Загрузка видео...

Не удалось загрузить видео

На главную

This is why you want an execution layer share your tools, auth and approvals across all agents you use Check out me adding the Axiom MCP server, authing to it, and calling it from Claude Code and OpenCode in under a minute - all locally on my computer `npm...

RhysSullivan's profile picture

Rhys

56,834 subscribers

29,958 просмотров • 3 месяцев назад •via X (Twitter)

LIVE

Anya Rossi• Live Now

Private livecam show

Комментарии: 0

Нет доступных комментариев

Здесь появятся комментарии из оригинального поста

Похожие видео

i hate the way tool calling works today, so i built a fix for it its called `executor` and it lets your agent run code to call any API / MCP / GraphQL server rather than installing CLIs this is a soft launch, you can try it with npm i -g executor but im still making it great
1:19

i hate the way tool calling works today, so i built a fix for it its called `executor` and it lets your agent run code to call any API / MCP / GraphQL server rather than installing CLIs this is a soft launch, you can try it with npm i -g executor but im still making it great

Rhys

128,964 просмотров • 3 месяцев назад

this tweet misses the gold of executor! the whole point is you don't want CLI / scripts, you want API / MCP servers in this video i ask executor to configure itself and add an MCP server - it pops up a browser for me to OAuth through based on the MCP elicitation spec
1:50

this tweet misses the gold of executor! the whole point is you don't want CLI / scripts, you want API / MCP servers in this video i ask executor to configure itself and add an MCP server - it pops up a browser for me to OAuth through based on the MCP elicitation spec

Rhys

33,637 просмотров • 3 месяцев назад

MCP sucking is a harness problem, not an MCP problem MCP unlocks behavior that is fundamentally impossible to get via CLI or APIs Bad auth, too much context usage, all get solved with an execution layer - your agent writes code to progressively discover and call tools
0:48

MCP sucking is a harness problem, not an MCP problem MCP unlocks behavior that is fundamentally impossible to get via CLI or APIs Bad auth, too much context usage, all get solved with an execution layer - your agent writes code to progressively discover and call tools

Rhys

376,442 просмотров • 3 месяцев назад

Big update for Claude Desktop and Cursor users! Now you can connect all AI apps via a common memory layer in a minute. I used the Graphiti MCP server that runs 100% locally to cross-operate across AI apps like Claude Desktop and Cursor without losing context. (setup below)
0:46

Big update for Claude Desktop and Cursor users! Now you can connect all AI apps via a common memory layer in a minute. I used the Graphiti MCP server that runs 100% locally to cross-operate across AI apps like Claude Desktop and Cursor without losing context. (setup below)

Avi Chawla

122,824 просмотров • 7 месяцев назад

Doom is now an MCP App 🎮 Waiting for the build in Visual Studio Code just got fun with your favorite arcade games! Runs on all MCP Apps hosts: Claude, goose, Postman, and more. My server pulls the games from the awesome Internet Archive. Check it out!
0:28

Doom is now an MCP App 🎮 Waiting for the build in Visual Studio Code just got fun with your favorite arcade games! Runs on all MCP Apps hosts: Claude, goose, Postman, and more. My server pulls the games from the awesome Internet Archive. Check it out!

Ido Salomon

15,003 просмотров • 4 месяцев назад

Check this!! An MCP server to control Jupyter notebooks from Claude! It lets you: - Create code cells - Execute code cells - Create markdown cells Completely free to use!
0:23

Check this!! An MCP server to control Jupyter notebooks from Claude! It lets you: - Create code cells - Execute code cells - Create markdown cells Completely free to use!

Avi Chawla

56,911 просмотров • 1 год назад

Write 100x better code using AI (for free!) Install the Codacy extension (VSCode, Cursor, WindSurf). This will install an MCP Server locally on your computer. Your coding copilot will use this server with the Codacy CLI to check every line of code it generates. The way this works is pretty awesome (assuming you are using Cursor): 1. You ask Cursor to generate some code 2. Cursor writes the code 3. Cursor then calls Codacy's MCP Server to check it 4. The MCP Server will use the Codacy CLI to find any issues 5. Cursor will fix the issues and call the MCP server again 6. 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! Codacy has been doing this for a long, long time, and their Guardrails MCP Server checks for a lot of potential issues: • Security issues • Quality standards • Pattern violations • Linting issues You can see more information about the extension here: I recorded a quick video to show you how it works. Thanks to the Codacy team for helping me set up everything, answering my 1,000 questions, and collaborating with me on this post.
5:21

Write 100x better code using AI (for free!) Install the Codacy extension (VSCode, Cursor, WindSurf). This will install an MCP Server locally on your computer. Your coding copilot will use this server with the Codacy CLI to check every line of code it generates. The way this works is pretty awesome (assuming you are using Cursor): 1. You ask Cursor to generate some code 2. Cursor writes the code 3. Cursor then calls Codacy's MCP Server to check it 4. The MCP Server will use the Codacy CLI to find any issues 5. Cursor will fix the issues and call the MCP server again 6. 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! Codacy has been doing this for a long, long time, and their Guardrails MCP Server checks for a lot of potential issues: • Security issues • Quality standards • Pattern violations • Linting issues You can see more information about the extension here: I recorded a quick video to show you how it works. Thanks to the Codacy team for helping me set up everything, answering my 1,000 questions, and collaborating with me on this post.

Santiago

63,240 просмотров • 1 год 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:
6:15

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

215,727 просмотров • 1 год назад

AI Agents can now connect to 100,000+ APIs instantly. Postman's AI Agent builder can turn any API into an MCP server in seconds without writing a single line of Code. You can use it in Claude, Cursor, Windsurf and other AI tools.
0:21

AI Agents can now connect to 100,000+ APIs instantly. Postman's AI Agent builder can turn any API into an MCP server in seconds without writing a single line of Code. You can use it in Claude, Cursor, Windsurf and other AI tools.

Shubham Saboo

79,784 просмотров • 1 год назад

I've been waiting for this. The Unified MCP is here! Rube is a universal MCP server to connect your AI agents to all *your* apps. Works with your favorite IDE, Claude Code, and other MCP clients. Watch it research YouTube vids → generate a full content strategy doc. Insane!
1:55

I've been waiting for this. The Unified MCP is here! Rube is a universal MCP server to connect your AI agents to all *your* apps. Works with your favorite IDE, Claude Code, and other MCP clients. Watch it research YouTube vids → generate a full content strategy doc. Insane!

elvis

62,262 просмотров • 10 месяцев назад

Wanted to learn more about MCPs this weekend. So I built this mini-tool called Here is how it works: 1️⃣ Tell the AI what you want. 2️⃣ It creates an MCP server with various tools. 3️⃣ Talk with AI to add/remove/modify these tools. 4️⃣ Click on Deploy and get your own MCP Server URL. You can directly use in url in Cursor / Claude Desktop or anywhere you want. Completely powered by Cloudflare Developers Durable Objects + Workers for Platforms
0:34

Wanted to learn more about MCPs this weekend. So I built this mini-tool called Here is how it works: 1️⃣ Tell the AI what you want. 2️⃣ It creates an MCP server with various tools. 3️⃣ Talk with AI to add/remove/modify these tools. 4️⃣ Click on Deploy and get your own MCP Server URL. You can directly use in url in Cursor / Claude Desktop or anywhere you want. Completely powered by Cloudflare Developers Durable Objects + Workers for Platforms

Bhanu Teja P

108,366 просмотров • 1 год назад

Coding agents shouldn't run on a laptop. That's why I've been building Cave. It's a self-hosted platform for running OpenCode agents in isolated sandboxes on your own server. You give it a GitHub repo, it creates an isolated sandbox, clones the code, sets everything up, and gives you a coding agent. Check in from your phone, run multiple agents side by side. It's not open source yet (it will be), but I'm opening a private beta so you can install it on your own server already. I've been dogfooding it since day one. If you're running a software factory: multiple agents working in parallel across repos, Cave gives you one place to monitor and manage all of it. Now, I'm looking for people who want to give it a spin. DM me, happy to set you up 😊 🚀
0:59

Coding agents shouldn't run on a laptop. That's why I've been building Cave. It's a self-hosted platform for running OpenCode agents in isolated sandboxes on your own server. You give it a GitHub repo, it creates an isolated sandbox, clones the code, sets everything up, and gives you a coding agent. Check in from your phone, run multiple agents side by side. It's not open source yet (it will be), but I'm opening a private beta so you can install it on your own server already. I've been dogfooding it since day one. If you're running a software factory: multiple agents working in parallel across repos, Cave gives you one place to monitor and manage all of it. Now, I'm looking for people who want to give it a spin. DM me, happy to set you up 😊 🚀

André König

36,082 просмотров • 2 месяцев назад

The founder of Claude Code (@ bcherny) made a thread last week going over 10 ways he and his team use Claude Code (Opus 4.5) to its maximum potential This 10 minute video is all the information you need to max out your Claude Code skills straight from the team who made it.
10:25

The founder of Claude Code (@ bcherny) made a thread last week going over 10 ways he and his team use Claude Code (Opus 4.5) to its maximum potential This 10 minute video is all the information you need to max out your Claude Code skills straight from the team who made it.

ashen

62,490 просмотров • 4 месяцев назад

You can use a 100% open source and MUCH cheaper/free alternative to Claude Code and Opus 4.5 OpenCode + MiniMax M2.1 can even build a 3D website using pure vibe coding. Steps are really simple: 1. Install OpenCode using the command 'npm i -g opencode-ai' 2. Get your MiniMax API key here: 3. Configure the MiniMax (official) mode Just type "opencode connect minimax" Coding plans start at $2… 10x cheaper than Claude Code (yes). You can also invite friends so they can get 10% off and you’ll get 10% API credits. (You can also use it locally if you have the config) And you're ready to build and iterate almost endlessly since the model is both way faster and cheaper than Opus in CC.
12:07

You can use a 100% open source and MUCH cheaper/free alternative to Claude Code and Opus 4.5 OpenCode + MiniMax M2.1 can even build a 3D website using pure vibe coding. Steps are really simple: 1. Install OpenCode using the command 'npm i -g opencode-ai' 2. Get your MiniMax API key here: 3. Configure the MiniMax (official) mode Just type "opencode connect minimax" Coding plans start at $2… 10x cheaper than Claude Code (yes). You can also invite friends so they can get 10% off and you’ll get 10% API credits. (You can also use it locally if you have the config) And you're ready to build and iterate almost endlessly since the model is both way faster and cheaper than Opus in CC.

Paul Couvert

42,443 просмотров • 5 месяцев назад

Open Interpreter is like ChatGPT Code Interpreter, but you can run it locally on your computer, and it is 100% open-source. Write and execute code, analyze data, control your computer, and more. Great work killian Here's how to install and use it: 👇
1:30

Open Interpreter is like ChatGPT Code Interpreter, but you can run it locally on your computer, and it is 100% open-source. Write and execute code, analyze data, control your computer, and more. Great work killian Here's how to install and use it: 👇

Matthew Berman

142,901 просмотров • 2 лет назад

Bash is all you need! Which is why I'm introducing my holiday project: just-bash just-bash is a pretty complete implementation of bash in TypeScript designed to be used as a bash tool by AI agents. Because it turns out agents love exploring data via shell scripts, even beyond coding. It comes with grep, sed, awk and the 99th percentile features that an agent like Claude Code or Cursor would use. In fact, Claude Code can use it for secure bash execution. In the package - A bash-tool for AI SDK - A binary for use by yourself or your coding agents - An overlay filesystem to feed files to your agent securely - A Vercel Sandbox compatible API, so you can quickly upgrade to a real VM if you need to run binaries - An example AI agent that explores the just-bash code base using just-bash - I imported the Oils shell bash compatibility suite and just-bash passes a very good chunk What is interesting about this codebase: It was essentially entirely written by Opus 4.5. Coding agents love bash and they are good at reproducing it. They are also great at text-book recursive descent parsers and AST tweet-walk interpreters. That said, it is, like, a lot of code and I didn't read it all 😅. This is very much a hack, but it also seems to be _really_ useful. I haven't really found anything agents want to use that it doesn't support and it's fast and secure (caveats apply). It doesn't have write access to your computer and the filesystem is given a root that the agent cannot escape from. Find it at Related: Our recent blog post how we migrated our data analysis agent to bash tools and achieved incredible quality improvements The video shows the example agent investigating the just-bash code base
1:47

Bash is all you need! Which is why I'm introducing my holiday project: just-bash just-bash is a pretty complete implementation of bash in TypeScript designed to be used as a bash tool by AI agents. Because it turns out agents love exploring data via shell scripts, even beyond coding. It comes with grep, sed, awk and the 99th percentile features that an agent like Claude Code or Cursor would use. In fact, Claude Code can use it for secure bash execution. In the package - A bash-tool for AI SDK - A binary for use by yourself or your coding agents - An overlay filesystem to feed files to your agent securely - A Vercel Sandbox compatible API, so you can quickly upgrade to a real VM if you need to run binaries - An example AI agent that explores the just-bash code base using just-bash - I imported the Oils shell bash compatibility suite and just-bash passes a very good chunk What is interesting about this codebase: It was essentially entirely written by Opus 4.5. Coding agents love bash and they are good at reproducing it. They are also great at text-book recursive descent parsers and AST tweet-walk interpreters. That said, it is, like, a lot of code and I didn't read it all 😅. This is very much a hack, but it also seems to be _really_ useful. I haven't really found anything agents want to use that it doesn't support and it's fast and secure (caveats apply). It doesn't have write access to your computer and the filesystem is given a root that the agent cannot escape from. Find it at Related: Our recent blog post how we migrated our data analysis agent to bash tools and achieved incredible quality improvements The video shows the example agent investigating the just-bash code base

Malte Ubl

124,713 просмотров • 6 месяцев назад

The easiest way to build an MCP Server using Google DeepMind Gemini 2.5 Pro and get started! 1. Use Gitingest to get all the code and docs from the FastMCP repo 2. Download the code into a txt file 3. Go to AI Studio, upload the file, define what kind of MCP Server you want to build 4. Gemini 2.5 Pro builds it for you.
0:40

The easiest way to build an MCP Server using Google DeepMind Gemini 2.5 Pro and get started! 1. Use Gitingest to get all the code and docs from the FastMCP repo 2. Download the code into a txt file 3. Go to AI Studio, upload the file, define what kind of MCP Server you want to build 4. Gemini 2.5 Pro builds it for you.

Philipp Schmid

75,968 просмотров • 1 год назад

So many people wanted to see my knowledge management system in Claude code, so here it is. All you need is Obsidian, Claude Code, GitHub, and the will to live and you too can set up your second brain in under 2 hours. If you want to learn how to build this, here’s the demo link:
1:34

So many people wanted to see my knowledge management system in Claude code, so here it is. All you need is Obsidian, Claude Code, GitHub, and the will to live and you too can set up your second brain in under 2 hours. If you want to learn how to build this, here’s the demo link:

Allie K. Miller

51,555 просмотров • 2 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!
2:43

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

141,952 просмотров • 1 год назад

Building AI agents and tools is getting ridiculously easy! Here is a pretty impressive FREE and Open-Source AI agent builder that runs on your computer. It's a desktop app you can install on Mac or Windows. Link: • Free • 100% no-code: You can type what you want, and the tool will build everything for you. • You can use local models like DeepSeek or Qwen, or connect to cloud models like GPT-4o, Claude, or Gemini with your API key. • You can turn the tools you build into MCP Servers. I recorded a quick video to show you how easy it is to build something from scratch.
4:10

Building AI agents and tools is getting ridiculously easy! Here is a pretty impressive FREE and Open-Source AI agent builder that runs on your computer. It's a desktop app you can install on Mac or Windows. Link: • Free • 100% no-code: You can type what you want, and the tool will build everything for you. • You can use local models like DeepSeek or Qwen, or connect to cloud models like GPT-4o, Claude, or Gemini with your API key. • You can turn the tools you build into MCP Servers. I recorded a quick video to show you how easy it is to build something from scratch.

Santiago

56,815 просмотров • 1 год назад