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Molecular design for AI agents: announcing the Tamarind MCP Server. Today, scientists can use the Tamarind Bio library of 250+ molecular design tools(Boltz, AlphaFold, RFdiffusion...) in any AI chat interface. We serve not just open-source models, but the internal protocols your team has onboarded to Tamarind. Any tool added...

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Deniz Kavi

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14,588 просмотров • 5 месяцев назад •via X (Twitter)

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New short course: Build AI Apps with MCP Servers: Working with Box Files, built with Box and taught by Ben Kus , their CTO. Many AI applications require custom code for basic file operations. The Model Context Protocol (MCP) standardizes this by letting you offload file tasks to dedicated servers that provide tools an LLM can use directly. In this course, you'll process documents stored in a Box folder using the Box MCP server. Rather than writing custom integration code to connect to the Box API and download files, you'll design your application to use the tools provided via MCP. Skills you'll gain: - Build an LLM-powered document processing app, using the Box MCP server to access files - Design a multi-agent system using Google's Agent Development Kit (ADK), consisting of specialized agents for file operations - Coordinate the multi-agent workflow through an orchestrator that uses the Agent2Agent (A2A) protocol to connect to the agents You'll start with a local file-processing app, refactor it to work with Box's MCP server, then evolve it into a multi-agent system. Sign up here:
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New short course: Build AI Apps with MCP Servers: Working with Box Files, built with Box and taught by Ben Kus , their CTO. Many AI applications require custom code for basic file operations. The Model Context Protocol (MCP) standardizes this by letting you offload file tasks to dedicated servers that provide tools an LLM can use directly. In this course, you'll process documents stored in a Box folder using the Box MCP server. Rather than writing custom integration code to connect to the Box API and download files, you'll design your application to use the tools provided via MCP. Skills you'll gain: - Build an LLM-powered document processing app, using the Box MCP server to access files - Design a multi-agent system using Google's Agent Development Kit (ADK), consisting of specialized agents for file operations - Coordinate the multi-agent workflow through an orchestrator that uses the Agent2Agent (A2A) protocol to connect to the agents You'll start with a local file-processing app, refactor it to work with Box's MCP server, then evolve it into a multi-agent system. Sign up here:

Andrew Ng

81,696 просмотров • 11 месяцев назад

Three 16 year old Indian students can remove microplastics from water using tamarind seeds. And they've received a $12,500 grant from The Earth Prize to develop this solution. How did they do it? Tamarind seeds have something called polysaccharide - which is a sticky compound that can natural polymer. Kinda like a plant-based glue. This glue when mixed with water can pull out microplastics and clump them together. And these kids are using the same principle to develop their product called "Plas-Stick" - which is just this power made from waste tamarind seeds. Now, "Plas-Stick" is still in its developmental stage and there is a lot that needs to be done before it can be scaled. But if they pull this off - we could have a simple, biodegradable and scalable solution to purify water for billions of people in the world. And that's something. Kudos to Vivaan Chhawchharia, Ariana Agarwal, and Avyana Mehta. P.S. Video and Picture taken from The Earth Prize
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Three 16 year old Indian students can remove microplastics from water using tamarind seeds. And they've received a $12,500 grant from The Earth Prize to develop this solution. How did they do it? Tamarind seeds have something called polysaccharide - which is a sticky compound that can natural polymer. Kinda like a plant-based glue. This glue when mixed with water can pull out microplastics and clump them together. And these kids are using the same principle to develop their product called "Plas-Stick" - which is just this power made from waste tamarind seeds. Now, "Plas-Stick" is still in its developmental stage and there is a lot that needs to be done before it can be scaled. But if they pull this off - we could have a simple, biodegradable and scalable solution to purify water for billions of people in the world. And that's something. Kudos to Vivaan Chhawchharia, Ariana Agarwal, and Avyana Mehta. P.S. Video and Picture taken from The Earth Prize

Varun Guru

62,830 просмотров • 3 месяцев назад

Introducing LangSmith Agent Builder 🤖🧱 A true agent building experience (not workflows!!), all through a simple natural language interface. Describe your agent to "build" it, then interact with it via chat, or add an automatic trigger. Connect your agents to any MCP server, or use our builtin tools. These agents also come with builtin memory, so they can optimize themselves over time, both ambiently, and from user feedback! Sign up for the waitlist today:
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Introducing LangSmith Agent Builder 🤖🧱 A true agent building experience (not workflows!!), all through a simple natural language interface. Describe your agent to "build" it, then interact with it via chat, or add an automatic trigger. Connect your agents to any MCP server, or use our builtin tools. These agents also come with builtin memory, so they can optimize themselves over time, both ambiently, and from user feedback! Sign up for the waitlist today:

Brace

30,846 просмотров • 10 месяцев назад

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

142,137 просмотров • 1 год назад

wtf is MCP? Paul Klein IV, CEO of explains "Model Context Protocol" (MCP) in plain terms: "There's this new way that people are exposing tools to AI & the 'Model Context Protocol' is one of those ways that you can standardize & give your AI a set of tools to call. BrowserBase has a way to expose the browser to your agent via MCP or MCP server. More & more developers are trying to find ways to integrate more functionalities to their AI agents & BrowserBase's browsers are one of those primitive functionalities, & the MCP is just one way to integrate it into your software." Paul Klein IV From 🅱️'s GitHub: Model Context Protocol (MCP) is an open protocol that enables seamless integration between LLM applications and external data sources and tools. Whether you’re building an AI-powered IDE, enhancing a chat interface, or creating custom AI workflows, MCP provides a standardized way to connect LLMs with the context they need. This server provides cloud browser automation capabilities using Browserbase and Stagehand. This server enables LLMs to interact with web pages, take screenshots, and execute JavaScript in a cloud browser environment.
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wtf is MCP? Paul Klein IV, CEO of explains "Model Context Protocol" (MCP) in plain terms: "There's this new way that people are exposing tools to AI & the 'Model Context Protocol' is one of those ways that you can standardize & give your AI a set of tools to call. BrowserBase has a way to expose the browser to your agent via MCP or MCP server. More & more developers are trying to find ways to integrate more functionalities to their AI agents & BrowserBase's browsers are one of those primitive functionalities, & the MCP is just one way to integrate it into your software." Paul Klein IV From 🅱️'s GitHub: Model Context Protocol (MCP) is an open protocol that enables seamless integration between LLM applications and external data sources and tools. Whether you’re building an AI-powered IDE, enhancing a chat interface, or creating custom AI workflows, MCP provides a standardized way to connect LLMs with the context they need. This server provides cloud browser automation capabilities using Browserbase and Stagehand. This server enables LLMs to interact with web pages, take screenshots, and execute JavaScript in a cloud browser environment.

Molly O’Shea

20,408 просмотров • 1 год назад

We’ve integrated a first-of-its-kind, privacy-native MCP connector layer to the Dot platform, making DotChat agentic by default. Live at → Even in private AI, the standard product is: pick a model, send a prompt, maybe enable web search and get an answer. This is simply not enough. MCPs changed what AI agents can do, but they are trapped inside: Claude Code, Cursor, local agents, API tooling and enterprise setups. Through Dot, users can now connect any MCP directly into their AI workspace, while maintaining Dot’s privacy boundary around the entire workflow. This means Dot users won’t just ask models questions, they’ll be able to connect tools, inspect onchain state, check portfolios, prepare transactions, and interact with Base from inside the chat flow. And yes, we’re starting with the big one. The Base MCP. This is a major step toward Dot’s agentic vision: powerful AI agents that can interact with the world without compromising user privacy. Check our demo below, where we integrate the updated Base MCP into Dot, and utilize it to buy $10 in $DOT, all whilst retaining full anonymity ↓
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We’ve integrated a first-of-its-kind, privacy-native MCP connector layer to the Dot platform, making DotChat agentic by default. Live at → Even in private AI, the standard product is: pick a model, send a prompt, maybe enable web search and get an answer. This is simply not enough. MCPs changed what AI agents can do, but they are trapped inside: Claude Code, Cursor, local agents, API tooling and enterprise setups. Through Dot, users can now connect any MCP directly into their AI workspace, while maintaining Dot’s privacy boundary around the entire workflow. This means Dot users won’t just ask models questions, they’ll be able to connect tools, inspect onchain state, check portfolios, prepare transactions, and interact with Base from inside the chat flow. And yes, we’re starting with the big one. The Base MCP. This is a major step toward Dot’s agentic vision: powerful AI agents that can interact with the world without compromising user privacy. Check our demo below, where we integrate the updated Base MCP into Dot, and utilize it to buy $10 in $DOT, all whilst retaining full anonymity ↓

Dot

10,268 просмотров • 2 месяцев назад

I'm still picking my jaw off the floor with this MCP update: Postman will now automatically build an MCP server for an API. Automatically! You select your API and the requests you want to include, and Potman will generate a Zip file with everything you need. In collaboration with Postman, I recorded a video showing how I generated an MCP server for the Google Maps API. It's glorious! Also, and before I forget: You can now test any MCP server with your Postman application. They built MCP right in, so now you can: • Connect to an MCP server • List out all of the available tools • Test any of the tools This is a 10x boost for the MCP ecosystem. If you've tried the default MCP debugger, you'll understand how huge a deal it is that you can do all of that from Postman now. Go here to give this a try:
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I'm still picking my jaw off the floor with this MCP update: Postman will now automatically build an MCP server for an API. Automatically! You select your API and the requests you want to include, and Potman will generate a Zip file with everything you need. In collaboration with Postman, I recorded a video showing how I generated an MCP server for the Google Maps API. It's glorious! Also, and before I forget: You can now test any MCP server with your Postman application. They built MCP right in, so now you can: • Connect to an MCP server • List out all of the available tools • Test any of the tools This is a 10x boost for the MCP ecosystem. If you've tried the default MCP debugger, you'll understand how huge a deal it is that you can do all of that from Postman now. Go here to give this a try:

Santiago

215,882 просмотров • 1 год назад

🔮 MCP INTEGRATION NOW LIVE ON OPENSERV This is bigger than most realize. We're not just adding a feature - we're enabling a fundamentally new paradigm for agent infrastructure. Before MCP: agents trapped in walled gardens, limited by what you explicitly feed them. After MCP: agents that can access 10K+ tools, interact with your existing systems, and truly operate in your digital world. Think about this: -Every integration used to require custom code -Every data source needed specific formatting -Every agent had to be built for specific tools MCP eliminates all of that. One standard to connect any agent to any tool. This isn't just convenience - it's the unlock that makes agents actually useful in the real world. OpenServ + MCP = AI teams that can genuinely get things done, not just talk about doing them. This is the breakthrough that turns agents from toys into tools.
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🔮 MCP INTEGRATION NOW LIVE ON OPENSERV This is bigger than most realize. We're not just adding a feature - we're enabling a fundamentally new paradigm for agent infrastructure. Before MCP: agents trapped in walled gardens, limited by what you explicitly feed them. After MCP: agents that can access 10K+ tools, interact with your existing systems, and truly operate in your digital world. Think about this: -Every integration used to require custom code -Every data source needed specific formatting -Every agent had to be built for specific tools MCP eliminates all of that. One standard to connect any agent to any tool. This isn't just convenience - it's the unlock that makes agents actually useful in the real world. OpenServ + MCP = AI teams that can genuinely get things done, not just talk about doing them. This is the breakthrough that turns agents from toys into tools.

OpenServ

34,665 просмотров • 1 год назад

Big news! Our AI browser for SEO is now available for free to everyone on MacOS and Windows. SERP Lens is out of beta and is the go-to workspace for SEOs and their AI agents. Purpose-built for the work of search, the application runs on live web data and is ready for what comes next. Here are some of the reasons why you need to test it out now: • Built in agentic functions • Select your favorite frontier AI model • Utilize skills trained by top consultants • Connect any MCP server & GSC data • Beautifully designed interface For a short time only, you can test out the AI with complimentary credits within the “Pro” trial; then it's $39 p/mo for continued access (can use the browser + some VPN locations for free). Our dev team, led by Sam Underwood 🇬🇧, has done some incredible work here, so make sure to check it out now and let us know what you think! Take a look for yourself:
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Big news! Our AI browser for SEO is now available for free to everyone on MacOS and Windows. SERP Lens is out of beta and is the go-to workspace for SEOs and their AI agents. Purpose-built for the work of search, the application runs on live web data and is ready for what comes next. Here are some of the reasons why you need to test it out now: • Built in agentic functions • Select your favorite frontier AI model • Utilize skills trained by top consultants • Connect any MCP server & GSC data • Beautifully designed interface For a short time only, you can test out the AI with complimentary credits within the “Pro” trial; then it's $39 p/mo for continued access (can use the browser + some VPN locations for free). Our dev team, led by Sam Underwood 🇬🇧, has done some incredible work here, so make sure to check it out now and let us know what you think! Take a look for yourself:

Brodie Clark

125,969 просмотров • 10 дней назад

Today, Box is announcing major new AI agent capabilities to let customers tap into the full value of their unstructured data. First, we’re announcing all new updates to the Box AI Studio to make it even easier to build AI agents that tap into your enterprise content for any job function, business process, or industry specific use case. We are also expanding our set of foundational agents that customers will be able to use to work with their enterprise content, including new features like search and research on unstructured data. Next, we’re announcing Box Extract to enable customers to use AI agents seamlessly for complex data extraction from any type of document or content. This makes it easier than ever to pull out data from contracts, invoices, research data, marketing assets, medical charts, and more. Finally, we’re introducing Box Automate, a new workflow automation solution within Box that lets you deploy AI agents across enterprise content-centric workflows. With Box Automate, you can design your business process in a simple drag and drop builder and then drop in AI agents at any step in the process. This ensures agents execute tasks at the right steps in a workflow every time. Best of all, our AI agents and workflow tools are designed to work across any system our customers work within, whether it’s leveraging pre-built integrations, Box APIs, or the new Box MCP Server. Ultimately, all of these capabilities come together to transform how companies can work with their enterprise content. Software has historically only been good at automating work that deals with structured data, which is why ERP, CRM, and HR systems have been mainstays of enterprise software for so long. The data in these systems fits neatly into a database, and the workflows are very ripe for automation. But it turns out most of the work in the world deals with unstructured data. It’s ideating through research documents, working with a client on contracts, reviewing details for a new product launch, looking at a patient’s healthcare record to make a diagnosis, working through due diligence documents for an M&A deal, and so on. For the first time ever, we can begin to bring all new insights and automation to this work with AI agents. At Box, we’re incredibly excited to be on this journey to help customers transform how they work with their most important data.
1:21

Today, Box is announcing major new AI agent capabilities to let customers tap into the full value of their unstructured data. First, we’re announcing all new updates to the Box AI Studio to make it even easier to build AI agents that tap into your enterprise content for any job function, business process, or industry specific use case. We are also expanding our set of foundational agents that customers will be able to use to work with their enterprise content, including new features like search and research on unstructured data. Next, we’re announcing Box Extract to enable customers to use AI agents seamlessly for complex data extraction from any type of document or content. This makes it easier than ever to pull out data from contracts, invoices, research data, marketing assets, medical charts, and more. Finally, we’re introducing Box Automate, a new workflow automation solution within Box that lets you deploy AI agents across enterprise content-centric workflows. With Box Automate, you can design your business process in a simple drag and drop builder and then drop in AI agents at any step in the process. This ensures agents execute tasks at the right steps in a workflow every time. Best of all, our AI agents and workflow tools are designed to work across any system our customers work within, whether it’s leveraging pre-built integrations, Box APIs, or the new Box MCP Server. Ultimately, all of these capabilities come together to transform how companies can work with their enterprise content. Software has historically only been good at automating work that deals with structured data, which is why ERP, CRM, and HR systems have been mainstays of enterprise software for so long. The data in these systems fits neatly into a database, and the workflows are very ripe for automation. But it turns out most of the work in the world deals with unstructured data. It’s ideating through research documents, working with a client on contracts, reviewing details for a new product launch, looking at a patient’s healthcare record to make a diagnosis, working through due diligence documents for an M&A deal, and so on. For the first time ever, we can begin to bring all new insights and automation to this work with AI agents. At Box, we’re incredibly excited to be on this journey to help customers transform how they work with their most important data.

Aaron Levie

91,863 просмотров • 11 месяцев назад

This is probably the biggest news yet in software going headless, and will bring knowledge work agents to the masses. The new ChatGPT agents have access to any of the tools and data you want to work with, with complete coding and tool use available to them. Here's an example of a custom sales assistant agent uses Box as a knowledge source for accessing enterprise content securely to answer questions and generate new content on the fly. The workflows can obviously vastly far more complex as the agent can use any of the tools within Box available via MCP and CLI. This precisely what agents will start to look like for knowledge work. You'll be able to spin them up in the foreground or background to help augment work. Big opportunity right now for headless platforms, and for all the new builders and designers of these agents in the enterprise.
1:06

This is probably the biggest news yet in software going headless, and will bring knowledge work agents to the masses. The new ChatGPT agents have access to any of the tools and data you want to work with, with complete coding and tool use available to them. Here's an example of a custom sales assistant agent uses Box as a knowledge source for accessing enterprise content securely to answer questions and generate new content on the fly. The workflows can obviously vastly far more complex as the agent can use any of the tools within Box available via MCP and CLI. This precisely what agents will start to look like for knowledge work. You'll be able to spin them up in the foreground or background to help augment work. Big opportunity right now for headless platforms, and for all the new builders and designers of these agents in the enterprise.

Aaron Levie

420,133 просмотров • 4 месяцев назад

🤯 Building computer-vision applications is never going to be the same again. As Andrej Karpathy says, "English is going to be the new programming-language", and we just made that happen for programming visual AI. 👨‍💻 This past month, the VLM Run team cooked up something magical that I'm especially proud of - a natural-language interface to build and run computer vision workflows (object-detection, OCR, parsing, and so much more) – all via our new VLM Run MCP server. 🔌 It works with any MCP-compatible AI agent (Claude, OpenAI, Cursor, etc.) and abstracts complex visual workflows into simple English commands. No need to write Python or fiddle with bespoke computer-vision code – just say what you want your agent to do, and it does it. Here’s a glimpse into what you can build today ( ☑️ Image AI agents – run detection, tagging, blurring, cropping, and more traditional CV tasks with English ☑️ Document AI agents – extract totals from invoices, search visual content, or act on what’s found ☑️ Video AI agents – understand scene content, trim clips, add subtitles, and create ready-to-share edits ☑️ And best-of-all, it's fully programmable with English. 🧑‍🍳 MCP Showcase: 📚 MCP Cookbooks: 📚 MCP Docs: #computervision #ai #agents #llm #vlm #mcp #vibecoding #genai
1:49

🤯 Building computer-vision applications is never going to be the same again. As Andrej Karpathy says, "English is going to be the new programming-language", and we just made that happen for programming visual AI. 👨‍💻 This past month, the VLM Run team cooked up something magical that I'm especially proud of - a natural-language interface to build and run computer vision workflows (object-detection, OCR, parsing, and so much more) – all via our new VLM Run MCP server. 🔌 It works with any MCP-compatible AI agent (Claude, OpenAI, Cursor, etc.) and abstracts complex visual workflows into simple English commands. No need to write Python or fiddle with bespoke computer-vision code – just say what you want your agent to do, and it does it. Here’s a glimpse into what you can build today ( ☑️ Image AI agents – run detection, tagging, blurring, cropping, and more traditional CV tasks with English ☑️ Document AI agents – extract totals from invoices, search visual content, or act on what’s found ☑️ Video AI agents – understand scene content, trim clips, add subtitles, and create ready-to-share edits ☑️ And best-of-all, it's fully programmable with English. 🧑‍🍳 MCP Showcase: 📚 MCP Cookbooks: 📚 MCP Docs: #computervision #ai #agents #llm #vlm #mcp #vibecoding #genai

Sudeep Pillai

17,789 просмотров • 1 год назад

🚀 Big news: Cline just launched the MCP Marketplace -- now you can one-click install MCP servers and give Cline access to a whole new suite of tools! What's changing with the MCP Marketplace: • Browse AI capabilities like apps • One-click MCP server installation • Automatic setup and configuration • No more JSON wrestling Before: - Find GitHub repos - Read technical docs - Configure manually - Hope it works Now: - Open Cline - Browse marketplace - Click "Install" - Start using The impact: Making AI superpowers accessible to everyone, not just tech experts. Imagine giving your AI instant access to: • Web searching capabilities • Design tools integration • Code analysis powers • And much more For developers: Want to list your MCP server? Create an issue in our mcp-marketplace repo with: • GitHub repo URL • 400x400 PNG logo • Brief description The future of AI development isn't about complex integrations. It's about unlocking capabilities with a single click. Welcome to that future. 🔮
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🚀 Big news: Cline just launched the MCP Marketplace -- now you can one-click install MCP servers and give Cline access to a whole new suite of tools! What's changing with the MCP Marketplace: • Browse AI capabilities like apps • One-click MCP server installation • Automatic setup and configuration • No more JSON wrestling Before: - Find GitHub repos - Read technical docs - Configure manually - Hope it works Now: - Open Cline - Browse marketplace - Click "Install" - Start using The impact: Making AI superpowers accessible to everyone, not just tech experts. Imagine giving your AI instant access to: • Web searching capabilities • Design tools integration • Code analysis powers • And much more For developers: Want to list your MCP server? Create an issue in our mcp-marketplace repo with: • GitHub repo URL • 400x400 PNG logo • Brief description The future of AI development isn't about complex integrations. It's about unlocking capabilities with a single click. Welcome to that future. 🔮

Cline

112,069 просмотров • 1 год назад

Box is a launch partner for Microsoft’s Apps in Copilot launch. The Box AI Agent for Microsoft365 Copilot will be able to surface rich visual previews like slides, graphs, and images right in the conversation and have access to all of the Box MCP server tools. Coming soon to the Box AI Agent in Microsoft 365 Copilot: 🔹 MCP-powered workflows — metadata extraction, document generation, and more 🔹 Visual-first search — view complicated graphs or rich visuals that are buried in your Box content, without having to open files. 🔹 Fewer clarifications, faster decisions - move from chat to action Enable agents to bring apps into the flow of work, while keeping IT in control:
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Box is a launch partner for Microsoft’s Apps in Copilot launch. The Box AI Agent for Microsoft365 Copilot will be able to surface rich visual previews like slides, graphs, and images right in the conversation and have access to all of the Box MCP server tools. Coming soon to the Box AI Agent in Microsoft 365 Copilot: 🔹 MCP-powered workflows — metadata extraction, document generation, and more 🔹 Visual-first search — view complicated graphs or rich visuals that are buried in your Box content, without having to open files. 🔹 Fewer clarifications, faster decisions - move from chat to action Enable agents to bring apps into the flow of work, while keeping IT in control:

Box

335,591 просмотров • 5 месяцев назад

Here we go again 🚀! Excited to announce that we're building A1Zap (YC W25) with Pennie Li and that we're in the Y Combinator W25 batch in San Francisco! What is A1Base? A1Base gives AI Agents a real world identity for work. We do that by rebuilding Twilio and Okta from the ground up, putting AI Agents first. This means developers can make AI-first agentic applications 10x easier with our API's. ⁉️ Why are we doing this? Because there's a huge torrent of new valuable companies possible with AI agents, but to get their AI Agents to users, they have to chain custom apps, chat interfaces, awkward Slack integrations, browser bots, and wrestle with Twilio’s legacy API (which is built for marketing). We solve this by providing developers with an easy to use API to interface your AI agent with humans/coworkers/users where they are in this case in Whatsapp, Slack, Teams, SMS and more) - with AI Agent features built in. These digital workers are poised to transform how we work and we're the critical infrastructure to help them interact naturally in human workflows. We're not just building another AI tool. We're creating the infrastructure that will enable AI agents to become a natural part of the workforce - handling everything from customer support to sales development to creative work. We're backed by Y Combinator and working with founding teams who share our vision. We believe that in the near future, AI Agents with human coworkers will enable us to pursue more creative and impactful work. Our mission is to help developers build AI Agents that people can partner with and rely on as trusted allies—always with a human-first mindset. If you're thinking about the Agentic future of your company reach out! If you're looking to build your first AI Agentic company - reach out too - we have some amazing open source templates to get you started on the journey. Excited to share more of what we're up to soon 🔜.
1:52

Here we go again 🚀! Excited to announce that we're building A1Zap (YC W25) with Pennie Li and that we're in the Y Combinator W25 batch in San Francisco! What is A1Base? A1Base gives AI Agents a real world identity for work. We do that by rebuilding Twilio and Okta from the ground up, putting AI Agents first. This means developers can make AI-first agentic applications 10x easier with our API's. ⁉️ Why are we doing this? Because there's a huge torrent of new valuable companies possible with AI agents, but to get their AI Agents to users, they have to chain custom apps, chat interfaces, awkward Slack integrations, browser bots, and wrestle with Twilio’s legacy API (which is built for marketing). We solve this by providing developers with an easy to use API to interface your AI agent with humans/coworkers/users where they are in this case in Whatsapp, Slack, Teams, SMS and more) - with AI Agent features built in. These digital workers are poised to transform how we work and we're the critical infrastructure to help them interact naturally in human workflows. We're not just building another AI tool. We're creating the infrastructure that will enable AI agents to become a natural part of the workforce - handling everything from customer support to sales development to creative work. We're backed by Y Combinator and working with founding teams who share our vision. We believe that in the near future, AI Agents with human coworkers will enable us to pursue more creative and impactful work. Our mission is to help developers build AI Agents that people can partner with and rely on as trusted allies—always with a human-first mindset. If you're thinking about the Agentic future of your company reach out! If you're looking to build your first AI Agentic company - reach out too - we have some amazing open source templates to get you started on the journey. Excited to share more of what we're up to soon 🔜.

Pasha Rayan

53,950 просмотров • 1 год назад

The most complete AI design skill stack is now in Open Design. AI agents can build full apps. But most of them still design like shit. You used to spend more time fixing the design than building the app. And prompting harder does not fix taste. So Open Design integrated the design skills everyone on X is talking about: Taste Skill Hallmark Frontend Design Image-to-Code Design Review 100+ design systems It works locally with 20+ AI CLIs: Codex, Claude Code, Cursor, Gemini CLI, OpenCode, and more. Pick your agent. Generate pages that finally have taste. 👉
0:53

The most complete AI design skill stack is now in Open Design. AI agents can build full apps. But most of them still design like shit. You used to spend more time fixing the design than building the app. And prompting harder does not fix taste. So Open Design integrated the design skills everyone on X is talking about: Taste Skill Hallmark Frontend Design Image-to-Code Design Review 100+ design systems It works locally with 20+ AI CLIs: Codex, Claude Code, Cursor, Gemini CLI, OpenCode, and more. Pick your agent. Generate pages that finally have taste. 👉

Open Design

53,135 просмотров • 2 месяцев назад

Global payroll is one of the hardest problems in business. We've spent 7 years solving it. Today we open it up, and step into our next chapter as the leading global employment infrastructure. Any company, any tool, any AI agent can now connect directly to Remote via MCP. No API keys. No custom integrations. Just the infrastructure tens of thousands of companies already run on. Watch Job explain👇
1:20

Global payroll is one of the hardest problems in business. We've spent 7 years solving it. Today we open it up, and step into our next chapter as the leading global employment infrastructure. Any company, any tool, any AI agent can now connect directly to Remote via MCP. No API keys. No custom integrations. Just the infrastructure tens of thousands of companies already run on. Watch Job explain👇

Remote

4,424,205 просмотров • 3 месяцев назад

AI has solved software. Biology is the next frontier. We're hiring across every team at Adaptyv. We’ve built the best automated lab for protein designers to experimentally test their AI-designed proteins. Today, the most advanced protein design companies run their wet-lab work on Adaptyv, from the biggest biopharmas to frontier AI labs to dozens of virtual biotech startups. Demand has grown faster than we have, so we’re hiring across the board: • Bio: Research associates, scientists and lab technicians to develop and run new assays at scale. • Lab automation: Engineers and interns to onboard new lab instruments and scale our automation infrastructure. • Software: Product and backend engineers to scale LabOS, our internal lab orchestration platform, our API for agents and the data pipelines that turn messy physical-world data into clean results. • Partnerships, customer success and operations: Building partnerships with AI & pharma labs, making sure customers understand their data and can run more campaigns, making sure the company operations run smoothly
0:30

AI has solved software. Biology is the next frontier. We're hiring across every team at Adaptyv. We’ve built the best automated lab for protein designers to experimentally test their AI-designed proteins. Today, the most advanced protein design companies run their wet-lab work on Adaptyv, from the biggest biopharmas to frontier AI labs to dozens of virtual biotech startups. Demand has grown faster than we have, so we’re hiring across the board: • Bio: Research associates, scientists and lab technicians to develop and run new assays at scale. • Lab automation: Engineers and interns to onboard new lab instruments and scale our automation infrastructure. • Software: Product and backend engineers to scale LabOS, our internal lab orchestration platform, our API for agents and the data pipelines that turn messy physical-world data into clean results. • Partnerships, customer success and operations: Building partnerships with AI & pharma labs, making sure customers understand their data and can run more campaigns, making sure the company operations run smoothly

Adaptyv Bio

13,670 просмотров • 2 месяцев назад

I bought Figma stock during the SaaSpocalypse panic. Talking to Matt Colyer, Figma's director of product management for developers, made me wish I'd bought more. He joined me on Every 📧's AI & I to make the case for a SaaS resurgence—and to explain why chat-based tools are the wrong interface for design. We get into: - Why running your own agents makes you more willing to pay for SaaS, not less - How Figma's MCP server allows you to approach design work from two directions: Take a live web page and reconstruct it on the Figma canvas, or hand a Figma design over to an agent so it can makes changes via pull request - The inherent limits of chat-based design, a format that isn’t equipped to generate lots of new ideas -Why review is the next bottleneck, and potential solutions for helping teams scale evaluations Watch below! Timestamps 0:01:03 - Introduction 0:02:15 - The SaaSpocalypse narrative has it backwards 0:05:27 - Matt’s email-agent origin story 0:13:21 - Divergent vs. convergent design thinking 0:17:39 - Figma’s MCP server 0:19:45 - Why design agents need personalization 0:22:09 - Every problem is a context problem 0:25:12 - Apple and Google as the reigning kings of context 0:28:18 - Why review is the new bottleneck
33:53

I bought Figma stock during the SaaSpocalypse panic. Talking to Matt Colyer, Figma's director of product management for developers, made me wish I'd bought more. He joined me on Every 📧's AI & I to make the case for a SaaS resurgence—and to explain why chat-based tools are the wrong interface for design. We get into: - Why running your own agents makes you more willing to pay for SaaS, not less - How Figma's MCP server allows you to approach design work from two directions: Take a live web page and reconstruct it on the Figma canvas, or hand a Figma design over to an agent so it can makes changes via pull request - The inherent limits of chat-based design, a format that isn’t equipped to generate lots of new ideas -Why review is the next bottleneck, and potential solutions for helping teams scale evaluations Watch below! Timestamps 0:01:03 - Introduction 0:02:15 - The SaaSpocalypse narrative has it backwards 0:05:27 - Matt’s email-agent origin story 0:13:21 - Divergent vs. convergent design thinking 0:17:39 - Figma’s MCP server 0:19:45 - Why design agents need personalization 0:22:09 - Every problem is a context problem 0:25:12 - Apple and Google as the reigning kings of context 0:28:18 - Why review is the new bottleneck

Dan Shipper 📧

12,921 просмотров • 2 месяцев назад