rack can communicate with another rack in a different building at the same bandwidth and latency as if they were the same machine. Memory can sit in one physical location, compute in another, networking in a third and a software orchestration layer composes the exact ratio the workload needs, on the fly, in real time. Murphy called this a data center without distance, a globally optically interconnected infrastructure where the rigid physical boundaries of today's servers begin to disappear entirely, and data centers function as one unified system. That is not a 10 year vision because Marvell's CPO (co-packaged optics) products are sampling in 2027 with volume shipments beginning 2028. Nvidia's Vera Rubin platform has already adopted Spectrum-X Ethernet Photonics, the first CPO switch in commercial production. The reason this makes Marvell's TAM almost impossible to cap is the following. Right now, Marvell's addressable market is the optical interconnect market, a segment projected to be worth $200 billion per year by end of decade. But if the data center without distance architecture actually materializes and the evidence suggests it will, then Marvell's TAM is not just the optical interconnect market but rather every connection in every data center on earth. Bullish on Marvel! Come join Milk Road Pro for just a $1, If you want the full Marvell breakdown on where it sits in our AI infrastructure portfolio, and our entire AI thesis. Link below!
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In 2016, Marvell's largest design win was a Wi-Fi chip in the Barbie Dream House (Save this). That is a documented fact about one of the most remarkable corporate transformations in semiconductor history. Ten years and $36 billion in acquisitions later, Marvell is now the company that Jensen Huang invites onto the COMPUTEX stage, the same stage where he announced a $2 billion strategic investment into the company. Over 75% of Marvell's revenue today comes from data centers. To understand what Marvell actually is now, you need to understand what Matt Murphy did when he walked in as CEO in 2016. The company had stagnant growth, governance scandals, and a business model built around chips for hard drives, printers, and consumer electronics, exactly the wrong place to be as the cloud era was beginning. Murphy made a ruthless decision to kill every low margin consumer business and go all in on data infrastructure. Then he went shopping. 2018 - Acquired Cavium for $6 billion, bringing ARM-based network processors and the foundation for cloud infrastructure compute. 2019 - Acquired Avera Semiconductor, formerly IBM's custom silicon team, which gave Marvell the ability to design bespoke ASICs for hyperscalers. This is what opened the door to Amazon, Microsoft, and Google design wins. 2021 - Acquired Inphi for $8.2 billion, securing leadership in high-speed optical interconnect, the technology that moves data between and within data centers at the speed of light. 2021 - Acquired Innovium, adding cloud-optimized Ethernet switching to the portfolio. 2025/2026 - Acquired Celestial AI for $3.25 billion, bringing photonic fabric technology that places optical connections directly inside the chip package itself. Each acquisition followed the same formula, buy the technology that will be absolutely essential in the next generation of computing before anyone else is paying attention. Now here's the vision Murphy laid out at COMPUTEX 2026, and why it's the most important thing he's ever said publicly. He made one central argument, AI scaling is no longer limited by compute or memory but rather limited by connectivity. Training a frontier model requires tens of thousands and eventually millions of processors working as a single engine and making that happen is a connectivity problem above all else. Today, data centers are constrained by copper. Copper traces connecting chips inside a server can only move data so far, so fast, before bandwidth collapses and latency rises, that's why today's AI servers have to bundle everything, CPUs, GPUs, memory onto the same physical board sitting centimeters apart. When you replace copper with optics, distance disappears entirely. An optically connected server rack can communicate with another rack in a different building at the same bandwidth and latency as if they were the same machine. Memory can sit in one physical location, compute in another, networking in a third and a software orchestration layer composes the exact ratio the workload needs, on the fly, in real time. Murphy called this a data center without distance, a globally optically interconnected infrastructure where the rigid physical boundaries of today's servers begin to disappear entirely, and data centers function as one unified system. That is not a 10 year vision because Marvell's CPO (co-packaged optics) products are sampling in 2027 with volume shipments beginning 2028. Nvidia's Vera Rubin platform has already adopted Spectrum-X Ethernet Photonics, the first CPO switch in commercial production. The reason this makes Marvell's TAM almost impossible to cap is the following. Right now, Marvell's addressable market is the optical interconnect market, a segment projected to be worth $200 billion per year by end of decade. But if the data center without distance architecture actually materializes and the evidence suggests it will, then Marvell's TAM is not just the optical interconnect market but rather every connection in every data center on earth. Bullish on Marvel! Come join Milk Road Pro for just a $1, If you want the full Marvell breakdown on where it sits in our AI infrastructure portfolio, and our entire AI thesis. Link below!

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