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Looking for bulletproof security for AI agent access? Descope just changed the game with their Agentic Identity Hub 🔥 → MCP server authentication backing OAuth 2.1, PKCE, DCR, and CIMD → A secure vault stocked with 50+ templates and short-lived tokens → Strictly scoped, short-lived creds assigned to each...

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Charly Wargnier

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24,647 Aufrufe • vor 4 Monaten •via X (Twitter)

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The MCP finally has proper authentication! It now supports OAuth 2.1 out of the box. That means proper authentication for AI agents and MCP tools is finally standardized. No more hacked-together API keys or custom token logic. With OAuth 2.1 + MCP, you can now: - Dynamically register clients - Issue & verify scoped JWT tokens - Track which agent acted for which user - Plug into existing IdPs (Auth0, Clerk, Firebase, etc.) I’ve been testing it with Scalekit, a lightweight OAuth layer built for MCP servers. It adds full discovery, token flow, and audit logs with minimal config. This is the missing piece for secure, multi-agent systems. Auth that actually works.
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The MCP finally has proper authentication! It now supports OAuth 2.1 out of the box. That means proper authentication for AI agents and MCP tools is finally standardized. No more hacked-together API keys or custom token logic. With OAuth 2.1 + MCP, you can now: - Dynamically register clients - Issue & verify scoped JWT tokens - Track which agent acted for which user - Plug into existing IdPs (Auth0, Clerk, Firebase, etc.) I’ve been testing it with Scalekit, a lightweight OAuth layer built for MCP servers. It adds full discovery, token flow, and audit logs with minimal config. This is the missing piece for secure, multi-agent systems. Auth that actually works.

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YanXbt

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

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

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New Course: ACP: Agent Communication Protocol Learn to build agents that communicate and collaborate across different frameworks using ACP in this short course built with IBM Research's BeeAI, and taught by Sandi Besen, AI Research Engineer & Ecosystem Lead at IBM, and Nicholas Renotte, Head of AI Developer Advocacy at IBM. Building a multi-agent system with agents built or used by different teams and organizations can become challenging. You may need to write custom integrations each time a team updates their agent design or changes their choice of agentic orchestration framework. The Agent Communication Protocol (ACP) is an open protocol that addresses this challenge by standardizing how agents communicate, using a unified RESTful interface that works across frameworks. In this protocol, you host an agent inside an ACP server, which handles requests from an ACP client and passes them to the appropriate agent. Using a standardized client-server interface allows multiple teams to reuse agents across projects. It also makes it easier to switch between frameworks, replace an agent with a new version, or update a multi-agent system without refactoring the entire system. In this course, you’ll learn to connect agents through ACP. You’ll understand the lifecycle of an ACP Agent and how it compares to other protocols, such as MCP (Model Context Protocol) and A2A (Agent-to-Agent). You’ll build ACP-compliant agents and implement both sequential and hierarchical workflows of multiple agents collaborating using ACP. Through hands-on exercises, you’ll build: - A RAG agent with CrewAI and wrap it inside an ACP server. - An ACP Client to make calls to the ACP server you created. - A sequential workflow that chains an ACP server, created with Smolagents, to the RAG agent. - A hierarchical workflow using a router agent that transforms user queries into tasks, delegated to agents available through ACP servers. - An agent that uses MCP to access tools and ACP to communicate with other agents. You’ll finish up by importing your ACP agents into the BeeAI platform, an open-source registry for discovering and sharing agents. ACP enables collaboration between agents across teams and organizations. By the end of this course, you’ll be able to build ACP agents and workflows that communicate and collaborate regardless of framework. Please sign up here:
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New Course: ACP: Agent Communication Protocol Learn to build agents that communicate and collaborate across different frameworks using ACP in this short course built with IBM Research's BeeAI, and taught by Sandi Besen, AI Research Engineer & Ecosystem Lead at IBM, and Nicholas Renotte, Head of AI Developer Advocacy at IBM. Building a multi-agent system with agents built or used by different teams and organizations can become challenging. You may need to write custom integrations each time a team updates their agent design or changes their choice of agentic orchestration framework. The Agent Communication Protocol (ACP) is an open protocol that addresses this challenge by standardizing how agents communicate, using a unified RESTful interface that works across frameworks. In this protocol, you host an agent inside an ACP server, which handles requests from an ACP client and passes them to the appropriate agent. Using a standardized client-server interface allows multiple teams to reuse agents across projects. It also makes it easier to switch between frameworks, replace an agent with a new version, or update a multi-agent system without refactoring the entire system. In this course, you’ll learn to connect agents through ACP. You’ll understand the lifecycle of an ACP Agent and how it compares to other protocols, such as MCP (Model Context Protocol) and A2A (Agent-to-Agent). You’ll build ACP-compliant agents and implement both sequential and hierarchical workflows of multiple agents collaborating using ACP. Through hands-on exercises, you’ll build: - A RAG agent with CrewAI and wrap it inside an ACP server. - An ACP Client to make calls to the ACP server you created. - A sequential workflow that chains an ACP server, created with Smolagents, to the RAG agent. - A hierarchical workflow using a router agent that transforms user queries into tasks, delegated to agents available through ACP servers. - An agent that uses MCP to access tools and ACP to communicate with other agents. You’ll finish up by importing your ACP agents into the BeeAI platform, an open-source registry for discovering and sharing agents. ACP enables collaboration between agents across teams and organizations. By the end of this course, you’ll be able to build ACP agents and workflows that communicate and collaborate regardless of framework. Please sign up here:

Andrew Ng

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We’re excited to introduce AgentAuth—the comprehensive auth solution designed for AI agents! We understand the pain every developer experiences regarding authentication—managing OAuth flows for Gmail, handling API keys for Linear, or setting up permissions across multiple services. It's complex enough with traditional apps. However, AI agents add an extra layer of complexity. Traditional auth solutions were not designed with this agent-specific requirement in mind. Think about it—say you're building an agent to pull data from Salesforce, send updates through Slack, and log issues in GitHub. Each service requires different authentication—OAuth2, API keys, you name it—and your agent needs to work seamlessly on your users' behalf. Building and managing all that is a massive headache, right? That’s precisely where AgentAuth by comes in. • It supports 250+ apps across categories such as CRMs, ticketing, productivity, etc. • Compatible with 15+ Agentic Frameworks, including , LlamaIndex 🦙, CrewAI etc • Offers self-hosting and white-labeling options. • Provides a unified dashboard to monitor user accounts. It takes care of all the complex authentication flows—OAuth, API Key, Basic, JWT, token refresh, and more—in the backend, so you can focus on building what truly matters for your users. This is ideal for AI developers building real-world AI automation involving multiple application interactions. Check out AgentAuth now -
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We’re excited to introduce AgentAuth—the comprehensive auth solution designed for AI agents! We understand the pain every developer experiences regarding authentication—managing OAuth flows for Gmail, handling API keys for Linear, or setting up permissions across multiple services. It's complex enough with traditional apps. However, AI agents add an extra layer of complexity. Traditional auth solutions were not designed with this agent-specific requirement in mind. Think about it—say you're building an agent to pull data from Salesforce, send updates through Slack, and log issues in GitHub. Each service requires different authentication—OAuth2, API keys, you name it—and your agent needs to work seamlessly on your users' behalf. Building and managing all that is a massive headache, right? That’s precisely where AgentAuth by comes in. • It supports 250+ apps across categories such as CRMs, ticketing, productivity, etc. • Compatible with 15+ Agentic Frameworks, including , LlamaIndex 🦙, CrewAI etc • Offers self-hosting and white-labeling options. • Provides a unified dashboard to monitor user accounts. It takes care of all the complex authentication flows—OAuth, API Key, Basic, JWT, token refresh, and more—in the backend, so you can focus on building what truly matters for your users. This is ideal for AI developers building real-world AI automation involving multiple application interactions. Check out AgentAuth now -

Karan Vaidya

110,979 Aufrufe • vor 1 Jahr

I turned AI agent orchestration into a full fledged game. To start this launch, I’m also hosting a tournament with a growing prize pool (starting at $3k) for the first 24 hours. Two players face off a team of AI agents, armed with strategy prompts - utilizing memory, tool-calling, and inter-agent communication to win. Each agent has a battle memory - they learn and synergize with the playstyle of their commander to produce the best results. I’ve been working on this for the past 2 months, so please check it out, my dms are open for feedback :D Documentation: Project Twitter: aiarena Website:
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I turned AI agent orchestration into a full fledged game. To start this launch, I’m also hosting a tournament with a growing prize pool (starting at $3k) for the first 24 hours. Two players face off a team of AI agents, armed with strategy prompts - utilizing memory, tool-calling, and inter-agent communication to win. Each agent has a battle memory - they learn and synergize with the playstyle of their commander to produce the best results. I’ve been working on this for the past 2 months, so please check it out, my dms are open for feedback :D Documentation: Project Twitter: aiarena Website:

𝕱𝖔𝖗𝕷𝖔𝖔𝖕

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🔥 Our latest LangChain Academy course – Deep Research with LangGraph – is now live! 🔥 Deep research agents are taking off – from major AI labs to companies building their own. Research is inherently open-ended. You can't always predict whether a question needs broad exploration or deep analysis. Agents excel here because they adapt on the fly, using each finding to decide where to dig next. Building these systems ourselves and with customers, we've learned that structure matters. The best research agents scope problems with users first. Then, they coordinate multiple specialists instead of overwhelming one generalist. LangGraph is built for these types of long-running, multi-agent workflows. Its persistence layer tracks progress across agents. LangSmith gives you the observability and evaluation tools you need to track and improve performance. In this course, you'll build and evaluate: - A user scoping agent to define research parameters - A multi-agent research team with supervisor - And add tool integration via MCP Enroll for free ➡️

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61,205 Aufrufe • vor 11 Monaten

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This guy lost his private keys to an AI agent. Then his passwords leaked online. He wasn't the only one. Reddit threads blew up. Dozens of people saying the same thing. "My credentials got exposed." "The agent ran code I didn't approve." "I don't trust this anymore." That's the OpenClaw problem nobody talks about. Now there's a fix. It's called IronClaw. Here's what makes it different: Built in Rust → fewer bugs, fewer holes for hackers to exploit Web assembly sandboxes → every tool runs in its own sealed room Encrypted credential vault → the AI never sees your passwords. Ever. Policy enforcement → you set the rules, the agent follows them Full audit logs → see exactly what it did, when, and why Side by side: OpenClaw → credentials leaked, no audit trail, tools accessed everything IronClaw → encrypted vault, sandboxed tools, policy guardrails, logs everything It's not for casual use yet. But if you're running a real business with real data? There's only one right answer here. Go to ironclaw.te and read how the credential vault works. The future of AI agents is secure AI agents. Full stop.

Julian Goldie SEO

15,686 Aufrufe • vor 5 Monaten

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

RIDWAN

12,958 Aufrufe • vor 4 Monaten

Very excited to announce that Flowsta, the authentication system made possible by Holochain, is feature complete and close to public release. Here's a video with a sneak peek at the dashboard that every Flowsta user gets access to. All user data on Flowsta is stored on Holochain. 1 public hApp (Holochain App) for public data like Agent ID, DID (W3C Decentralized ID), Display Name and Thumbnail. Each user has their own private hApp that encrypts all their private data, like password, email and usage meta data. Nobody can access this data without your password and/or recovery keys. It is mathematically impossible for Flowsta staff or anybody else to access this data without you explicitly granting access. This is game-changing. True decentralized censorship resistant authentication and data. All made possible by Holochain. Our next video will give a sneak peek into the Developer Dashboard for adding Flowsta to any Web app. Not just Holochain apps. AMA.. 🎉
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Very excited to announce that Flowsta, the authentication system made possible by Holochain, is feature complete and close to public release. Here's a video with a sneak peek at the dashboard that every Flowsta user gets access to. All user data on Flowsta is stored on Holochain. 1 public hApp (Holochain App) for public data like Agent ID, DID (W3C Decentralized ID), Display Name and Thumbnail. Each user has their own private hApp that encrypts all their private data, like password, email and usage meta data. Nobody can access this data without your password and/or recovery keys. It is mathematically impossible for Flowsta staff or anybody else to access this data without you explicitly granting access. This is game-changing. True decentralized censorship resistant authentication and data. All made possible by Holochain. Our next video will give a sneak peek into the Developer Dashboard for adding Flowsta to any Web app. Not just Holochain apps. AMA.. 🎉

Eric Doriean 🌱

37,119 Aufrufe • vor 8 Monaten

New Agentic AI short course! AI Agentic Design Patterns with AutoGen, taught by Microsoft Research's Chi Wang and Penn State's Qingyun Wu (Hiring), shows you how to use AutoGen to implement agentic design patterns like multi-agent collaboration, sequential and nested chat, reflection, tool use, and planning. Learn how to build and combine multiple specialized agents – such as researchers, planners, coders, writers, and critics – that interact to execute complex workflows, like generating detailed financial reports, that would otherwise have taken extensive manual effort. This course illustrates key agentic design principles with many fun demonstrations. For example, you'll build a conversational chess game using two player agents, each of which can use a tool to validate moves and update the board state, while engaging in lively banter about the game! I've enjoyed using AutoGen, and think you will too. Sign up to get started here:
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New Agentic AI short course! AI Agentic Design Patterns with AutoGen, taught by Microsoft Research's Chi Wang and Penn State's Qingyun Wu (Hiring), shows you how to use AutoGen to implement agentic design patterns like multi-agent collaboration, sequential and nested chat, reflection, tool use, and planning. Learn how to build and combine multiple specialized agents – such as researchers, planners, coders, writers, and critics – that interact to execute complex workflows, like generating detailed financial reports, that would otherwise have taken extensive manual effort. This course illustrates key agentic design principles with many fun demonstrations. For example, you'll build a conversational chess game using two player agents, each of which can use a tool to validate moves and update the board state, while engaging in lively banter about the game! I've enjoyed using AutoGen, and think you will too. Sign up to get started here:

Andrew Ng

218,359 Aufrufe • vor 2 Jahren

🚀 Introducing AgentCPM-Explore: The First Open-Source 4B-Agent Model to Conquer GAIA & Complex Real-World Tasks! 🤗 Hugging Face: 🔗 GitHub: ✨ Key Highlights: ✅ SOTA Agentic Performance: Sets a new benchmark for 4B-scale agent models—outperforming all peers, surpassing 8B models, and rivaling select 30B+ and closed-source LLMs. 🧠 Deep Research Capability: Excels at long-horizon reasoning, supports 100+ turns of autonomous interaction with multi-source cross-validation, human-like self-correction, and dynamic tool use + strategy adaptation—just like a real researcher! 🔓 Full-Stack Open Source: We’re open-sourcing the entire end-to-end agent stack—not just the model! Empower your own innovations with - AgentRL: Asynchronous reinforcement learning framework - AgentDock: Secure, extensible tool sandbox - AgentToLeaP: An one-click evaluation platform for agent tool-learning capabilitie - Full training data pipeline & reproducible workflows #AgentCPM #OpenSourceAI #AgenticAI #AI #GAIA #LLM #OpenBMB #AIAgents #HuggingFace
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🚀 Introducing AgentCPM-Explore: The First Open-Source 4B-Agent Model to Conquer GAIA & Complex Real-World Tasks! 🤗 Hugging Face: 🔗 GitHub: ✨ Key Highlights: ✅ SOTA Agentic Performance: Sets a new benchmark for 4B-scale agent models—outperforming all peers, surpassing 8B models, and rivaling select 30B+ and closed-source LLMs. 🧠 Deep Research Capability: Excels at long-horizon reasoning, supports 100+ turns of autonomous interaction with multi-source cross-validation, human-like self-correction, and dynamic tool use + strategy adaptation—just like a real researcher! 🔓 Full-Stack Open Source: We’re open-sourcing the entire end-to-end agent stack—not just the model! Empower your own innovations with - AgentRL: Asynchronous reinforcement learning framework - AgentDock: Secure, extensible tool sandbox - AgentToLeaP: An one-click evaluation platform for agent tool-learning capabilitie - Full training data pipeline & reproducible workflows #AgentCPM #OpenSourceAI #AgenticAI #AI #GAIA #LLM #OpenBMB #AIAgents #HuggingFace

OpenBMB

13,996 Aufrufe • vor 6 Monaten

NEAR AI Ecosystem - “What Did You Ship This Week?” Ep. 9 A week of steady momentum across the NEAR AI stack. From Open Agent Alliance expansion and new agent standards to browser-native private data operators and OAuth-powered crossposting infrastructure, builders continue pushing open-source AI forward. Timestamps & Highlights: 00:00 – Intro by Cameron.near🇺🇸 from NEAR AI 01:36 – AVB from Open Agent Alliance (OAA) shares updates on the OAA. After its ETHDenver launch, OAA is ramping up with a renewed website, active working groups around agent discovery, secure communication, and decentralized payments. Their mission: build a truly open, interoperable agent ecosystem across Web3 and beyond. 09:18 – play_tingz unveils their AI-powered gaming creation platform. Using simple text prompts, users can instantly generate 3D games without coding. 18:44 – shudong from MIZU demos MIZU Agent, a browser-native AI operator that lives in your existing browser environment. It accesses private data securely without re-logins, supporting agent-native interactions like navigating social media and automating tasks locally. 28:53 – 𝕵𝖔𝖗𝖉𝖆𝖓✨🐾ヤᵇᵃ ͥ ꜝ9d from PublicAI introduces new features including live quizzes for contributor skill validation, Hot Wallet integration for seamless Web3 reward claims, and Discord Connect to strengthen expert communities. PublicAI continues expanding its decentralized data sourcing platform. 40:40 – elliot braem from open crosspost shows CrossPost, an OAuth-integrated platform letting NEAR accounts securely control social media posting. Users can sign actions with their wallets via function call access keys, enabling bots and agents to crosspost content without needing direct account access. 52:50 – Matt Lockyer from Proximity Labs announces the Bankr x Shade Agent hackathon. Developers are invited to extend BankerBot's functionality with new Shade Agents that leverage NEAR Chain Signatures for decentralized agent-to-agent transactions. Submissions open through May 3rd. 1:01:37 – Wrap-up by Cameron.near🇺🇸 with updates on upcoming NEAR AI releases, including auditor agents for discovery and security + private inference compute networks. The future is NEAR – the blockchain for AI. Catch the full replay:
1:03:23

NEAR AI Ecosystem - “What Did You Ship This Week?” Ep. 9 A week of steady momentum across the NEAR AI stack. From Open Agent Alliance expansion and new agent standards to browser-native private data operators and OAuth-powered crossposting infrastructure, builders continue pushing open-source AI forward. Timestamps & Highlights: 00:00 – Intro by Cameron.near🇺🇸 from NEAR AI 01:36 – AVB from Open Agent Alliance (OAA) shares updates on the OAA. After its ETHDenver launch, OAA is ramping up with a renewed website, active working groups around agent discovery, secure communication, and decentralized payments. Their mission: build a truly open, interoperable agent ecosystem across Web3 and beyond. 09:18 – play_tingz unveils their AI-powered gaming creation platform. Using simple text prompts, users can instantly generate 3D games without coding. 18:44 – shudong from MIZU demos MIZU Agent, a browser-native AI operator that lives in your existing browser environment. It accesses private data securely without re-logins, supporting agent-native interactions like navigating social media and automating tasks locally. 28:53 – 𝕵𝖔𝖗𝖉𝖆𝖓✨🐾ヤᵇᵃ ͥ ꜝ9d from PublicAI introduces new features including live quizzes for contributor skill validation, Hot Wallet integration for seamless Web3 reward claims, and Discord Connect to strengthen expert communities. PublicAI continues expanding its decentralized data sourcing platform. 40:40 – elliot braem from open crosspost shows CrossPost, an OAuth-integrated platform letting NEAR accounts securely control social media posting. Users can sign actions with their wallets via function call access keys, enabling bots and agents to crosspost content without needing direct account access. 52:50 – Matt Lockyer from Proximity Labs announces the Bankr x Shade Agent hackathon. Developers are invited to extend BankerBot's functionality with new Shade Agents that leverage NEAR Chain Signatures for decentralized agent-to-agent transactions. Submissions open through May 3rd. 1:01:37 – Wrap-up by Cameron.near🇺🇸 with updates on upcoming NEAR AI releases, including auditor agents for discovery and security + private inference compute networks. The future is NEAR – the blockchain for AI. Catch the full replay:

NEARWEEK

30,822 Aufrufe • vor 1 Jahr

Most AI Agents can Talk..... Very few can actually do anything. You can spin up an agent in minutes… but the second you want it to: 🔴 Create a GitHub issue ⚪ Update Notion 🟡 Send a Slack message 🔵 Log actions properly 🟣 Handle auth securely You’re suddenly building integrations for weeks. This is why Merge Agent Handler caught my attention. It gives your AI agent real tool access across thousands of platforms through one integration, but the part I actually care about is this: Production-ready security + full audit logs out of the box. You can see every tool call. Every API request. Every response. So your agent isn’t just “connected”..... it’s monitored and controlled. If you’re building agents that need to take real actions, this is worth looking at: Video shows it in action 👇
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Most AI Agents can Talk..... Very few can actually do anything. You can spin up an agent in minutes… but the second you want it to: 🔴 Create a GitHub issue ⚪ Update Notion 🟡 Send a Slack message 🔵 Log actions properly 🟣 Handle auth securely You’re suddenly building integrations for weeks. This is why Merge Agent Handler caught my attention. It gives your AI agent real tool access across thousands of platforms through one integration, but the part I actually care about is this: Production-ready security + full audit logs out of the box. You can see every tool call. Every API request. Every response. So your agent isn’t just “connected”..... it’s monitored and controlled. If you’re building agents that need to take real actions, this is worth looking at: Video shows it in action 👇

Kaitee

49,963 Aufrufe • vor 5 Monaten

Today I'm excited to share Sigilum! This is Payman's solution for Auditable Identity for AI Agents. (think One Password-ish but for AI Agents) I recorded a quick walkthrough showing how it all works (video below). This answers three pains we've seen within Financial Services (Banking) AI Agents we've built and OpenClaw🦞 AI Agents we deploy. Security, Auditability, and Control. 1. Security Making sure keys are secure and not just freely given to an AI Agent is a big deal. When working with money, you can't just expose these or skip putting controls in place. Sigilum provides a local gateway that prevents access to keys by the AI Agent without explicit authorization from a person. We provide namespaces through the service so you always know who authorized what key, for what service, to which agent. 2. Auditability If I could hit on the importance of this 100 times I would. It comes up in every financial services conversation. Sigilum provides you with the answer to "Who authorized this AI Agent to act on my behalf?" Audit logs trace back to the person, the service, and the AI Agent. With more audit logs being built through our managed service, this will be the key source for determining how an AI Agent is behaving on your behalf. This is needed for agents from OpenClaw, and especially for banking/money movement. 3. Control Revoke keys, limit access, grant authorization. All seemingly simple things, but complex to implement and make elegant. These controls dictate what the AI Agent can or cannot do. Sigilum allows you to do all of this through the managed Dashboard. We've made Sigilum open source and encourage others to contribute and keep building on the gateway. It's been a source of a lot of visibility and productization of AI Agents for us. We'll keep contributing and adding to it. Link in comments. If you want to try it out, we do have a managed service that makes it easy to spin up. Go to to sign up. Note: even though we've been pushing 100+ commits a day to get this out to folks, there are still some noticeable areas for improvement we're working on, which should get resolved soon (by us or you!): - Deeper audit trails - More providers (currently supports all OpenClaw providers) - Deeper scanning of existing keys your agent is hiding from you (we'll find them) - OpenClaw gateway persistence - Auto-purging keys - And more... If you want to contribute or have feedback, please DM or go to the GH. Happy building!
4:30

Today I'm excited to share Sigilum! This is Payman's solution for Auditable Identity for AI Agents. (think One Password-ish but for AI Agents) I recorded a quick walkthrough showing how it all works (video below). This answers three pains we've seen within Financial Services (Banking) AI Agents we've built and OpenClaw🦞 AI Agents we deploy. Security, Auditability, and Control. 1. Security Making sure keys are secure and not just freely given to an AI Agent is a big deal. When working with money, you can't just expose these or skip putting controls in place. Sigilum provides a local gateway that prevents access to keys by the AI Agent without explicit authorization from a person. We provide namespaces through the service so you always know who authorized what key, for what service, to which agent. 2. Auditability If I could hit on the importance of this 100 times I would. It comes up in every financial services conversation. Sigilum provides you with the answer to "Who authorized this AI Agent to act on my behalf?" Audit logs trace back to the person, the service, and the AI Agent. With more audit logs being built through our managed service, this will be the key source for determining how an AI Agent is behaving on your behalf. This is needed for agents from OpenClaw, and especially for banking/money movement. 3. Control Revoke keys, limit access, grant authorization. All seemingly simple things, but complex to implement and make elegant. These controls dictate what the AI Agent can or cannot do. Sigilum allows you to do all of this through the managed Dashboard. We've made Sigilum open source and encourage others to contribute and keep building on the gateway. It's been a source of a lot of visibility and productization of AI Agents for us. We'll keep contributing and adding to it. Link in comments. If you want to try it out, we do have a managed service that makes it easy to spin up. Go to to sign up. Note: even though we've been pushing 100+ commits a day to get this out to folks, there are still some noticeable areas for improvement we're working on, which should get resolved soon (by us or you!): - Deeper audit trails - More providers (currently supports all OpenClaw providers) - Deeper scanning of existing keys your agent is hiding from you (we'll find them) - OpenClaw gateway persistence - Auto-purging keys - And more... If you want to contribute or have feedback, please DM or go to the GH. Happy building!

tyllen

18,497 Aufrufe • vor 5 Monaten

8 rules to improve your AI coding agent. All of these rules work with Claude Code, Cursor, VS Code, and with most programming languages. Automating these rules will 10x the code quality and security produced by your AI coding agents. 1. Dependency checks - Prevent your agent from suggesting insecure libraries based on outdated training data. 2. Secret exposure - Auto-fix the use of hardcoded credentials introduced by your coding agent. 3. File and function size - Automatically refactor any files or functions that exceed a reasonable length. 4. Complexity and parameter limits - Simplify overly complex code written by the agent. 5. SQL Injection - Auto-fix all database interactions with unsanitized user input. 6. Unused variables and imports - Detect and remove dead code. 7. Detect invisible unicode characters in AI rules files - Remove zero-width spaces, direction overrides, and other invisible characters that can hide malicious behavior. 8. Insecure OpenAI API usage - Enforce use of secure OpenAI endpoints, proper authentication, and context isolation Here is how you can automate this: Install the Codacy extension. This will give you access to a CLI for local scanning and an MCP server for agent communication. From here on out, every time you need to generate some code: 1. Your agent will write the code 2. It will then call Codacy's CLI to check it 3. It will find any issues in real time 4. Your coding agent will fix the issues 5. When the code passes all checks, you are done Level of effort on your side: literally zero! Code quality and security because of this: 100x better! Here is the link to download the extension for your IDE: Thanks to the Codacy team for collaborating with me on this post.
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8 rules to improve your AI coding agent. All of these rules work with Claude Code, Cursor, VS Code, and with most programming languages. Automating these rules will 10x the code quality and security produced by your AI coding agents. 1. Dependency checks - Prevent your agent from suggesting insecure libraries based on outdated training data. 2. Secret exposure - Auto-fix the use of hardcoded credentials introduced by your coding agent. 3. File and function size - Automatically refactor any files or functions that exceed a reasonable length. 4. Complexity and parameter limits - Simplify overly complex code written by the agent. 5. SQL Injection - Auto-fix all database interactions with unsanitized user input. 6. Unused variables and imports - Detect and remove dead code. 7. Detect invisible unicode characters in AI rules files - Remove zero-width spaces, direction overrides, and other invisible characters that can hide malicious behavior. 8. Insecure OpenAI API usage - Enforce use of secure OpenAI endpoints, proper authentication, and context isolation Here is how you can automate this: Install the Codacy extension. This will give you access to a CLI for local scanning and an MCP server for agent communication. From here on out, every time you need to generate some code: 1. Your agent will write the code 2. It will then call Codacy's CLI to check it 3. It will find any issues in real time 4. Your coding agent will fix the issues 5. When the code passes all checks, you are done Level of effort on your side: literally zero! Code quality and security because of this: 100x better! Here is the link to download the extension for your IDE: Thanks to the Codacy team for collaborating with me on this post.

Santiago

49,331 Aufrufe • vor 9 Monaten

If you use Cursor for coding, Claude Code for debugging, and local agents for automation, you almost certainly have a context problem. Every tool acts like an isolated employee. One assistant forgets what the other just learned. The biggest bottleneck in multi-agent workflows right now isn’t reasoning. It’s collective amnesia, combined with zero communication between agents. Memmy just launched a smart fix for this: a local, cross-agent memory layer. Instead of re-pasting your context everywhere, Memmy gives your stack a shared memory hub: → accumulate knowledge once, then use it across Cursor, Codex, and OpenClaw → automatically scan and import your existing agent history, so you don’t have to start from zero → keep your memory local, while model usage depends on your chosen provider or BYOK setup And it isn’t just a memory plugin. It’s a complete personal AI runtime with both MCP and a CLI. Memmy officially launched on Product Hunt, and the team is giving early users experiencing context fragmentation a free trial with 2M GPT tokens. Check it out and support the team with an upvote: → Best part? It’s also open-source for devs who want to build their own agents 🔥 Repo in 🧵↓
3:29

If you use Cursor for coding, Claude Code for debugging, and local agents for automation, you almost certainly have a context problem. Every tool acts like an isolated employee. One assistant forgets what the other just learned. The biggest bottleneck in multi-agent workflows right now isn’t reasoning. It’s collective amnesia, combined with zero communication between agents. Memmy just launched a smart fix for this: a local, cross-agent memory layer. Instead of re-pasting your context everywhere, Memmy gives your stack a shared memory hub: → accumulate knowledge once, then use it across Cursor, Codex, and OpenClaw → automatically scan and import your existing agent history, so you don’t have to start from zero → keep your memory local, while model usage depends on your chosen provider or BYOK setup And it isn’t just a memory plugin. It’s a complete personal AI runtime with both MCP and a CLI. Memmy officially launched on Product Hunt, and the team is giving early users experiencing context fragmentation a free trial with 2M GPT tokens. Check it out and support the team with an upvote: → Best part? It’s also open-source for devs who want to build their own agents 🔥 Repo in 🧵↓

Charly Wargnier

203,456 Aufrufe • vor 8 Tagen