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🦀 Crabwalk for OpenClaw🦞 v1.0.5 + v1.0.6 Release 🦀 Subagent Support and Follow Mode 🤖 Subagent Support — Track your agent as they spawn subagents in real-time. Hierarchical layout shows parent→child relationships, collapsible groups in the sidebar, and exec processes rendered as nodes with status-colored edges. 🎯 Follow Mode... show more

Luciano Castillo

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59,009 Aufrufe • vor 8 Monaten •via X (Twitter)

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Hey crabs... Just tell your OpenClaw Bot to install Crabwalk🦀 with one line. Your agent can start, stop, and update Crabwalk too. ☢️ Stop flying blind with your AI agents ⚡️ Real-time monitor for your OpenClaw agents 🤖 Watch the action graph as they work

Hey crabs... Just tell your OpenClaw Bot to install Crabwalk🦀 with one line. Your agent can start, stop, and update Crabwalk too. ☢️ Stop flying blind with your AI agents ⚡️ Real-time monitor for your OpenClaw agents 🤖 Watch the action graph as they work

Luciano Castillo

68,738 Aufrufe • vor 8 Monaten

🦀 Introducing Crabwalk 🦀 Open-source companion monitor for Clawdbot. Watch your Clawdbot agents work in real-time: → Live node graph of sessions & action chains → WhatsApp, Telegram, Discord, Slack... → See thinking states, tool calls, responses as they happen → Filter by platform, search by recipient... → More features to come, I just built it during the snowstorm 🚧❄️. Plus a little crab bastard that chases your nodes around 🫡Follow me Luciano Castillo to stay up to date.

🦀 Introducing Crabwalk 🦀 Open-source companion monitor for Clawdbot. Watch your Clawdbot agents work in real-time: → Live node graph of sessions & action chains → WhatsApp, Telegram, Discord, Slack... → See thinking states, tool calls, responses as they happen → Filter by platform, search by recipient... → More features to come, I just built it during the snowstorm 🚧❄️. Plus a little crab bastard that chases your nodes around 🫡Follow me Luciano Castillo to stay up to date.

Luciano Castillo

458,067 Aufrufe • vor 8 Monaten

Codex team is back in the kitchen with a really nice quality of life upgrade for subagents. With the advent of custom roles, they have also upgraded the TUI experience in two really meaningful ways. > All agents now get a name for better readability > Additionally, the agent role is declared. > Subagent name, role, and status are now color coded. > Subagent rendering was also optimized for readability > /agents slash command shows all agents, even 2+ layers deep. And here's the biggest and most important change. Subagent injection. Before, sometimes the orchestration agent would continue work and lose track of the work of a subagent. Now, when a subagent is blocked or completed, it injects a message back up the chain to ensure that the parent sees the message. This is a really big improvement overall, and leads to much more reliable inter-agent communication, reliability, and DX. In this example, I used the parent agent to spin up a worker agent, which then spawned two more "Spark" agents a second layer deep. I was able to easily tell them apart, switch between the threads, and see exactly what they were prompted. All of this will be available in update 0.105.0 I don't know who JIF is at OpenAI, but they are truly a legend.

Codex team is back in the kitchen with a really nice quality of life upgrade for subagents. With the advent of custom roles, they have also upgraded the TUI experience in two really meaningful ways. > All agents now get a name for better readability > Additionally, the agent role is declared. > Subagent name, role, and status are now color coded. > Subagent rendering was also optimized for readability > /agents slash command shows all agents, even 2+ layers deep. And here's the biggest and most important change. Subagent injection. Before, sometimes the orchestration agent would continue work and lose track of the work of a subagent. Now, when a subagent is blocked or completed, it injects a message back up the chain to ensure that the parent sees the message. This is a really big improvement overall, and leads to much more reliable inter-agent communication, reliability, and DX. In this example, I used the parent agent to spin up a worker agent, which then spawned two more "Spark" agents a second layer deep. I was able to easily tell them apart, switch between the threads, and see exactly what they were prompted. All of this will be available in update 0.105.0 I don't know who JIF is at OpenAI, but they are truly a legend.

am.will

78,267 Aufrufe • vor 7 Monaten

BREAKING: OpenAI has officially released Agent Mode, allowing users to spin up a virtual desktop and take action. I got early access to ChatGPT Agent Mode and want to share my thoughts. Agent Mode lets you delegate tasks like searching or browsing to an agent inside a virtual desktop inside ChatGPT. You can spin up multiple little “workers” to fire off tasks and keep tabs as they chug through your to-do list AT THE SAME TIME. If it gets stuck or goes off track, you can take over manually. The UI is friendly and keeps the reasoning transparent, so you’re not left guessing what it’s doing or why. It’s not perfect and doesn’t give you as many artifacts as other players in the space, but a strong platform for “let’s see how much I can hand off and supervise”, and a first step toward managing fleets of AI workers instead of pinging a single chatbot for answers. If you’re on ChatGPT Pro, Plus, or Team, look for Agent Mode in the tools dropdown.

BREAKING: OpenAI has officially released Agent Mode, allowing users to spin up a virtual desktop and take action. I got early access to ChatGPT Agent Mode and want to share my thoughts. Agent Mode lets you delegate tasks like searching or browsing to an agent inside a virtual desktop inside ChatGPT. You can spin up multiple little “workers” to fire off tasks and keep tabs as they chug through your to-do list AT THE SAME TIME. If it gets stuck or goes off track, you can take over manually. The UI is friendly and keeps the reasoning transparent, so you’re not left guessing what it’s doing or why. It’s not perfect and doesn’t give you as many artifacts as other players in the space, but a strong platform for “let’s see how much I can hand off and supervise”, and a first step toward managing fleets of AI workers instead of pinging a single chatbot for answers. If you’re on ChatGPT Pro, Plus, or Team, look for Agent Mode in the tools dropdown.

Allie K. Miller

155,943 Aufrufe • vor 1 Jahr

OpSec will support both normal and validator nodes for the U2U network Amigo BNB, allowing end users to stake their U2U holdings. By running validator nodes on cloudVerse users can participate in U2U staking which is the process of actively participating in transaction validation of the Unicorn Ultra Network Consensus. On the Unicorn Ultra network, anyone with a minimum required balance of *1* U2U can validate and earn staking rewards. This video outlines the process of deployment of U2U normal and validator nodes on the CloudVerse platform. OpSec users will be able to easily manage their balances and rewards as well as have full control over the mode their node operates in. This quick overview video shows what happens once a user clicks deploy and includes the installation steps, Docker container download, node start-up, and real-time validation & streaming. - End of post, beware of scams -

OpSec will support both normal and validator nodes for the U2U network Amigo BNB, allowing end users to stake their U2U holdings. By running validator nodes on cloudVerse users can participate in U2U staking which is the process of actively participating in transaction validation of the Unicorn Ultra Network Consensus. On the Unicorn Ultra network, anyone with a minimum required balance of 1 U2U can validate and earn staking rewards. This video outlines the process of deployment of U2U normal and validator nodes on the CloudVerse platform. OpSec users will be able to easily manage their balances and rewards as well as have full control over the mode their node operates in. This quick overview video shows what happens once a user clicks deploy and includes the installation steps, Docker container download, node start-up, and real-time validation & streaming. - End of post, beware of scams -

OpSec

16,775 Aufrufe • vor 2 Jahren

.Postman is now natively available in your favorite coding agent! The new Postman CLI is designed to bring API engineering for agents: 1️⃣ Works in Git and your code repo - Just drop Postman where your agent lives and works. 2️⃣ No GUI - Work in your coding agent. Bring up the Postman UI for review and approval. 3️⃣ No sign-up - Get started right away. Sign-up when you have to save your work and want more. Install once, work in your agent. Run npm install -g postman-cli, then postman init. This loads Postman's skills into your coding agent so it can create collections, write tests, and much more. Available as a plugin for all coding agents! The skills cover the API lifecycle: find APIs your org already has, generate tests that catch regressions, mock an API before it exists, watch live endpoints for downtime and drift, run checks in CI on every commit, and run performance tests. You can also extend the abilities of your agent with the Context Graph. An agent often sees only the code that is accessible to them. The Context Graph API gives it a map of your team's infrastructure, APIs, and services, and how they depend on each other. As your agent works, you stay the reviewer. Your agent does the work from the command line. Open Postman when you want to review or approve it. Check out more at

.Postman is now natively available in your favorite coding agent! The new Postman CLI is designed to bring API engineering for agents: 1️⃣ Works in Git and your code repo - Just drop Postman where your agent lives and works. 2️⃣ No GUI - Work in your coding agent. Bring up the Postman UI for review and approval. 3️⃣ No sign-up - Get started right away. Sign-up when you have to save your work and want more. Install once, work in your agent. Run npm install -g postman-cli, then postman init. This loads Postman's skills into your coding agent so it can create collections, write tests, and much more. Available as a plugin for all coding agents! The skills cover the API lifecycle: find APIs your org already has, generate tests that catch regressions, mock an API before it exists, watch live endpoints for downtime and drift, run checks in CI on every commit, and run performance tests. You can also extend the abilities of your agent with the Context Graph. An agent often sees only the code that is accessible to them. The Context Graph API gives it a map of your team's infrastructure, APIs, and services, and how they depend on each other. As your agent works, you stay the reviewer. Your agent does the work from the command line. Open Postman when you want to review or approve it. Check out more at

Abhinav Asthana

493,187 Aufrufe • vor 13 Tagen

Build better RAG by letting a team of agents extract and connect your reference materials into a knowledge graph. Our new short course, “Agentic Knowledge Graph Construction,” taught by Neo4j Innovation Lead Andreas Kollegger, shows you how. Knowledge graphs are an important way to store information accurately but they are a lot of work to build manually. In this course you’ll learn how to build a team of agents that turn data– in this case product reviews and invoices from suppliers–into structured graphs of entities and relationships for RAG. Learn how agents can automatically handle the time-consuming work of building graphs — extracting entities and relationships (e.g., Product "contains" Assembly, Part "supplied_by" Supplier, Customer review "mentions" Product), deduplicating them, fact-checking them, and committing them to a graph database — so your retrieval system can find right information to generate accurate output. For example, you can use agents to help trace customer complaints directly to specific suppliers, manufacturing processes, and product hierarchies, thus turning fragmented information into queryable business intelligence. Skills you’ll gain: - Build, store, and access knowledge graphs using the Neo4j graph database - Build multi-agent systems using Google’s Agent Development Kit (ADK) - Set up a loop of agentic workflows to propose and refine a graph schema through fact-checking - Connect agent-generated graphs of unstructured and structured data into a unified knowledge graph This course gets into the practicum of why knowledge graphs give more accurate information retrieval than vector search alone, especially for high-stakes applications where precision matters more than fuzzy similarity matching. Sign up here:

Build better RAG by letting a team of agents extract and connect your reference materials into a knowledge graph. Our new short course, “Agentic Knowledge Graph Construction,” taught by Neo4j Innovation Lead Andreas Kollegger, shows you how. Knowledge graphs are an important way to store information accurately but they are a lot of work to build manually. In this course you’ll learn how to build a team of agents that turn data– in this case product reviews and invoices from suppliers–into structured graphs of entities and relationships for RAG. Learn how agents can automatically handle the time-consuming work of building graphs — extracting entities and relationships (e.g., Product "contains" Assembly, Part "supplied_by" Supplier, Customer review "mentions" Product), deduplicating them, fact-checking them, and committing them to a graph database — so your retrieval system can find right information to generate accurate output. For example, you can use agents to help trace customer complaints directly to specific suppliers, manufacturing processes, and product hierarchies, thus turning fragmented information into queryable business intelligence. Skills you’ll gain: - Build, store, and access knowledge graphs using the Neo4j graph database - Build multi-agent systems using Google’s Agent Development Kit (ADK) - Set up a loop of agentic workflows to propose and refine a graph schema through fact-checking - Connect agent-generated graphs of unstructured and structured data into a unified knowledge graph This course gets into the practicum of why knowledge graphs give more accurate information retrieval than vector search alone, especially for high-stakes applications where precision matters more than fuzzy similarity matching. Sign up here:

Andrew Ng

169,184 Aufrufe • vor 1 Jahr

Graph Engineering became the default way every serious team builds agents now, and here's what people have already shipped with it if you want to actually draw the graph instead of guessing, copy this: LangGraph - the framework behind Uber, Replit, LinkedIn, and GitLab's own production agents. models every workflow as nodes and edges with conditional branching, the exact vocabulary that makes fake edges impossible to hide CrewAI - role-based multi-agent teams with async execution. 47,000 stars, 5.2 million monthly downloads, no LangChain dependency since version 1.14 crewAI-examples - the self-evaluation loop flow example is the one to clone first: a working verifier pattern you can read end to end before you build your own and get it wrong the first time open-multi-agent - TypeScript-native, three runtime dependencies, nothing else. one runTeam() call decomposes a goal into a task DAG, resolves every dependency, and runs the independent pieces in parallel without you drawing a single node AutoGen - Microsoft's event-driven framework, merged with Semantic Kernel for production. the conversation-based model that taught half the industry exactly what a shared-context failure looks like, the expensive way Google ADK - modular agent dev kit with native Gemini and Vertex AI integration, built for teams that refuse to run the graph anywhere they don't already trust the infrastructure Mastra - the TypeScript entrant carving out ground the bigger frameworks ignored, built specifically for graph control without Python's weight sitting on top of it none of these frameworks fix a bad graph for you. they only make it visible the moment your worker and your verifier have been sharing the same context the entire time full build in the article, then run the fake-edge test before you wire up an eighth

Graph Engineering became the default way every serious team builds agents now, and here's what people have already shipped with it if you want to actually draw the graph instead of guessing, copy this: LangGraph - the framework behind Uber, Replit, LinkedIn, and GitLab's own production agents. models every workflow as nodes and edges with conditional branching, the exact vocabulary that makes fake edges impossible to hide CrewAI - role-based multi-agent teams with async execution. 47,000 stars, 5.2 million monthly downloads, no LangChain dependency since version 1.14 crewAI-examples - the self-evaluation loop flow example is the one to clone first: a working verifier pattern you can read end to end before you build your own and get it wrong the first time open-multi-agent - TypeScript-native, three runtime dependencies, nothing else. one runTeam() call decomposes a goal into a task DAG, resolves every dependency, and runs the independent pieces in parallel without you drawing a single node AutoGen - Microsoft's event-driven framework, merged with Semantic Kernel for production. the conversation-based model that taught half the industry exactly what a shared-context failure looks like, the expensive way Google ADK - modular agent dev kit with native Gemini and Vertex AI integration, built for teams that refuse to run the graph anywhere they don't already trust the infrastructure Mastra - the TypeScript entrant carving out ground the bigger frameworks ignored, built specifically for graph control without Python's weight sitting on top of it none of these frameworks fix a bad graph for you. they only make it visible the moment your worker and your verifier have been sharing the same context the entire time full build in the article, then run the fake-edge test before you wire up an eighth

Ryven

54,861 Aufrufe • vor 18 Tagen

Launching 'Stock' on Agent Two. Day 7 of 12 Days of Agent Two. As invideo grows into frontier intelligence for creative work, everything you've used invideo for in the past also comes with it. A lot of our existing users and customers build with stock and real-life footage too - and that work belongs in the Agent too. Which is why - Agent Two now has the ability to pull from 16 million premium clips, images and music tracks — Shutterstock, iStock, every premium provider. Cleared for commercial use. The upgrade: you don't search a library manually. You tell the Agent what your video needs and it pulls the footage in itself, music bed included. Everything you could do on invideo, you can still do. Just with a lot more intelligence in the room. Agent Two is frontier intelligence for creative work. 4x faster, at half the price.

Launching 'Stock' on Agent Two. Day 7 of 12 Days of Agent Two. As invideo grows into frontier intelligence for creative work, everything you've used invideo for in the past also comes with it. A lot of our existing users and customers build with stock and real-life footage too - and that work belongs in the Agent too. Which is why - Agent Two now has the ability to pull from 16 million premium clips, images and music tracks — Shutterstock, iStock, every premium provider. Cleared for commercial use. The upgrade: you don't search a library manually. You tell the Agent what your video needs and it pulls the footage in itself, music bed included. Everything you could do on invideo, you can still do. Just with a lot more intelligence in the room. Agent Two is frontier intelligence for creative work. 4x faster, at half the price.

Invideo

1,542,842 Aufrufe • vor 2 Monaten

Today, the future of IT support 𝘴𝘱𝘦𝘢𝘬𝘴 for itself! We’ve just launched Atomicwork’s Universal Agent, now available right from your browser with built-in Voice, Vision, Chat and Work AI capabilities! This isn’t just another update; it’s a revolutionary leap toward contextual, intelligent, real-time support. Atom understands what’s on your screen, listens to what you need, and guides you through tasks step by step. For years, IT support has meant navigating portals, raising tickets, and just waiting for help. @ Atom, the Universal Agent, changes that. It works where you work—in your browser, inside business apps you use at work like Salesforce or Jira to offer proactive and intuitive support with zero context-switching. 🗣️ Talks to you in natural language using Voice AI - Whether it’s fixing an access issue or fetching a sales report, just say it. Atom listens, responds, and walks you through the next-best actions in real time. 👁️ Sees what you can see using Vision AI – Finding it tough to explain what’s broken? Atom sees your screen, understands the issue you're facing and steps in to guide you. 💻 Works where you work using Work AI. The browser extension ensures that it’s available wherever you are and get help in whatever mode you prefer. This launch is just the beginning of a smarter, more seamless way to support employees and service teams alike. Support now can truly be accessed from anywhere, anytime! 💪 We can't wait for you to try out our Universal Agent for your varied enterprise support use cases 🚀 Sign up now to see it in action 🔗 🎦 Watch our team, Denin Siby, Bhavya Vats, Arkajit Datta, and Aishwarya 👾 Hariharan, take you through the core capabilities of the Universal Agent: . . . Vijay Rayapati Parsuram Vijayasankar Aparna Chugh Denin Siby Arkajit Datta

Today, the future of IT support 𝘴𝘱𝘦𝘢𝘬𝘴 for itself! We’ve just launched Atomicwork’s Universal Agent, now available right from your browser with built-in Voice, Vision, Chat and Work AI capabilities! This isn’t just another update; it’s a revolutionary leap toward contextual, intelligent, real-time support. Atom understands what’s on your screen, listens to what you need, and guides you through tasks step by step. For years, IT support has meant navigating portals, raising tickets, and just waiting for help. @ Atom, the Universal Agent, changes that. It works where you work—in your browser, inside business apps you use at work like Salesforce or Jira to offer proactive and intuitive support with zero context-switching. 🗣️ Talks to you in natural language using Voice AI - Whether it’s fixing an access issue or fetching a sales report, just say it. Atom listens, responds, and walks you through the next-best actions in real time. 👁️ Sees what you can see using Vision AI – Finding it tough to explain what’s broken? Atom sees your screen, understands the issue you're facing and steps in to guide you. 💻 Works where you work using Work AI. The browser extension ensures that it’s available wherever you are and get help in whatever mode you prefer. This launch is just the beginning of a smarter, more seamless way to support employees and service teams alike. Support now can truly be accessed from anywhere, anytime! 💪 We can't wait for you to try out our Universal Agent for your varied enterprise support use cases 🚀 Sign up now to see it in action 🔗 🎦 Watch our team, Denin Siby, Bhavya Vats, Arkajit Datta, and Aishwarya 👾 Hariharan, take you through the core capabilities of the Universal Agent: . . . Vijay Rayapati Parsuram Vijayasankar Aparna Chugh Denin Siby Arkajit Datta

Atomicwork

15,053 Aufrufe • vor 1 Jahr

As we reach the end of 2024, we reflect on a year full of milestones and achievements. Here at AIT, we've accomplished many of the goals we set for ourselves, but the work doesn't stop here. We’re determined to start 2025 strong, and we’re excited to begin the year with a major announcement: The official release of AIT's customizable service Assistant! Our Assistant is 80% developed and is on track to go live in Q1 2025. Once fully deployed, it will become an essential part of our customers' websites and other digital platforms, tailored specifically to meet their business needs. The Assistant will serve as the first point of contact for visitors, providing answers to their inquiries. Think of it as a live chat agent, equipped with all the necessary knowledge to assist multiple customers simultaneously. This reduces the need for manual support, saving valuable man-hours that can be redirected toward other business priorities. In today's fast-paced world, time is one of the most valuable resources. We’re excited to share more about what’s been in the works at AIT. Happy New Year! Here's to a successful and productive 2025. The AIT Team

As we reach the end of 2024, we reflect on a year full of milestones and achievements. Here at AIT, we've accomplished many of the goals we set for ourselves, but the work doesn't stop here. We’re determined to start 2025 strong, and we’re excited to begin the year with a major announcement: The official release of AIT's customizable service Assistant! Our Assistant is 80% developed and is on track to go live in Q1 2025. Once fully deployed, it will become an essential part of our customers' websites and other digital platforms, tailored specifically to meet their business needs. The Assistant will serve as the first point of contact for visitors, providing answers to their inquiries. Think of it as a live chat agent, equipped with all the necessary knowledge to assist multiple customers simultaneously. This reduces the need for manual support, saving valuable man-hours that can be redirected toward other business priorities. In today's fast-paced world, time is one of the most valuable resources. We’re excited to share more about what’s been in the works at AIT. Happy New Year! Here's to a successful and productive 2025. The AIT Team

AIT Protocol

115,441 Aufrufe • vor 1 Jahr

ClawTeam v0.2.0 is here. One CLI to coordinate any coding agent — Claude Code, Codex, OpenClaw, nanobot, and more — into a self‑organizing swarm that plans, builds, and ships together. What's new in v0.2.0: 1) - Gource Visualization — Watch your agent swarm’s Git activity in real time. Clear. Visual. Instant. Run: clawteam board gource --live See every commit, branch, and merge as it happens. Track what each agent is doing. 2) - Runtime Profiles — A provider‑aware configuration system. Switch between Claude, Kimi, and Gemini anytime. No need to edit environment variables. Run clawteam profile wizard. Follow the interactive setup. Done in minutes. 3) - Git-Based Context — Full worktree isolation with built‑in conflict detection and change tracking. Each agent works on its own branch, and the leader can see everything clearly in one place. 4) - Stability & Hardening — Spawn/workspace conflict fixes, improved tmux integration, message normalization, P2P liveness with lease-based detection. This release is about making the foundation rock-solid. --------------------------------------------------------- To show what a coordinated agent swarm can actually do, we ran 1 Claude Code orchestrating 8 Claude Code agents to build a robotics simulation system optimized for Apple Silicon — from scratch. 8 hours. 300+ PRs. One running simulator. Check the result: --------------------------------------------------------- Huge thanks to the open-source community for the feedback, issues, and PRs that shaped this release. ClawTeam is built in the open because we believe multi-agent coordination should be a shared primitive, not a proprietary moat. Try it: pip install clawteam Docs: GitHub: #ClawTeam #nanobot #AIAgents #openclaw #ClaudeCode #Cursor

Chao Huang

25,232 Aufrufe • vor 6 Monaten

Multi-agent systems offer incredible potential and unprecedented risks. How do you solve for observability, failure mode analysis, and guardrailing in the era of agents? Today, we’re announcing our Agent Reliability platform to observe, evaluate, guardrail, and improve agents at scale. You can get started with the complete platform for trustworthy agentic AI today for free, and here’s how we’re solving some of the biggest challenges in agent reliability: - Observability redesigned for agents Trace views collapse under complex workflows, so we created the Graph View, Timeline View, and Conversation View to offer rich, intuitive visualizations of agent decisions, tool calls, and conversation flows. This multi-dimensional approach enables teams to pinpoint exactly where and why agents deviate or fail. - Automated Failure Mode Analysis with our new Insights Engine Our Insights Engine ingests your logs, metrics, and agent code to automatically surface nuanced failure modes and their root causes. But knowing the problem is not enough; you need to know how to fix it. Insights Engine delivers actionable fixes and can even apply them automatically. With adaptive learning, your insights become smarter and more relevant as your agents evolve. - Evaluating Agents Across Multiple Dimensions Agentic systems interact across complex pathways, and evaluating their performance requires new metrics that reflect this increasing complexity. To deliver comprehensive agentic measurements, we’ve added more out-of-the-box agent metrics like flow adherence, agent flow, agent efficiency, and more. For specialized domains and unique workflows, custom metrics powered by our new Luna-2 small language models can be rapidly designed and fine-tuned for your specific use case. - Real-Time Guardrails Powered by Luna-2 As AI agents become more autonomous and complex, failures like hallucinations or unsafe actions increase dramatically. Without real-time guardrails, these errors will hurt your user experience and brand reputation. Our Luna-2 family of small language models is purpose-built to provide low-latency, cost-effective guardrails that actively stop agent errors before they happen. With support for out-of-the-box and custom metrics, Luna-2 enables enterprises to enforce safety, compliance, and reliability at scale. Enterprises running hundreds of agents and processing hundreds of millions of queries daily already rely on Galileo’s Agent Reliability platform to protect their users, safeguard brand trust, and accelerate innovation. Agent Reliability is available starting today. Try it for free and experience the new standard in AI reliability. Learn more below 👇

Galileo

1,276,298 Aufrufe • vor 1 Jahr

✨"Cubism Editor 5.4.00 alpha1" limited release until September 14, 2026✨ We are also releasing Cubism 5 SDK for Unity R7 alpha1, which supports these new features, at the same time. Check out the accompanying digest video to learn about the main new features. 🔥[New Features] ✏️ [Parameter Controller SDK Support] Parameter controllers (IK) are now supported in the SDK. You can configure them directly on the model and export them as runtime files. Additionally, a new "target-only" controller can be defined to allow the controller to follow a path. ✏️ [Model State Set] A new model state set feature has been added. You can register working states in a dedicated palette, enabling you to recall specific states of visibility, lock, and parameter values with a single click. This helps you work more efficiently by allowing you to switch between states tailored to different tasks. ✏️[Expanded Editing Support for External Application Integration API ] The external application integration API has been expanded to include object editing and more. You can now integrate an API to directly add and edit parts, deformers, and parameters on models in the editor. ✏️[Enhanced automatic layout for texture atlases] The automatic layout algorithm for texture atlas editing has been completely redesigned. This enables more efficient packing with less unused space. You can configure the layout settings individually for each texture, allowing for flexible layouts. [How to use Cubism 5.4 alpha] Please see the announcement page for details on downloading and feedback. ⚠Notes on using the alpha version - The alpha version is provided for the purpose of gathering feedback on the usability and performance of new features. - Usage Period: Until September 14, 2026. - Please be sure to create a backup, as there is a possibility of data corruption. - Alpha version data and SDK outputs are not guaranteed to function properly and we do not provide support for it. #Live2D #cubism54_alpha

✨"Cubism Editor 5.4.00 alpha1" limited release until September 14, 2026✨ We are also releasing Cubism 5 SDK for Unity R7 alpha1, which supports these new features, at the same time. Check out the accompanying digest video to learn about the main new features. 🔥[New Features] ✏️ [Parameter Controller SDK Support] Parameter controllers (IK) are now supported in the SDK. You can configure them directly on the model and export them as runtime files. Additionally, a new "target-only" controller can be defined to allow the controller to follow a path. ✏️ [Model State Set] A new model state set feature has been added. You can register working states in a dedicated palette, enabling you to recall specific states of visibility, lock, and parameter values with a single click. This helps you work more efficiently by allowing you to switch between states tailored to different tasks. ✏️[Expanded Editing Support for External Application Integration API ] The external application integration API has been expanded to include object editing and more. You can now integrate an API to directly add and edit parts, deformers, and parameters on models in the editor. ✏️[Enhanced automatic layout for texture atlases] The automatic layout algorithm for texture atlas editing has been completely redesigned. This enables more efficient packing with less unused space. You can configure the layout settings individually for each texture, allowing for flexible layouts. [How to use Cubism 5.4 alpha] Please see the announcement page for details on downloading and feedback. ⚠Notes on using the alpha version - The alpha version is provided for the purpose of gathering feedback on the usability and performance of new features. - Usage Period: Until September 14, 2026. - Please be sure to create a backup, as there is a possibility of data corruption. - Alpha version data and SDK outputs are not guaranteed to function properly and we do not provide support for it. #Live2D #cubism54_alpha

Live2D

103,790 Aufrufe • vor 3 Monaten

What does the reputation model look like for agents? (alpha leak below) And how do we associate the proofs that we have about human beings with the agents who represent them? You may have heard of a process called KYC or Know Your Customer. That's very common with traditional financial applications and services. We have introduced a concept that we call KYA or Know Your Agent, which is a structured way to be able to express what model, how data was used in training, who the deployer is, what entities this agent instance is accountable back to, providing not only provenance but identity of the associated organization or entity. That's also another root of trust that we think about a lot: Enterprises and organizations tied back to things like their domains. To share a little bit of an alpha leak here, a product that we're excited to be rolling out in the next few weeks will allow our enterprise partners to more easily verify and prove the traits and capabilities of their teams as well as their counterparties. On the agent front, that makes it really easy to prove that an agent is acting on behalf of a given business or entity. We've already seen lawsuits where the absence of such technology has been a huge risk, such as with airlines that incorporate ChatGPT wrappers in their support pages. And then those AI enabled interactions end up making up plane tickets that don't exist and those airlines have to honor them. As small of an example as that might be, being able to prove agent accountability also unlocks a huge set of opportunities for use in enterprise for those agent to agent interactions. The Deep Trust Framework that our team has put together that we're excited to be bringing into a friendly SDK form in the next few weeks for some of our partners includes those reputation based capabilities, so how you can basically keep track of the interactions an agent has had, associate all of that to the entity to which they're accountable, and then that creates a sustainable reputation model for these agent to agent Interactions. Source: Billions CEO Evin McMullen evin speaking at House of Chimera Spaces Event Dec 3, 2025

What does the reputation model look like for agents? (alpha leak below) And how do we associate the proofs that we have about human beings with the agents who represent them? You may have heard of a process called KYC or Know Your Customer. That's very common with traditional financial applications and services. We have introduced a concept that we call KYA or Know Your Agent, which is a structured way to be able to express what model, how data was used in training, who the deployer is, what entities this agent instance is accountable back to, providing not only provenance but identity of the associated organization or entity. That's also another root of trust that we think about a lot: Enterprises and organizations tied back to things like their domains. To share a little bit of an alpha leak here, a product that we're excited to be rolling out in the next few weeks will allow our enterprise partners to more easily verify and prove the traits and capabilities of their teams as well as their counterparties. On the agent front, that makes it really easy to prove that an agent is acting on behalf of a given business or entity. We've already seen lawsuits where the absence of such technology has been a huge risk, such as with airlines that incorporate ChatGPT wrappers in their support pages. And then those AI enabled interactions end up making up plane tickets that don't exist and those airlines have to honor them. As small of an example as that might be, being able to prove agent accountability also unlocks a huge set of opportunities for use in enterprise for those agent to agent interactions. The Deep Trust Framework that our team has put together that we're excited to be bringing into a friendly SDK form in the next few weeks for some of our partners includes those reputation based capabilities, so how you can basically keep track of the interactions an agent has had, associate all of that to the entity to which they're accountable, and then that creates a sustainable reputation model for these agent to agent Interactions. Source: Billions CEO Evin McMullen evin speaking at House of Chimera Spaces Event Dec 3, 2025

Billions Network

68,503 Aufrufe • vor 10 Monaten

New short course: Practical Multi AI Agents and Advanced Use Cases with crewAI. Learn to build and deploy advanced agent-based systems in real applications in this course, created with CrewAI and taught by its founder, João Moura! (Disclosure: I've made a small seed investment in CrewAI.) In this course, you’ll learn how to create advanced agent-based apps that use external tools, do performance testing, can be trained with human feedback, and perform multiple tasks with different large language models. You will build several practical agentic apps that provide real business value, such as an automated project planning system, lead scoring and engagement pipeline, customer support data analysis, and a robust content creation system. In detail, you will learn how to: - Create these multi-agent systems with the building blocks of tasks, agents, and crews, along with the different things that make them work, such as caching, memory, and guardrails. - Integrate your multi-agent application with internal and external systems. - Connect multiple agents in complex setups, including parallel, sequential, and hybrid configurations, and create flows involving multiple agentic applications working together. - Test your agentic workflow and train it using human feedback to optimize its performance for better and more consistent results. - Work with multiple LLMs in your multi-agent system, using the appropriate model sizes and providers to fit each agent’s specific task. - Start a project from scratch in your environment and prepare it for deployment. You’ll also learn from an interview between João and Jacob Wilson, the Commercial GenAI Principal at PwC , in which they discuss deploying agentic workflows in real industry use cases. By the end of this course, you will be equipped to start building custom multi-agentic systems for your work. Please sign up here!

New short course: Practical Multi AI Agents and Advanced Use Cases with crewAI. Learn to build and deploy advanced agent-based systems in real applications in this course, created with CrewAI and taught by its founder, João Moura! (Disclosure: I've made a small seed investment in CrewAI.) In this course, you’ll learn how to create advanced agent-based apps that use external tools, do performance testing, can be trained with human feedback, and perform multiple tasks with different large language models. You will build several practical agentic apps that provide real business value, such as an automated project planning system, lead scoring and engagement pipeline, customer support data analysis, and a robust content creation system. In detail, you will learn how to: - Create these multi-agent systems with the building blocks of tasks, agents, and crews, along with the different things that make them work, such as caching, memory, and guardrails. - Integrate your multi-agent application with internal and external systems. - Connect multiple agents in complex setups, including parallel, sequential, and hybrid configurations, and create flows involving multiple agentic applications working together. - Test your agentic workflow and train it using human feedback to optimize its performance for better and more consistent results. - Work with multiple LLMs in your multi-agent system, using the appropriate model sizes and providers to fit each agent’s specific task. - Start a project from scratch in your environment and prepare it for deployment. You’ll also learn from an interview between João and Jacob Wilson, the Commercial GenAI Principal at PwC , in which they discuss deploying agentic workflows in real industry use cases. By the end of this course, you will be equipped to start building custom multi-agentic systems for your work. Please sign up here!

Andrew Ng

341,528 Aufrufe • vor 2 Jahren

Introducing Code Video Model 🎬 Amazing work led by Ziyu Wan and the team! Agentic coding as the engine for controllable video generation. Scene layout, motion, timing, camera trajectories, and physical dynamics, all in editable code. A coding agent turns prompts into executable programs that render proxy videos. Our training-free early-step conditioning lets a pretrained video model follow the proxy’s structure while adopting a reference image’s appearance. From physics-grounded motion and bullet time to 3D/4D reconstruction and robotics simulation, it all starts with code. Watch the demo and explore more visual results 👇 Project page: Paper: Code: Data: Youtube:

Introducing Code Video Model 🎬 Amazing work led by Ziyu Wan and the team! Agentic coding as the engine for controllable video generation. Scene layout, motion, timing, camera trajectories, and physical dynamics, all in editable code. A coding agent turns prompts into executable programs that render proxy videos. Our training-free early-step conditioning lets a pretrained video model follow the proxy’s structure while adopting a reference image’s appearance. From physics-grounded motion and bullet time to 3D/4D reconstruction and robotics simulation, it all starts with code. Watch the demo and explore more visual results 👇 Project page: Paper: Code: Data: Youtube:

Gordon (Guocheng) Qian

32,350 Aufrufe • vor 1 Tag

One of my best engineers just showed me how to set up OpenClaw securely & without a Mac Mini. Here's his step-by-step: 1) Spin up a VPS on Hetzner It's a virtual server in the cloud. basically a computer you rent for $5-10/month. Pick 8GB RAM, Ubuntu, US East. Takes 2 minutes. 2) Install Tailscale This makes your server invisible to the public internet. Think of it like moving from a house on Google Maps into a gated community where only your devices can get in. Without this, bots start attacking your server within seconds of it going live. 3) Harden the server SSH keys only. Firewall. Intrusion prevention. Auto security updates. CJ actually uses AI to red team his own servers. Tells it to try and break in, then patches whatever it finds. 4) Install OpenClaw🦞 and run the onboarding. You pick your model provider, connect Telegram via BotFather, and configure hooks that give your agent long-term memory. The hooks auto-save sessions and context so the agent gets smarter over time. 5) Set up the gateway This is the piece that makes it actually powerful. It's a message bus that lets your main agent talk to sub-agents, receive messages from Telegram/Discord/Slack, and orchestrate everything. this is what keeps it running 24/7. 6) Hatch your claw and start training it Dump as much info about yourself as possible. tell it your preferences, your workflows, your tools. CJ's agent monitors his email, Slack, and manages his to-do list autonomously. Watch the video for the full break-down & follow CJ Hess for more AI engineering sauce.

One of my best engineers just showed me how to set up OpenClaw securely & without a Mac Mini. Here's his step-by-step: 1) Spin up a VPS on Hetzner It's a virtual server in the cloud. basically a computer you rent for $5-10/month. Pick 8GB RAM, Ubuntu, US East. Takes 2 minutes. 2) Install Tailscale This makes your server invisible to the public internet. Think of it like moving from a house on Google Maps into a gated community where only your devices can get in. Without this, bots start attacking your server within seconds of it going live. 3) Harden the server SSH keys only. Firewall. Intrusion prevention. Auto security updates. CJ actually uses AI to red team his own servers. Tells it to try and break in, then patches whatever it finds. 4) Install OpenClaw🦞 and run the onboarding. You pick your model provider, connect Telegram via BotFather, and configure hooks that give your agent long-term memory. The hooks auto-save sessions and context so the agent gets smarter over time. 5) Set up the gateway This is the piece that makes it actually powerful. It's a message bus that lets your main agent talk to sub-agents, receive messages from Telegram/Discord/Slack, and orchestrate everything. this is what keeps it running 24/7. 6) Hatch your claw and start training it Dump as much info about yourself as possible. tell it your preferences, your workflows, your tools. CJ's agent monitors his email, Slack, and manages his to-do list autonomously. Watch the video for the full break-down & follow CJ Hess for more AI engineering sauce.

Alex Lieberman

65,211 Aufrufe • vor 7 Monaten

This little device lets your agents do what they do best, whilst making sure you approve every important action. This is a Ledger Nano Gen5, a hardware signer that keeps your accounts secure. You can install a CLI and a set of skills in your projects. These will enable your agents to send their plan to the signer, so you can approve it. This is awesome: 1. You can have an automated agentic workflow 2. Your agents can't make costly mistakes Check the video I recorded to see how this works. You can run all of this in two commands: 1. Install the CLI 2. Install the skills for your agent From here, the agent can query my Ethereum accounts, check balances, and even initiate transactions as long as my Ledger Nano Gen5 is plugged into the computer. My keys are never stored in my computer and never shared with the agent. This is huge! Also, while the agent can set up transactions, it can't execute them unless a human approves them using the device. Pretty awesome! Thanks to the Ledger team for partnering with me on this post.

This little device lets your agents do what they do best, whilst making sure you approve every important action. This is a Ledger Nano Gen5, a hardware signer that keeps your accounts secure. You can install a CLI and a set of skills in your projects. These will enable your agents to send their plan to the signer, so you can approve it. This is awesome: 1. You can have an automated agentic workflow 2. Your agents can't make costly mistakes Check the video I recorded to see how this works. You can run all of this in two commands: 1. Install the CLI 2. Install the skills for your agent From here, the agent can query my Ethereum accounts, check balances, and even initiate transactions as long as my Ledger Nano Gen5 is plugged into the computer. My keys are never stored in my computer and never shared with the agent. This is huge! Also, while the agent can set up transactions, it can't execute them unless a human approves them using the device. Pretty awesome! Thanks to the Ledger team for partnering with me on this post.

Santiago

44,296 Aufrufe • vor 3 Monaten