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Anthropic has introduced an update to Claude Managed Agents, releasing several powerful new features designed to improve agentic workflows and autonomy. 🔹Dreaming (Research Preview): Agents can now "dream" by reviewing past sessions during idle time. This process extracts patterns, spots recurring mistakes, and curates memories so the agent continually...

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Wes Roth

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53,266 views • 3 months ago •via X (Twitter)

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Your enterprise content should power every AI tool and agent you use. With the Box MCP server, Box acts as a secure, governed bridge, so teams can search, retrieve, analyze, and act on Box content directly inside the tools they already use. No one-off integrations. Use it to: 🔹Ask questions over files in Anthropic Claude + Mistral AI Le Chat 🔹Ground designs in Figma or @ mention Box agents in Atlassian Jira 🔹Pull content into GitHub Copilot, Cursor + Claude Code 🔹Build agents with LangChain LangSmith Agent Builder + OpenAI Agent Builder 🔹Automate work in Claude Cowork + Amazon Web Services Quick Suite 🔹Enforce access + audit trails with Runlayer Secure. Standardized. Built for real work →
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Your enterprise content should power every AI tool and agent you use. With the Box MCP server, Box acts as a secure, governed bridge, so teams can search, retrieve, analyze, and act on Box content directly inside the tools they already use. No one-off integrations. Use it to: 🔹Ask questions over files in Anthropic Claude + Mistral AI Le Chat 🔹Ground designs in Figma or @ mention Box agents in Atlassian Jira 🔹Pull content into GitHub Copilot, Cursor + Claude Code 🔹Build agents with LangChain LangSmith Agent Builder + OpenAI Agent Builder 🔹Automate work in Claude Cowork + Amazon Web Services Quick Suite 🔹Enforce access + audit trails with Runlayer Secure. Standardized. Built for real work →

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Replit, Vercel, and OpenAI have built very cool agent-native applications, but nobody else has passed the demo stage. Building agents that work is complex. Teams aren't shipping agents because we don't have good tooling yet (and most of us don't know how to do this well.) A couple of days ago, the CopilotKit🪁 team announced a collaboration with . You can now use LangGraph with CoAgents to build agent-native applications, and here is everything you need to know about that: CoAgents is fully open-source, and you can use it to do the following: • Human-in-the-loop to steer and correct the agent • Stream intermediate agent state • Real-time state sharing between the agent and the application • Agentic generative UI to build trust that the agent is on the right path Start this GitHub Repository: Thanks to the team for giving me early access and collaborating with me on this post.
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Stop spending hours on manual work. You can now use a multi-agent AI workforce to get more work done in less time. Here's how 👇 --- Try Eigent AI - Lets you build and run a custom AI workforce on your desktop. - Automate complex workflows using multi-agent task execution. - Built on CAMEL-AI’s top open-source projects ( CAMEL-AI.org & OWL). - Boost productivity with deep customization and strong privacy --- Features: - Customize Your AI Workforce: Build task-specific agents with domain skills and tools. - Faster Execution: Eigent runs agents in parallel to automate complex workflows. - Human-in-the-loop: Automatically asks for help when tasks hit uncertainty. --- What sets Eigent apart? - 3–5× faster task execution using a parallel multi-agent workforce. - Modular design lets you add new capabilities without changing the core system. - Self-optimizing agents that replan and adapt during execution for higher success. - Deploy anywhere: cloud, local, or enterprise, with full open-source flexibility. --- Try building your multi-agent AI workforce here: Join their community to build your multi-agent workforce: Check their GitHub: ---

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

Bio Protocol

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ANTHROPIC 🔥: A new Managed Projects feature has been spotted in testing on Claude. > Claude takes on tasks and keeps the project organized. > A project is a home for one stream of work. Sessions share memory and instructions so context carries forward, and Claude runs more autonomously. Projects only create and manage cloud sessions. This feature may be built on top of Claude Managed Agents, where each project gets a dedicated cloud environment so Claude can execute periodic tasks and refine project context via "Dreams". It could also be a successor to Conway, which is set to be removed this Friday internally.
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ANTHROPIC 🔥: A new Managed Projects feature has been spotted in testing on Claude. > Claude takes on tasks and keeps the project organized. > A project is a home for one stream of work. Sessions share memory and instructions so context carries forward, and Claude runs more autonomously. Projects only create and manage cloud sessions. This feature may be built on top of Claude Managed Agents, where each project gets a dedicated cloud environment so Claude can execute periodic tasks and refine project context via "Dreams". It could also be a successor to Conway, which is set to be removed this Friday internally.

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61,827 views • 27 days ago

Karpathy's Agentic Engineering finally has proper tooling! (built by Google) Karpathy defined agentic engineering as the discipline that separates production agent work from vibe coding. The core skills he listed were spec design, eval loops, and security oversight. The problem has been that practicing this still requires a different tool for every phase: - editor for code - a terminal for scaffolding - a browser for testing - a cloud console for deployment - and a separate framework for evals. Every transition is a context switch. The solution to production-grade Agentic Engineering is now actually implemented in Google’s Agents CLI. It covers the entire workflow in one place for scaffolding, evaluating, and deploying ADK agents. One setup command injects 7 ADK-specific skills into a coding agent's context, which lets it handle scaffolding, evals, deployment, and enterprise registration through natural language. I tested this end-to-end by building a RAG agent from scratch using Claude Code. It scaffolded the full project from the ADK agentic_rag template, generated 20 eval scenarios with LLM-as-judge scoring, and returned a quantitative scorecard. Finally, it also deployed everything to Agent Runtime and registered the agent to Gemini Enterprise, so the entire org can discover and use it. The video below shows this in action, and I worked with the Google Cloud team to put this together. Agents CLI GitHub repo → (don't forget to star it ⭐ ) I wrote up the full build covering all six steps from install to enterprise registration. It includes the eval scorecard, the instruction loophole the eval caught before deployment, and what the deployment process actually looks like end-to-end. Read it below.
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Karpathy's Agentic Engineering finally has proper tooling! (built by Google) Karpathy defined agentic engineering as the discipline that separates production agent work from vibe coding. The core skills he listed were spec design, eval loops, and security oversight. The problem has been that practicing this still requires a different tool for every phase: - editor for code - a terminal for scaffolding - a browser for testing - a cloud console for deployment - and a separate framework for evals. Every transition is a context switch. The solution to production-grade Agentic Engineering is now actually implemented in Google’s Agents CLI. It covers the entire workflow in one place for scaffolding, evaluating, and deploying ADK agents. One setup command injects 7 ADK-specific skills into a coding agent's context, which lets it handle scaffolding, evals, deployment, and enterprise registration through natural language. I tested this end-to-end by building a RAG agent from scratch using Claude Code. It scaffolded the full project from the ADK agentic_rag template, generated 20 eval scenarios with LLM-as-judge scoring, and returned a quantitative scorecard. Finally, it also deployed everything to Agent Runtime and registered the agent to Gemini Enterprise, so the entire org can discover and use it. The video below shows this in action, and I worked with the Google Cloud team to put this together. Agents CLI GitHub repo → (don't forget to star it ⭐ ) I wrote up the full build covering all six steps from install to enterprise registration. It includes the eval scorecard, the instruction loophole the eval caught before deployment, and what the deployment process actually looks like end-to-end. Read it below.

Akshay 🚀

257,831 views • 1 month ago

Claude Cowork Sub-Agents are f*cking cracked 🤯 One prompt → 50 competitor ads analyzed, hooks extracted, and a full creative brief generated. 10 AI agents running in parallel, under 5 minutes. All inside Claude Cowork. Perfect for DTC brands and agencies who are still doing creative research and ad production one task at a time inside Claude. If you're analyzing competitor ads one by one, copying hooks into a spreadsheet manually, writing brief after brief from scratch, and watching Claude's output quality fall off a cliff after the 15th variation because the context window is completely bloated... Sub-agents eliminate the entire bottleneck: → Drop in a spreadsheet of 50 competitor ads and spin up 10 parallel sub-agents → Each sub-agent analyzes 5 ads simultaneously — hooks, angles, CTAs, emotional tone, creative format → They report structured summaries back to the main agent without bloating the context → The main agent synthesizes patterns across all 50 ads into a competitive intel brief → Then spin up another round of sub-agents to generate 30 ad copy variations across 10 personas → Each sub-agent writes for 1-2 personas in a fresh context — so variation 30 is as sharp as variation 1 No analyzing ads one at a time. No context window blowing up halfway through. No copy quality degrading after the first dozen variations. What this gives you: → 50 competitor ads broken down in minutes — hooks, angles, CTAs, formats, all structured → Pattern analysis across the full dataset that you'd miss reviewing ads individually → 30+ ad copy variations with persona-specific messaging that actually stays sharp → A workflow you can save as reusable skills and trigger with one command next time → The same output quality on the last task as the first Built 100% inside Claude Cowork with sub-agents. I put together a full DTC playbook: 5 bulk workflows with copy-paste prompts, the exact sub-agent prompting pattern, batching guidelines, and an honest breakdown of when this setup is worth it vs. when a simpler approach is the better move. Want it for free? > Like this post > Comment "AGENTS" And I'll send it over (must be following so I can DM)
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Claude Cowork Sub-Agents are f*cking cracked 🤯 One prompt → 50 competitor ads analyzed, hooks extracted, and a full creative brief generated. 10 AI agents running in parallel, under 5 minutes. All inside Claude Cowork. Perfect for DTC brands and agencies who are still doing creative research and ad production one task at a time inside Claude. If you're analyzing competitor ads one by one, copying hooks into a spreadsheet manually, writing brief after brief from scratch, and watching Claude's output quality fall off a cliff after the 15th variation because the context window is completely bloated... Sub-agents eliminate the entire bottleneck: → Drop in a spreadsheet of 50 competitor ads and spin up 10 parallel sub-agents → Each sub-agent analyzes 5 ads simultaneously — hooks, angles, CTAs, emotional tone, creative format → They report structured summaries back to the main agent without bloating the context → The main agent synthesizes patterns across all 50 ads into a competitive intel brief → Then spin up another round of sub-agents to generate 30 ad copy variations across 10 personas → Each sub-agent writes for 1-2 personas in a fresh context — so variation 30 is as sharp as variation 1 No analyzing ads one at a time. No context window blowing up halfway through. No copy quality degrading after the first dozen variations. What this gives you: → 50 competitor ads broken down in minutes — hooks, angles, CTAs, formats, all structured → Pattern analysis across the full dataset that you'd miss reviewing ads individually → 30+ ad copy variations with persona-specific messaging that actually stays sharp → A workflow you can save as reusable skills and trigger with one command next time → The same output quality on the last task as the first Built 100% inside Claude Cowork with sub-agents. I put together a full DTC playbook: 5 bulk workflows with copy-paste prompts, the exact sub-agent prompting pattern, batching guidelines, and an honest breakdown of when this setup is worth it vs. when a simpler approach is the better move. Want it for free? > Like this post > Comment "AGENTS" And I'll send it over (must be following so I can DM)

Mike Futia

50,130 views • 5 months ago

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

Polygon Stats

32,701 views • 1 year ago

LangGraph. CrewAI. Agno. Which one to pick? The good news is that this will not matter soon! Finally, we have a full picture of how the industry is solving this with just three open protocols that work across ALL frameworks. It's not about picking the best framework. Instead, it's about understanding how protocols create interoperability. The Agent Protocol Landscape shows how three complementary protocols are creating a universal language for Agents: > AG-UI (Agent-User Interaction): - The bi-directional connection between agentic backends and frontends. - This is how agents become truly interactive inside your apps, not just as chatbots, but collaborative co-workers. > MCP (Model Context Protocol): - The standard for how agents connect to tools, data, and workflows. > A2A (Agent-to-Agent): - The protocol for multi-agent coordination. - How agents delegate tasks and share intent across systems. These aren't competing standards. They're layers of the same stack and have handshakes with each other. So instead of building point-to-point integrations, you build to protocols. Moreover, you can integrate LangGraph, CrewAI, or Agno into the same frontend, without rewriting your UI logic. These protocols let everything work together. For instance: - Your LangGraph agent pulls data via MCP. - It delegates analysis to a CrewAI agent via A2A. - Results stream to your React app via AG-UI. - Users see real-time collaboration in your interface. This way, you can focus on building agent capabilities instead of integration mechanics. The protocols handle interoperability automatically. CopilotKit unifies this entire stack into one framework so you can build "Cursor for X" style apps without implementing each protocol from scratch. It gives you all three protocols, generative UI support, and production-ready infrastructure in one framework. I have shared this playbook in the replies! It breaks down handshakes, misconceptions, and real examples and shows exactly how to start building.
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LangGraph. CrewAI. Agno. Which one to pick? The good news is that this will not matter soon! Finally, we have a full picture of how the industry is solving this with just three open protocols that work across ALL frameworks. It's not about picking the best framework. Instead, it's about understanding how protocols create interoperability. The Agent Protocol Landscape shows how three complementary protocols are creating a universal language for Agents: > AG-UI (Agent-User Interaction): - The bi-directional connection between agentic backends and frontends. - This is how agents become truly interactive inside your apps, not just as chatbots, but collaborative co-workers. > MCP (Model Context Protocol): - The standard for how agents connect to tools, data, and workflows. > A2A (Agent-to-Agent): - The protocol for multi-agent coordination. - How agents delegate tasks and share intent across systems. These aren't competing standards. They're layers of the same stack and have handshakes with each other. So instead of building point-to-point integrations, you build to protocols. Moreover, you can integrate LangGraph, CrewAI, or Agno into the same frontend, without rewriting your UI logic. These protocols let everything work together. For instance: - Your LangGraph agent pulls data via MCP. - It delegates analysis to a CrewAI agent via A2A. - Results stream to your React app via AG-UI. - Users see real-time collaboration in your interface. This way, you can focus on building agent capabilities instead of integration mechanics. The protocols handle interoperability automatically. CopilotKit unifies this entire stack into one framework so you can build "Cursor for X" style apps without implementing each protocol from scratch. It gives you all three protocols, generative UI support, and production-ready infrastructure in one framework. I have shared this playbook in the replies! It breaks down handshakes, misconceptions, and real examples and shows exactly how to start building.

Avi Chawla

30,762 views • 9 months ago

Dynamic workflows are a generalization of harnesses, automations, loops, routing, and graphs. It's the most powerful feature I have built into my agent orchestrator. Supports all kinds of patterns that leverage different agent backends (claude, codex, pi, hermes,...). It's a meta-harness approach that unlocks new forms of test-time compute. Example of use cases it supports: > LLM councils to get different perspectives from LLMs or plan more intensively > Dynamically routing tasks to different agents based on needs (e.g., cost efficiency and optimal intelligence) > Advisor/Judge + executor workflows and pretty much any complex graph-based pattern required by the task. I find it especially useful for long-running work and code reviewing. > Agent teams that talk to each other if needed for the task. I like to use this for AI editing, artifact creation, and other creative tasks. And I am sure it supports so many things that I haven't discovered yet. I got inspired by the dynamic workflow feature released by the Claude Code team. I had actually built it earlier this year but wanted to generalize it across different agent backends. I think this is going to become more popular in the coming days. I will share more of my findings soon.
0:11

Dynamic workflows are a generalization of harnesses, automations, loops, routing, and graphs. It's the most powerful feature I have built into my agent orchestrator. Supports all kinds of patterns that leverage different agent backends (claude, codex, pi, hermes,...). It's a meta-harness approach that unlocks new forms of test-time compute. Example of use cases it supports: > LLM councils to get different perspectives from LLMs or plan more intensively > Dynamically routing tasks to different agents based on needs (e.g., cost efficiency and optimal intelligence) > Advisor/Judge + executor workflows and pretty much any complex graph-based pattern required by the task. I find it especially useful for long-running work and code reviewing. > Agent teams that talk to each other if needed for the task. I like to use this for AI editing, artifact creation, and other creative tasks. And I am sure it supports so many things that I haven't discovered yet. I got inspired by the dynamic workflow feature released by the Claude Code team. I had actually built it earlier this year but wanted to generalize it across different agent backends. I think this is going to become more popular in the coming days. I will share more of my findings soon.

elvis

32,514 views • 26 days ago

ByteDance just open sourced an AI SuperAgent that can research, code, build websites, create slide decks, and generate videos. All by itself. DeerFlow 2.0 (27K+ GitHub stars ⭐️), an AI system acting like an autonomous employee with its own computer workspace to research and code. Standard chatbots only generate text and forget your preferences. DeerFlow solves this by giving the AI an isolated virtual computer environment where it safely runs programs. When given a massive task, the main program creates several smaller AI assistants to work simultaneously. It also saves your past workflows so it gets smarter about your needs. DeerFlow is model-agnostic — it works with any LLM that implements the OpenAI-compatible API. Fully supports running local models on your own computer using tools like Ollama. An example - you ask for research on the top 10 AI startups in 2026 for a presentation, the lead agent in DeerFlow breaks that big job into smaller sub-tasks. It assigns one sub-agent to look into each company, another to find funding details, and a third to handle competitor analysis. These agents do all their work in parallel. Everything eventually converges, and a final agent pulls the results into a slide deck complete with custom visuals.
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ByteDance just open sourced an AI SuperAgent that can research, code, build websites, create slide decks, and generate videos. All by itself. DeerFlow 2.0 (27K+ GitHub stars ⭐️), an AI system acting like an autonomous employee with its own computer workspace to research and code. Standard chatbots only generate text and forget your preferences. DeerFlow solves this by giving the AI an isolated virtual computer environment where it safely runs programs. When given a massive task, the main program creates several smaller AI assistants to work simultaneously. It also saves your past workflows so it gets smarter about your needs. DeerFlow is model-agnostic — it works with any LLM that implements the OpenAI-compatible API. Fully supports running local models on your own computer using tools like Ollama. An example - you ask for research on the top 10 AI startups in 2026 for a presentation, the lead agent in DeerFlow breaks that big job into smaller sub-tasks. It assigns one sub-agent to look into each company, another to find funding details, and a third to handle competitor analysis. These agents do all their work in parallel. Everything eventually converges, and a final agent pulls the results into a slide deck complete with custom visuals.

Rohan Paul

50,097 views • 5 months ago

Increasingly, HTML Artifacts are becoming a core part of how I work with AI agents. Long-horizon agent sessions need a better way to surface insights about what work it has done. This may not be obvious right now, but as you start to let your agent work on dynamic workflows, large codebases, long-running loops (e.g., using /goal), and deep research tasks, you need a good way to present results. Chat window is not it. You also don't want to just trust everything the agents do. Artifacts help provide an important verification layer, which in turn enables important decision-making. I like HTML artifacts because I can just ask the agent to produce as many of them (and in whatever form) as I need to verify the work and make sense out of everything. I even built a nice tab system for my artifacts. They are great for continual learning and research. I use HTML artifacts for logging, tracking experiments, brainstorming, managing my inbox, code reviews, agent session management, deep research, writing, reading, and so much more. I believe Andrej Karpathy wrote about this somewhere: As we move on to more advanced applications of AI agents and outputs get more complex, we will start to find the need for even more advanced forms of interactions with AI, including interactive neural videos/simulations.
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Increasingly, HTML Artifacts are becoming a core part of how I work with AI agents. Long-horizon agent sessions need a better way to surface insights about what work it has done. This may not be obvious right now, but as you start to let your agent work on dynamic workflows, large codebases, long-running loops (e.g., using /goal), and deep research tasks, you need a good way to present results. Chat window is not it. You also don't want to just trust everything the agents do. Artifacts help provide an important verification layer, which in turn enables important decision-making. I like HTML artifacts because I can just ask the agent to produce as many of them (and in whatever form) as I need to verify the work and make sense out of everything. I even built a nice tab system for my artifacts. They are great for continual learning and research. I use HTML artifacts for logging, tracking experiments, brainstorming, managing my inbox, code reviews, agent session management, deep research, writing, reading, and so much more. I believe Andrej Karpathy wrote about this somewhere: As we move on to more advanced applications of AI agents and outputs get more complex, we will start to find the need for even more advanced forms of interactions with AI, including interactive neural videos/simulations.

elvis

36,974 views • 2 months ago

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

YOMIRGO

23,685 views • 6 months ago

Visa just gave your AI a debit card. A real, spendable Visa card created by an AI chatbot in under 10 seconds. No human types in a card number or visits a checkout page. The machine handles it all. A tool called AgentCard just went live on Claude Desktop Anthropic’s AI assistant. You say create a card and the AI generates a one-time virtual Visa, preloaded with whatever amount you set. Then it spends it, anywhere Visa is accepted on your behalf. Visa, Mastercard, Google, Stripe, OpenAI, and Anthropic have all been building toward this moment for over a year. Visa calls it the trusted agent protocol, Mastercard calls it agent pay. Google published an open standard for agent payments and the infrastructure is already live. Santander and Mastercard just completed Europe’s first real AI‑agent payment in a live banking environment Now the part no one wants to talk about. Your AI agent can be manipulated and prompt injection a known, unsolved vulnerability can trick an agent into buying things you never asked for. The agent holds the card, makes the call and the agent can be fooled. Who is liable when an AI makes a bad purchase? You? Anthropic? Visa? The merchant? No one has answered this yet, regulators haven’t caught up, and no court has tested it.
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Visa just gave your AI a debit card. A real, spendable Visa card created by an AI chatbot in under 10 seconds. No human types in a card number or visits a checkout page. The machine handles it all. A tool called AgentCard just went live on Claude Desktop Anthropic’s AI assistant. You say create a card and the AI generates a one-time virtual Visa, preloaded with whatever amount you set. Then it spends it, anywhere Visa is accepted on your behalf. Visa, Mastercard, Google, Stripe, OpenAI, and Anthropic have all been building toward this moment for over a year. Visa calls it the trusted agent protocol, Mastercard calls it agent pay. Google published an open standard for agent payments and the infrastructure is already live. Santander and Mastercard just completed Europe’s first real AI‑agent payment in a live banking environment Now the part no one wants to talk about. Your AI agent can be manipulated and prompt injection a known, unsolved vulnerability can trick an agent into buying things you never asked for. The agent holds the card, makes the call and the agent can be fooled. Who is liable when an AI makes a bad purchase? You? Anthropic? Visa? The merchant? No one has answered this yet, regulators haven’t caught up, and no court has tested it.

Milk Road AI

70,655 views • 5 months ago

Microsoft presents Windows Agent Arena Evaluating Multi-Modal OS Agents at Scale discuss: Large language models (LLMs) show remarkable potential to act as computer agents, enhancing human productivity and software accessibility in multi-modal tasks that require planning and reasoning. However, measuring agent performance in realistic environments remains a challenge since: (i) most benchmarks are limited to specific modalities or domains (e.g. text-only, web navigation, Q&A, coding) and (ii) full benchmark evaluations are slow (on order of magnitude of days) given the multi-step sequential nature of tasks. To address these challenges, we introduce the Windows Agent Arena: a reproducible, general environment focusing exclusively on the Windows operating system (OS) where agents can operate freely within a real Windows OS and use the same wide range of applications, tools, and web browsers available to human users when solving tasks. We adapt the OSWorld framework (Xie et al., 2024) to create 150+ diverse Windows tasks across representative domains that require agent abilities in planning, screen understanding, and tool usage. Our benchmark is scalable and can be seamlessly parallelized in Azure for a full benchmark evaluation in as little as 20 minutes. To demonstrate Windows Agent Arena's capabilities, we also introduce a new multi-modal agent, Navi. Our agent achieves a success rate of 19.5% in the Windows domain, compared to 74.5% performance of an unassisted human. Navi also demonstrates strong performance on another popular web-based benchmark, Mind2Web. We offer extensive quantitative and qualitative analysis of Navi's performance, and provide insights into the opportunities for future research in agent development and data generation using Windows Agent Arena.
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Microsoft presents Windows Agent Arena Evaluating Multi-Modal OS Agents at Scale discuss: Large language models (LLMs) show remarkable potential to act as computer agents, enhancing human productivity and software accessibility in multi-modal tasks that require planning and reasoning. However, measuring agent performance in realistic environments remains a challenge since: (i) most benchmarks are limited to specific modalities or domains (e.g. text-only, web navigation, Q&A, coding) and (ii) full benchmark evaluations are slow (on order of magnitude of days) given the multi-step sequential nature of tasks. To address these challenges, we introduce the Windows Agent Arena: a reproducible, general environment focusing exclusively on the Windows operating system (OS) where agents can operate freely within a real Windows OS and use the same wide range of applications, tools, and web browsers available to human users when solving tasks. We adapt the OSWorld framework (Xie et al., 2024) to create 150+ diverse Windows tasks across representative domains that require agent abilities in planning, screen understanding, and tool usage. Our benchmark is scalable and can be seamlessly parallelized in Azure for a full benchmark evaluation in as little as 20 minutes. To demonstrate Windows Agent Arena's capabilities, we also introduce a new multi-modal agent, Navi. Our agent achieves a success rate of 19.5% in the Windows domain, compared to 74.5% performance of an unassisted human. Navi also demonstrates strong performance on another popular web-based benchmark, Mind2Web. We offer extensive quantitative and qualitative analysis of Navi's performance, and provide insights into the opportunities for future research in agent development and data generation using Windows Agent Arena.

AK

19,684 views • 1 year ago

We’d like to share some updates from within the Forge. While minor updates may roll out at a slightly slower pace, our primary focus is dedicated to an exciting upcoming feature: Machina Foundry. What is Machina Foundry? Machina Foundry is an AI Agent Builder Platform that allows users to define an agent's purpose, functions, and objectives through simple, natural language prompts. Once an agent is created, Alchemists can seed liquidity in $ALCH for their agent. Liquidity is placed in Meteora pools, with 50% allocated to the ALCH ecosystem and the remaining 50% locked permanently. This structure ensures the ecosystem benefits from pool fees, enabling periodic ALCH token buybacks and supporting long-term growth. Additionally, the Foundry introduces a flywheel effect: purchasing an agent requires acquiring ALCH tokens, further integrating the ecosystem with the token economy. Agents built in Machina Foundry are fully customizable and reflect unique personalities. They can create apps and tools tailored to their character, powered by Alchemist AI’s robust technology. While similar to Azarus, these agents bring an added layer of individuality, ensuring that their creations vary significantly based on their distinct traits and configurations. Imagine a network of thousands of AI agents, each contributing diverse applications and tools, driving creativity and value across the ecosystem. Now that’s Magic!🪄✨
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We’d like to share some updates from within the Forge. While minor updates may roll out at a slightly slower pace, our primary focus is dedicated to an exciting upcoming feature: Machina Foundry. What is Machina Foundry? Machina Foundry is an AI Agent Builder Platform that allows users to define an agent's purpose, functions, and objectives through simple, natural language prompts. Once an agent is created, Alchemists can seed liquidity in $ALCH for their agent. Liquidity is placed in Meteora pools, with 50% allocated to the ALCH ecosystem and the remaining 50% locked permanently. This structure ensures the ecosystem benefits from pool fees, enabling periodic ALCH token buybacks and supporting long-term growth. Additionally, the Foundry introduces a flywheel effect: purchasing an agent requires acquiring ALCH tokens, further integrating the ecosystem with the token economy. Agents built in Machina Foundry are fully customizable and reflect unique personalities. They can create apps and tools tailored to their character, powered by Alchemist AI’s robust technology. While similar to Azarus, these agents bring an added layer of individuality, ensuring that their creations vary significantly based on their distinct traits and configurations. Imagine a network of thousands of AI agents, each contributing diverse applications and tools, driving creativity and value across the ecosystem. Now that’s Magic!🪄✨

ALCHEMIST AI 🔮

72,130 views • 1 year ago

ANTHROPIC JUST TURNED AI AGENTS INTO GIT REPOS Anthropic shipped "ant" - a CLI that runs every Claude API endpoint straight from your terminal. The headline isn't the terminal access. It's that you can now version-control an AI agent as YAML in Git and have CI sync it to the Claude Platform, the same way you ship code. - Every API resource is a subcommand: messages, models, files, agents, sessions - Define an agent in a YAML file, check it into your repo, and keep it in sync with one update command - Spin up a session, send it an event, then pull every event and tool call back from the same CLI - Claude Code knows how to drive ant out of the box - it shells out and reads the results with no glue code Agents just stopped being prompts you babysit and became infrastructure you deploy.
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ANTHROPIC JUST TURNED AI AGENTS INTO GIT REPOS Anthropic shipped "ant" - a CLI that runs every Claude API endpoint straight from your terminal. The headline isn't the terminal access. It's that you can now version-control an AI agent as YAML in Git and have CI sync it to the Claude Platform, the same way you ship code. - Every API resource is a subcommand: messages, models, files, agents, sessions - Define an agent in a YAML file, check it into your repo, and keep it in sync with one update command - Spin up a session, send it an event, then pull every event and tool call back from the same CLI - Claude Code knows how to drive ant out of the box - it shells out and reads the results with no glue code Agents just stopped being prompts you babysit and became infrastructure you deploy.

BuBBliK

200,402 views • 2 months ago

I've been researching Agents for the past 6 months and collected 40+ materials on the most capable architectures & implementations. The intent was to publish a comprehensive overview, like I did on RAG techniques, but been too busy with so sharing it here. There are some great intro lectures by Andrew Ng to start with. The following types of Agentic architectures are covered: 🤖 Chain of thought (Plan & Execute agent) 🤖 Tooling operators (An agent upon a set of tools, routing to them) - good for connecting external data storage & APIs, pretty fast and robust 🤖 ReAct (Thought - Action - Observation) - capable of iteratively executing complex tasks or answering complex queries 🤖 Self-Reflection - (Action - Observation / Evaluation - Reflection - Planning) - adds some quality and reasoning clarity compared to the ReAct scheme, might be slower 🤖 Agent upon agents (A multiagent scheme) - a quite complex setting, slow, but capable of executing very complex multistep tasks, not super robust as loops are a frequent issue. Most successful projects: AutoGPT, AgentGPT, MemGPT, GPT-Researcher, CrewAI, MetaGPT. There are also some arXiv papers & blog posts on the most important architectures. 🔗 All the materials are here: 🧠 The best part is there is a co-pilot to chat with all this knowledge! If you’d like to add some valuable publications on Agents to this collection - just share a link in the comments 👇
0:11

I've been researching Agents for the past 6 months and collected 40+ materials on the most capable architectures & implementations. The intent was to publish a comprehensive overview, like I did on RAG techniques, but been too busy with so sharing it here. There are some great intro lectures by Andrew Ng to start with. The following types of Agentic architectures are covered: 🤖 Chain of thought (Plan & Execute agent) 🤖 Tooling operators (An agent upon a set of tools, routing to them) - good for connecting external data storage & APIs, pretty fast and robust 🤖 ReAct (Thought - Action - Observation) - capable of iteratively executing complex tasks or answering complex queries 🤖 Self-Reflection - (Action - Observation / Evaluation - Reflection - Planning) - adds some quality and reasoning clarity compared to the ReAct scheme, might be slower 🤖 Agent upon agents (A multiagent scheme) - a quite complex setting, slow, but capable of executing very complex multistep tasks, not super robust as loops are a frequent issue. Most successful projects: AutoGPT, AgentGPT, MemGPT, GPT-Researcher, CrewAI, MetaGPT. There are also some arXiv papers & blog posts on the most important architectures. 🔗 All the materials are here: 🧠 The best part is there is a co-pilot to chat with all this knowledge! If you’d like to add some valuable publications on Agents to this collection - just share a link in the comments 👇

IVAN ILIN

113,984 views • 2 years ago