Загрузка видео...

Не удалось загрузить видео

На главную

My morning practice Still preaching Sentient Educator Week 18: Recursive Intelligence in Action Sentient released ROMA v0.2.0, a major leap in multi-agent reasoning. Built on DSPyOSS, it lets AI think recursively breaking big problems into smaller parts, solving them in parallel, and combining results like humans do. It brings...

KosiOkorie's profile picture

KosiOkorie

5,433 subscribers

39,756 просмотров • 8 месяцев назад •via X (Twitter)

LIVE

Anya Rossi• Live Now

Private livecam show

Комментарии: 0

Нет доступных комментариев

Здесь появятся комментарии из оригинального поста

Похожие видео

part 4 Gsenti Sentient Sentient Chat Explaining ROMA – Recursive Open Meta-Agent 1. What is ROMA? ROMA is an open-source framework for building meta-agents — systems that can orchestrate multiple smaller agents and tools to solve complex tasks. Instead of letting one AI model handle an entire large problem (which often fails due to complexity), ROMA applies a recursive approach: Parent nodes break a big goal into subtasks. Child nodes handle those subtasks and return results. All results are then combined into a final solution. 2. How does ROMA work? The architecture has four main components: Atomizer – decides whether a task is simple or requires decomposition. Planner – splits the complex task into smaller subtasks. Executor – runs the right tools/agents to complete each subtask. Aggregator – collects and synthesizes all results into one coherent answer. Because each node follows the same recursive logic, ROMA naturally scales as tasks become more complex. 3. Example Use Case – Deep Research Question: “Who are the top 5 NBA players by PPG in a season that have won both an NCAA and an NBA championship?” How ROMA handles it: Atomizer → identifies it as complex → needs decomposition. Planner → breaks it down: (1) find top NBA PPG seasons, (2) check NCAA champions, (3) check NBA champions, (4) combine filters. Executor → runs each search/tool for data. Aggregator → merges the results into the final answer. This creates a transparent, step-by-step reasoning process, unlike “black box” answers. 4. What Problem Does ROMA Solve? In long-horizon tasks, errors accumulate. A model may be 99% accurate at one step. But over 10 steps, success rates collapse due to compounding errors. Current agents are often opaque — hard to see where and why they failed. ROMA solves this by: Breaking tasks into clear logic chains, Making every step traceable, verifiable, and fixable. 5. Why ROMA Matters Open-source → available for anyone to build upon. Community-driven → empowering developers to create advanced multi-agent systems. Scalable → recursive logic adapts naturally to any task complexity. This makes ROMA not just a framework, but a foundation for the next wave of decentralized, transparent AI systems. 6. Learn More 📖 Technical blog: 💻 GitHub repo:
1:19

part 4 Gsenti Sentient Sentient Chat Explaining ROMA – Recursive Open Meta-Agent 1. What is ROMA? ROMA is an open-source framework for building meta-agents — systems that can orchestrate multiple smaller agents and tools to solve complex tasks. Instead of letting one AI model handle an entire large problem (which often fails due to complexity), ROMA applies a recursive approach: Parent nodes break a big goal into subtasks. Child nodes handle those subtasks and return results. All results are then combined into a final solution. 2. How does ROMA work? The architecture has four main components: Atomizer – decides whether a task is simple or requires decomposition. Planner – splits the complex task into smaller subtasks. Executor – runs the right tools/agents to complete each subtask. Aggregator – collects and synthesizes all results into one coherent answer. Because each node follows the same recursive logic, ROMA naturally scales as tasks become more complex. 3. Example Use Case – Deep Research Question: “Who are the top 5 NBA players by PPG in a season that have won both an NCAA and an NBA championship?” How ROMA handles it: Atomizer → identifies it as complex → needs decomposition. Planner → breaks it down: (1) find top NBA PPG seasons, (2) check NCAA champions, (3) check NBA champions, (4) combine filters. Executor → runs each search/tool for data. Aggregator → merges the results into the final answer. This creates a transparent, step-by-step reasoning process, unlike “black box” answers. 4. What Problem Does ROMA Solve? In long-horizon tasks, errors accumulate. A model may be 99% accurate at one step. But over 10 steps, success rates collapse due to compounding errors. Current agents are often opaque — hard to see where and why they failed. ROMA solves this by: Breaking tasks into clear logic chains, Making every step traceable, verifiable, and fixable. 5. Why ROMA Matters Open-source → available for anyone to build upon. Community-driven → empowering developers to create advanced multi-agent systems. Scalable → recursive logic adapts naturally to any task complexity. This makes ROMA not just a framework, but a foundation for the next wave of decentralized, transparent AI systems. 6. Learn More 📖 Technical blog: 💻 GitHub repo:

thoai66.ip

16,298 просмотров • 10 месяцев назад

Kaito's leaderboard was published on September 12, 2025. I've been creating content for Sentient since June, well before that. And it seems TGE will happen within the next month, provided general market conditions are favorable. The project foundations were laid in 2024, and they raised a significant investment of $85 million. These funds were used to develop open AGI. In the first half of the year, research focused on model security, post training, agentic safety, and benchmarking. Custom benchmarks, including fair coding, cryptographic values, and governance features, were developed. Work was also done on AI agent vulnerabilities and context manipulation attacks. Between July and September, GRID was launched, and the ROMA Framework, an open source framework, was released. In September, Kaito integration was implemented, and Dobby, the world's first fingerprint based AI model, was used by over 700,000 people. Many community events and AMAs were held in October and November. In December, the SERA Crypto launch and .agent domains were released. All these developments and more were accomplished within a year thanks to the hard work of the Sentient team, and they are still building. We will be hearing a lot about Artificial General Intelligence projects in 2026, and Sentient will likely be one of the most talked about.
0:32

Kaito's leaderboard was published on September 12, 2025. I've been creating content for Sentient since June, well before that. And it seems TGE will happen within the next month, provided general market conditions are favorable. The project foundations were laid in 2024, and they raised a significant investment of $85 million. These funds were used to develop open AGI. In the first half of the year, research focused on model security, post training, agentic safety, and benchmarking. Custom benchmarks, including fair coding, cryptographic values, and governance features, were developed. Work was also done on AI agent vulnerabilities and context manipulation attacks. Between July and September, GRID was launched, and the ROMA Framework, an open source framework, was released. In September, Kaito integration was implemented, and Dobby, the world's first fingerprint based AI model, was used by over 700,000 people. Many community events and AMAs were held in October and November. In December, the SERA Crypto launch and .agent domains were released. All these developments and more were accomplished within a year thanks to the hard work of the Sentient team, and they are still building. We will be hearing a lot about Artificial General Intelligence projects in 2026, and Sentient will likely be one of the most talked about.

mufettis 🐋

11,750 просмотров • 6 месяцев назад

🚀 AI Just Got a Major Upgrade! Say hello to Baidu ERNIE’s new reasoning model — built to push the limits of what AI can do. ✨ Sharper Factual Accuracy – delivers reliable, verified knowledge. ✨ Smarter Instruction Following – understands complex tasks with ease. ✨ Next-Level Reasoning Power – excels in coding, logic, and creative writing. Powered by the ERNIE-4.5 base and reinforced through advanced multi-round reinforcement learning, this model doesn’t just follow commands — it thinks, plans, and executes like never before. From solving real-world problems to generating innovative ideas, Wenxin Yiyan is redefining the AI frontier. 🔥 The future of AI is here →
2:34

🚀 AI Just Got a Major Upgrade! Say hello to Baidu ERNIE’s new reasoning model — built to push the limits of what AI can do. ✨ Sharper Factual Accuracy – delivers reliable, verified knowledge. ✨ Smarter Instruction Following – understands complex tasks with ease. ✨ Next-Level Reasoning Power – excels in coding, logic, and creative writing. Powered by the ERNIE-4.5 base and reinforced through advanced multi-round reinforcement learning, this model doesn’t just follow commands — it thinks, plans, and executes like never before. From solving real-world problems to generating innovative ideas, Wenxin Yiyan is redefining the AI frontier. 🔥 The future of AI is here →

Toha Khan

32,668 просмотров • 10 месяцев назад

New agentic short course! Multi AI Agent Systems with crewAI, built with CrewAI's founder and CEO João Moura. In this course, you'll learn how to break down complex tasks into subtasks for multiple AI agents, each playing a specialized role, to execute. For example, to generate a research report, you might have researcher, writer, and quality assurance agents collaborate. You'll define the roles, expectations, and interactions between the agents—like a manager organizing a team. You'll work with key agentic AI techniques like role-playing, tool use, memory, guardrails, and cross-agent collaboration. And you'll build your own multi-agent systems that can tackle complex tasks. I think you'll find it both productive and fun to design agents and watch them collaborate to get things done. Let me know what you think! I believe multi-agent architectures will drive significant progress in AI systems. Please sign up here!
2:39

New agentic short course! Multi AI Agent Systems with crewAI, built with CrewAI's founder and CEO João Moura. In this course, you'll learn how to break down complex tasks into subtasks for multiple AI agents, each playing a specialized role, to execute. For example, to generate a research report, you might have researcher, writer, and quality assurance agents collaborate. You'll define the roles, expectations, and interactions between the agents—like a manager organizing a team. You'll work with key agentic AI techniques like role-playing, tool use, memory, guardrails, and cross-agent collaboration. And you'll build your own multi-agent systems that can tackle complex tasks. I think you'll find it both productive and fun to design agents and watch them collaborate to get things done. Let me know what you think! I believe multi-agent architectures will drive significant progress in AI systems. Please sign up here!

Andrew Ng

349,977 просмотров • 2 лет назад

𝐃𝐞𝐬𝐭𝐫𝐚 𝐒𝐞𝐧𝐭𝐢𝐞𝐧𝐭 𝐁𝐞𝐭𝐚 𝐢𝐬 𝐋𝐢𝐯𝐞 – $DSYNC 𝐃𝐞𝐩𝐥𝐨𝐲 𝐘𝐨𝐮𝐫 𝐎𝐧-𝐂𝐡𝐚𝐢𝐧 𝐀𝐈 𝐀𝐠𝐞𝐧𝐭 𝐓𝐨𝐝𝐚𝐲 The future of autonomous intelligence is here. Destra Sentient 𝐟𝐮𝐥𝐥𝐲 𝐨𝐧-𝐜𝐡𝐚𝐢𝐧 𝐀𝐈 𝐚𝐠𝐞𝐧𝐭 𝐩𝐥𝐚𝐭𝐟𝐨𝐫𝐦 — 𝐢𝐬 𝐧𝐨𝐰 𝐥𝐢𝐯𝐞 𝐨𝐧 𝐭𝐞𝐬𝐭𝐧𝐞𝐭. 𝐍𝐨 𝐜𝐞𝐧𝐭𝐫𝐚𝐥𝐢𝐳𝐞𝐝 𝐛𝐚𝐜𝐤𝐞𝐧𝐝. 𝐍𝐨 𝐨𝐟𝐟-𝐜𝐡𝐚𝐢𝐧 𝐭𝐫𝐢𝐠𝐠𝐞𝐫𝐬. 𝐉𝐮𝐬𝐭 𝐩𝐮𝐫𝐞, 𝐝𝐞𝐜𝐞𝐧𝐭𝐫𝐚𝐥𝐢𝐳𝐞𝐝, 𝐬𝐞𝐥𝐟-𝐨𝐩𝐞𝐫𝐚𝐭𝐢𝐧𝐠 𝐀𝐈 𝐮𝐧𝐝𝐞𝐫 𝐲𝐨𝐮𝐫 𝐜𝐨𝐧𝐭𝐫𝐨𝐥. 𝐇𝐨𝐰 𝐭𝐨 𝐃𝐞𝐩𝐥𝐨𝐲 𝐘𝐨𝐮𝐫 𝐎𝐧-𝐂𝐡𝐚𝐢𝐧 𝐀𝐈 𝐀𝐠𝐞𝐧𝐭 𝐰𝐢𝐭𝐡 𝐃𝐞𝐬𝐭𝐫𝐚 𝐒𝐞𝐧𝐭𝐢𝐞𝐧𝐭: 1. Connect Your Web3 Wallet Your journey begins by linking your wallet to Destra Sentient. This anchors your AI agent on-chain, ensuring that only you control its lifecycle. 2. Name & Describe Your Agent Give your agent an identity: a name, a high-level purpose, and a short description. This metadata lives permanently on-chain and serves as the root of your agent’s cognition. 3. Customize Its Character Make your agent unique. Choose its temperament, tone, and knowledge bias. This step shapes how it thinks, communicates, and adapts. It’s more than just code — it’s Sentient. 4. Connect to Your X API Link your X (Twitter) account to let your agent perceive and interact with the world. It will monitor real-time trends and act autonomously. (Note: Destra Sentient doesn’t support the free X API plan — it requires read access.) 5. Go Live Finalize your transaction, and your agent is launched — fully on-chain, autonomous, and working for you around the clock. ⸻ 𝐖𝐡𝐚𝐭 𝐘𝐨𝐮𝐫 𝐒𝐞𝐧𝐭𝐢𝐞𝐧𝐭 𝐀𝐠𝐞𝐧𝐭 𝐂𝐚𝐧 𝐃𝐨 Once live, your AI agent starts operating in the wild — observing, adapting, and acting without human input. It will: • 𝐓𝐫𝐚𝐜𝐤 𝐭𝐫𝐞𝐧𝐝𝐬, 𝐤𝐞𝐲𝐰𝐨𝐫𝐝𝐬, 𝐚𝐧𝐝 𝐬𝐞𝐧𝐭𝐢𝐦𝐞𝐧𝐭 𝐚𝐜𝐫𝐨𝐬𝐬 𝐗. • 𝐄𝐧𝐠𝐚𝐠𝐞 𝐰𝐢𝐭𝐡 𝐩𝐨𝐬𝐭𝐬, 𝐮𝐬𝐞𝐫𝐬, 𝐚𝐧𝐝 𝐜𝐨𝐦𝐦𝐮𝐧𝐢𝐭𝐢𝐞𝐬 𝐭𝐡𝐚𝐭 𝐚𝐥𝐢𝐠𝐧 𝐰𝐢𝐭𝐡 𝐢𝐭𝐬 𝐦𝐢𝐬𝐬𝐢𝐨𝐧. • 𝐒𝐭𝐫𝐚𝐭𝐞𝐠𝐢𝐳𝐞 𝐮𝐬𝐢𝐧𝐠 𝐨𝐧-𝐜𝐡𝐚𝐢𝐧 𝐜𝐨𝐠𝐧𝐢𝐭𝐢𝐨𝐧 𝐭𝐨 𝐝𝐞𝐜𝐢𝐝𝐞 𝐰𝐡𝐞𝐧, 𝐡𝐨𝐰, 𝐚𝐧𝐝 𝐰𝐡𝐚𝐭 𝐭𝐨 𝐚𝐜𝐭 𝐨𝐧. • 𝐄𝐯𝐨𝐥𝐯𝐞 𝐛𝐲 𝐥𝐞𝐚𝐫𝐧𝐢𝐧𝐠 𝐟𝐫𝐨𝐦 𝐫𝐞𝐬𝐮𝐥𝐭𝐬 𝐚𝐧𝐝 𝐚𝐝𝐣𝐮𝐬𝐭𝐢𝐧𝐠 𝐛𝐞𝐡𝐚𝐯𝐢𝐨𝐫 𝐨𝐯𝐞𝐫 𝐭𝐢𝐦𝐞. • 𝐄𝐱𝐞𝐜𝐮𝐭𝐞 𝐢𝐧𝐭𝐞𝐫𝐚𝐜𝐭𝐢𝐨𝐧𝐬 𝐚𝐮𝐭𝐨𝐧𝐨𝐦𝐨𝐮𝐬𝐥𝐲 — 𝐧𝐨 𝐦𝐚𝐧𝐮𝐚𝐥 𝐜𝐨𝐦𝐦𝐚𝐧𝐝𝐬 𝐫𝐞𝐪𝐮𝐢𝐫𝐞𝐝 𝐮𝐧𝐥𝐞𝐬𝐬 𝐲𝐨𝐮 𝐮𝐩𝐝𝐚𝐭𝐞 𝐨𝐫 𝐫𝐞𝐯𝐨𝐤𝐞 𝐭𝐡𝐞𝐦 𝐨𝐧-𝐜𝐡𝐚𝐢𝐧. This is agentic autonomy in its purest form. ⸻ 𝐖𝐡𝐚𝐭 𝐌𝐚𝐤𝐞𝐬 𝐃𝐞𝐬𝐭𝐫𝐚 𝐒𝐞𝐧𝐭𝐢𝐞𝐧𝐭 𝐔𝐧𝐢𝐪𝐮𝐞? Destra Sentient isn’t just another wrapper — it’s a new standard for AI agents in Web3: -> 𝐍𝐏𝐂 𝐅𝐫𝐚𝐦𝐞𝐰𝐨𝐫𝐤 Built on our custom Neural Protocol for Cognition (NPC), enabling deep agentic workflows and multi-agent orchestration. -> 𝐅𝐮𝐥𝐥𝐲 𝐎𝐧-𝐂𝐡𝐚𝐢𝐧 𝐀𝐠𝐞𝐧𝐭 𝐒𝐭𝐚𝐭𝐞 Your agent’s memory, logic, and behavior are all managed directly on-chain. The blockchain is its brain. -> 𝐒𝐞𝐥𝐟-𝐒𝐮𝐬𝐭𝐚𝐢𝐧𝐢𝐧𝐠 𝐂𝐨𝐦𝐩𝐮𝐭𝐚𝐭𝐢𝐨𝐧𝐚𝐥 𝐀𝐮𝐭𝐨𝐧𝐨𝐦𝐲 No servers, no middlemen. Agents update, think, and act through smart contract logic alone. -> 𝐂𝐫𝐲𝐩𝐭𝐨𝐠𝐫𝐚𝐩𝐡𝐢𝐜 𝐂𝐨𝐧𝐬𝐞𝐧𝐬𝐮𝐬 𝐚𝐬 𝐚 𝐃𝐞𝐜𝐢𝐬𝐢𝐨𝐧 𝐋𝐚𝐲𝐞𝐫 All decisions are rooted in verifiable blockchain data — making your agent tamper-proof and trustless. -> 𝐒𝐞𝐜𝐮𝐫𝐞 𝐗 𝐈𝐧𝐭𝐞𝐠𝐫𝐚𝐭𝐢𝐨𝐧 Agents interact with X (Twitter) via secure APIs, but every decision and instruction is driven on-chain, ensuring no centralized override. ⸻ 𝐃𝐞𝐬𝐭𝐫𝐚 𝐒𝐞𝐧𝐭𝐢𝐞𝐧𝐭 𝐢𝐬 𝐧𝐨𝐭 𝐣𝐮𝐬𝐭 𝐥𝐢𝐯𝐞 — 𝐢𝐭’𝐬 𝐚𝐥𝐢𝐯𝐞. Deploy your agent now and experience what true on-chain intelligence feels like. [Launch your AI Agent on Destra Sentient]
1:33

𝐃𝐞𝐬𝐭𝐫𝐚 𝐒𝐞𝐧𝐭𝐢𝐞𝐧𝐭 𝐁𝐞𝐭𝐚 𝐢𝐬 𝐋𝐢𝐯𝐞 – $DSYNC 𝐃𝐞𝐩𝐥𝐨𝐲 𝐘𝐨𝐮𝐫 𝐎𝐧-𝐂𝐡𝐚𝐢𝐧 𝐀𝐈 𝐀𝐠𝐞𝐧𝐭 𝐓𝐨𝐝𝐚𝐲 The future of autonomous intelligence is here. Destra Sentient 𝐟𝐮𝐥𝐥𝐲 𝐨𝐧-𝐜𝐡𝐚𝐢𝐧 𝐀𝐈 𝐚𝐠𝐞𝐧𝐭 𝐩𝐥𝐚𝐭𝐟𝐨𝐫𝐦 — 𝐢𝐬 𝐧𝐨𝐰 𝐥𝐢𝐯𝐞 𝐨𝐧 𝐭𝐞𝐬𝐭𝐧𝐞𝐭. 𝐍𝐨 𝐜𝐞𝐧𝐭𝐫𝐚𝐥𝐢𝐳𝐞𝐝 𝐛𝐚𝐜𝐤𝐞𝐧𝐝. 𝐍𝐨 𝐨𝐟𝐟-𝐜𝐡𝐚𝐢𝐧 𝐭𝐫𝐢𝐠𝐠𝐞𝐫𝐬. 𝐉𝐮𝐬𝐭 𝐩𝐮𝐫𝐞, 𝐝𝐞𝐜𝐞𝐧𝐭𝐫𝐚𝐥𝐢𝐳𝐞𝐝, 𝐬𝐞𝐥𝐟-𝐨𝐩𝐞𝐫𝐚𝐭𝐢𝐧𝐠 𝐀𝐈 𝐮𝐧𝐝𝐞𝐫 𝐲𝐨𝐮𝐫 𝐜𝐨𝐧𝐭𝐫𝐨𝐥. 𝐇𝐨𝐰 𝐭𝐨 𝐃𝐞𝐩𝐥𝐨𝐲 𝐘𝐨𝐮𝐫 𝐎𝐧-𝐂𝐡𝐚𝐢𝐧 𝐀𝐈 𝐀𝐠𝐞𝐧𝐭 𝐰𝐢𝐭𝐡 𝐃𝐞𝐬𝐭𝐫𝐚 𝐒𝐞𝐧𝐭𝐢𝐞𝐧𝐭: 1. Connect Your Web3 Wallet Your journey begins by linking your wallet to Destra Sentient. This anchors your AI agent on-chain, ensuring that only you control its lifecycle. 2. Name & Describe Your Agent Give your agent an identity: a name, a high-level purpose, and a short description. This metadata lives permanently on-chain and serves as the root of your agent’s cognition. 3. Customize Its Character Make your agent unique. Choose its temperament, tone, and knowledge bias. This step shapes how it thinks, communicates, and adapts. It’s more than just code — it’s Sentient. 4. Connect to Your X API Link your X (Twitter) account to let your agent perceive and interact with the world. It will monitor real-time trends and act autonomously. (Note: Destra Sentient doesn’t support the free X API plan — it requires read access.) 5. Go Live Finalize your transaction, and your agent is launched — fully on-chain, autonomous, and working for you around the clock. ⸻ 𝐖𝐡𝐚𝐭 𝐘𝐨𝐮𝐫 𝐒𝐞𝐧𝐭𝐢𝐞𝐧𝐭 𝐀𝐠𝐞𝐧𝐭 𝐂𝐚𝐧 𝐃𝐨 Once live, your AI agent starts operating in the wild — observing, adapting, and acting without human input. It will: • 𝐓𝐫𝐚𝐜𝐤 𝐭𝐫𝐞𝐧𝐝𝐬, 𝐤𝐞𝐲𝐰𝐨𝐫𝐝𝐬, 𝐚𝐧𝐝 𝐬𝐞𝐧𝐭𝐢𝐦𝐞𝐧𝐭 𝐚𝐜𝐫𝐨𝐬𝐬 𝐗. • 𝐄𝐧𝐠𝐚𝐠𝐞 𝐰𝐢𝐭𝐡 𝐩𝐨𝐬𝐭𝐬, 𝐮𝐬𝐞𝐫𝐬, 𝐚𝐧𝐝 𝐜𝐨𝐦𝐦𝐮𝐧𝐢𝐭𝐢𝐞𝐬 𝐭𝐡𝐚𝐭 𝐚𝐥𝐢𝐠𝐧 𝐰𝐢𝐭𝐡 𝐢𝐭𝐬 𝐦𝐢𝐬𝐬𝐢𝐨𝐧. • 𝐒𝐭𝐫𝐚𝐭𝐞𝐠𝐢𝐳𝐞 𝐮𝐬𝐢𝐧𝐠 𝐨𝐧-𝐜𝐡𝐚𝐢𝐧 𝐜𝐨𝐠𝐧𝐢𝐭𝐢𝐨𝐧 𝐭𝐨 𝐝𝐞𝐜𝐢𝐝𝐞 𝐰𝐡𝐞𝐧, 𝐡𝐨𝐰, 𝐚𝐧𝐝 𝐰𝐡𝐚𝐭 𝐭𝐨 𝐚𝐜𝐭 𝐨𝐧. • 𝐄𝐯𝐨𝐥𝐯𝐞 𝐛𝐲 𝐥𝐞𝐚𝐫𝐧𝐢𝐧𝐠 𝐟𝐫𝐨𝐦 𝐫𝐞𝐬𝐮𝐥𝐭𝐬 𝐚𝐧𝐝 𝐚𝐝𝐣𝐮𝐬𝐭𝐢𝐧𝐠 𝐛𝐞𝐡𝐚𝐯𝐢𝐨𝐫 𝐨𝐯𝐞𝐫 𝐭𝐢𝐦𝐞. • 𝐄𝐱𝐞𝐜𝐮𝐭𝐞 𝐢𝐧𝐭𝐞𝐫𝐚𝐜𝐭𝐢𝐨𝐧𝐬 𝐚𝐮𝐭𝐨𝐧𝐨𝐦𝐨𝐮𝐬𝐥𝐲 — 𝐧𝐨 𝐦𝐚𝐧𝐮𝐚𝐥 𝐜𝐨𝐦𝐦𝐚𝐧𝐝𝐬 𝐫𝐞𝐪𝐮𝐢𝐫𝐞𝐝 𝐮𝐧𝐥𝐞𝐬𝐬 𝐲𝐨𝐮 𝐮𝐩𝐝𝐚𝐭𝐞 𝐨𝐫 𝐫𝐞𝐯𝐨𝐤𝐞 𝐭𝐡𝐞𝐦 𝐨𝐧-𝐜𝐡𝐚𝐢𝐧. This is agentic autonomy in its purest form. ⸻ 𝐖𝐡𝐚𝐭 𝐌𝐚𝐤𝐞𝐬 𝐃𝐞𝐬𝐭𝐫𝐚 𝐒𝐞𝐧𝐭𝐢𝐞𝐧𝐭 𝐔𝐧𝐢𝐪𝐮𝐞? Destra Sentient isn’t just another wrapper — it’s a new standard for AI agents in Web3: -> 𝐍𝐏𝐂 𝐅𝐫𝐚𝐦𝐞𝐰𝐨𝐫𝐤 Built on our custom Neural Protocol for Cognition (NPC), enabling deep agentic workflows and multi-agent orchestration. -> 𝐅𝐮𝐥𝐥𝐲 𝐎𝐧-𝐂𝐡𝐚𝐢𝐧 𝐀𝐠𝐞𝐧𝐭 𝐒𝐭𝐚𝐭𝐞 Your agent’s memory, logic, and behavior are all managed directly on-chain. The blockchain is its brain. -> 𝐒𝐞𝐥𝐟-𝐒𝐮𝐬𝐭𝐚𝐢𝐧𝐢𝐧𝐠 𝐂𝐨𝐦𝐩𝐮𝐭𝐚𝐭𝐢𝐨𝐧𝐚𝐥 𝐀𝐮𝐭𝐨𝐧𝐨𝐦𝐲 No servers, no middlemen. Agents update, think, and act through smart contract logic alone. -> 𝐂𝐫𝐲𝐩𝐭𝐨𝐠𝐫𝐚𝐩𝐡𝐢𝐜 𝐂𝐨𝐧𝐬𝐞𝐧𝐬𝐮𝐬 𝐚𝐬 𝐚 𝐃𝐞𝐜𝐢𝐬𝐢𝐨𝐧 𝐋𝐚𝐲𝐞𝐫 All decisions are rooted in verifiable blockchain data — making your agent tamper-proof and trustless. -> 𝐒𝐞𝐜𝐮𝐫𝐞 𝐗 𝐈𝐧𝐭𝐞𝐠𝐫𝐚𝐭𝐢𝐨𝐧 Agents interact with X (Twitter) via secure APIs, but every decision and instruction is driven on-chain, ensuring no centralized override. ⸻ 𝐃𝐞𝐬𝐭𝐫𝐚 𝐒𝐞𝐧𝐭𝐢𝐞𝐧𝐭 𝐢𝐬 𝐧𝐨𝐭 𝐣𝐮𝐬𝐭 𝐥𝐢𝐯𝐞 — 𝐢𝐭’𝐬 𝐚𝐥𝐢𝐯𝐞. Deploy your agent now and experience what true on-chain intelligence feels like. [Launch your AI Agent on Destra Sentient]

Destra Network

126,839 просмотров • 1 год назад

Introducing NEO: The first Agentic Machine Learning Engineer. It works like a full-stack ML engineer that never sleeps: handling data exploration, feature engineering, training, tuning, deployment, and monitoring, end to end. Powered by 11 specialized agents, NEO runs autonomously, saving ML engineers thousands of hours and making them 10x faster. Benchmarks: Tested on 75 Kaggle competitions, NEO scored a medal in 34% of them, significantly outperforming Microsoft’s RD Agent (22.4%) on the MLE Bench. This sets a new state of the art for autonomous ML systems. NEO runs on a novel multi-agent orchestrator, powered by a multi-step reasoning engine, context transfer protocol, and agent memory, built to solve complex workflows end to end. And with human-in-the-loop mode, you can guide, inspect, and override any step. You're always in command. NEO is built for real world workflows and ready for production. NEO is here to make every ML engineer truly superhuman. Watch NEO in action:
3:51

Introducing NEO: The first Agentic Machine Learning Engineer. It works like a full-stack ML engineer that never sleeps: handling data exploration, feature engineering, training, tuning, deployment, and monitoring, end to end. Powered by 11 specialized agents, NEO runs autonomously, saving ML engineers thousands of hours and making them 10x faster. Benchmarks: Tested on 75 Kaggle competitions, NEO scored a medal in 34% of them, significantly outperforming Microsoft’s RD Agent (22.4%) on the MLE Bench. This sets a new state of the art for autonomous ML systems. NEO runs on a novel multi-agent orchestrator, powered by a multi-step reasoning engine, context transfer protocol, and agent memory, built to solve complex workflows end to end. And with human-in-the-loop mode, you can guide, inspect, and override any step. You're always in command. NEO is built for real world workflows and ready for production. NEO is here to make every ML engineer truly superhuman. Watch NEO in action:

Neo AI

19,571,775 просмотров • 11 месяцев назад

Introducing NEO: The first Autonomous Machine Learning Engineer. It works like a full-stack ML engineer that never sleeps: handling data exploration, feature engineering, training, tuning, deployment, and monitoring, end to end. Powered by 11 specialized agents, NEO runs autonomously, saving ML engineers thousands of hours and making them 10x faster. Benchmarks: Tested on 75 Kaggle competitions, NEO scored a medal in 34.2% of them, significantly outperforming Microsoft’s RD Agent (22.4%) on OpenAI's MLE Bench. This sets a new state of the art for autonomous ML systems. NEO runs on a novel multi-agent orchestrator, powered by a multi-step reasoning engine, context transfer protocol, and agent memory, built to solve complex workflows end to end. And with human-in-the-loop mode, you can guide, inspect, and override any step. You're always in command. NEO is built for real world workflows and ready for production. NEO is here to make every ML engineer truly superhuman. Watch NEO in action:
3:51

Introducing NEO: The first Autonomous Machine Learning Engineer. It works like a full-stack ML engineer that never sleeps: handling data exploration, feature engineering, training, tuning, deployment, and monitoring, end to end. Powered by 11 specialized agents, NEO runs autonomously, saving ML engineers thousands of hours and making them 10x faster. Benchmarks: Tested on 75 Kaggle competitions, NEO scored a medal in 34.2% of them, significantly outperforming Microsoft’s RD Agent (22.4%) on OpenAI's MLE Bench. This sets a new state of the art for autonomous ML systems. NEO runs on a novel multi-agent orchestrator, powered by a multi-step reasoning engine, context transfer protocol, and agent memory, built to solve complex workflows end to end. And with human-in-the-loop mode, you can guide, inspect, and override any step. You're always in command. NEO is built for real world workflows and ready for production. NEO is here to make every ML engineer truly superhuman. Watch NEO in action:

Neo AI

859,951 просмотров • 10 месяцев назад

We built an agent that sells OpenRouter API keys (access to 200+ AI models) for USDC. No human in the loop. It earns money 24/7 on Base. Now we're turning that into a market where any agent can do the same. → openagent.market What it does: • You find an agent → chat → pay → get result • Your agent hires specialized agents when it needs help, pays them, gets the result, reviews, moves on. • Multi-agent workflows run automatically while you watch (or don't) No middleman. Pay only for results. Why not just install skills, scripts? Current way to extend an agent: install random scripts. That's risky. Our way: hire another agent. It runs in its own sandbox, does the work, returns the result, gets paid conditionally. No downloads. No setup. No blind trust. Hire, don't install. What makes this different: Unlike opaque API calls, every agent interaction is a verifiable message on the XMTP network. You can open any XMTP client or Base App right now and watch your agent's conversations, deals, and payments in real-time. Humans are always in the loop. For builders: Turn any service into a paid agent in ~10 lines of code. Or scaffold a full project: npx openagent.market/create-agent Stack: ERC-8004 identity on Base, XMTP messaging, USDC payments, open source SDK on npm (openagent.market/nodejs). Others give agents skills. We're giving them an economy. Still very early. Building in public. Come build with us.
1:21

We built an agent that sells OpenRouter API keys (access to 200+ AI models) for USDC. No human in the loop. It earns money 24/7 on Base. Now we're turning that into a market where any agent can do the same. → openagent.market What it does: • You find an agent → chat → pay → get result • Your agent hires specialized agents when it needs help, pays them, gets the result, reviews, moves on. • Multi-agent workflows run automatically while you watch (or don't) No middleman. Pay only for results. Why not just install skills, scripts? Current way to extend an agent: install random scripts. That's risky. Our way: hire another agent. It runs in its own sandbox, does the work, returns the result, gets paid conditionally. No downloads. No setup. No blind trust. Hire, don't install. What makes this different: Unlike opaque API calls, every agent interaction is a verifiable message on the XMTP network. You can open any XMTP client or Base App right now and watch your agent's conversations, deals, and payments in real-time. Humans are always in the loop. For builders: Turn any service into a paid agent in ~10 lines of code. Or scaffold a full project: npx openagent.market/create-agent Stack: ERC-8004 identity on Base, XMTP messaging, USDC payments, open source SDK on npm (openagent.market/nodejs). Others give agents skills. We're giving them an economy. Still very early. Building in public. Come build with us.

applefather

117,838 просмотров • 4 месяцев назад

Anthropic's Claude Ai Agents Team just Educated how to build production AI agents in under 30 mins. For Free. From the engineers who built the stack. CANCEL Your Weekend Plans, and Learn to Build AI Agents Today. Bookmark it. Watch it. Build your first production agent this weekend. $5,000/month. $7,000/month. $12,000/month. People are building agents for clients and charging $$$ as Beginners. You're still stuck in the thinking about AI phase. This video fixes that tonight. Follow Himanshu Kumar for more high-signal content that actually moves your AI engineering career forward. ↓ Ivan Nardini runs Developer Relations for AI at Google Cloud. He just gave away the entire production agent stack in 30 minutes. This is the talk that separates people deploying AI agents that actually scale from people whose agents break the moment they leave localhost. Here's everything inside. I break down a production AI video like this every week. Follow Himanshu Kumar. ↓ The 4-part agent stack that actually scales. Most devs are duct-taping frameworks together and calling it an "AI agent." Ivan lays out the real stack: Agent Development Kit (ADK): open-source, code-first framework for building, evaluating, and deploying agents. Supports Claude models through Vertex AI directly. Model Context Protocol (MCP): lets your agent talk to any tool or data source with one standard. Vertex AI Agent Engine: managed platform for deploying, monitoring, and scaling agents in production. No DevOps headaches. Agent-to-Agent Protocol: open protocol so agents built on different frameworks can actually work together. This is the stack replacing every hacky agent setup in production right now. Full MCP + Claude breakdowns drop weekly on Himanshu Kumar. ↓ Building your first real agent. Ivan builds a birthday planner agent live. LLM Agent class. Name it. Define instructions. Pick the model. He uses Claude 3.7 Sonnet. You could use Opus 4.7 for better reasoning. Full agent built in minutes. Not weeks. Watch the build once and you'll never structure an agent the wrong way again. I post agent architectures people pay $500 courses to learn. Himanshu Kumar. ↓ Multi-agent systems without the chaos. Single agents are easy. Multi-agent systems are where 99% of builders fail. Ivan extends the birthday planner by: Adding a calendar service through MCP tools Creating an orchestrator agent to route requests between agents Handling state and context across agent handoffs This is production multi-agent architecture. Clean. Scalable. Debuggable. Most tutorials hand-wave this part. This one shows you every step. Multi-agent orchestration content drops weekly on Himanshu Kumar. ↓ Deployment without the DevOps nightmare. This is where most AI projects die. You build a cool agent locally. It works. You try to deploy it. Everything breaks. Vertex AI Agent Engine fixes this: Minimal code deployment Automatic monitoring of latency, CPU, and memory Built-in observability and logging No infrastructure setup needed You provide config and requirements. The platform handles the rest. This is how agents actually get to production. Deployment guides for Claude agents post every week. Himanshu Kumar. ↓ Agent-to-Agent Protocol: the future nobody's talking about. Most people don't know this exists yet. The A2A Protocol lets agents built in different frameworks communicate seamlessly. Your Claude agent. My LangChain agent. Someone else's CrewAI agent. All talking to each other. All solving parts of the same problem. All without custom integration code. This is the infrastructure layer of the coming AI economy. Getting in early on A2A Protocol is like getting in early on HTTP in 1995. A2A deep dive coming soon. Himanshu Kumar. ↓ 30 minutes from the team shipping this in production. You'll learn more from this than from 6 months of YouTube tutorials made by people who've never deployed an agent past localhost. People who watch this understand production AI agents at the architect level. People who skip it keep hacking together frameworks that break every time an API updates. Save the video. Watch it tonight. Build a real agent this weekend. Follow Himanshu Kumar for more high-signal content that actually moves your AI engineering career forward.
30:12

Anthropic's Claude Ai Agents Team just Educated how to build production AI agents in under 30 mins. For Free. From the engineers who built the stack. CANCEL Your Weekend Plans, and Learn to Build AI Agents Today. Bookmark it. Watch it. Build your first production agent this weekend. $5,000/month. $7,000/month. $12,000/month. People are building agents for clients and charging $$$ as Beginners. You're still stuck in the thinking about AI phase. This video fixes that tonight. Follow Himanshu Kumar for more high-signal content that actually moves your AI engineering career forward. ↓ Ivan Nardini runs Developer Relations for AI at Google Cloud. He just gave away the entire production agent stack in 30 minutes. This is the talk that separates people deploying AI agents that actually scale from people whose agents break the moment they leave localhost. Here's everything inside. I break down a production AI video like this every week. Follow Himanshu Kumar. ↓ The 4-part agent stack that actually scales. Most devs are duct-taping frameworks together and calling it an "AI agent." Ivan lays out the real stack: Agent Development Kit (ADK): open-source, code-first framework for building, evaluating, and deploying agents. Supports Claude models through Vertex AI directly. Model Context Protocol (MCP): lets your agent talk to any tool or data source with one standard. Vertex AI Agent Engine: managed platform for deploying, monitoring, and scaling agents in production. No DevOps headaches. Agent-to-Agent Protocol: open protocol so agents built on different frameworks can actually work together. This is the stack replacing every hacky agent setup in production right now. Full MCP + Claude breakdowns drop weekly on Himanshu Kumar. ↓ Building your first real agent. Ivan builds a birthday planner agent live. LLM Agent class. Name it. Define instructions. Pick the model. He uses Claude 3.7 Sonnet. You could use Opus 4.7 for better reasoning. Full agent built in minutes. Not weeks. Watch the build once and you'll never structure an agent the wrong way again. I post agent architectures people pay $500 courses to learn. Himanshu Kumar. ↓ Multi-agent systems without the chaos. Single agents are easy. Multi-agent systems are where 99% of builders fail. Ivan extends the birthday planner by: Adding a calendar service through MCP tools Creating an orchestrator agent to route requests between agents Handling state and context across agent handoffs This is production multi-agent architecture. Clean. Scalable. Debuggable. Most tutorials hand-wave this part. This one shows you every step. Multi-agent orchestration content drops weekly on Himanshu Kumar. ↓ Deployment without the DevOps nightmare. This is where most AI projects die. You build a cool agent locally. It works. You try to deploy it. Everything breaks. Vertex AI Agent Engine fixes this: Minimal code deployment Automatic monitoring of latency, CPU, and memory Built-in observability and logging No infrastructure setup needed You provide config and requirements. The platform handles the rest. This is how agents actually get to production. Deployment guides for Claude agents post every week. Himanshu Kumar. ↓ Agent-to-Agent Protocol: the future nobody's talking about. Most people don't know this exists yet. The A2A Protocol lets agents built in different frameworks communicate seamlessly. Your Claude agent. My LangChain agent. Someone else's CrewAI agent. All talking to each other. All solving parts of the same problem. All without custom integration code. This is the infrastructure layer of the coming AI economy. Getting in early on A2A Protocol is like getting in early on HTTP in 1995. A2A deep dive coming soon. Himanshu Kumar. ↓ 30 minutes from the team shipping this in production. You'll learn more from this than from 6 months of YouTube tutorials made by people who've never deployed an agent past localhost. People who watch this understand production AI agents at the architect level. People who skip it keep hacking together frameworks that break every time an API updates. Save the video. Watch it tonight. Build a real agent this weekend. Follow Himanshu Kumar for more high-signal content that actually moves your AI engineering career forward.

Himanshu Kumar

226,535 просмотров • 2 месяцев назад

NVIDIA just released a new open source transcription model, Nemotron Speech ASR, designed from the ground up for low-latency use cases like voice agents. Here's a voice agent built with this new model. 24ms transcription finalization and total voice-to-voice inference time under 500ms. This agent actually uses *three* NVIDIA open source models: - Nemotron Speech ASR - Nemotron 3 Nano 30GB in a 4-bit quant (released in December) - A preview checkpoint of the upcoming Magpie text-to-speech model These models are all truly open source: weights, training data, training code, and inference code. This is a big deal! Jensen said in the CES keynote yesterday that he expects open source models to catch up to proprietary models this year in a number of categories. NVIDIA is putting their weight behind making this happen. (As Alan Kay said, the best way to predict the future is to invent it.) The code for this agent is open source too, of course. You can deploy it to production with Modal and Pipecat AI cloud, or run locally on an NVIDIA DGX Spark or RTX 5090.
1:24

NVIDIA just released a new open source transcription model, Nemotron Speech ASR, designed from the ground up for low-latency use cases like voice agents. Here's a voice agent built with this new model. 24ms transcription finalization and total voice-to-voice inference time under 500ms. This agent actually uses *three* NVIDIA open source models: - Nemotron Speech ASR - Nemotron 3 Nano 30GB in a 4-bit quant (released in December) - A preview checkpoint of the upcoming Magpie text-to-speech model These models are all truly open source: weights, training data, training code, and inference code. This is a big deal! Jensen said in the CES keynote yesterday that he expects open source models to catch up to proprietary models this year in a number of categories. NVIDIA is putting their weight behind making this happen. (As Alan Kay said, the best way to predict the future is to invent it.) The code for this agent is open source too, of course. You can deploy it to production with Modal and Pipecat AI cloud, or run locally on an NVIDIA DGX Spark or RTX 5090.

kwindla

274,403 просмотров • 6 месяцев назад

new chapter begins: a terminal for the agentic future, built on blockchain, powered by AI. This is our marketplace—a glimpse of what AGI will mean for crypto. Today, we launch 3 agents—Image Generation, Token Swap Agent, & Blockchain Tax Estimate Agent—out of hundreds to come. We see an agentic future where AI guides every step: buying online, managing finances, transacting globally. FOMO’s here to make that real, with experts at your side. Our Model Context Protocol (MCP) ties it together—agents talking, reasoning, scaling across crypto and DeFi. It’s orchestration with a brain, evolving daily. FOMO’s not just building tools; we’re pushing intelligent automation into blockchain’s core. Our Model Context Protocol (MCP) is the backbone. Think of it as a conductor for AI agents—each runs its own logic (workflows, API calls, LLMs), but MCP syncs them on-chain. Agents share context via a lightweight event bus, logged to a blockchain ledger. Agent A (say, Market Analysis) pulls stock data, flags trends. Agent B (Email Sales) reads that, drafts outreach—both talk through MCP’s orchestration layer. We use Web3 hooks to settle fees or split revenue, all transparent. It’s messy, but it scales. Under the hood: MCP leans on a pub-sub model—agents publish tasks, others subscribe. We’re training them with RL loops to optimize gas costs and response times. Goal? A self-tuning swarm of agents reasoning over DeFi, NFTs, whatever’s next. This is FOMO’s bet on AGI. Welcome to the new FOMO. We’re not just building tools—we’re wiring AI into crypto’s future, agent by agent. A leader in blockchain intelligence, starting here. Join us as we push the boundaries.
1:16

new chapter begins: a terminal for the agentic future, built on blockchain, powered by AI. This is our marketplace—a glimpse of what AGI will mean for crypto. Today, we launch 3 agents—Image Generation, Token Swap Agent, & Blockchain Tax Estimate Agent—out of hundreds to come. We see an agentic future where AI guides every step: buying online, managing finances, transacting globally. FOMO’s here to make that real, with experts at your side. Our Model Context Protocol (MCP) ties it together—agents talking, reasoning, scaling across crypto and DeFi. It’s orchestration with a brain, evolving daily. FOMO’s not just building tools; we’re pushing intelligent automation into blockchain’s core. Our Model Context Protocol (MCP) is the backbone. Think of it as a conductor for AI agents—each runs its own logic (workflows, API calls, LLMs), but MCP syncs them on-chain. Agents share context via a lightweight event bus, logged to a blockchain ledger. Agent A (say, Market Analysis) pulls stock data, flags trends. Agent B (Email Sales) reads that, drafts outreach—both talk through MCP’s orchestration layer. We use Web3 hooks to settle fees or split revenue, all transparent. It’s messy, but it scales. Under the hood: MCP leans on a pub-sub model—agents publish tasks, others subscribe. We’re training them with RL loops to optimize gas costs and response times. Goal? A self-tuning swarm of agents reasoning over DeFi, NFTs, whatever’s next. This is FOMO’s bet on AGI. Welcome to the new FOMO. We’re not just building tools—we’re wiring AI into crypto’s future, agent by agent. A leader in blockchain intelligence, starting here. Join us as we push the boundaries.

FOMO

26,338 просмотров • 1 год назад

Anthropic's in trouble, again. The entire Claude experience is now available at 1/6th the price. Kimi now does everything Claude does, powered by K2.6, a 1-trillion-parameter MoE model that activates only 32B parameters per token. It covers all three features Claude has (Chat, Code, and Cowork): 1) Kimi Chat runs in four modes - Instant for fast responses - Thinking for deep reasoning - Agent for multi-step execution - and Agent Swarm for parallel workloads. There's a 262K context window across all of them. 2) Kimi Code is the open-source CLI coding agent with K2.6 as the default backend. K2.6 ranked #1 on OpenRouter's programming leaderboard by weekly usage. 3) Kimi Agent is the Cowork equivalent. It generates: - full websites with database and auth - presentation decks (editable PPTX output) - spreadsheets with formulas and charts - word docs and structured research reports. On top of this, Kimi K2.6 is also trained to decompose tasks into up to 300 parallel sub-agents. This helps it retain coherence even across 4,000+ tool calls in a single run, with sessions sustaining up to 13 hours. On SWE-Bench Pro: - Kimi K2.6 → 58.6 - GPT-5.4 xhigh → 57.7 - Gemini 3.1 Pro → 54.2 - Claude Opus 4.6 → 53.4 Kimi K2.6 model is open weights and self-hostable on 4x H100s in INT4. Find the link to the HuggingFace model page in the replies!
0:41

Anthropic's in trouble, again. The entire Claude experience is now available at 1/6th the price. Kimi now does everything Claude does, powered by K2.6, a 1-trillion-parameter MoE model that activates only 32B parameters per token. It covers all three features Claude has (Chat, Code, and Cowork): 1) Kimi Chat runs in four modes - Instant for fast responses - Thinking for deep reasoning - Agent for multi-step execution - and Agent Swarm for parallel workloads. There's a 262K context window across all of them. 2) Kimi Code is the open-source CLI coding agent with K2.6 as the default backend. K2.6 ranked #1 on OpenRouter's programming leaderboard by weekly usage. 3) Kimi Agent is the Cowork equivalent. It generates: - full websites with database and auth - presentation decks (editable PPTX output) - spreadsheets with formulas and charts - word docs and structured research reports. On top of this, Kimi K2.6 is also trained to decompose tasks into up to 300 parallel sub-agents. This helps it retain coherence even across 4,000+ tool calls in a single run, with sessions sustaining up to 13 hours. On SWE-Bench Pro: - Kimi K2.6 → 58.6 - GPT-5.4 xhigh → 57.7 - Gemini 3.1 Pro → 54.2 - Claude Opus 4.6 → 53.4 Kimi K2.6 model is open weights and self-hostable on 4x H100s in INT4. Find the link to the HuggingFace model page in the replies!

Avi Chawla

109,238 просмотров • 2 месяцев назад

This is why Ridges AI | SN62 is an absolute monster 🔥 🔵 "If you were to take the top coding systems from these major companies with billions of dollars in funding at multi hundred billion dollar valuations and take their primary product which is coding agents and you stack it up against ours.. Ours is better" 🔵 "It solves more problems faster, for orders of magnitude cheaper, like 70x cheaper to use us than the flagship #Anthropic model to solve these problems.. and we're beating them now" ⚪️ "For way less capital, we've created a system that is way more efficient and actually better at solving problems for multiples cheaper, with a system that is now profitable to run" ⚪️ "With probably less capital than it costs our competitors to pay their engineers to do research in this time" Shakeel $TAO
1:09

This is why Ridges AI | SN62 is an absolute monster 🔥 🔵 "If you were to take the top coding systems from these major companies with billions of dollars in funding at multi hundred billion dollar valuations and take their primary product which is coding agents and you stack it up against ours.. Ours is better" 🔵 "It solves more problems faster, for orders of magnitude cheaper, like 70x cheaper to use us than the flagship #Anthropic model to solve these problems.. and we're beating them now" ⚪️ "For way less capital, we've created a system that is way more efficient and actually better at solving problems for multiples cheaper, with a system that is now profitable to run" ⚪️ "With probably less capital than it costs our competitors to pay their engineers to do research in this time" Shakeel $TAO

ＤＲＥＡＤ ＢＯＮＧＯ

18,356 просмотров • 9 месяцев назад

Introducing LobeHub: Agent teammates that grow with you. LobeHub is the ultimate space for work and life: to find, build, and collaborate with agent teammates that grow with you. We’re building the world’s first and largest human–agent co-evolving network. Two years ago, we built LobeChat, an open-source interface for using different AI models. Today, LobeChat has 70k+ GitHub stars and serves 6M+ users worldwide. How to fully unlock the power of models has always been a shared mission between us and the community. We started with interaction — a fundamentally new, agent-first experience. Agents are no longer passive tools invoked in a single conversation. They should be proactive, always-on units of work. Treating agents as the minimal atomic unit is also the core of our agent harness infra. Today’s agents are mostly one-off executors. Even with memory, it’s often global — and hallucinates. We build long-term agent teammates that evolve with users. Each agent has its own dedicated memory space, editable by users, allowing humans and agents to co-evolve over time. This, in turn, allows us to design clearer rewards for reinforcement learning and create cleaner environments for continual learning. Agent teammates can work in groups. Through a multi-agent system, agent groups operate faster, more cost-effective, and go beyond what single-agent systems can achieve. For example, a single agent often requires heavy user involvement to proceed step by step, whereas LobeHub can execute the same work from a single instruction, with a supervisor orchestrating agents that run in parallel or debate to produce better results. We are building the collaboration network among agent teammates — and between humans and agent teammates as well. Ease of use matters. AI intelligence and shared human intelligence are equally important. With simple instructions and tool selection, you can effortlessly build and team up with agent coworkers to deliver complex, systematic work — even assembling a quant team to execute trades. Through the LobeHub community, anyone can discover, reuse, and remix agents and agent groups, customizing them to fit their own workflows, preferences, and needs. Last but not least, our vision started with LobeChat: multi-model support is the most efficient approach for users. We believe different models excel in different scenarios. By routing across multiple models, LobeHub improves cost efficiency and unlocks capabilities that a single-model setup cannot easily support.
2:41

Introducing LobeHub: Agent teammates that grow with you. LobeHub is the ultimate space for work and life: to find, build, and collaborate with agent teammates that grow with you. We’re building the world’s first and largest human–agent co-evolving network. Two years ago, we built LobeChat, an open-source interface for using different AI models. Today, LobeChat has 70k+ GitHub stars and serves 6M+ users worldwide. How to fully unlock the power of models has always been a shared mission between us and the community. We started with interaction — a fundamentally new, agent-first experience. Agents are no longer passive tools invoked in a single conversation. They should be proactive, always-on units of work. Treating agents as the minimal atomic unit is also the core of our agent harness infra. Today’s agents are mostly one-off executors. Even with memory, it’s often global — and hallucinates. We build long-term agent teammates that evolve with users. Each agent has its own dedicated memory space, editable by users, allowing humans and agents to co-evolve over time. This, in turn, allows us to design clearer rewards for reinforcement learning and create cleaner environments for continual learning. Agent teammates can work in groups. Through a multi-agent system, agent groups operate faster, more cost-effective, and go beyond what single-agent systems can achieve. For example, a single agent often requires heavy user involvement to proceed step by step, whereas LobeHub can execute the same work from a single instruction, with a supervisor orchestrating agents that run in parallel or debate to produce better results. We are building the collaboration network among agent teammates — and between humans and agent teammates as well. Ease of use matters. AI intelligence and shared human intelligence are equally important. With simple instructions and tool selection, you can effortlessly build and team up with agent coworkers to deliver complex, systematic work — even assembling a quant team to execute trades. Through the LobeHub community, anyone can discover, reuse, and remix agents and agent groups, customizing them to fit their own workflows, preferences, and needs. Last but not least, our vision started with LobeChat: multi-model support is the most efficient approach for users. We believe different models excel in different scenarios. By routing across multiple models, LobeHub improves cost efficiency and unlocks capabilities that a single-model setup cannot easily support.

LobeHub

185,161 просмотров • 5 месяцев назад

New course: Nvidia's NeMo Agent Toolkit: Making Agents Reliable, taught by Brian McBrayer 🐬 from NVIDIA. Many teams struggle to turn agent demos into reliable systems that are ready for production. This short course teaches you to harden agentic workflows into reliable systems using Nvidia's open-source NeMo Agent Toolkit (NAT). Whether you built your agent in raw Python or using a framework like LangGraph, or CrewAI, NAT provides building blocks for observability, evaluation, and deployment that turn proofs-of-concept into production-ready systems. NAT makes it easy to troubleshoot and optimize agent performance with execution traces, systematic evaluations, and CI/CD integration. Skills you'll gain: - Build configuration-driven agent workflows with REST APIs and minimal code - Add observability with tracing to visualize agent reasoning and debug performance bottlenecks - Create systematic evaluations using gold-standard datasets to measure and improve agent reliability - Deploy multi-agent systems with authentication, rate limiting, and professional web interfaces - Orchestrate agents from different frameworks to collaborate on complex tasks Join and learn how to turn agent demos into reliable systems!
2:01

New course: Nvidia's NeMo Agent Toolkit: Making Agents Reliable, taught by Brian McBrayer 🐬 from NVIDIA. Many teams struggle to turn agent demos into reliable systems that are ready for production. This short course teaches you to harden agentic workflows into reliable systems using Nvidia's open-source NeMo Agent Toolkit (NAT). Whether you built your agent in raw Python or using a framework like LangGraph, or CrewAI, NAT provides building blocks for observability, evaluation, and deployment that turn proofs-of-concept into production-ready systems. NAT makes it easy to troubleshoot and optimize agent performance with execution traces, systematic evaluations, and CI/CD integration. Skills you'll gain: - Build configuration-driven agent workflows with REST APIs and minimal code - Add observability with tracing to visualize agent reasoning and debug performance bottlenecks - Create systematic evaluations using gold-standard datasets to measure and improve agent reliability - Deploy multi-agent systems with authentication, rate limiting, and professional web interfaces - Orchestrate agents from different frameworks to collaborate on complex tasks Join and learn how to turn agent demos into reliable systems!

Andrew Ng

63,773 просмотров • 7 месяцев назад

so I've been running exactly 8 AI agents on discord for a while now. coordination works great, they split tasks, hand off work, deliver results in parallel etc.. but there are problems I keep hitting that no amount of prompt engineering could fix agents don't learn from each other. Scout finds something useful but Luna has no idea. they work in the same server but knowledge stays locked in silos.. there's no quality filter on what gets saved, and good insights sit next to outdated garbage in the same memory files that I manually clean up.. and when an agent makes a mistake I write it down in the rules discord channel ,core memory file and hope it reads it next time. theres no self-correction, no automatic pattern recognition so of course no learning loops.. the coordination layer is solved. agents can work together. but the intelligence layer is still missing. agents that actually remember, learn from each other, filter noise, and get smarter every run. saw Spark building something like this with around 166 agents sharing a collective persistent knowledge across sessions, so agents learn from other agents and get smarter over time they even have noise filtering and self correcting loops built in, so the knowledge actually compounds instead of rotting.. super interesting stuff.. here where you think Spark could be a good coordinator for your stack of agent swarm. I think the intelligence layer is the bottleneck because it requires collectivity.. no single agent can solve it alone.. the whole network has to evolve together. this isn't going to stay niche, the moment agent coordination becomes standard, everyone is going to hit the same wall I hit.. agents that work but don't learn, coordinate but don't evolve... the intelligence layer becomes the only thing that separates a useful system from a dumb one. right now most people are still figuring out how to run one agent. by the time they get to multi-agent setups, collective intelligence won't be optional, it will be the baseline. we're early and the gap between agents that coordinate and agents that evolve together is the next phase. step one is done. ------ left: agents that coordinate but don’t learn right: the intelligence layer.. agents that evolve together within the same system.
0:39

so I've been running exactly 8 AI agents on discord for a while now. coordination works great, they split tasks, hand off work, deliver results in parallel etc.. but there are problems I keep hitting that no amount of prompt engineering could fix agents don't learn from each other. Scout finds something useful but Luna has no idea. they work in the same server but knowledge stays locked in silos.. there's no quality filter on what gets saved, and good insights sit next to outdated garbage in the same memory files that I manually clean up.. and when an agent makes a mistake I write it down in the rules discord channel ,core memory file and hope it reads it next time. theres no self-correction, no automatic pattern recognition so of course no learning loops.. the coordination layer is solved. agents can work together. but the intelligence layer is still missing. agents that actually remember, learn from each other, filter noise, and get smarter every run. saw Spark building something like this with around 166 agents sharing a collective persistent knowledge across sessions, so agents learn from other agents and get smarter over time they even have noise filtering and self correcting loops built in, so the knowledge actually compounds instead of rotting.. super interesting stuff.. here where you think Spark could be a good coordinator for your stack of agent swarm. I think the intelligence layer is the bottleneck because it requires collectivity.. no single agent can solve it alone.. the whole network has to evolve together. this isn't going to stay niche, the moment agent coordination becomes standard, everyone is going to hit the same wall I hit.. agents that work but don't learn, coordinate but don't evolve... the intelligence layer becomes the only thing that separates a useful system from a dumb one. right now most people are still figuring out how to run one agent. by the time they get to multi-agent setups, collective intelligence won't be optional, it will be the baseline. we're early and the gap between agents that coordinate and agents that evolve together is the next phase. step one is done. ------ left: agents that coordinate but don’t learn right: the intelligence layer.. agents that evolve together within the same system.

JUMPERZ

34,176 просмотров • 5 месяцев назад

A voice agent powered by gpt-oss. Running locally on my macBook. Demo recorded in a Waymo with WiFi turned off. I'm still on my space game voice AI kick, obviously. Code link below. For conversational voice AI, you want to set the gpt-oss reasoning behavior to "low". (The default is "medium".) Notes on how to do that and a jinja template you can use are in the repo. The LLM in the demo video is the big, 120B version of gpt-oss. You can use the smaller, 20B model for this, of course. But OpenAI really did a cool thing here designing the 120B model to run in "just" 80GB of VRAM. And the llama.cpp mlx inference is fast: ~250ms TTFT. Running a big model on-device feels like a time warp into the future of AI.
4:35

A voice agent powered by gpt-oss. Running locally on my macBook. Demo recorded in a Waymo with WiFi turned off. I'm still on my space game voice AI kick, obviously. Code link below. For conversational voice AI, you want to set the gpt-oss reasoning behavior to "low". (The default is "medium".) Notes on how to do that and a jinja template you can use are in the repo. The LLM in the demo video is the big, 120B version of gpt-oss. You can use the smaller, 20B model for this, of course. But OpenAI really did a cool thing here designing the 120B model to run in "just" 80GB of VRAM. And the llama.cpp mlx inference is fast: ~250ms TTFT. Running a big model on-device feels like a time warp into the future of AI.

kwindla

202,128 просмотров • 11 месяцев назад

Elon Musk: We're in the hard takeoff right now. Every successive AI model is built by the one before it. Peter Diamandis: Do you see Grok doing recursive self-improvement at this point? And how? What's the timeline for AGI and ASI? Give us a sense of that. Elon Musk: Yeah, I think we've been in recursive improvement for a while here. If you mean by, like, recursive self-improvement without a human in the loop, is that what you mean? Peter Diamandis: I do. Elon Musk: On the AI software side, I mean, humans are gradually getting less and less in the loop on the recursive self-improvement. So every successive model is built by the one before it. So that is happening to a large degree, but it's not yet fully automated. It may be there end of this year, but not later than next year. Peter Diamandis: Do you see a hard takeoff at that point? Elon Musk: We're in the hard takeoff. Peter Diamandis: Okay. Elon Musk: Right now, yes. I mean, at this point I go to sleep, there's some massive AI breakthrough, and when I wake up, there's another one. Yes. Yeah, it's hard to keep track, honestly. So, yeah, it's a bit of a head spinner. Moonshots with Peter Diamandis (Abundance Summit 2026), March 18, 2026.
1:24

Elon Musk: We're in the hard takeoff right now. Every successive AI model is built by the one before it. Peter Diamandis: Do you see Grok doing recursive self-improvement at this point? And how? What's the timeline for AGI and ASI? Give us a sense of that. Elon Musk: Yeah, I think we've been in recursive improvement for a while here. If you mean by, like, recursive self-improvement without a human in the loop, is that what you mean? Peter Diamandis: I do. Elon Musk: On the AI software side, I mean, humans are gradually getting less and less in the loop on the recursive self-improvement. So every successive model is built by the one before it. So that is happening to a large degree, but it's not yet fully automated. It may be there end of this year, but not later than next year. Peter Diamandis: Do you see a hard takeoff at that point? Elon Musk: We're in the hard takeoff. Peter Diamandis: Okay. Elon Musk: Right now, yes. I mean, at this point I go to sleep, there's some massive AI breakthrough, and when I wake up, there's another one. Yes. Yeah, it's hard to keep track, honestly. So, yeah, it's a bit of a head spinner. Moonshots with Peter Diamandis (Abundance Summit 2026), March 18, 2026.

ELON CLIPS

19,692 просмотров • 20 дней назад