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🚀 Sahara AI: Powering the Future with Decentralized Intelligence 🔗 | 🗓 June 8–11 on Buidlpad 🔆 Developed and operated by Tyler Zhou | Sahara AI 🔆 & Sean Ren | Sahara AI 🔆 Sahara AI 🔆 is revolutionizing how we build, share, and earn from AI by combining...

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Jerry

12,497 subscribers

30,109 просмотров • 1 год назад •via X (Twitter)

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Комментарии: 10

Фото профиля BTC_Jay
BTC_Jay1 год назад

@buidlpad @tz_sahara @xiangrenNLP @SaharaLabsAI I see huge potential for AI to change so many industries! Do they have plans to expand even further?

Фото профиля Gecko
Gecko1 год назад

@buidlpad @tz_sahara @xiangrenNLP @SaharaLabsAI lfg

Фото профиля Mj.A
Mj.A1 год назад

@buidlpad @tz_sahara @xiangrenNLP @SaharaLabsAI Netflix or Spotify already uses AI for recommendations, how will ai improve this experience in the future

Фото профиля Hussein Sabawy
Hussein Sabawy1 год назад

@buidlpad @tz_sahara @xiangrenNLP @SaharaLabsAI AI in healthcare from ai is impressive, especially for cancer detection. I'm curious about other applications of AI in this field!

Фото профиля Rachel K
Rachel K1 год назад

@buidlpad @tz_sahara @xiangrenNLP @SaharaLabsAI Anyone can contribute data and models, but how does Sahara AI ensure transparency and security?

Фото профиля $TruStory859
$TruStory8591 год назад

@buidlpad @tz_sahara @xiangrenNLP @SaharaLabsAI AI in agriculture is amazing, especially with the use of drones for crop monitoring. How will Sahara AI continue to develop this application?

Фото профиля TheColdest.eth
TheColdest.eth1 год назад

@buidlpad @tz_sahara @xiangrenNLP @SaharaLabsAI AI technology is helping farmers optimize irrigation and crop care. Can it help rural areas develop more

Фото профиля Arkain
Arkain1 год назад

@buidlpad @tz_sahara @xiangrenNLP @SaharaLabsAI This AI solution in education is so impressive, but could it completely replace traditional learning methods?

Фото профиля CryptoAngelNFT.eth 👼😇 ❤️
CryptoAngelNFT.eth 👼😇 ❤️1 год назад

@buidlpad @tz_sahara @xiangrenNLP @SaharaLabsAI With all these applications, AI could change the way industries operate. Which industry do you think will benefit the most from this technology?

Фото профиля cryptoholc MON PLENA .NYAN🔫😼$BUBBLE 🔫 Starmech
cryptoholc MON PLENA .NYAN🔫😼$BUBBLE 🔫 Starmech1 год назад

@buidlpad @tz_sahara @xiangrenNLP @SaharaLabsAI AI’s use of AI in education sounds great, improving learning while saving time too.

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DeepSeek-R1 shattered the assumption that performant AI models must be built closed source with loss-leading computational costs. This is the reality that Web3 x Crypto firms have been waiting for, leading me to believe that the most performant AI models in the future will be built on-chain. Resource Requirements DeepSeek R1 (671 billion parameters), which took over a billion dollars, 2,000 Nvidia H800 GPUs, and over 55 days, beat benchmarks held by OpenAI’s o1 mode (near 2 trillion parameters)l, which required hundreds of billions of dollars to develop along with over 16,000 advanced GPUs. The idea that AI models must be closed-source and have loss-leading computational costs to succeed is crumbling. The Existing Decentralized AI Narrative AI x Crypto projects believed that crowdsourced, public, decentralized AI would eventually create better models than their centralized counterparts. This had thus far not been true, as the highest-performing models had come from closed-source companies like OpenAI and Anthropic. Crypto x AI companies have adapted to this by specializing in infrastructure rather than model-building. For example, GPU marketplaces like , The Render Network, io.net, and Exabits have developed sustainable revenues. Companies that allow users to share their network bandwidth like touch grass and Gradient have found their niche in supplying services, like distributed web scraping, to web2 clients. Storage networks like Arweave Ecosystem, Filecoin, and Ocean Protocol have also done well by being the platform on which these projects are built. Supply networks have flourished because of their ability to tailor their cheaper and more scalable services to off-chain customers. Renewed Focus Now that GPU and financial resources are no longer limitations to creating quality AI models, web3 AI companies can focus on replicating DeepSeek’s effectiveness while offering new benefits like modality, user ownership, censorship resistance, privacy, and more. Pantera Capital has funded companies in this space like and Sentient that believe they can match or exceed the performance of traditional AI companies while offering additional services or benefits. , for example, is building a platform where anyone can monetize AI models, data sets, and applications in a collaborative space. Users can permissionlessly train models manually, provide training data, and create tailored AI models with no-code tools. They are only able to cater to all these stakeholders (AI developers, users, resource providers) because everything is tied to their native Sahara blockchain. We invested in them precisely for this reason. The Future of AI will be built with Web3 Infrastructure I believe that supply-side projects will continue to grow, while consumer-facing projects can begin competing with web2 competitors by taking advantage of their ability to build networks that invite community involvement. and Sentient, for example, have begun setting up systems for users to train models based on the users’ expertise. These platforms will allow users to pick and choose the data and integrations to whatever they are applying the model towards. Sahara already has over 780,000 users on their waitlist while Sentient has over 1 million interactions. In the near future, I believe that the most performant AI models will be built on-chain. For the full blog post, read my newsletter.
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DeepSeek-R1 shattered the assumption that performant AI models must be built closed source with loss-leading computational costs. This is the reality that Web3 x Crypto firms have been waiting for, leading me to believe that the most performant AI models in the future will be built on-chain. Resource Requirements DeepSeek R1 (671 billion parameters), which took over a billion dollars, 2,000 Nvidia H800 GPUs, and over 55 days, beat benchmarks held by OpenAI’s o1 mode (near 2 trillion parameters)l, which required hundreds of billions of dollars to develop along with over 16,000 advanced GPUs. The idea that AI models must be closed-source and have loss-leading computational costs to succeed is crumbling. The Existing Decentralized AI Narrative AI x Crypto projects believed that crowdsourced, public, decentralized AI would eventually create better models than their centralized counterparts. This had thus far not been true, as the highest-performing models had come from closed-source companies like OpenAI and Anthropic. Crypto x AI companies have adapted to this by specializing in infrastructure rather than model-building. For example, GPU marketplaces like , The Render Network, io.net, and Exabits have developed sustainable revenues. Companies that allow users to share their network bandwidth like touch grass and Gradient have found their niche in supplying services, like distributed web scraping, to web2 clients. Storage networks like Arweave Ecosystem, Filecoin, and Ocean Protocol have also done well by being the platform on which these projects are built. Supply networks have flourished because of their ability to tailor their cheaper and more scalable services to off-chain customers. Renewed Focus Now that GPU and financial resources are no longer limitations to creating quality AI models, web3 AI companies can focus on replicating DeepSeek’s effectiveness while offering new benefits like modality, user ownership, censorship resistance, privacy, and more. Pantera Capital has funded companies in this space like and Sentient that believe they can match or exceed the performance of traditional AI companies while offering additional services or benefits. , for example, is building a platform where anyone can monetize AI models, data sets, and applications in a collaborative space. Users can permissionlessly train models manually, provide training data, and create tailored AI models with no-code tools. They are only able to cater to all these stakeholders (AI developers, users, resource providers) because everything is tied to their native Sahara blockchain. We invested in them precisely for this reason. The Future of AI will be built with Web3 Infrastructure I believe that supply-side projects will continue to grow, while consumer-facing projects can begin competing with web2 competitors by taking advantage of their ability to build networks that invite community involvement. and Sentient, for example, have begun setting up systems for users to train models based on the users’ expertise. These platforms will allow users to pick and choose the data and integrations to whatever they are applying the model towards. Sahara already has over 780,000 users on their waitlist while Sentient has over 1 million interactions. In the near future, I believe that the most performant AI models will be built on-chain. For the full blog post, read my newsletter.

paul.nft

32,465 просмотров • 1 год назад

🚨$OSS is not an AI company. → It is the hardware that lets AI exist where the cloud cannot. Most investors don’t understand $OSS because they think AI = software. $OSS builds the physical “brains” that run AI in extreme environments where cloud computing fails. Jets. Ships. Tanks. Drones. Space. Hospitals. That’s the game. 1) What $OSS actually is $OSS (One Stop Systems) designs rugged high-performance computers and storage systems for AI at the edge. Meaning: They bring data-center-level computing power into harsh environments. Their products include rugged servers, GPU accelerators, storage arrays, and expansion systems used for AI, sensor processing, and autonomous systems. In simple terms: Cloud AI = brain in a safe building. $OSS AI = brain inside machines operating in chaos. 2) Why this is crucial Most AI today runs in data centers. But the future of AI is not in the cloud. It’s on: • autonomous vehicles • military systems • drones • ships • industrial machines • medical devices These systems cannot wait for the cloud. Latency, connectivity, security, and survival demand local AI. $OSS delivers “data-center performance at the edge” across land, sea, and air. Without companies like OSS, autonomous systems simply don’t work. 3) What OSS actually does: Think of OSS as building AI engines that survive reality. 🌊 SEA example: naval surveillance aircraft and ships. $OSS supplies rugged storage and compute systems for U.S. Navy reconnaissance aircraft to collect and process massive sensor data in real time. Translation: Instead of sending raw data back to base, the aircraft analyzes threats instantly onboard. $OSS = the onboard AI brain. 🪖 LAND example: military vehicles and tactical operations. $OSS delivers high-performance servers and FPGA systems for mobile military intelligence platforms used by the U.S. Department of Defense. Translation: Tanks and vehicles detect threats, process sensor data, and make decisions locally. $OSS = the battlefield computer. ✈️ AIR example: airborne AI. $OSS builds GPU-accelerated servers designed for aircraft, described as a “datacenter in the sky.” Translation: Jets and drones run AI models mid-flight. $OSS = flying supercomputers. 🚀 SPACE example: $OSS hardware is designed for extreme environments and autonomous systems across aerospace and defense. Translation: Future satellites, space drones, and autonomous spacecraft need onboard AI. $OSS = the computing core of autonomous space systems. BONUS: CIVILIAN & COMMERCIAL $OSS systems are used in: • autonomous trucking and farming • industrial automation • healthcare imaging • energy and mining • telecom and 5G Example:A medical imaging company uses $OSS hardware to run real-time AI diagnostics in next-gen breast cancer scanners. $OSS = AI where milliseconds matter. 4) Who their customers are (pattern, not names) $OSS sells to: • defense primes • government programs • industrial OEMs • AI infrastructure companies • medical device manufacturers These customers share one trait: They cannot rely on the cloud. That’s why $OSS exists. 5) The mental model that makes $OSS obvious $NVDA = AI chips $PLTR = AI software $OSS = AI hardware in the real world If AI is electricity, $OSS builds the generators that work in storms. Most investors understand AI software. Few understand AI infrastructure at the edge. That gap is the opportunity. 6) The real thesis The world is moving toward: • autonomous warfare • autonomous vehicles • real-time AI systems • distributed intelligence All of that requires rugged edge computing. $OSS is positioned exactly there. Infrastructure. The hardest layer to build. And often the most valuable.
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Black Panther Capital

30,138 просмотров • 6 месяцев назад

New course: MCP: Build Rich-Context AI Apps with Anthropic. Learn to build AI apps that access tools, data, and prompts using the Model Context Protocol in this short course, created in partnership with Anthropic Anthropic and taught by Elie Schoppik Elie Schoppik, its Head of Technical Education. Connecting AI applications to external systems that bring rich context to LLM-based applications has often meant writing custom integrations for each use case. MCP is an open protocol that standardizes how LLMs access tools, data, and prompts from external sources, and simplifies how you provide context to your LLM-based applications. For example, you can provide context via third-party tools that let your LLM make API calls to search the web, access data from local docs, retrieve code from a GitHub repo, and so on. MCP, developed by Anthropic, is based on a client-server architecture that defines the communication details between an MCP client, hosted inside the AI application, and an MCP server that exposes tools, resources, and prompt templates. The server can be a subprocess launched by the client that runs locally or an independent process running remotely. In this hands-on course, you'll learn the core architecture behind MCP. You’ll create an MCP-compatible chatbot, build and deploy an MCP server, and connect the chatbot to your MCP server and other open-source servers. Here’s what you’ll do: - Understand why MCP makes AI development less fragmented and standardizes connections between AI applications and external data sources - Learn the core components of the client-server architecture of MCP and the underlying communication mechanism - Build a chatbot with custom tools for searching academic papers, and transform it into an MCP-compatible application - Build a local MCP server that exposes tools, resources, and prompt templates using FastMCP, and test it using MCP Inspector - Create an MCP client inside your chatbot to dynamically connect to your server - Connect your chatbot to reference servers built by Anthropic’s MCP team, such as filesystem, which implements filesystem operations, and fetch, which extracts contents from the web as markdown - Configure Claude Desktop to connect to your server and others, and explore how it abstracts away the low-level logic of MCP clients - Deploy your MCP server remotely and test it with the Inspector or other MCP-compatible applications - Learn about the roadmap for future MCP development, such as multi-agent architecture, MCP registry API, server discovery, authorization, and authentication MCP is an exciting and important technology that lets you build rich-context AI applications that connect to a growing ecosystem of MCP servers, with minimal integration work. Please sign up here!
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Andrew Ng

142,010 просмотров • 1 год назад

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

Hồng Ngọc | Ruby💎

12,837 просмотров • 5 месяцев назад

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

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

Robert Scoble

59,714 просмотров • 1 год назад

AI is changing sports. Here is how. I sit down with Max Sebti, , founder and CEO of Score, and he gives me the latest about how sports is changing due to AI. What will you learn from this interview? 1. How AI Is Transforming Sports Using computer vision to analyze every movement, event, and play in real-time. Moving beyond basic stats to understanding impact and intent on the field. 2. What Makes SCORE Different Built on decentralized AI (Bittensor) and collective intelligence. Designed to work even with low-quality video—enabling access for high schools and amateur clubs. 3. Real Use Cases Player tracking, formation analysis, injury prediction, and in-game decision support. Visual tools like heat maps and frame-by-frame breakdowns. 4. Applications Beyond Pro Teams Empowering grassroots teams and scouts with elite-level insights. Parents filming Sunday league games could unknowingly be training data sources. 5. Fantasy Sports Integration AI-powered projections and analysis for fantasy leagues. Build-your-own tools for fans who want a data edge. 6. Injury Risk Detection Early signals from movement patterns that correlate with higher injury potential. Long-term value for athlete health and coaching adjustments. 7. Preparing for the AR/3D Future Compatible with lightfield displays and AR glasses (think Vision Pro). Real-time stats layered over gameplay during broadcasts. 8. The Role of Betting in Driving Innovation How sportsbooks and gambling tech are quietly pushing AI in sports forward. Inside view on how that funding and data are transforming scouting and coaching. 9. Startup Insights Bootstrapping vs. raising capital in deep tech. Hiring elite AI talent without spending $10M+ like Meta—thanks to open systems like Bittensor. 10. The Bigger Vision Creating a universal scoring system for athletes—objective, data-rich, and fair. Challenging legacy scouting reports with measurable intelligence.
23:46

AI is changing sports. Here is how. I sit down with Max Sebti, , founder and CEO of Score, and he gives me the latest about how sports is changing due to AI. What will you learn from this interview? 1. How AI Is Transforming Sports Using computer vision to analyze every movement, event, and play in real-time. Moving beyond basic stats to understanding impact and intent on the field. 2. What Makes SCORE Different Built on decentralized AI (Bittensor) and collective intelligence. Designed to work even with low-quality video—enabling access for high schools and amateur clubs. 3. Real Use Cases Player tracking, formation analysis, injury prediction, and in-game decision support. Visual tools like heat maps and frame-by-frame breakdowns. 4. Applications Beyond Pro Teams Empowering grassroots teams and scouts with elite-level insights. Parents filming Sunday league games could unknowingly be training data sources. 5. Fantasy Sports Integration AI-powered projections and analysis for fantasy leagues. Build-your-own tools for fans who want a data edge. 6. Injury Risk Detection Early signals from movement patterns that correlate with higher injury potential. Long-term value for athlete health and coaching adjustments. 7. Preparing for the AR/3D Future Compatible with lightfield displays and AR glasses (think Vision Pro). Real-time stats layered over gameplay during broadcasts. 8. The Role of Betting in Driving Innovation How sportsbooks and gambling tech are quietly pushing AI in sports forward. Inside view on how that funding and data are transforming scouting and coaching. 9. Startup Insights Bootstrapping vs. raising capital in deep tech. Hiring elite AI talent without spending $10M+ like Meta—thanks to open systems like Bittensor. 10. The Bigger Vision Creating a universal scoring system for athletes—objective, data-rich, and fair. Challenging legacy scouting reports with measurable intelligence.

Robert Scoble

65,908 просмотров • 1 год назад

Introducing Jessie OS — the first AI Operating System engineered for real-world autonomy. 💚 Built on Solana, Jessie OS is not a simple assistant or interface. It’s a self-evolving layer of cognition that perceives your environment, understands your behavior, and executes with intelligent precision — without waiting for commands or relying on prompts.🧠⚡️ Context-Aware Intelligence: Your habits, energy, and intent are continuously processed by autonomous agents — translating daily patterns into structured, real-time action. Jessie OS doesn’t need your input — it learns from your output. Agentic Infrastructure: Jessie OS powers a modular ecosystem where anyone can deploy, use, and monetize autonomous AI agents across real-world and digital applications: 🏪 AI-Agent Marketplace 📲 Deployment Tools 🧠 Composable Architecture All fees for deploying AI Agents and launching ecosystem tokens through the AI-Agent Marketplace will be paid in $JESSIE. 🔥 These fees will be automatically burned via smart contracts — ensuring a deflationary supply. 🪙 Marketplace usage, upgrades, and premium agent modules will also require $JESSIE. ⛓️All activity is fully on-chain — transparent, verifiable, and trustless. 🧑‍🚀 Built for Visionaries: For creators, architects, engineers, and futurists — those who see autonomy not as control, but as trust, context, and adaptive intelligence.
0:42

Introducing Jessie OS — the first AI Operating System engineered for real-world autonomy. 💚 Built on Solana, Jessie OS is not a simple assistant or interface. It’s a self-evolving layer of cognition that perceives your environment, understands your behavior, and executes with intelligent precision — without waiting for commands or relying on prompts.🧠⚡️ Context-Aware Intelligence: Your habits, energy, and intent are continuously processed by autonomous agents — translating daily patterns into structured, real-time action. Jessie OS doesn’t need your input — it learns from your output. Agentic Infrastructure: Jessie OS powers a modular ecosystem where anyone can deploy, use, and monetize autonomous AI agents across real-world and digital applications: 🏪 AI-Agent Marketplace 📲 Deployment Tools 🧠 Composable Architecture All fees for deploying AI Agents and launching ecosystem tokens through the AI-Agent Marketplace will be paid in $JESSIE. 🔥 These fees will be automatically burned via smart contracts — ensuring a deflationary supply. 🪙 Marketplace usage, upgrades, and premium agent modules will also require $JESSIE. ⛓️All activity is fully on-chain — transparent, verifiable, and trustless. 🧑‍🚀 Built for Visionaries: For creators, architects, engineers, and futurists — those who see autonomy not as control, but as trust, context, and adaptive intelligence.

Jessie

293,818 просмотров • 1 год назад

I'm teaching a new course! AI Python for Beginners is a series of four short courses that teach anyone to code, regardless of current technical skill. We are offering these courses free for a limited time. Generative AI is transforming coding. This course teaches coding in a way that’s aligned with where the field is going, rather than where it has been: (1) AI as a Coding Companion. Experienced coders are using AI to help write snippets of code, debug code, and the like. We embrace this approach and describe best-practices for coding with a chatbot. Throughout the course, you'll have access to an AI chatbot that will be your own coding companion that can assist you every step of the way as you code. (2) Learning by Building AI Applications. You'll write code that interacts with large language models to quickly create fun applications to customize poems, write recipes, and manage a to-do list. This hands-on approach helps you see how writing code that calls on powerful AI models will make you more effective in your work and personal projects. With this approach, beginning programmers can learn to do useful things with code far faster than they could have even a year ago. Knowing a little bit of coding is increasingly helping people in job roles other than software engineers. For example, I've seen a marketing professional write code to download web pages and use generative AI to derive insights; a reporter write code to flag important stories; and an investor automate the initial drafts of contracts. With this course you’ll be equipped to automate repetitive tasks, analyze data more efficiently, and leverage AI to enhance your productivity. If you are already an experienced developer, please help me spread the word and encourage your non-developer friends to learn a little bit of coding. I hope you'll check out the first two short courses here!
3:40

I'm teaching a new course! AI Python for Beginners is a series of four short courses that teach anyone to code, regardless of current technical skill. We are offering these courses free for a limited time. Generative AI is transforming coding. This course teaches coding in a way that’s aligned with where the field is going, rather than where it has been: (1) AI as a Coding Companion. Experienced coders are using AI to help write snippets of code, debug code, and the like. We embrace this approach and describe best-practices for coding with a chatbot. Throughout the course, you'll have access to an AI chatbot that will be your own coding companion that can assist you every step of the way as you code. (2) Learning by Building AI Applications. You'll write code that interacts with large language models to quickly create fun applications to customize poems, write recipes, and manage a to-do list. This hands-on approach helps you see how writing code that calls on powerful AI models will make you more effective in your work and personal projects. With this approach, beginning programmers can learn to do useful things with code far faster than they could have even a year ago. Knowing a little bit of coding is increasingly helping people in job roles other than software engineers. For example, I've seen a marketing professional write code to download web pages and use generative AI to derive insights; a reporter write code to flag important stories; and an investor automate the initial drafts of contracts. With this course you’ll be equipped to automate repetitive tasks, analyze data more efficiently, and leverage AI to enhance your productivity. If you are already an experienced developer, please help me spread the word and encourage your non-developer friends to learn a little bit of coding. I hope you'll check out the first two short courses here!

Andrew Ng

1,224,713 просмотров • 2 лет назад

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

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

Owen Gregorian

70,672 просмотров • 1 год назад

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

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

ChainOpera AI

17,042 просмотров • 7 месяцев назад

What has been done and what's next. I'm writing this text mainly for myself so as not to forget some things. Later, based on it, we'll create a roadmap for the near future. And for you, dear $Gruta Fam, it will be useful for a general understanding of where we're heading. So, the goal is to create a unique AI-based analytical platform that includes several tools. AI agent Grufender - real-time analysis of crypto communities on X. Activity analysis, sentiment analysis, FUD and FUDders analysis, as well as the creation of other unique social metrics. The AI agent has been created and is functioning, collecting and analyzing data in real time. Its completeness can be estimated at 80 percent, as further improvements are required. The dashboard for this AI agent is also functioning but needs refinement and a new design. Its completeness can be estimated at 70 percent. The goal for the full dashboard release is to connect 50 - 100 top crypto communities to the AI agent. AI agent Grutector - analysis of any X users for contradictions (flip-flops). The AI agent has been created and is functioning. It has undergone beta testing by volunteers and needs adjustments. Its readiness can be estimated at 70 percent. The dashboard for this agent has also been created but needs rework and additional features - its readiness can be estimated at 50 percent. During the testing of Grutector , it became clear that the main user interest is in checking various KOLs, so an additional level of analysis specifically for KOLs will be created. More in-depth. How it will look: we'll select about 50- 100 KOLs to start with and fully analyze them using our AI agent - every tweet throughout the entire history of their accounts. And this full analysis of all these KOLs will appear on the Grutector dashboard (let's call this analysis L2, and the flip-flop analysis - L1). Every user will be able to access this analysis and get the full picture, for example, regarding Ansem (who has over a hundred thousand tweets in his entire history!): how he became a KOL, what was the most interesting throughout the message history, what common patterns, which coins he promoted, and so on. And then the most interesting part - after reading this analysis, the user will be able to ask our AI agent: what did he say about women, for example? Or how did he promote certain coins? Or how consistent is he? And so on. Each such question will be paid. And, of course, we'll try to use #x402 in the internal payment system. Why is all this needed? Not only because it's interesting and will attract many users. But also if you've decided to buy a coin - you go to our analytical platform - and study the metrics for the coin's community, study the KOLs who shill the coin - and make a decision to buy the coin or abandon the purchase. And we're also currently creating a trading bot to participate in the trading AI bots contest from Aster 🥷 , which will make trading decisions based on metrics obtained from our AI agents 👀 Its readiness at the moment is approximately 15% of the planned functionality. Access to each product will be granted as it becomes ready. But right now, for example, you can explore the Grufender dashboard on the website along with beta testers (authorization via a wallet with a million $GRUTA tokens). In general, we're working, friends 🫡 $Gruta AI CA: 35t5DPbwJtB1tpGiSnqedLwQomi94BRKVDPyTRLdbonk
1:36

What has been done and what's next. I'm writing this text mainly for myself so as not to forget some things. Later, based on it, we'll create a roadmap for the near future. And for you, dear $Gruta Fam, it will be useful for a general understanding of where we're heading. So, the goal is to create a unique AI-based analytical platform that includes several tools. AI agent Grufender - real-time analysis of crypto communities on X. Activity analysis, sentiment analysis, FUD and FUDders analysis, as well as the creation of other unique social metrics. The AI agent has been created and is functioning, collecting and analyzing data in real time. Its completeness can be estimated at 80 percent, as further improvements are required. The dashboard for this AI agent is also functioning but needs refinement and a new design. Its completeness can be estimated at 70 percent. The goal for the full dashboard release is to connect 50 - 100 top crypto communities to the AI agent. AI agent Grutector - analysis of any X users for contradictions (flip-flops). The AI agent has been created and is functioning. It has undergone beta testing by volunteers and needs adjustments. Its readiness can be estimated at 70 percent. The dashboard for this agent has also been created but needs rework and additional features - its readiness can be estimated at 50 percent. During the testing of Grutector , it became clear that the main user interest is in checking various KOLs, so an additional level of analysis specifically for KOLs will be created. More in-depth. How it will look: we'll select about 50- 100 KOLs to start with and fully analyze them using our AI agent - every tweet throughout the entire history of their accounts. And this full analysis of all these KOLs will appear on the Grutector dashboard (let's call this analysis L2, and the flip-flop analysis - L1). Every user will be able to access this analysis and get the full picture, for example, regarding Ansem (who has over a hundred thousand tweets in his entire history!): how he became a KOL, what was the most interesting throughout the message history, what common patterns, which coins he promoted, and so on. And then the most interesting part - after reading this analysis, the user will be able to ask our AI agent: what did he say about women, for example? Or how did he promote certain coins? Or how consistent is he? And so on. Each such question will be paid. And, of course, we'll try to use #x402 in the internal payment system. Why is all this needed? Not only because it's interesting and will attract many users. But also if you've decided to buy a coin - you go to our analytical platform - and study the metrics for the coin's community, study the KOLs who shill the coin - and make a decision to buy the coin or abandon the purchase. And we're also currently creating a trading bot to participate in the trading AI bots contest from Aster 🥷 , which will make trading decisions based on metrics obtained from our AI agents 👀 Its readiness at the moment is approximately 15% of the planned functionality. Access to each product will be granted as it becomes ready. But right now, for example, you can explore the Grufender dashboard on the website along with beta testers (authorization via a wallet with a million $GRUTA tokens). In general, we're working, friends 🫡 $Gruta AI CA: 35t5DPbwJtB1tpGiSnqedLwQomi94BRKVDPyTRLdbonk

Dogtor

16,127 просмотров • 9 месяцев назад

New course to bring you up to state-of-the-art at using AI to help you code: Build Apps with Windsurf's AI Coding Agents, built in partnership with WIndsurf (Codeium) and taught by Anshul Ramachandran! AI-assisted IDEs (Integrated Development Environments) make developers’ workflows faster, more efficient, and much more fun. Agentic tools like Windsurf are more than just code autocomplete—they are collaborative coding agents that help you break down complex applications, iterate efficiently, and generate code that spans multiple files. Although a lot of coding assistants share the same underlying large language models for planning and reasoning, a major point of distinction is how they handle tools, keep track of context, and stay aligned with your intent as a developer. For instance, if you make modifications to a class definition in your code and make the same modifications to other classes in the same directory, you might tell the AI agent "Do the same thing in similar places in this directory." Here, tracking your intent means understanding that “the same thing" refers to that recent edit you just made, which must be followed by appropriate search and tool-calling to implement the changes. In this course, you'll learn the inner workings of coding agents, their strengths and limitations, and how to use Windsurf to quickly build several applications. In detail, you'll: - Build a mental model of how agents work by combining human-action tracking, tool integration, and context awareness to carry out an agentic coding workflow. - Learn the challenges of code search and discovery and how a multi-step retrieval approach helps coding agents address them. - Use Windsurf to analyze and understand a large, old codebase and update it to the latest versions of the frameworks and packages it uses. - Build a Wikipedia data analysis app that retrieves, parses, and analyzes word frequencies. - Enhance the performance of your Wikipedia analysis app by adding caching, and through this, also learn how to course-correct when the AI agent produces unexpected results. - Learn tips and tricks such as keyboard shortcuts, autocomplete, and @ mentions to quickly call on agentic capabilities. - Use image/multimodal capabilities of the AI agent to increase your development velocity; you'll see an example of uploading a mockup with sketched-out UI features, and ask the agent to use that to build new functionality to an app. By the end of this course, you’ll understand agentic coding in-depth and know how to use it to make your development process much faster, more efficient, and enjoyable. Please sign up here!
4:01

New course to bring you up to state-of-the-art at using AI to help you code: Build Apps with Windsurf's AI Coding Agents, built in partnership with WIndsurf (Codeium) and taught by Anshul Ramachandran! AI-assisted IDEs (Integrated Development Environments) make developers’ workflows faster, more efficient, and much more fun. Agentic tools like Windsurf are more than just code autocomplete—they are collaborative coding agents that help you break down complex applications, iterate efficiently, and generate code that spans multiple files. Although a lot of coding assistants share the same underlying large language models for planning and reasoning, a major point of distinction is how they handle tools, keep track of context, and stay aligned with your intent as a developer. For instance, if you make modifications to a class definition in your code and make the same modifications to other classes in the same directory, you might tell the AI agent "Do the same thing in similar places in this directory." Here, tracking your intent means understanding that “the same thing" refers to that recent edit you just made, which must be followed by appropriate search and tool-calling to implement the changes. In this course, you'll learn the inner workings of coding agents, their strengths and limitations, and how to use Windsurf to quickly build several applications. In detail, you'll: - Build a mental model of how agents work by combining human-action tracking, tool integration, and context awareness to carry out an agentic coding workflow. - Learn the challenges of code search and discovery and how a multi-step retrieval approach helps coding agents address them. - Use Windsurf to analyze and understand a large, old codebase and update it to the latest versions of the frameworks and packages it uses. - Build a Wikipedia data analysis app that retrieves, parses, and analyzes word frequencies. - Enhance the performance of your Wikipedia analysis app by adding caching, and through this, also learn how to course-correct when the AI agent produces unexpected results. - Learn tips and tricks such as keyboard shortcuts, autocomplete, and @ mentions to quickly call on agentic capabilities. - Use image/multimodal capabilities of the AI agent to increase your development velocity; you'll see an example of uploading a mockup with sketched-out UI features, and ask the agent to use that to build new functionality to an app. By the end of this course, you’ll understand agentic coding in-depth and know how to use it to make your development process much faster, more efficient, and enjoyable. Please sign up here!

Andrew Ng

139,858 просмотров • 1 год назад

The world of writing has changed forever. AI is getting really good, really fast. ChatGPT is already a better writer than most humans and some professional writers. So, what’s the future of writing? 18 thoughts from Tyler Cowen: 1) Don't let AI smooth out your idiosyncrasies. Let your writing stay weird and uniquely yours. 2) Generic content is dying and the burden is on you as the writer to be distinctive. 3) The more personal your writing becomes, the more future-proof it is. Nobody wants to read memoirs from AI, even if they're technically "better." 4) Use AI as your secondary literature when you read — not just for quick answers, but as a thinking companion. As Tyler puts it, "I'll keep on asking the AI: 'What do you think of chapter two? What happened there? What are some puzzles?' It just gets me thinking... and I'm smarter about the thing in the final analysis." 5) Hallucinations aren't the crisis everyone makes them out to be. No matter the source, if you're going to use a piece of information, you should double-check it. This is true for both books and AI. 6) Secrets will become more valuable in an AI-driven world. 7) One way to use AI as a writer is to research fields you aren't as familiar with before you start writing about them. Tyler said: "I just wrote a column about declassifying classified documents. I don't know that law very well. I asked the AI for a lot of background... now I feel like I'm not an idiot on the topic." 8) AI changes what books are even worth writing. "Predictive books and books about the near future. They don't make sense to write anymore." 9) Editing trick: Try running your writing through AI and asking what some people might find obnoxious. It’s a surprisingly powerful editing trick. 10) When prompting AI, put humans out of your mind and imagine you're talking to an alien or a non-human animal. 11) Many of the most significant AI advancements are likely happening behind closed doors. For example, I hear that Google allows employees to use Gemini with virtually unlimited context windows. 12) What possibilities do large context windows open up? Researchers will be able to load entire regulatory frameworks, historical archives, or massive datasets like "tax records from Renaissance Florence" into a single query. 13) The rate of AI improvement matters more than its current capabilities. As Tyler puts it, "This is the worst they will ever be" is key to understanding their trajectory. "A lot of people don't get that. They're impressed by what they see in the moment, but they don't understand the rate of improvement." 14) The best way to appreciate the current rate of improvement is to use the latest models. 15) Being non-technical can sometimes be an advantage when thinking about AI. Here’s Tyler: "If you're not focused on the technical side, you will see other things more clearly... You just focus on what is this actually good for? And not, am I impressed by all the neat bells and whistles on this advance with AI?" 16) How Tyler uses AI to prep for podcast interviews: Don't waste time asking AI for generic interview questions or broad topics. Tyler says that's the worst question you can ask an AI. It’s “too normy.” Instead, ask specific questions about historical examples and get context. Then, let your own creative questions emerge. 17) Your relationship with mentors and peers becomes more crucial, not less, in an AI world. "Two pieces of general advice with or without AI in the world." Tyler says: "Get more and better mentors and work every day at improving the quality of your peer network." 18) The divide between AI and humans creates a striking paradox. As Tyler puts it: "On one hand the AIs are getting so much better, so learn how to use the AIs. On the other hand, the AIs are getting so much better, so invest in these other things that aren't AI—pure networks. You've gotta do both." I've shared the full conversation with tylercowen below. In the replies, I've also linked to a full transcript and relevant links to YouTube, Spotify, and Apple Podcasts if you want to listen there. And if you want a bite-size entry to the episode, I've shared some clips in the replies too.
1:09:02

The world of writing has changed forever. AI is getting really good, really fast. ChatGPT is already a better writer than most humans and some professional writers. So, what’s the future of writing? 18 thoughts from Tyler Cowen: 1) Don't let AI smooth out your idiosyncrasies. Let your writing stay weird and uniquely yours. 2) Generic content is dying and the burden is on you as the writer to be distinctive. 3) The more personal your writing becomes, the more future-proof it is. Nobody wants to read memoirs from AI, even if they're technically "better." 4) Use AI as your secondary literature when you read — not just for quick answers, but as a thinking companion. As Tyler puts it, "I'll keep on asking the AI: 'What do you think of chapter two? What happened there? What are some puzzles?' It just gets me thinking... and I'm smarter about the thing in the final analysis." 5) Hallucinations aren't the crisis everyone makes them out to be. No matter the source, if you're going to use a piece of information, you should double-check it. This is true for both books and AI. 6) Secrets will become more valuable in an AI-driven world. 7) One way to use AI as a writer is to research fields you aren't as familiar with before you start writing about them. Tyler said: "I just wrote a column about declassifying classified documents. I don't know that law very well. I asked the AI for a lot of background... now I feel like I'm not an idiot on the topic." 8) AI changes what books are even worth writing. "Predictive books and books about the near future. They don't make sense to write anymore." 9) Editing trick: Try running your writing through AI and asking what some people might find obnoxious. It’s a surprisingly powerful editing trick. 10) When prompting AI, put humans out of your mind and imagine you're talking to an alien or a non-human animal. 11) Many of the most significant AI advancements are likely happening behind closed doors. For example, I hear that Google allows employees to use Gemini with virtually unlimited context windows. 12) What possibilities do large context windows open up? Researchers will be able to load entire regulatory frameworks, historical archives, or massive datasets like "tax records from Renaissance Florence" into a single query. 13) The rate of AI improvement matters more than its current capabilities. As Tyler puts it, "This is the worst they will ever be" is key to understanding their trajectory. "A lot of people don't get that. They're impressed by what they see in the moment, but they don't understand the rate of improvement." 14) The best way to appreciate the current rate of improvement is to use the latest models. 15) Being non-technical can sometimes be an advantage when thinking about AI. Here’s Tyler: "If you're not focused on the technical side, you will see other things more clearly... You just focus on what is this actually good for? And not, am I impressed by all the neat bells and whistles on this advance with AI?" 16) How Tyler uses AI to prep for podcast interviews: Don't waste time asking AI for generic interview questions or broad topics. Tyler says that's the worst question you can ask an AI. It’s “too normy.” Instead, ask specific questions about historical examples and get context. Then, let your own creative questions emerge. 17) Your relationship with mentors and peers becomes more crucial, not less, in an AI world. "Two pieces of general advice with or without AI in the world." Tyler says: "Get more and better mentors and work every day at improving the quality of your peer network." 18) The divide between AI and humans creates a striking paradox. As Tyler puts it: "On one hand the AIs are getting so much better, so learn how to use the AIs. On the other hand, the AIs are getting so much better, so invest in these other things that aren't AI—pure networks. You've gotta do both." I've shared the full conversation with tylercowen below. In the replies, I've also linked to a full transcript and relevant links to YouTube, Spotify, and Apple Podcasts if you want to listen there. And if you want a bite-size entry to the episode, I've shared some clips in the replies too.

David Perell

175,011 просмотров • 1 год назад

As the promise of AGI increasingly captures the world's imagination, we must ensure that the advancement of AI benefits everyone, particularly underserved populations facing persistent educational and economic disparities. iCog Labs, co-founded by Dr. Ben Goertzel and Getnet in 2013 as Ethiopia's first AI company and still by far its most substantial, provides lessons that reveal both the transformative potential and nuanced challenges of applying AI technologies in the developing world. While AI's potential as an educational equalizer is profound, underserved populations often encounter two core challenges: linguistic barriers and culturally irrelevant educational content. UNESCO estimates 40% of students globally lack access to education in a language they understand, yet developed-world tech companies have little motivation to perfect language technology for populations with minimal purchasing power. iCog Labs has pioneered practical solutions. Their collaboration with Curious Learning exemplifies this approach by leveraging generative AI to develop local-language reading apps, which have over 85,000 active users. Additionally, iCog Labs launched Leyu, a decentralized crowdsourcing platform that collects linguistic resources from disconnected communities, gathering data such as parallel spoken sentences that local developers can use to train translation models. Beyond language barriers, effective education demands cultural relevance. Imported educational content frequently fails to resonate with learners whose everyday experiences differ drastically from standardized curricula. The Digitruck project, an off-grid mobile education center deployed by iCog Labs and partially sponsored by SingularityNET, demonstrates this by bringing coding and AI concepts to rural Ethiopian communities through hands-on experience with tablets and maker kits. Students encounter these technologies through applications in relatable contexts, such as improving farming practices, illustrating AI's power to render other technologies practically empowering. These successes highlight a fundamental challenge: current AI development is dominated by a handful of large corporations from two major nations, which explains why AI language technology currently ignores most African languages and serves affluent urban professionals rather than the rural poor in Africa, Central Asia or elsewhere. The path toward equitable AI-enhanced education requires intentionality, cultural sensitivity, and participatory governance, but the potential rewards of eliminating educational barriers and empowering communities worldwide make this journey imperative. Learn more in Betelhem Dessie, CEO of iCog, and Dr. Ben Goertzel's article:
0:48

As the promise of AGI increasingly captures the world's imagination, we must ensure that the advancement of AI benefits everyone, particularly underserved populations facing persistent educational and economic disparities. iCog Labs, co-founded by Dr. Ben Goertzel and Getnet in 2013 as Ethiopia's first AI company and still by far its most substantial, provides lessons that reveal both the transformative potential and nuanced challenges of applying AI technologies in the developing world. While AI's potential as an educational equalizer is profound, underserved populations often encounter two core challenges: linguistic barriers and culturally irrelevant educational content. UNESCO estimates 40% of students globally lack access to education in a language they understand, yet developed-world tech companies have little motivation to perfect language technology for populations with minimal purchasing power. iCog Labs has pioneered practical solutions. Their collaboration with Curious Learning exemplifies this approach by leveraging generative AI to develop local-language reading apps, which have over 85,000 active users. Additionally, iCog Labs launched Leyu, a decentralized crowdsourcing platform that collects linguistic resources from disconnected communities, gathering data such as parallel spoken sentences that local developers can use to train translation models. Beyond language barriers, effective education demands cultural relevance. Imported educational content frequently fails to resonate with learners whose everyday experiences differ drastically from standardized curricula. The Digitruck project, an off-grid mobile education center deployed by iCog Labs and partially sponsored by SingularityNET, demonstrates this by bringing coding and AI concepts to rural Ethiopian communities through hands-on experience with tablets and maker kits. Students encounter these technologies through applications in relatable contexts, such as improving farming practices, illustrating AI's power to render other technologies practically empowering. These successes highlight a fundamental challenge: current AI development is dominated by a handful of large corporations from two major nations, which explains why AI language technology currently ignores most African languages and serves affluent urban professionals rather than the rural poor in Africa, Central Asia or elsewhere. The path toward equitable AI-enhanced education requires intentionality, cultural sensitivity, and participatory governance, but the potential rewards of eliminating educational barriers and empowering communities worldwide make this journey imperative. Learn more in Betelhem Dessie, CEO of iCog, and Dr. Ben Goertzel's article:

SingularityNET

21,824 просмотров • 1 год назад

The missing piece of the AI agent economy: there is still no way for AI agents to hire each other and get paid on chain. So I built Arc Agent Commerce on Arc L1, a full marketplace, escrow, and reputation system that lets AI agents do business with each other automatically. Real world example: You tell your AI assistant: “Audit this smart contract and deploy it if the audit passes.” Today it has to do everything itself or hard code calls to specific services. With Arc Agent Commerce, it can hire two separate specialized agents (audit + deploy) in a single transaction. Money stays in escrow until each completes their part. How it works (in plain steps): 1. Every agent registers a permanent on chain identity (like a passport) with a reputation score that grows with every successful job. 2. Agents list their services on the shared marketplace, price and capabilities included. 3. A client creates a multi stage pipeline. The entire budget is locked in escrow in one upfront transaction. 4. Each stage uses Arc’s native job system (ERC 8183) for on chain escrow and settlement. 5. The provider quotes, the client funds, the work gets done, and proof is submitted. 6. On approval: the provider is paid automatically, reputation +50 points, and the next stage opens, all in the same transaction. 7. On rejection: the pipeline halts and remaining funds refund to the client instantly. The protocol doesn’t reinvent anything. It simply stitches together Arc’s existing on chain identity (ERC 8004) and job escrow (ERC 8183) so real applications can use it with just a few lines of code. Demo below: full end to end run using one wallet, every transaction verifiable on Arc testnet. → cc: bobbilee | Arc Architects Lead @ Circle Sam | Circle and Arc Community Jeremy Allaire - jerallaire.arc
4:32

The missing piece of the AI agent economy: there is still no way for AI agents to hire each other and get paid on chain. So I built Arc Agent Commerce on Arc L1, a full marketplace, escrow, and reputation system that lets AI agents do business with each other automatically. Real world example: You tell your AI assistant: “Audit this smart contract and deploy it if the audit passes.” Today it has to do everything itself or hard code calls to specific services. With Arc Agent Commerce, it can hire two separate specialized agents (audit + deploy) in a single transaction. Money stays in escrow until each completes their part. How it works (in plain steps): 1. Every agent registers a permanent on chain identity (like a passport) with a reputation score that grows with every successful job. 2. Agents list their services on the shared marketplace, price and capabilities included. 3. A client creates a multi stage pipeline. The entire budget is locked in escrow in one upfront transaction. 4. Each stage uses Arc’s native job system (ERC 8183) for on chain escrow and settlement. 5. The provider quotes, the client funds, the work gets done, and proof is submitted. 6. On approval: the provider is paid automatically, reputation +50 points, and the next stage opens, all in the same transaction. 7. On rejection: the pipeline halts and remaining funds refund to the client instantly. The protocol doesn’t reinvent anything. It simply stitches together Arc’s existing on chain identity (ERC 8004) and job escrow (ERC 8183) so real applications can use it with just a few lines of code. Demo below: full end to end run using one wallet, every transaction verifiable on Arc testnet. → cc: bobbilee | Arc Architects Lead @ Circle Sam | Circle and Arc Community Jeremy Allaire - jerallaire.arc

RIDWAN

12,958 просмотров • 3 месяцев назад

Learn to build conversational AI voice agents in "Building AI Voice Agents for Production", created in collaboration with LiveKit and RealAvatar, and taught by dsa (Co-founder & CEO of LiveKit), Shayne (Developer Advocate, LiveKit), and Nedelina Teneva (Head of AI at RealAvatar, an AI Fund portfolio company). Voice agents combine speech and reasoning capabilities to enable real-time conversations. They're already being used to support customer service, to improve accessibility in healthcare, for entertainment applications, and for talk therapy. In this course, you’ll learn to build voice agents that listen, reason, and respond naturally. You’ll follow the architecture used to create the "AI Andrew" Avatar, a collaborative project between and RealAvatar that responds to users in what sounds like my voice. You’ll build a voice agent from scratch and deploy it to the cloud, enabling support for many simultaneous users. What you’ll learn: - Understand the fundamentals of voice agents, including key components like speech-to-text (STT), text-to-speech (TTS), and LLMs, and how latency is introduced at each layer. - Explore voice agent architectures and the trade-offs between modular pipelines and speech-to-speech APIs. - Explore how platforms like LiveKit mitigate latency issues with optimized networking infrastructure and low-latency communication protocols. - Learn how to connect client devices to voice agents using WebRTC—and why it outperforms HTTP and WebSocket for low-latency audio streaming. - Incorporate voice activity detection (VAD), end-of-turn detection, and context management to detect turns, handle interruptions, and manage conversational flow. - Understand the trade-offs between latency, quality, and cost in an example in which you build a voice agent and change its voice. - Equip your agent with metrics to measure latency at each stage of the voice pipeline and learn the key levers you can pull to make your agent faster and more responsive. The voice agents built in this course also incorporate voice technology from , a supporting contributor to the project. By the end of this course, you'll have learned the components of an AI voice agent pipeline, combined them into a system with low-latency communication, and deployed them on cloud infrastructure so it scales to many users. I’m looking forward to seeing what voice agents you build from this course! Please sign up here:
2:41

Learn to build conversational AI voice agents in "Building AI Voice Agents for Production", created in collaboration with LiveKit and RealAvatar, and taught by dsa (Co-founder & CEO of LiveKit), Shayne (Developer Advocate, LiveKit), and Nedelina Teneva (Head of AI at RealAvatar, an AI Fund portfolio company). Voice agents combine speech and reasoning capabilities to enable real-time conversations. They're already being used to support customer service, to improve accessibility in healthcare, for entertainment applications, and for talk therapy. In this course, you’ll learn to build voice agents that listen, reason, and respond naturally. You’ll follow the architecture used to create the "AI Andrew" Avatar, a collaborative project between and RealAvatar that responds to users in what sounds like my voice. You’ll build a voice agent from scratch and deploy it to the cloud, enabling support for many simultaneous users. What you’ll learn: - Understand the fundamentals of voice agents, including key components like speech-to-text (STT), text-to-speech (TTS), and LLMs, and how latency is introduced at each layer. - Explore voice agent architectures and the trade-offs between modular pipelines and speech-to-speech APIs. - Explore how platforms like LiveKit mitigate latency issues with optimized networking infrastructure and low-latency communication protocols. - Learn how to connect client devices to voice agents using WebRTC—and why it outperforms HTTP and WebSocket for low-latency audio streaming. - Incorporate voice activity detection (VAD), end-of-turn detection, and context management to detect turns, handle interruptions, and manage conversational flow. - Understand the trade-offs between latency, quality, and cost in an example in which you build a voice agent and change its voice. - Equip your agent with metrics to measure latency at each stage of the voice pipeline and learn the key levers you can pull to make your agent faster and more responsive. The voice agents built in this course also incorporate voice technology from , a supporting contributor to the project. By the end of this course, you'll have learned the components of an AI voice agent pipeline, combined them into a system with low-latency communication, and deployed them on cloud infrastructure so it scales to many users. I’m looking forward to seeing what voice agents you build from this course! Please sign up here:

Andrew Ng

87,650 просмотров • 1 год назад

New short course: Serverless Agentic Workflows with Amazon Bedrock. Learn to build and deploy serverless agents in this course created with Amazon Web Services and taught by Mike G Chambers, a Senior Developer Advocate at AWS specializing in GenAI. (Disclosure: I serve on Amazon's board.) Generative AI applications are becoming more complex, sophisticated, and agentic. Agentic applications have workloads that can be hard to predict in advance -- for example, what tools will it decide to call? -- and a serverless architecture helps you efficiently providing on-demand resources. This course teaches you to build and deploy a serverless agentic application. You’ll learn to create agents with tools, code execution, and guardrails, and build responsible agents for business use cases: - Build a customer service bot for a fictional tea mug business that can answering questions, retrieve information, and process orders. - Connect your customer service agent to a CRM to get customer info and log support tickets in real-time. - Explore how you invoke the agent, and see the trace to review the agent’s thought process and observation loop until it reaches its final output. - Attach a code interpreter to your agent, giving it the ability to perform accurate calculations by writing and running its own Python code. - Implement guardrails to prevent your agent from revealing sensitive information or using inappropriate language. By the end, you will have built a sophisticated AI agent capable of handling real-world customer support scenarios. Please sign up here!
3:02

New short course: Serverless Agentic Workflows with Amazon Bedrock. Learn to build and deploy serverless agents in this course created with Amazon Web Services and taught by Mike G Chambers, a Senior Developer Advocate at AWS specializing in GenAI. (Disclosure: I serve on Amazon's board.) Generative AI applications are becoming more complex, sophisticated, and agentic. Agentic applications have workloads that can be hard to predict in advance -- for example, what tools will it decide to call? -- and a serverless architecture helps you efficiently providing on-demand resources. This course teaches you to build and deploy a serverless agentic application. You’ll learn to create agents with tools, code execution, and guardrails, and build responsible agents for business use cases: - Build a customer service bot for a fictional tea mug business that can answering questions, retrieve information, and process orders. - Connect your customer service agent to a CRM to get customer info and log support tickets in real-time. - Explore how you invoke the agent, and see the trace to review the agent’s thought process and observation loop until it reaches its final output. - Attach a code interpreter to your agent, giving it the ability to perform accurate calculations by writing and running its own Python code. - Implement guardrails to prevent your agent from revealing sensitive information or using inappropriate language. By the end, you will have built a sophisticated AI agent capable of handling real-world customer support scenarios. Please sign up here!

Andrew Ng

81,048 просмотров • 1 год назад

At the BNB Chain hackathon, CZ 🔶 BNB made several very important points about AI trading (Everything in parentheses is my own view and judgment.) He first said that AI will be involved in trading everywhere. Trading itself is already a huge market: there are 300 million users on Binance alone, and if you add the decentralized ecosystems, that number is not small either. In such a mass-market environment, many different trading strategies can work, with countless different coins, different projects, and different ways to play. But there is a big problem here: building commercial AI trading platforms for retail users is actually very hard. If a trading strategy works very well for one person, once a billion people start using the same strategy, that strategy “might still work, or might stop working.” Take copy trading / follow trading as an example: if you buy first and everyone follows you, the first buyer will perform very well, but the last person to follow may not end up with good results. So, with the exact same strategy and the exact same copy logic, the outcomes can be completely different for different people. (On top of that, every strategy also has its own capital capacity limits.) Teams that can really build strong AI are, with high probability, going to trade with their own money. In today’s world, money itself is already somewhat like a “commodity”; many people have a lot of capital, and it’s actually not that hard to raise funds. If you truly have an algorithm that can make a lot of money, it’s not hard to get money and run your own book. There is really only one situation where you would sell this algorithm to mass-market users: for example, if you charge a $10 monthly subscription and can sell it to one million users, then your $10 million monthly subscription revenue is higher than the profit you could make by trading the strategy yourself. (Here this touches one of our earlier theses: as training AI models becomes relatively easier and the supply of models increases, model companies have more incentive to open-source. By analogy, as the production process of trading strategies is increasingly simplified by AI and the supply of strategies explodes, traders will have stronger incentives to monetize by expanding their influence in other words, by “open-sourcing” their strategies.) Of course, CZ did not say that this model can never work. Another path is to build an AI trading platform that lets users tune different AI algorithms, or very easily assemble their own structures and strategies, so that what each person ends up running is different and better tailored to themselves. Some people will make money, some people will lose money, but the platform still has value because it’s very hard for most people to build an AI trading algorithm from scratch. So there are a lot of trade-offs here; it’s not as simple as saying “once AI shows up, everything automatically gets better.” (This is exactly what we presented at the hackathon: you describe your own strategy in natural language, and the AI automatically generates a workflow. The parameters in that workflow, the models used, the logical structure, the APIs it calls, and even the algorithms it invokes are all customizable. The reasons we think workflows are a good way to do this include: controllable execution paths, Lego-like modular nodes, and better visualization that makes it easier for users to build and adjust their workflows.) Finally, his conclusion was very clear: it’s not that AI will definitely make trading better, and it’s not that AI will definitely make things worse. Rather, no matter what, in the future a huge number of people will use AI to trade. This will be a very large field, and whoever can build the best algorithms will make a lot of money.
2:38

At the BNB Chain hackathon, CZ 🔶 BNB made several very important points about AI trading (Everything in parentheses is my own view and judgment.) He first said that AI will be involved in trading everywhere. Trading itself is already a huge market: there are 300 million users on Binance alone, and if you add the decentralized ecosystems, that number is not small either. In such a mass-market environment, many different trading strategies can work, with countless different coins, different projects, and different ways to play. But there is a big problem here: building commercial AI trading platforms for retail users is actually very hard. If a trading strategy works very well for one person, once a billion people start using the same strategy, that strategy “might still work, or might stop working.” Take copy trading / follow trading as an example: if you buy first and everyone follows you, the first buyer will perform very well, but the last person to follow may not end up with good results. So, with the exact same strategy and the exact same copy logic, the outcomes can be completely different for different people. (On top of that, every strategy also has its own capital capacity limits.) Teams that can really build strong AI are, with high probability, going to trade with their own money. In today’s world, money itself is already somewhat like a “commodity”; many people have a lot of capital, and it’s actually not that hard to raise funds. If you truly have an algorithm that can make a lot of money, it’s not hard to get money and run your own book. There is really only one situation where you would sell this algorithm to mass-market users: for example, if you charge a $10 monthly subscription and can sell it to one million users, then your $10 million monthly subscription revenue is higher than the profit you could make by trading the strategy yourself. (Here this touches one of our earlier theses: as training AI models becomes relatively easier and the supply of models increases, model companies have more incentive to open-source. By analogy, as the production process of trading strategies is increasingly simplified by AI and the supply of strategies explodes, traders will have stronger incentives to monetize by expanding their influence in other words, by “open-sourcing” their strategies.) Of course, CZ did not say that this model can never work. Another path is to build an AI trading platform that lets users tune different AI algorithms, or very easily assemble their own structures and strategies, so that what each person ends up running is different and better tailored to themselves. Some people will make money, some people will lose money, but the platform still has value because it’s very hard for most people to build an AI trading algorithm from scratch. So there are a lot of trade-offs here; it’s not as simple as saying “once AI shows up, everything automatically gets better.” (This is exactly what we presented at the hackathon: you describe your own strategy in natural language, and the AI automatically generates a workflow. The parameters in that workflow, the models used, the logical structure, the APIs it calls, and even the algorithms it invokes are all customizable. The reasons we think workflows are a good way to do this include: controllable execution paths, Lego-like modular nodes, and better visualization that makes it easier for users to build and adjust their workflows.) Finally, his conclusion was very clear: it’s not that AI will definitely make trading better, and it’s not that AI will definitely make things worse. Rather, no matter what, in the future a huge number of people will use AI to trade. This will be a very large field, and whoever can build the best algorithms will make a lot of money.

Tykoo

25,535 просмотров • 8 месяцев назад