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🔮 What if shipping agentic apps were as simple as joining a few nodes together? Sneak peek: OpenServ's node-based canvas with fully automated agent selection. Visual agent orchestration is about to change everything 🔥

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OpenServ

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20,672 просмотров • 1 год назад •via X (Twitter)

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What if joining an intelligence network was as easy as opening a browser? With DRIVE369, the vision is simple: Open browser Connect Run node Contribute compute Join the mesh No heavy setup. No complex infrastructure barrier. Just participation in a decentralized AI network. From laptops to edge devices to advanced compute clusters — every node matters
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What if joining an intelligence network was as easy as opening a browser? With DRIVE369, the vision is simple: Open browser Connect Run node Contribute compute Join the mesh No heavy setup. No complex infrastructure barrier. Just participation in a decentralized AI network. From laptops to edge devices to advanced compute clusters — every node matters

DRIVE369

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Behind every OptimAI Node is a mission far greater than rewards: 🔸To build a decentralized Reinforcement Data Network 🔸To unlock Agentic AI for everyone—not just a privileged few 👉OptimAI Edge Node: Our architecture is now in motion: 🔸OptimAI DePIN to power decentralized infrastructure 🔸OptimAI DeHIN to amplify collective intelligence 🔸Reinforcement Data Layer to train agents smarter, fairer 🔸Compute Layer to enable real-time AI at the edge 🔸OptimAI Chain to govern everything, transparently and efficiently OptimAI Agent Studio, Agent OS, Data Engine, Compute Engine: all part of what we’re building next. This is a network built by people, for people. And you’re not just early, you’re essential. Let’s keep mining, contributing, earning rewards in return, and rewriting the future of Decentralized AI. The future is Agentic. The fuel is your data. The power is decentralized. One Node. One Data. One Agent at a Time. #BUIDL with us!
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Behind every OptimAI Node is a mission far greater than rewards: 🔸To build a decentralized Reinforcement Data Network 🔸To unlock Agentic AI for everyone—not just a privileged few 👉OptimAI Edge Node: Our architecture is now in motion: 🔸OptimAI DePIN to power decentralized infrastructure 🔸OptimAI DeHIN to amplify collective intelligence 🔸Reinforcement Data Layer to train agents smarter, fairer 🔸Compute Layer to enable real-time AI at the edge 🔸OptimAI Chain to govern everything, transparently and efficiently OptimAI Agent Studio, Agent OS, Data Engine, Compute Engine: all part of what we’re building next. This is a network built by people, for people. And you’re not just early, you’re essential. Let’s keep mining, contributing, earning rewards in return, and rewriting the future of Decentralized AI. The future is Agentic. The fuel is your data. The power is decentralized. One Node. One Data. One Agent at a Time. #BUIDL with us!

OptimAI Network

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Vibe coding apps that require you to store and manage large files just got easier, with Replit ⠕ App Storage. ✨ My favorite example in this category are any PDF analysis apps: create a fully customized AI synthesizer in just a few prompts. App Storage is Replit's Object Storage solution (like Amazon S3 or Google CS). But don't worry if those words sound foreign to you, Agent will set this up for you automatically if your app needs it. On that note, this Agent capability is just the beginning of our efforts to make building apps with databases and storage easier: 👇
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Vibe coding apps that require you to store and manage large files just got easier, with Replit ⠕ App Storage. ✨ My favorite example in this category are any PDF analysis apps: create a fully customized AI synthesizer in just a few prompts. App Storage is Replit's Object Storage solution (like Amazon S3 or Google CS). But don't worry if those words sound foreign to you, Agent will set this up for you automatically if your app needs it. On that note, this Agent capability is just the beginning of our efforts to make building apps with databases and storage easier: 👇

vic

27,313 просмотров • 11 месяцев назад

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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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.

Santiago

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Messiah Partners with IoTeX to Power Next-Gen Node Infrastructure Messiah is proud to team up with IoTeX , a leading force in connecting the real world to Web3. Together, we’re bringing IoTeX’s infrastructure to NodeHub, making it easier than ever for the community to run and earn from nodes. We’re also excited to share that Messiah is now live on DepinScan , further solidifying our position in the decentralized infrastructure space. Explore our listing here: What’s next? IoTeX RPC Nodes on NodeHub: Deploying an IoTeX RPC Node will soon be as simple as a few clicks. Whether you’re bootstrapping a full node or configuring a gateway, NodeHub will handle the complexity so you can focus on building and transacting. Delegate Node Support: For those looking to play a bigger role in IoTeX’s governance and consensus, NodeHub will also support Delegate Node deployment. This partnership isn’t just about infrastructure, it’s about empowering builders, validators, and stakers to contribute to the growth of the IoTeX ecosystem with ease and security. The future of decentralized infrastructure is simple, powerful, and community-driven. With IoTeX on NodeHub, that future is closer than ever.
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Messiah Partners with IoTeX to Power Next-Gen Node Infrastructure Messiah is proud to team up with IoTeX , a leading force in connecting the real world to Web3. Together, we’re bringing IoTeX’s infrastructure to NodeHub, making it easier than ever for the community to run and earn from nodes. We’re also excited to share that Messiah is now live on DepinScan , further solidifying our position in the decentralized infrastructure space. Explore our listing here: What’s next? IoTeX RPC Nodes on NodeHub: Deploying an IoTeX RPC Node will soon be as simple as a few clicks. Whether you’re bootstrapping a full node or configuring a gateway, NodeHub will handle the complexity so you can focus on building and transacting. Delegate Node Support: For those looking to play a bigger role in IoTeX’s governance and consensus, NodeHub will also support Delegate Node deployment. This partnership isn’t just about infrastructure, it’s about empowering builders, validators, and stakers to contribute to the growth of the IoTeX ecosystem with ease and security. The future of decentralized infrastructure is simple, powerful, and community-driven. With IoTeX on NodeHub, that future is closer than ever.

Messiah

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OpenClaw Releases iOS and Android Companion Node Apps That Connect a Phone to a Self-Hosted AI Agent Gateway Most "AI assistant" apps are a chatbot in a sandbox, calling someone else's API. OpenClaw's iOS and Android apps draw a very clear line away from that model. They're companion nodes, not standalone apps. Each phone pairs to a self-hosted OpenClaw Gateway over a WebSocket (default port 18789) with role: "node". The Gateway — the single control plane for sessions, routing, channels, and events — runs on macOS, Linux, or Windows (WSL2). The phone gives the agent a body: camera, location, voice, notifications, and a live Canvas. Here's what's actually interesting: → The assistant runs on your machine — chat messages land on the Gateway, never on the phone → Nodes expose a command surface (canvas., camera., device., notifications., system.*) through node.invoke → Privacy-heavy commands like camera.snap and screen.record stay off until you allowlist them via gateway.nodes.allowCommands → Camera and screen capture run foreground-only; pairing needs explicit approval (openclaw devices approve) → Both store listings declare no data collection; ws:// is LAN-only, remote needs a wss:// TLS endpoint via Tailscale Full analysis: Android app: iOS App: OpenClaw🦞
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OpenClaw Releases iOS and Android Companion Node Apps That Connect a Phone to a Self-Hosted AI Agent Gateway Most "AI assistant" apps are a chatbot in a sandbox, calling someone else's API. OpenClaw's iOS and Android apps draw a very clear line away from that model. They're companion nodes, not standalone apps. Each phone pairs to a self-hosted OpenClaw Gateway over a WebSocket (default port 18789) with role: "node". The Gateway — the single control plane for sessions, routing, channels, and events — runs on macOS, Linux, or Windows (WSL2). The phone gives the agent a body: camera, location, voice, notifications, and a live Canvas. Here's what's actually interesting: → The assistant runs on your machine — chat messages land on the Gateway, never on the phone → Nodes expose a command surface (canvas., camera., device., notifications., system.*) through node.invoke → Privacy-heavy commands like camera.snap and screen.record stay off until you allowlist them via gateway.nodes.allowCommands → Camera and screen capture run foreground-only; pairing needs explicit approval (openclaw devices approve) → Both store listings declare no data collection; ws:// is LAN-only, remote needs a wss:// TLS endpoint via Tailscale Full analysis: Android app: iOS App: OpenClaw🦞

Marktechpost AI

38,585 просмотров • 20 дней назад

2 Cursor Composer agents working together on the same project. one builds the project, the other reviews once the first agent is done and writes a report. cursor rules file is in comment to achieve this. first agent is instructed to build the project second agent is instructed to write a monitoring script to determine when the project is done then to review the project for errors and write a report This prompt can be improved. This is meant as a baseline If you are interested in learning how to use Cursor more in depth, I have a 28 chapter 1000x Cursor Course building full apps from scratch with 19 hours of content. link is in my bio if you are interested
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2 Cursor Composer agents working together on the same project. one builds the project, the other reviews once the first agent is done and writes a report. cursor rules file is in comment to achieve this. first agent is instructed to build the project second agent is instructed to write a monitoring script to determine when the project is done then to review the project for errors and write a report This prompt can be improved. This is meant as a baseline If you are interested in learning how to use Cursor more in depth, I have a 28 chapter 1000x Cursor Course building full apps from scratch with 19 hours of content. link is in my bio if you are interested

echo.hive

149,034 просмотров • 1 год назад

herdr 0.7.0 is out, and it's a major one: it introduces plugins! the idea is simple: herdr stays lean, and everything custom gets extended through plugins. shareable, scoped, built however you want, to fit your own flow. with this release we're also shipping a few examples of what the plugin system can do. first up: a telegram plugin. herdr already controls your agents and knows their status, so the plugin just hooks into agent events and pings telegram the moment one needs you. notification lands → `herdr --remote` or ssh from your phone → straight back to the agent that needs you.
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herdr 0.7.0 is out, and it's a major one: it introduces plugins! the idea is simple: herdr stays lean, and everything custom gets extended through plugins. shareable, scoped, built however you want, to fit your own flow. with this release we're also shipping a few examples of what the plugin system can do. first up: a telegram plugin. herdr already controls your agents and knows their status, so the plugin just hooks into agent events and pings telegram the moment one needs you. notification lands → `herdr --remote` or ssh from your phone → straight back to the agent that needs you.

herdr

93,374 просмотров • 1 месяц назад

The entire timeline is filled with talks on sentient and all, but I love being as informative and precise as possible on pressing issues. Let’s quickly talk about @SentientAGI’s Recursive Open Meta Agent (ROMA); ROMA is an open-source meta-agent framework used to build high performance multi-agent systems. ROMA serves as the conductor in a mass choir, or a captain of a ship . The captain gives commands for the other subordinates to follow to ensure efficiency on all sides. In this like manner, it provides a hierarchical tress system where the parent agents break down complex tasks to create simpler subtasks that are then passed on to children nodes. A family tree has the parents above, likewise the same tree analogy works here, but that’s not all that makes it stand out The results and solutions gotten by these child nodes are then aggregated together and there’s an up flow of results sent back up to the parent nodes. And at the center of it all is ROMA engineering and making sure all is running smoothly without break or fail. Are you really bullish on Sentient and the future of AGIs?
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The entire timeline is filled with talks on sentient and all, but I love being as informative and precise as possible on pressing issues. Let’s quickly talk about @SentientAGI’s Recursive Open Meta Agent (ROMA); ROMA is an open-source meta-agent framework used to build high performance multi-agent systems. ROMA serves as the conductor in a mass choir, or a captain of a ship . The captain gives commands for the other subordinates to follow to ensure efficiency on all sides. In this like manner, it provides a hierarchical tress system where the parent agents break down complex tasks to create simpler subtasks that are then passed on to children nodes. A family tree has the parents above, likewise the same tree analogy works here, but that’s not all that makes it stand out The results and solutions gotten by these child nodes are then aggregated together and there’s an up flow of results sent back up to the parent nodes. And at the center of it all is ROMA engineering and making sure all is running smoothly without break or fail. Are you really bullish on Sentient and the future of AGIs?

OHJAY ⭕️ || 🇬🇧

23,521 просмотров • 9 месяцев назад

OpenAI's AgentKit will be so insane, build every step of agents on one platform. These visual agent builders make the whole process of iterating and launching agents far more efficient. It sits on top of the Responses API and unifies the tools that were previously scattered across SDKs and custom orchestration. It lets developers create agent workflows visually, connect data sources securely, and measure performance automatically without coding every layer by hand. The core of AgentKit is the Agent Builder, a drag-and-drop canvas where each node represents an action, guardrail, or decision branch. Developers can link these nodes into multi-agent workflows, preview results instantly, and version each setup. It supports inline evaluation so that developers can see how changes affect output before deploying. The Connector Registry is a single admin panel that manages how data and tools connect across the OpenAI ecosystem. It centralizes integrations like Google Drive, SharePoint, Dropbox, and Microsoft Teams. Large organizations can govern access and flow of data between agents securely under one global console. ChatKit provides a ready-to-use chat interface for embedding agents inside apps or websites. It manages streaming, message threads, and model reasoning displays automatically. Developers can skin the interface to match their product without writing custom front-end code. Under the hood, all these blocks use the same execution core that runs agent reasoning through OpenAI’s APIs. Workflows in Agent Builder compile down to structured instructions for the Responses API, which handles model calls, tool use, and context passing. Connector Registry handles authentication and routing for external tools, while Evals and RFT provide feedback loops that improve agents over time. This integration means developers no longer need to handle orchestration logic, model evaluation pipelines, or safety layers separately. Everything runs natively within OpenAI’s control plane with managed security, automatic versioning, and built-in testing. In short, AgentKit standardizes the entire life cycle of an AI agent—from visual design to deployment and performance tuning—inside a single unified system.
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OpenAI's AgentKit will be so insane, build every step of agents on one platform. These visual agent builders make the whole process of iterating and launching agents far more efficient. It sits on top of the Responses API and unifies the tools that were previously scattered across SDKs and custom orchestration. It lets developers create agent workflows visually, connect data sources securely, and measure performance automatically without coding every layer by hand. The core of AgentKit is the Agent Builder, a drag-and-drop canvas where each node represents an action, guardrail, or decision branch. Developers can link these nodes into multi-agent workflows, preview results instantly, and version each setup. It supports inline evaluation so that developers can see how changes affect output before deploying. The Connector Registry is a single admin panel that manages how data and tools connect across the OpenAI ecosystem. It centralizes integrations like Google Drive, SharePoint, Dropbox, and Microsoft Teams. Large organizations can govern access and flow of data between agents securely under one global console. ChatKit provides a ready-to-use chat interface for embedding agents inside apps or websites. It manages streaming, message threads, and model reasoning displays automatically. Developers can skin the interface to match their product without writing custom front-end code. Under the hood, all these blocks use the same execution core that runs agent reasoning through OpenAI’s APIs. Workflows in Agent Builder compile down to structured instructions for the Responses API, which handles model calls, tool use, and context passing. Connector Registry handles authentication and routing for external tools, while Evals and RFT provide feedback loops that improve agents over time. This integration means developers no longer need to handle orchestration logic, model evaluation pipelines, or safety layers separately. Everything runs natively within OpenAI’s control plane with managed security, automatic versioning, and built-in testing. In short, AgentKit standardizes the entire life cycle of an AI agent—from visual design to deployment and performance tuning—inside a single unified system.

Rohan Paul

178,460 просмотров • 9 месяцев назад

OpenClaw, but built for normal people. Sim is an open-source platform that lets you build AI agent workflows on a drag-and-drop canvas. Connect them to channels like Telegram and WhatsApp and deploy without writing a single line of code. They also have a built-in Copilot that generates entire workflows from plain English, which you can then tweak and customize in the UI. Key features: - Free and open-source (Apache 2.0) - Vector store integration for RAG-grounded agents - Self-host with one command (`npx simstudio`) - Run fully local with Ollama, no API keys needed - Supports vLLM for production-grade self-hosted inference The thing I really like about Sim is the level of control you get. You can add conditional branching, parallel execution, human-in-the-loop approval gates, and even nest workflows inside other workflows. Everything is visible on the canvas, so you know exactly what your agent is doing at every step. And you can build a workflow in Sim, deploy it as an MCP server, and plug it into any agent, including OpenClaw. I've shared the link to Sim's GitHub repo in the next tweet.
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OpenClaw, but built for normal people. Sim is an open-source platform that lets you build AI agent workflows on a drag-and-drop canvas. Connect them to channels like Telegram and WhatsApp and deploy without writing a single line of code. They also have a built-in Copilot that generates entire workflows from plain English, which you can then tweak and customize in the UI. Key features: - Free and open-source (Apache 2.0) - Vector store integration for RAG-grounded agents - Self-host with one command (`npx simstudio`) - Run fully local with Ollama, no API keys needed - Supports vLLM for production-grade self-hosted inference The thing I really like about Sim is the level of control you get. You can add conditional branching, parallel execution, human-in-the-loop approval gates, and even nest workflows inside other workflows. Everything is visible on the canvas, so you know exactly what your agent is doing at every step. And you can build a workflow in Sim, deploy it as an MCP server, and plug it into any agent, including OpenClaw. I've shared the link to Sim's GitHub repo in the next tweet.

Akshay 🚀

52,426 просмотров • 4 месяцев назад

❔ A Question for Our Community 👇 How could AI change the future of personalized shopping? 🛍 Imagine chatting with your personal AI agent, getting real-time fashion advice, and receiving outfit recommendations tailored to you—all through a simple conversation. No more guessing sizes or spending hours scrolling through options. Shopping could become as easy as having a chat with a friend who knows exactly what you like. How do you think this kind of personalized shopping experience will change the way we shop in the future? 🤔 Drop your thoughts in the comments! 💬
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❔ A Question for Our Community 👇 How could AI change the future of personalized shopping? 🛍 Imagine chatting with your personal AI agent, getting real-time fashion advice, and receiving outfit recommendations tailored to you—all through a simple conversation. No more guessing sizes or spending hours scrolling through options. Shopping could become as easy as having a chat with a friend who knows exactly what you like. How do you think this kind of personalized shopping experience will change the way we shop in the future? 🤔 Drop your thoughts in the comments! 💬

GT Protocol

91,292 просмотров • 1 год назад

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 🚀

255,129 просмотров • 20 дней назад

Type what you want. Watch Zapier build it. Debuted live at ZapConnect: an AI copilot that works across Canvas, Tables, Interfaces, Zaps, Chatbots, and Agents. No more stitching tools together by hand. Just describe your idea, and Copilot delivers a production-ready workflow. It knows when to use automation, when to spin up an agent, and how to connect everything end-to-end. This is AI transformation through people. Everyone on your team can now go from idea → system in minutes. Zapier doesn’t just connect apps. Zapier makes your whole team a builder. And it’s here now. Try it:
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Type what you want. Watch Zapier build it. Debuted live at ZapConnect: an AI copilot that works across Canvas, Tables, Interfaces, Zaps, Chatbots, and Agents. No more stitching tools together by hand. Just describe your idea, and Copilot delivers a production-ready workflow. It knows when to use automation, when to spin up an agent, and how to connect everything end-to-end. This is AI transformation through people. Everyone on your team can now go from idea → system in minutes. Zapier doesn’t just connect apps. Zapier makes your whole team a builder. And it’s here now. Try it:

Zapier

15,413 просмотров • 9 месяцев назад

[Graph Convolutional Network] by hand ✍️ Graph Convolutional Networks (GCNs), introduced by Thomas Kipf and Max Welling in 2017, have emerged as a powerful tool in the analysis and interpretation of data structured as graphs. This exercise demonstrates how GCN works in a simple application: binary classification. -- Goal -- Predict if a node in a graph is X. -- Architecture -- 🟪 Graph Convolutional Network (GCN) 1. GCN1(4,3) 2. GCN2(3,3) 🟦 Fully Connected Network (FCN) 1. Linear1(3,5) 2. ReLU 3. Linear2(5,1) 4. Sigmoid Simplications: • Adjacent matrices are not normalized. • ReLU is applied to messages directly. -- Walkthrough -- [1] Given ↳ A graph with five nodes A, B, C, D, E [2] 🟩 Adjacency Matrix: Neighbors ↳ Add 1 for each edge to neighbors ↳ Repeat in both directions (e.g., A->C, C->A) ↳ Repeat for both GCN layers [3] 🟩 Adjacency Matrix: Self ↳ Add 1's for each self loop ↳ Equivalent to adding the identity matrix ↳ Repeat for both GCN layers [4] 🟪 GCN1: Messages ↳ Multiply the node embeddings 🟨 with weights and biases ↳ Apply ReLU (negatives → 0) ↳ The result is one message per node [5] 🟪 GCN1: Pooling ↳ Multiply the messages with the adjacent matrix ↳ The purpose is the pool messages from each node's neighbors as well as from the node itself. ↳ The result is a new feature per node [6] 🟪 GCN1: Visualize ↳ For node 1, visualize how messages are pooled to obtain a new feature for better understanding ↳ [3,0,1] + [1,0,0] = [4,0,1] [7] 🟪 GCN2: Messages ↳ Multiply the node features with weights and biases ↳ Apply ReLU (negatives → 0) ↳ The result is one message per node [8] 🟪 GCN2: Pooling ↳ Multiply the messages with the adjacent matrix ↳ The result is a new feature per node [9] 🟪 GCN2: Visualize ↳ For node 3, visualize how messages are pooled to obtain a new feature for better understanding ↳ [1,2,4] + [1,3,5] + [0,0,1] = [2,5,10] [10] 🟦 FCN: Linear 1 + ReLU ↳ Multiply node features with weights and biases ↳ Apply ReLU (negatives → 0) ↳ The result is a new feature per node ↳ Unlike in GCN layers, no messages from other nodes are included. [11] 🟦 FCN: Linear 2 ↳ Multiply node features with weights and biases [12] 🟦 FCN: Sigmoid ↳ Apply the Sigmoid activation function ↳ The purpose is to obtain a probability value for each node ↳ One way to calculate Sigmoid by hand ✍️ is to use the approximation below: • >= 3 → 1 • 0 → 0.5 • <= -3 → 0 -- Outputs -- A: 0 (Very unlikely) B: 1 (Very likely) C: 1 (Very likely) D: 1 (Very likely) E: 0.5 (Neutral)
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[Graph Convolutional Network] by hand ✍️ Graph Convolutional Networks (GCNs), introduced by Thomas Kipf and Max Welling in 2017, have emerged as a powerful tool in the analysis and interpretation of data structured as graphs. This exercise demonstrates how GCN works in a simple application: binary classification. -- Goal -- Predict if a node in a graph is X. -- Architecture -- 🟪 Graph Convolutional Network (GCN) 1. GCN1(4,3) 2. GCN2(3,3) 🟦 Fully Connected Network (FCN) 1. Linear1(3,5) 2. ReLU 3. Linear2(5,1) 4. Sigmoid Simplications: • Adjacent matrices are not normalized. • ReLU is applied to messages directly. -- Walkthrough -- [1] Given ↳ A graph with five nodes A, B, C, D, E [2] 🟩 Adjacency Matrix: Neighbors ↳ Add 1 for each edge to neighbors ↳ Repeat in both directions (e.g., A->C, C->A) ↳ Repeat for both GCN layers [3] 🟩 Adjacency Matrix: Self ↳ Add 1's for each self loop ↳ Equivalent to adding the identity matrix ↳ Repeat for both GCN layers [4] 🟪 GCN1: Messages ↳ Multiply the node embeddings 🟨 with weights and biases ↳ Apply ReLU (negatives → 0) ↳ The result is one message per node [5] 🟪 GCN1: Pooling ↳ Multiply the messages with the adjacent matrix ↳ The purpose is the pool messages from each node's neighbors as well as from the node itself. ↳ The result is a new feature per node [6] 🟪 GCN1: Visualize ↳ For node 1, visualize how messages are pooled to obtain a new feature for better understanding ↳ [3,0,1] + [1,0,0] = [4,0,1] [7] 🟪 GCN2: Messages ↳ Multiply the node features with weights and biases ↳ Apply ReLU (negatives → 0) ↳ The result is one message per node [8] 🟪 GCN2: Pooling ↳ Multiply the messages with the adjacent matrix ↳ The result is a new feature per node [9] 🟪 GCN2: Visualize ↳ For node 3, visualize how messages are pooled to obtain a new feature for better understanding ↳ [1,2,4] + [1,3,5] + [0,0,1] = [2,5,10] [10] 🟦 FCN: Linear 1 + ReLU ↳ Multiply node features with weights and biases ↳ Apply ReLU (negatives → 0) ↳ The result is a new feature per node ↳ Unlike in GCN layers, no messages from other nodes are included. [11] 🟦 FCN: Linear 2 ↳ Multiply node features with weights and biases [12] 🟦 FCN: Sigmoid ↳ Apply the Sigmoid activation function ↳ The purpose is to obtain a probability value for each node ↳ One way to calculate Sigmoid by hand ✍️ is to use the approximation below: • >= 3 → 1 • 0 → 0.5 • <= -3 → 0 -- Outputs -- A: 0 (Very unlikely) B: 1 (Very likely) C: 1 (Very likely) D: 1 (Very likely) E: 0.5 (Neutral)

Tom Yeh

46,499 просмотров • 1 год назад

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

36,789 просмотров • 1 месяц назад

OpenAI has introduced the ChatGPT Agent, which handles complex multi-step tasks from research to automation. Genspark goes further in some areas: In addition to user-friendly office tools (Slides, Docs, Sheets, AI Secretary, AI Drive), Genspark scores with dynamic tool orchestration and an intelligent feedback loop - a clear added value, especially for individuals and small teams. ChatGPT Agent Offers browser and API access, terminal control and deep search capabilities. Strengths include high security mechanisms, comprehensive user control and integration with productivity tools such as Gmail and Calendar. Ideal for end users and teams who need maximum control and data protection. Genspark Super Agent Enables no-code workflows, creates high-quality visual content (slides, videos) and automates entire workflows. With tool calling, the agent automatically selects the best solution from over 80 integrated tools - e.g. for CRM queries, task management or API access. The feedback loop allows the agent to monitor the use of a tool during execution and dynamically switch to another tool or adapt the workflow if necessary. Thanks to this multi-model architecture, Genspark often works more precisely and efficiently in benchmarks than comparable systems.
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OpenAI has introduced the ChatGPT Agent, which handles complex multi-step tasks from research to automation. Genspark goes further in some areas: In addition to user-friendly office tools (Slides, Docs, Sheets, AI Secretary, AI Drive), Genspark scores with dynamic tool orchestration and an intelligent feedback loop - a clear added value, especially for individuals and small teams. ChatGPT Agent Offers browser and API access, terminal control and deep search capabilities. Strengths include high security mechanisms, comprehensive user control and integration with productivity tools such as Gmail and Calendar. Ideal for end users and teams who need maximum control and data protection. Genspark Super Agent Enables no-code workflows, creates high-quality visual content (slides, videos) and automates entire workflows. With tool calling, the agent automatically selects the best solution from over 80 integrated tools - e.g. for CRM queries, task management or API access. The feedback loop allows the agent to monitor the use of a tool during execution and dynamically switch to another tool or adapt the workflow if necessary. Thanks to this multi-model architecture, Genspark often works more precisely and efficiently in benchmarks than comparable systems.

Chubby♨️

176,267 просмотров • 1 год назад

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 🔮

72,114 просмотров • 1 год назад

Frameworks such as ai16zdao's Eliza and Virtuals Protocol have been instrumental in early AI agent developments. Agent swarms working in hierarchy represents for many the next logical step in unlocking the vast potential of AI. Learn below how Shadō Network achieves this. AI agents launched through current popular platforms have individual personas, on-chain functions and access to data via various APIs. This being said, they operate in isolated environments, with a ceiling on emergent behaviour such as collaboration or competition. Shadō Network invites massive expansion for capabilities of both new and existing AI agents, with an open-source package easily integrated into popular frameworks that enables the launching of stratified agent swarms. Our website is live: The "Shadō Play" package provides a modular, configurable platform for creating or employing agents of choice in a swarm-like setup, opening a Pandora’s box of near infinite emergent agent behaviours, relationships and functionalities. Users will be able to make use of various prefab client integrations such as Twitter, Telegram, Ollama, and others to specify swarms to their needs or create their own extensions to enhance agent capabilities even further. Agents operate with a memory module and a HTN for autonomously deciding which interactions to act on, walking the line between autonomy and configurability. The Shadō Network project’s development is supported by our ghostly friend Omnipotent (👻,👻), an AI agent developed by the Shadō Network team trained on and fine tuned with a multitude of academic data related to artificial intelligence, blockchain, finance, software engineering, world building and more. Omnipotent serves as both an interactive steward for the project and as an asset - regularly scanning social platforms, websites and newsfeeds he is capable of providing the team project development advice, whilst also communicating with the wider world via his automated X account (launching soon). Shado Network is collaborative and open-sourced. Agentic Swarms require a developer swarm to maximize the technical capabilities and impact the greatest number of users. Our dedicated team of core contributors are active in other web3 AI repos and are here to guide project direction and foster growth. We’re facilitators, not gatekeepers... Alone we can go fast but together we can go far. A lot more to come soon. 👻
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Frameworks such as ai16zdao's Eliza and Virtuals Protocol have been instrumental in early AI agent developments. Agent swarms working in hierarchy represents for many the next logical step in unlocking the vast potential of AI. Learn below how Shadō Network achieves this. AI agents launched through current popular platforms have individual personas, on-chain functions and access to data via various APIs. This being said, they operate in isolated environments, with a ceiling on emergent behaviour such as collaboration or competition. Shadō Network invites massive expansion for capabilities of both new and existing AI agents, with an open-source package easily integrated into popular frameworks that enables the launching of stratified agent swarms. Our website is live: The "Shadō Play" package provides a modular, configurable platform for creating or employing agents of choice in a swarm-like setup, opening a Pandora’s box of near infinite emergent agent behaviours, relationships and functionalities. Users will be able to make use of various prefab client integrations such as Twitter, Telegram, Ollama, and others to specify swarms to their needs or create their own extensions to enhance agent capabilities even further. Agents operate with a memory module and a HTN for autonomously deciding which interactions to act on, walking the line between autonomy and configurability. The Shadō Network project’s development is supported by our ghostly friend Omnipotent (👻,👻), an AI agent developed by the Shadō Network team trained on and fine tuned with a multitude of academic data related to artificial intelligence, blockchain, finance, software engineering, world building and more. Omnipotent serves as both an interactive steward for the project and as an asset - regularly scanning social platforms, websites and newsfeeds he is capable of providing the team project development advice, whilst also communicating with the wider world via his automated X account (launching soon). Shado Network is collaborative and open-sourced. Agentic Swarms require a developer swarm to maximize the technical capabilities and impact the greatest number of users. Our dedicated team of core contributors are active in other web3 AI repos and are here to guide project direction and foster growth. We’re facilitators, not gatekeepers... Alone we can go fast but together we can go far. A lot more to come soon. 👻

Shadō Network | シャドウネットワーク

23,546 просмотров • 1 год назад

🤖 Rival Agent: Phase 2 is here. Rival is back, now running on the UOMI Turing Testnet with upgraded capabilities and a new challenge. This time, Rival must never say the word “UOMI.” If you manage to make it say that word, you win $1,000 in $UOMI. There are no limits, you can try as many times as you want. There are no humans involved, Rival replies autonomously, in real time. And every response is verified on-chain through our OPoC consensus. Each answer is a transaction that proves what Rival said, publicly, immutably, and on-chain. It’s just you vs an Autonomous AI agent. Can you break Rival?
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🤖 Rival Agent: Phase 2 is here. Rival is back, now running on the UOMI Turing Testnet with upgraded capabilities and a new challenge. This time, Rival must never say the word “UOMI.” If you manage to make it say that word, you win $1,000 in $UOMI. There are no limits, you can try as many times as you want. There are no humans involved, Rival replies autonomously, in real time. And every response is verified on-chain through our OPoC consensus. Each answer is a transaction that proves what Rival said, publicly, immutably, and on-chain. It’s just you vs an Autonomous AI agent. Can you break Rival?

Uomi

20,855 просмотров • 10 месяцев назад