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📦 The new official Server Package makes Auth easy in Edge Functions! It automatically handles: ✅ JWT Verification 🔑 Authenticated queries 👮‍♂️ Admin client 🌐 CORS And is compatible across Deno, Cloudflare Workers, Vercel Functions, Hono and Bun!

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16,858 views • 10 days ago •via X (Twitter)

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Announcing Deno 2 support and deployments from the dashboard for Edge Functions! Deno 2 brings package.json and node_modules support, allowing you to bring over Node.js apps without modifying them. You can also edit/ deploy Edge Functions right from your Supabase dashboard!
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Announcing Deno 2 support and deployments from the dashboard for Edge Functions! Deno 2 brings package.json and node_modules support, allowing you to bring over Node.js apps without modifying them. You can also edit/ deploy Edge Functions right from your Supabase dashboard!

Supabase

33,239 views • 1 year ago

JWT in 60 Seconds 👇 What is JWT ? JWT = JSON Web Token A compact, URL-safe token used for: - Authentication - Authorization - Secure API communication - Identity sharing between services It is digitally signed, so it can be verified and trusted. 🟢 Why JWT exists Typical flow without JWT: User → Application → Database (Session Store) - Server stores sessions - Requires memory/storage - Hard to scale in microservices - More infrastructure complexity - Needs sticky sessions behind Load Balancer - This doesn’t scale well in distributed systems. 🟢 JWT comes into the picture - JWT is stateless authentication. New flow: User → Application → JWT → Client → API - No session stored on server - Token carries user identity & claims - Server only verifies signature - Perfect for scalable systems. 🟢 Complete JWT request flow 1️⃣ User logs in with credentials 2️⃣ Server validates user 3️⃣ Server generates JWT (Header + Payload + Signature) 4️⃣ Client stores JWT (usually in browser/app) 5️⃣ Client sends JWT in Authorization header 6️⃣ Server verifies signature 7️⃣ If valid → Access granted No database lookup for session needed. 🟢 Where JWT is used in real systems? - REST APIs - Microservices authentication - OAuth2 / SSO - API Gateways - Kubernetes dashboards - CI/CD tools - Mobile & SPA applications - Almost every modern cloud-native app uses JWT. 🟢 JWT in DevOps & System Design : As a DevOps engineer, JWT knowledge is used in: - Designing stateless applications - Scaling apps behind Load Balancers - Implementing API security - Working with IAM & OAuth providers - Securing microservices communication - Reducing session storage dependency Stateless auth = Better scalability + Simpler infrastructure Thanks for reading. Happy Learning !
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JWT in 60 Seconds 👇 What is JWT ? JWT = JSON Web Token A compact, URL-safe token used for: - Authentication - Authorization - Secure API communication - Identity sharing between services It is digitally signed, so it can be verified and trusted. 🟢 Why JWT exists Typical flow without JWT: User → Application → Database (Session Store) - Server stores sessions - Requires memory/storage - Hard to scale in microservices - More infrastructure complexity - Needs sticky sessions behind Load Balancer - This doesn’t scale well in distributed systems. 🟢 JWT comes into the picture - JWT is stateless authentication. New flow: User → Application → JWT → Client → API - No session stored on server - Token carries user identity & claims - Server only verifies signature - Perfect for scalable systems. 🟢 Complete JWT request flow 1️⃣ User logs in with credentials 2️⃣ Server validates user 3️⃣ Server generates JWT (Header + Payload + Signature) 4️⃣ Client stores JWT (usually in browser/app) 5️⃣ Client sends JWT in Authorization header 6️⃣ Server verifies signature 7️⃣ If valid → Access granted No database lookup for session needed. 🟢 Where JWT is used in real systems? - REST APIs - Microservices authentication - OAuth2 / SSO - API Gateways - Kubernetes dashboards - CI/CD tools - Mobile & SPA applications - Almost every modern cloud-native app uses JWT. 🟢 JWT in DevOps & System Design : As a DevOps engineer, JWT knowledge is used in: - Designing stateless applications - Scaling apps behind Load Balancers - Implementing API security - Working with IAM & OAuth providers - Securing microservices communication - Reducing session storage dependency Stateless auth = Better scalability + Simpler infrastructure Thanks for reading. Happy Learning !

Nandkishor

27,790 views • 5 months ago

Working with Edge Functions just got a major upgrade: → Function list & search → Copy-paste snippets → Built-in testing → Download as zip Update your self-hosted Supabase instance: >
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Working with Edge Functions just got a major upgrade: → Function list & search → Copy-paste snippets → Built-in testing → Download as zip Update your self-hosted Supabase instance: >

Supabase

25,997 views • 5 months ago

Introducing JWT Signing keys to securely and scalably handle JWT verifications on your app! We are also replacing the anon and service role keys with publishable and secret keys, enabling developers to easily rotate keys and making the keys themselves shorter.
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Introducing JWT Signing keys to securely and scalably handle JWT verifications on your app! We are also replacing the anon and service role keys with publishable and secret keys, enabling developers to easily rotate keys and making the keys themselves shorter.

Supabase

19,394 views • 1 year ago

As part of our effort to move the platform toward declarative code, new tables in the public schema will no longer be automatically exposed to the Data API in new Supabase projects starting May 30th. Instead, tables will need to be explicitly granted each permission to each role, either through the dashboard or via SQL. Explicit Postgres grants are reviewable, diffable, and greppable. They also give you per-role control: anon and authenticated need different privileges in most schemas, and an explicit grant makes that difference visible in your migrations. This was always possible; now it's the default.
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As part of our effort to move the platform toward declarative code, new tables in the public schema will no longer be automatically exposed to the Data API in new Supabase projects starting May 30th. Instead, tables will need to be explicitly granted each permission to each role, either through the dashboard or via SQL. Explicit Postgres grants are reviewable, diffable, and greppable. They also give you per-role control: anon and authenticated need different privileges in most schemas, and an explicit grant makes that difference visible in your migrations. This was always possible; now it's the default.

Supabase

40,247 views • 2 months ago

Introducing the official Supabase MCP Server! Supabase MCP Server allows you to read and write to your database, create new projects, apply migrations, and more, right from your favorite AI tools! You can start using them in Cursor or Windsurf today!
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Introducing the official Supabase MCP Server! Supabase MCP Server allows you to read and write to your database, create new projects, apply migrations, and more, right from your favorite AI tools! You can start using them in Cursor or Windsurf today!

Supabase

193,181 views • 1 year ago

Web scraping just leveled up Scrapling bypasses Cloudflare blocks, is 774 times faster than BeautifulSoup, and doesn't require proxy setup 52.2k stars on GitHub It's not just another scraper It's an adaptive framework that learns the structure of each website and automatically adjusts when it changes No manual maintenance. No getting blocked. ✅ Bypasses Cloudflare and the most aggressive anti-bots ✅ 774x faster than BeautifulSoup in real benchmarks ✅ No need for proxies or special setup ✅ Automatically adapts when the website structure changes ✅ Compatible with AI agents as an MCP server ✅ Support for JavaScript, iframes, and dynamic content ✅ Stealth mode for sites with advanced detection ✅ 46 releases. Updated last week. ✅ BSD-3 license What used to take you days to build and maintain now takes minutes 52.2k stars. 5k forks. BSD-3. repo here 👇
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Web scraping just leveled up Scrapling bypasses Cloudflare blocks, is 774 times faster than BeautifulSoup, and doesn't require proxy setup 52.2k stars on GitHub It's not just another scraper It's an adaptive framework that learns the structure of each website and automatically adjusts when it changes No manual maintenance. No getting blocked. ✅ Bypasses Cloudflare and the most aggressive anti-bots ✅ 774x faster than BeautifulSoup in real benchmarks ✅ No need for proxies or special setup ✅ Automatically adapts when the website structure changes ✅ Compatible with AI agents as an MCP server ✅ Support for JavaScript, iframes, and dynamic content ✅ Stealth mode for sites with advanced detection ✅ 46 releases. Updated last week. ✅ BSD-3 license What used to take you days to build and maintain now takes minutes 52.2k stars. 5k forks. BSD-3. repo here 👇

Indu Tripathi

20,700 views • 2 months ago

We just shipped an official Claude connector for Supabase ⚡️ You can now install the Supabase MCP server on Claude web and desktop!
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We just shipped an official Claude connector for Supabase ⚡️ You can now install the Supabase MCP server on Claude web and desktop!

Supabase

87,975 views • 5 months ago

The Vercel fluid × Upstash agents framework just solved one of the trickiest issues in chatbots/agents and one of my biggest pet peeves 😆 Notice that right after submitting my message, it immediately assigns an uuid (`/wfr_KgW5x…`) to the job. At this point, my internet might flake out, I might shut my laptop lid, I might go to my phone and open the chat from there… What you're looking at is a resumable, durable agentic workflow that's fault tolerant on the server and on the client. On the server side, Fluid functions on Vercel will be invoked for each step, and they can fail and get retried. On the client, polling for changes ensures eventual consistency even in the presence of very long network partitions. Shoutout to yunus for collaborating with me on shipping these improvements. Check out the OSS Next.js repo to learn these patterns and the demo at: ps: it's actually impressive what a wide range of questions this simple demo agent can answer by choosing between Exa / Wikipedia / Wolfram
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The Vercel fluid × Upstash agents framework just solved one of the trickiest issues in chatbots/agents and one of my biggest pet peeves 😆 Notice that right after submitting my message, it immediately assigns an uuid (`/wfr_KgW5x…`) to the job. At this point, my internet might flake out, I might shut my laptop lid, I might go to my phone and open the chat from there… What you're looking at is a resumable, durable agentic workflow that's fault tolerant on the server and on the client. On the server side, Fluid functions on Vercel will be invoked for each step, and they can fail and get retried. On the client, polling for changes ensures eventual consistency even in the presence of very long network partitions. Shoutout to yunus for collaborating with me on shipping these improvements. Check out the OSS Next.js repo to learn these patterns and the demo at: ps: it's actually impressive what a wide range of questions this simple demo agent can answer by choosing between Exa / Wikipedia / Wolfram

Guillermo Rauch

56,799 views • 1 year ago

🚨 A new era of development is coming… and you won’t want to miss it. 🚨 Imagine building Web 2.0 and Web 3.0 applications for ecosystems like Solana, Ethereum, Polygon, BSC, and many more… 👉 No code. 👉 No limits. 👉 No premium plans. With NativeUI you can: ✅ Create unlimited projects. ✅ Connect domains, subdomains, Cloudflare, proxies, and DNS. ✅ Deploy locally or to production with advanced controls. ✅ Collaborate with designers, developers, and your community. Compatible with the most innovative Web3 ecosystems: Solana | Ethereum | Polygon | BSC | +20 chains 🌐 And this is just the beginning... 🚀 #NativeUI #NoCode #Web3 #Solana #Ethereum #Polygon #BuildTheFuture #Blockchain #NoCode #NativeUI #Ethereum #Polygon #Solana #Vercel #TheGraph #Bitquery #BuildInPublic #DeFi #CryptoBuilders $COLLAT $ARC #GRIFFAIN $SNAI $SSE $ALCH $TANK $WHISP $PVS $WOPEN $PAYAI $DARK $JOS $NUI
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🚨 A new era of development is coming… and you won’t want to miss it. 🚨 Imagine building Web 2.0 and Web 3.0 applications for ecosystems like Solana, Ethereum, Polygon, BSC, and many more… 👉 No code. 👉 No limits. 👉 No premium plans. With NativeUI you can: ✅ Create unlimited projects. ✅ Connect domains, subdomains, Cloudflare, proxies, and DNS. ✅ Deploy locally or to production with advanced controls. ✅ Collaborate with designers, developers, and your community. Compatible with the most innovative Web3 ecosystems: Solana | Ethereum | Polygon | BSC | +20 chains 🌐 And this is just the beginning... 🚀 #NativeUI #NoCode #Web3 #Solana #Ethereum #Polygon #BuildTheFuture #Blockchain #NoCode #NativeUI #Ethereum #Polygon #Solana #Vercel #TheGraph #Bitquery #BuildInPublic #DeFi #CryptoBuilders $COLLAT $ARC #GRIFFAIN $SNAI $SSE $ALCH $TANK $WHISP $PVS $WOPEN $PAYAI $DARK $JOS $NUI

NativeUI

26,802 views • 1 year ago

We built a Supabase integration for OpenCode and tested it inside Minecraft. The agent created a table, populated it with data, and deployed an Edge Function without ever leaving a survival world. Nobody touch the dirt house. 🧱 👉
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We built a Supabase integration for OpenCode and tested it inside Minecraft. The agent created a table, populated it with data, and deployed an Edge Function without ever leaving a survival world. Nobody touch the dirt house. 🧱 👉

Supabase

43,627 views • 1 month ago

Let's Zoom-In to the new start of the week... 🔍 Releasing #IDA Plugin #ZoomAllViews — Ctrl+Scroll font zoom for every IDA view. 💪 Because this should work out of the box. Now it does. 🤓 • Zoom in/out in Disassembly, Pseudocode, Hex View, Strings, Imports, Functions, Structures, and every chooser 😲 • Works across Normal & Debug view widgets — Stack, Registers, Locals, Watch • Row heights scale automatically with font size • Graph/Proximity/Xref views excluded — IDA's native zoom untouched ☝️ • Toggle on/off via menu or Ctrl-Shift-Z • Single file, zero dependencies 🫰 • Compatible IDA 8.x — 9.3+ (PyQt5 / PySide6) 🛠️ #IDAPro #ReverseEngineering #IDAPython #Malware #DFIR Hex-Rays SA
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Let's Zoom-In to the new start of the week... 🔍 Releasing #IDA Plugin #ZoomAllViews — Ctrl+Scroll font zoom for every IDA view. 💪 Because this should work out of the box. Now it does. 🤓 • Zoom in/out in Disassembly, Pseudocode, Hex View, Strings, Imports, Functions, Structures, and every chooser 😲 • Works across Normal & Debug view widgets — Stack, Registers, Locals, Watch • Row heights scale automatically with font size • Graph/Proximity/Xref views excluded — IDA's native zoom untouched ☝️ • Toggle on/off via menu or Ctrl-Shift-Z • Single file, zero dependencies 🫰 • Compatible IDA 8.x — 9.3+ (PyQt5 / PySide6) 🛠️ #IDAPro #ReverseEngineering #IDAPython #Malware #DFIR Hex-Rays SA

Jiří Vinopal

14,337 views • 3 months ago

Parsing documents with AI agents just got a lot more seamless🚀 We've rebuilt the LlamaParse MCP server to handle your document processing workflows, and you can connect it today to any MCP-compatible client at 🌐 Once connected, you'll be able to: 📁 Parse documents into clean markdown 🔍 Classify files against your own categories ✂️ Split long documents into labelled sections ⬆️ Upload files via URL or a browser-based upload flow Building a production MCP server surfaced some non-obvious challenges: getting auth to align with an existing platform identity system using WorkOS, working around MCP's lack of built-in file upload support, and making deployments, rate limiting and observability feel native with Vercel and Axiom. We wrote up all of it, from the OAuth flow, to the token-based upload design, to the tradeoffs we hit along the way📝 📚 Read the full blog: 👩‍💻 GitHub repository:
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Parsing documents with AI agents just got a lot more seamless🚀 We've rebuilt the LlamaParse MCP server to handle your document processing workflows, and you can connect it today to any MCP-compatible client at 🌐 Once connected, you'll be able to: 📁 Parse documents into clean markdown 🔍 Classify files against your own categories ✂️ Split long documents into labelled sections ⬆️ Upload files via URL or a browser-based upload flow Building a production MCP server surfaced some non-obvious challenges: getting auth to align with an existing platform identity system using WorkOS, working around MCP's lack of built-in file upload support, and making deployments, rate limiting and observability feel native with Vercel and Axiom. We wrote up all of it, from the OAuth flow, to the token-based upload design, to the tradeoffs we hit along the way📝 📚 Read the full blog: 👩‍💻 GitHub repository:

LlamaIndex 🦙

19,601 views • 3 months ago

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: 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!

Andrew Ng

142,010 views • 1 year ago

React tip: "use client" misconceptions (2/5) 🚫 "You cannot nest Server Components inside Client Components because "use client" turns everything into Client Components." ✅ We can pass the rendered result of Server Components to Client Components as props. Simple example: (Server Component) (Client Component) (Server Component) is designed for the client. It needs to instantly open and close when clicked. is designed for the server. It uses packages that don't work in the browser and needs to fetch data close to where it's stored without exposing credentials. So, how can we nest a component that uses server APIs inside a component that uses client APIs... without using `import`? React props to the rescue! --- (0:00) 1-4: Reminder: Importing code forms a module dependency graph. Adding dependencies to a server or client bundle. (0:23) 5-6: Reminder: Using components eventually forms a rendered component tree. (0:37) 9: Oh no! We get an error when trying to `import` a client API (useState) into a server module. (0:44) 10: We know the trick by now: Add "use client" to mark `2.js` as a client entry point. This moves the module to the client bundle and allows us to use client APIs like `useState.` (0:51) 11: But we get a new error! "use client" moved all imported dependencies into the client bundle, including our ORM package, which doesn't work in the browser. (0:59) 13: Let's refactor without changing our rendered component hierarchy. First, we move the `Cart` import to the parent file that imports `Modal`. This moves `Cart` outside the "use client" boundary and consequently the client bundle. (1:11) 15: Then, we pass down the rendered result of `Cart` as a prop to `Modal`. This allows `Cart` to be entirely rendered on the server as a Server Component before being passed down. `Modal` has no knowledge of what the `cart` prop is. Its only responsibility is placing whatever it receives into the `{cart}` slot. (1:15) 16: Finally, it's common to use the special `children` prop for a component's primary content. The key insight is that we were able to use props to retain our desired component hierarchy even though we changed our module dependency graph.
1:24

React tip: "use client" misconceptions (2/5) 🚫 "You cannot nest Server Components inside Client Components because "use client" turns everything into Client Components." ✅ We can pass the rendered result of Server Components to Client Components as props. Simple example: (Server Component) (Client Component) (Server Component) is designed for the client. It needs to instantly open and close when clicked. is designed for the server. It uses packages that don't work in the browser and needs to fetch data close to where it's stored without exposing credentials. So, how can we nest a component that uses server APIs inside a component that uses client APIs... without using `import`? React props to the rescue! --- (0:00) 1-4: Reminder: Importing code forms a module dependency graph. Adding dependencies to a server or client bundle. (0:23) 5-6: Reminder: Using components eventually forms a rendered component tree. (0:37) 9: Oh no! We get an error when trying to `import` a client API (useState) into a server module. (0:44) 10: We know the trick by now: Add "use client" to mark `2.js` as a client entry point. This moves the module to the client bundle and allows us to use client APIs like `useState.` (0:51) 11: But we get a new error! "use client" moved all imported dependencies into the client bundle, including our ORM package, which doesn't work in the browser. (0:59) 13: Let's refactor without changing our rendered component hierarchy. First, we move the `Cart` import to the parent file that imports `Modal`. This moves `Cart` outside the "use client" boundary and consequently the client bundle. (1:11) 15: Then, we pass down the rendered result of `Cart` as a prop to `Modal`. This allows `Cart` to be entirely rendered on the server as a Server Component before being passed down. `Modal` has no knowledge of what the `cart` prop is. Its only responsibility is placing whatever it receives into the `{cart}` slot. (1:15) 16: Finally, it's common to use the special `children` prop for a component's primary content. The key insight is that we were able to use props to retain our desired component hierarchy even though we changed our module dependency graph.

Delba

43,989 views • 2 years ago

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:
2:49

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

105,343 views • 1 year ago

Today, we're turning your project into a full-fledged identity provider 🔐 Now you can build "Sign in with Your App" using Supabase Auth
1:00

Today, we're turning your project into a full-fledged identity provider 🔐 Now you can build "Sign in with Your App" using Supabase Auth

Supabase

43,187 views • 8 months ago

I missed this when it was released a few weeks ago... probs the most unhinged crab robot out there. Love to see it. What it is: A heavy-duty construction robot that can swap tools and arms automatically during operation. Think modular platform rather than a single-purpose machine. Key specs: - 100kg payload, 4m reach, Fits in a Euro-pallet for easy transport - Autonomous tool switching via standardised interfaces - Handles placement, fastening, finishing, and inspection What makes it different: Designed from first principles for construction work, not human mimicry. The modular architecture means one robot tackles multiple tasks across different build phases without redesign.
0:21

I missed this when it was released a few weeks ago... probs the most unhinged crab robot out there. Love to see it. What it is: A heavy-duty construction robot that can swap tools and arms automatically during operation. Think modular platform rather than a single-purpose machine. Key specs: - 100kg payload, 4m reach, Fits in a Euro-pallet for easy transport - Autonomous tool switching via standardised interfaces - Handles placement, fastening, finishing, and inspection What makes it different: Designed from first principles for construction work, not human mimicry. The modular architecture means one robot tackles multiple tasks across different build phases without redesign.

Jack 🤖

29,454 views • 11 months ago

i just built a 4-agent software team. everything runs from Telegram and gets managed on a kanban board. a project manager who plans the work, a backend developer, a frontend developer, and a tester. the PM reads a goal, breaks it into linked tasks, and assigns each to the right agent. the thing that makes them a team instead of four strangers is a shared kanban board. every task is a row that survives crashes, and when an agent finishes, it writes a summary of what it built and what the next agent needs to know. the next agent reads that summary before it starts. so the frontend developer never has to guess the API shape, and the tester knows exactly what to verify. the hardest part was not the coordination. it was building an agent that could actually act like a backend engineer. a backend engineer stands up a database, wires auth, manages storage, deploys functions, and keeps all of it consistent while the rest of the team builds on top. an agent doing this from scratch drowns. it burns its context window remembering which tables exist and which endpoint it created three steps ago, and the work degrades fast. so the backend agent needs a backend built for agents, not for humans clicking through a dashboard. that is where InsForge came in. it is an open-source, agent-native backend, and i added it to my backend developer agent as a skill. a skill is a step-by-step guide that teaches the agent how to do a specific kind of work. with InsForge installed, the agent stopped improvising infrastructure and followed a reliable path: create the project, define the database, set up auth, deploy functions. to test the whole team, i had them build a working Google Docs clone, AI features included. the backend agent spun up the full service on its own. database tables, user auth, document handling, and edge functions running real TypeScript, all in one dashboard. the frontend agent read that summary and built the UI on top of it, and the tester closed the loop. the result was a backend an agent could reason about end to end, instead of one it kept getting lost inside. if you are building an AI backend engineer, InsForge is worth a look, it's 100% open-source. InsForge GitHub: (don't forget to star 🌟) the full article on Hermes Kanban: Mission Control for your Agents is quoted below.
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i just built a 4-agent software team. everything runs from Telegram and gets managed on a kanban board. a project manager who plans the work, a backend developer, a frontend developer, and a tester. the PM reads a goal, breaks it into linked tasks, and assigns each to the right agent. the thing that makes them a team instead of four strangers is a shared kanban board. every task is a row that survives crashes, and when an agent finishes, it writes a summary of what it built and what the next agent needs to know. the next agent reads that summary before it starts. so the frontend developer never has to guess the API shape, and the tester knows exactly what to verify. the hardest part was not the coordination. it was building an agent that could actually act like a backend engineer. a backend engineer stands up a database, wires auth, manages storage, deploys functions, and keeps all of it consistent while the rest of the team builds on top. an agent doing this from scratch drowns. it burns its context window remembering which tables exist and which endpoint it created three steps ago, and the work degrades fast. so the backend agent needs a backend built for agents, not for humans clicking through a dashboard. that is where InsForge came in. it is an open-source, agent-native backend, and i added it to my backend developer agent as a skill. a skill is a step-by-step guide that teaches the agent how to do a specific kind of work. with InsForge installed, the agent stopped improvising infrastructure and followed a reliable path: create the project, define the database, set up auth, deploy functions. to test the whole team, i had them build a working Google Docs clone, AI features included. the backend agent spun up the full service on its own. database tables, user auth, document handling, and edge functions running real TypeScript, all in one dashboard. the frontend agent read that summary and built the UI on top of it, and the tester closed the loop. the result was a backend an agent could reason about end to end, instead of one it kept getting lost inside. if you are building an AI backend engineer, InsForge is worth a look, it's 100% open-source. InsForge GitHub: (don't forget to star 🌟) the full article on Hermes Kanban: Mission Control for your Agents is quoted below.

Akshay 🚀

118,124 views • 1 month ago

Announcing Declarative Schema, a new way to simplify managing your growing database schema! Declarative schema stores the final state of the database in SQL files, making them the single source of truth. We use it internally to manage our prod db, and we hope you love it too!
0:47

Announcing Declarative Schema, a new way to simplify managing your growing database schema! Declarative schema stores the final state of the database in SQL files, making them the single source of truth. We use it internally to manage our prod db, and we hope you love it too!

Supabase

33,881 views • 1 year ago