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My 3rd online course is live! 🎉 I made this one because I kept getting the same question on my YT videos about Grafana MCP: how does this setup on my laptop become something I can actually run in production? There was almost no practical material, so I couldn't...

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Nemanja Magic

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134,786 görüntüleme • 9 gün önce •via X (Twitter)

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Big Sheddy 🦅9 gün önce

congratulations bro

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Nemanja Magic7 gün önce

My idolo!

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Big Sheddy 🦅7 gün önce

ah I’m not worthy o, happy for you bro

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catman9 gün önce

The best technical teaching connects the local demo to the production constraints, because that is where concepts like permissions, networking, and secrets become real.

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rafiki9 gün önce

Congratulations and well done friend! So proud 🥰🥰

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Nemanja Magic7 gün önce

❤️❤️

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Congratulations Man🙌🏾

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Benzer Videolar

This is incredibly useful for those using Claude Code! I have two computers, and sharing your Claude Code sessions is almost impossible without this. I often start working on something on my desktop, and then want to continue working on the same session (same context, conversation, etc.) on my laptop. The folks at Depot built an integration that solves this problem: You can now start a session on one computer and resume it anywhere else in the world! You can even share it with other team members so they can also contribute to it. Here is an article showing you how the Depot team uses this feature with Claude Code to automate their GitHub Actions Runner updates: Thanks to the team for the effort in building this integration and collaborating with me on this post.
4:29

This is incredibly useful for those using Claude Code! I have two computers, and sharing your Claude Code sessions is almost impossible without this. I often start working on something on my desktop, and then want to continue working on the same session (same context, conversation, etc.) on my laptop. The folks at Depot built an integration that solves this problem: You can now start a session on one computer and resume it anywhere else in the world! You can even share it with other team members so they can also contribute to it. Here is an article showing you how the Depot team uses this feature with Claude Code to automate their GitHub Actions Runner updates: Thanks to the team for the effort in building this integration and collaborating with me on this post.

Santiago

43,943 görüntüleme • 11 ay önce

Almost 20 years later, AWS is still the most popular cloud in the world. The reason is simple: it just works! They have four services focused on Generative AI: 1. Amazon Q 2. Amazon Bedrock 3. SageMaker JumpStart 4. PartyRock I've been using AWS for around 15 years (honestly, I don't remember well), and their Developer Center is a gold mine. If you open their Developer Center, you'll find a new learning path, "Generative AI for Developers." I'm linking to it below. This is not just a course. This is a collection of courses, examples, videos, tutorials, and code walkthroughs. They will teach you how to use Generative AI on AWS using the four services above. ↑ That right there is a huge selling point: These classes aren't just theoretical. You'll have a chance to learn using the same professional tools everyone else uses. By the way, there are many more resources in the Developer Center: • Machine Learning • Data Operations • DevOps All of these are free. Click, click, and start learning right away. One more thing before I forget: If you are building anything with AWS, check out Amazon Q, their coding assistant. This is the *best* coding assistant for AWS-related work, and it's not even close. It's a Visual Studio Code extension. Install it, and it works like any other code assistant, except this one knows a lot about AWS. Thanks to AWS for sponsoring a post about their free courses and learning resources. There's a special place in Developer Heaven for you.
2:10

Almost 20 years later, AWS is still the most popular cloud in the world. The reason is simple: it just works! They have four services focused on Generative AI: 1. Amazon Q 2. Amazon Bedrock 3. SageMaker JumpStart 4. PartyRock I've been using AWS for around 15 years (honestly, I don't remember well), and their Developer Center is a gold mine. If you open their Developer Center, you'll find a new learning path, "Generative AI for Developers." I'm linking to it below. This is not just a course. This is a collection of courses, examples, videos, tutorials, and code walkthroughs. They will teach you how to use Generative AI on AWS using the four services above. ↑ That right there is a huge selling point: These classes aren't just theoretical. You'll have a chance to learn using the same professional tools everyone else uses. By the way, there are many more resources in the Developer Center: • Machine Learning • Data Operations • DevOps All of these are free. Click, click, and start learning right away. One more thing before I forget: If you are building anything with AWS, check out Amazon Q, their coding assistant. This is the *best* coding assistant for AWS-related work, and it's not even close. It's a Visual Studio Code extension. Install it, and it works like any other code assistant, except this one knows a lot about AWS. Thanks to AWS for sponsoring a post about their free courses and learning resources. There's a special place in Developer Heaven for you.

Santiago

22,104 görüntüleme • 1 yıl önce

We’re excited to launch OpenMemory MCP, a private memory for MCP-compatible clients powered by mem0 Today, most AI assistants and dev tools operate without memory. You plan your roadmap in Claude, implement tasks in Cursor, but none of them know what the other did. Each tool operates in isolation, and your context disappears as soon as the session ends. OpenMemory MCP solves this. Built on the open Model Context Protocol (MCP), OpenMemory MCP runs 100% locally and provides a persistent, portable memory layer for all your AI tools. It enables agents and assistants to read from and write to a shared memory, securely and privately. Key capabilities: ✅Works across MCP clients like Cursor, Claude Desktop, Windsurf, Cline and more ✅Provides standardized memory operations (add_memories, search_memory, list_memories, delete_all_memories) ✅Stores data on your machine, fully private ✅Offers a centralized dashboard for visibility and control ✅Simple Docker-based setup with no vendor lock-in If you're building on MCP, OpenMemory MCP is the easiest way to add persistent, private memory to your clients with zero cloud dependencies and full local control. We’ve put together a short demo to show how it works Check it out in the links below 👇
2:24

We’re excited to launch OpenMemory MCP, a private memory for MCP-compatible clients powered by mem0 Today, most AI assistants and dev tools operate without memory. You plan your roadmap in Claude, implement tasks in Cursor, but none of them know what the other did. Each tool operates in isolation, and your context disappears as soon as the session ends. OpenMemory MCP solves this. Built on the open Model Context Protocol (MCP), OpenMemory MCP runs 100% locally and provides a persistent, portable memory layer for all your AI tools. It enables agents and assistants to read from and write to a shared memory, securely and privately. Key capabilities: ✅Works across MCP clients like Cursor, Claude Desktop, Windsurf, Cline and more ✅Provides standardized memory operations (add_memories, search_memory, list_memories, delete_all_memories) ✅Stores data on your machine, fully private ✅Offers a centralized dashboard for visibility and control ✅Simple Docker-based setup with no vendor lock-in If you're building on MCP, OpenMemory MCP is the easiest way to add persistent, private memory to your clients with zero cloud dependencies and full local control. We’ve put together a short demo to show how it works Check it out in the links below 👇

Taranjeet

472,273 görüntüleme • 1 yıl önce

People used to pay me on on average to build their apps for $30K USD in less than a month. I was the go-to-guy because I knew how to build exactly what they wanted with little instruction and I always met their insane deadlines. I was known as "the machine", I wasn't the 10x developer I was the 100x developer. But it took its toll on my health and I left that do tech education. Claude Code can now do it. No computer science degree. No dev team. No $30K invoice. But most non-tech people are still stuck on the outside looking in because there is a tech knowledge gap. What if someone showed you all the steps, used plain speak, provided technical strategy, technical pathing, technical guidance, made it practical, and takes you to production. That's what my Claude Code from Zero course would be. But I'll be honest with you, this won't be a Udemy $20 priced course. It will be $300, $500 or $1200 depending on your needs. But do you want me to build it?
2:19

People used to pay me on on average to build their apps for $30K USD in less than a month. I was the go-to-guy because I knew how to build exactly what they wanted with little instruction and I always met their insane deadlines. I was known as "the machine", I wasn't the 10x developer I was the 100x developer. But it took its toll on my health and I left that do tech education. Claude Code can now do it. No computer science degree. No dev team. No $30K invoice. But most non-tech people are still stuck on the outside looking in because there is a tech knowledge gap. What if someone showed you all the steps, used plain speak, provided technical strategy, technical pathing, technical guidance, made it practical, and takes you to production. That's what my Claude Code from Zero course would be. But I'll be honest with you, this won't be a Udemy $20 priced course. It will be $300, $500 or $1200 depending on your needs. But do you want me to build it?

Andrew Brown

26,969 görüntüleme • 6 ay önce

THIS IS THE GREATEST GAME I'VE EVER MADE WITH AI. WHAT MODEL DO YOU THINK I USED? This is not some one-shot slop Minecraft remake on a browser, but this is a complete mini game that I've put about a week of time into, made entirely out of the Unity CLI Every single asset you're seeing here was custom-made using the Meshy API and then rigged and animated with the Blender MCP and built on Unity Craziest part: I did this entire thing on the $100/month plan within one week's usage I also took a few days to recreate this exact same game with a different model that I'll be posting tomorrow and somehow that one did an even better job Play the entire game below for free ⇣
3:46

THIS IS THE GREATEST GAME I'VE EVER MADE WITH AI. WHAT MODEL DO YOU THINK I USED? This is not some one-shot slop Minecraft remake on a browser, but this is a complete mini game that I've put about a week of time into, made entirely out of the Unity CLI Every single asset you're seeing here was custom-made using the Meshy API and then rigged and animated with the Blender MCP and built on Unity Craziest part: I did this entire thing on the $100/month plan within one week's usage I also took a few days to recreate this exact same game with a different model that I'll be posting tomorrow and somehow that one did an even better job Play the entire game below for free ⇣

ashen

61,959 görüntüleme • 29 gün önce

I didn’t start my company NextLink Labs because I wanted to be a founder. I started it because production kept falling over and no one could explain why. At my last job, I was a solo developer on a critical system. Servers were going down. The service was unstable. Customers were feeling it. So I started digging. What I found wasn’t a bad engineer or a bad decision. It was something worse. The application was running on extremely old servers, filled with things that had nothing to do with the service. When I asked why we hadn’t migrated to the new servers we bought 18 months earlier, the answer stunned me: “We bought them… but no one knows how to set them up.” No documentation. No CI/CD. No infrastructure as code. No one actually knew what was running in production, so no one felt confident touching it. To make it harder, the servers were in Los Angeles. I was in Pittsburgh. I couldn’t even get hands-on to fix it myself. That was the moment it clicked. This wasn’t a one-off failure. This was how a lot of companies operated. I kept pushing for a better way. More structure. More clarity. Systems that didn’t rely on tribal knowledge. Eventually, I “wore out my welcome.” My manager didn’t fire me, but he did say, “Maybe you should do what you’re building on the side… full time.” Once I landed one more client, I resigned within four weeks. That was 11 years ago. Here's the lesson I took with me: Most outages aren’t caused by bad engineers. They’re caused by undocumented systems and neglected foundations. At NextLink Labs, everything we do comes back to that belief. Build systems people understand. Make infrastructure boring and predictable. And design for execution, not heroics. That’s how you scale without things constantly breaking.
2:18

I didn’t start my company NextLink Labs because I wanted to be a founder. I started it because production kept falling over and no one could explain why. At my last job, I was a solo developer on a critical system. Servers were going down. The service was unstable. Customers were feeling it. So I started digging. What I found wasn’t a bad engineer or a bad decision. It was something worse. The application was running on extremely old servers, filled with things that had nothing to do with the service. When I asked why we hadn’t migrated to the new servers we bought 18 months earlier, the answer stunned me: “We bought them… but no one knows how to set them up.” No documentation. No CI/CD. No infrastructure as code. No one actually knew what was running in production, so no one felt confident touching it. To make it harder, the servers were in Los Angeles. I was in Pittsburgh. I couldn’t even get hands-on to fix it myself. That was the moment it clicked. This wasn’t a one-off failure. This was how a lot of companies operated. I kept pushing for a better way. More structure. More clarity. Systems that didn’t rely on tribal knowledge. Eventually, I “wore out my welcome.” My manager didn’t fire me, but he did say, “Maybe you should do what you’re building on the side… full time.” Once I landed one more client, I resigned within four weeks. That was 11 years ago. Here's the lesson I took with me: Most outages aren’t caused by bad engineers. They’re caused by undocumented systems and neglected foundations. At NextLink Labs, everything we do comes back to that belief. Build systems people understand. Make infrastructure boring and predictable. And design for execution, not heroics. That’s how you scale without things constantly breaking.

Jordan Saunders

417,369 görüntüleme • 7 ay önce

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

142,256 görüntüleme • 1 yıl önce

Ever since I wired Claude Code to WhatsApp 3 weeks ago, I built a stupidly large infra around it. I mean, opus built it. No clue how the code even looks. The entire thing was vibe coded using my phone. I wanted to see how far I could push it without touching the computer. Everything via WhatsApp. Build what I need on the fly. So the resulting infrastructure will already be battle tested for software development. The entire thing was streamlined with nearly no manual interventions, everything was communicated via WhatsApp using a single script establishing this connection. If the script is down, I need to get home to start it again to resume the development. Claude was upgrading it, debugging it, restarting it while maintaining constant uptime so it could keep communicating with me. I stressed Claude about it, telling it that it will be “in the dark” and other words that deliberately sound scary about losing communications if the script dies. I also refused git and refused cloning the code, I wanted to see Claude adapting to work on a *LIVING* system. The way this whole thing works: Claude has its own dedicated phone number that I am paying for. A real WhatsApp account for it is installed on a real iPhone that is sitting on my desk. All is registered under my name, this is legit setup with no hacks and tricks. I’ve set up a WhatsApp “Community” and multiple different groups under it. Both me and Claude are the admins, so Claude could edit it on my behalf. Each group is a project I am working on and has its own isolated context. The Group description is a system prompt that gets auto-appended to the larger system prompt explaining this setup in general. When I send a message it’s an instant interrupt to Claude Code’s process, just like in the terminal. Voice notes are seamlessly transcribed with a local Whisper model. Images are used with multimodal reading in an isolated parallel session. Multiple groups running in parallel so I can work on all projects at the same time. No cross-talking, everything has an isolated context and history. And because it’s local on my own machine: Everything is REAL. The browser is REAL. I am connected as myself on it to all services because I actually use it in real life. Claude has unlimited internet access, just like humans who use actual browsers. It utilizes custom-made browser tools that I made to control any browser session it wants. Depending on the situation, it can either connect to my existing session or create one for its own. (You can tell it ‘look at my browser for a sec’ then talk about the current page you are on and it just works, pretty cool) My custom browser tools are not perfect (not by a long shot) but I managed to make them work well to the point they are somewhat reliable. This gives Claude full access to my real creds and all the services I actually use. I’m productive AS HELL with this. It really feels like a personal assistant. I ask it to read my emails and msgs, check x .com for news, research arxiv papers, write code, run experiments for me, investigate and reverse engineer github repos, even use my credit card and order things. [I try not to do this one a lot lol so far no disasters]. All from my phone. Super convenient. This is not a product or an open source project (maybe soon of it will make sense). This is just an ugly script I hacked the entire thing is ~600 lines. (ok maybe i did look at the code, but i swear i didn’t edit!) You can also vibe code this from scratch pretty fast and it will probably even end up better. This is just a cool thing so I’m sharing. It is a real speed booster for many things I do on daily basis, mostly boring things. Forcing my routine into some new “agent platform” just didn’t feel right for me. WhatsApp is where I already communicate and look for messages, so I decided that my agents will live there too. AGI in my pocket 24/7.
0:52

Ever since I wired Claude Code to WhatsApp 3 weeks ago, I built a stupidly large infra around it. I mean, opus built it. No clue how the code even looks. The entire thing was vibe coded using my phone. I wanted to see how far I could push it without touching the computer. Everything via WhatsApp. Build what I need on the fly. So the resulting infrastructure will already be battle tested for software development. The entire thing was streamlined with nearly no manual interventions, everything was communicated via WhatsApp using a single script establishing this connection. If the script is down, I need to get home to start it again to resume the development. Claude was upgrading it, debugging it, restarting it while maintaining constant uptime so it could keep communicating with me. I stressed Claude about it, telling it that it will be “in the dark” and other words that deliberately sound scary about losing communications if the script dies. I also refused git and refused cloning the code, I wanted to see Claude adapting to work on a *LIVING* system. The way this whole thing works: Claude has its own dedicated phone number that I am paying for. A real WhatsApp account for it is installed on a real iPhone that is sitting on my desk. All is registered under my name, this is legit setup with no hacks and tricks. I’ve set up a WhatsApp “Community” and multiple different groups under it. Both me and Claude are the admins, so Claude could edit it on my behalf. Each group is a project I am working on and has its own isolated context. The Group description is a system prompt that gets auto-appended to the larger system prompt explaining this setup in general. When I send a message it’s an instant interrupt to Claude Code’s process, just like in the terminal. Voice notes are seamlessly transcribed with a local Whisper model. Images are used with multimodal reading in an isolated parallel session. Multiple groups running in parallel so I can work on all projects at the same time. No cross-talking, everything has an isolated context and history. And because it’s local on my own machine: Everything is REAL. The browser is REAL. I am connected as myself on it to all services because I actually use it in real life. Claude has unlimited internet access, just like humans who use actual browsers. It utilizes custom-made browser tools that I made to control any browser session it wants. Depending on the situation, it can either connect to my existing session or create one for its own. (You can tell it ‘look at my browser for a sec’ then talk about the current page you are on and it just works, pretty cool) My custom browser tools are not perfect (not by a long shot) but I managed to make them work well to the point they are somewhat reliable. This gives Claude full access to my real creds and all the services I actually use. I’m productive AS HELL with this. It really feels like a personal assistant. I ask it to read my emails and msgs, check x .com for news, research arxiv papers, write code, run experiments for me, investigate and reverse engineer github repos, even use my credit card and order things. [I try not to do this one a lot lol so far no disasters]. All from my phone. Super convenient. This is not a product or an open source project (maybe soon of it will make sense). This is just an ugly script I hacked the entire thing is ~600 lines. (ok maybe i did look at the code, but i swear i didn’t edit!) You can also vibe code this from scratch pretty fast and it will probably even end up better. This is just a cool thing so I’m sharing. It is a real speed booster for many things I do on daily basis, mostly boring things. Forcing my routine into some new “agent platform” just didn’t feel right for me. WhatsApp is where I already communicate and look for messages, so I decided that my agents will live there too. AGI in my pocket 24/7.

Yam Peleg

420,390 görüntüleme • 9 ay önce

How I Actually Make Money... A lot of people assume I made all my $$ being on YT, crypto, influencing AKA big loud exciting stuff. In reality I made it all doing what you see below ( this is a demo video for HYROS). For nearly a decade I've just sat around and figured how to make media buyers more revenue from their ads by improving their attribution. I'm not a crypto expert or some YT expert. I am an attribution and software sales expert. No one really knows me for this tho. Because it isn't loud. It's not sexy. Most people reading this don't even know what a Facebook ad attribution software is. I've gone into a remote office with around 10 other leadership team mates and built tracking tools based on my talks with customers...then sold the tracking tools... This has generated 150 million+ in networth for me. I just wanted to point this out because people usually assume the LOUD things online are making money. When in reality its just waking up and grinding out solutions for customers...for a very long time...and being the best at it. I'm on YT/here for fun. When I'm serious about generating money, this is what I do. Stop being loud. Focus on a customer and then focus on solving their problems better than anyone else and then drive them to your product with extreme efficiency. This is where the big $$ is made. <3
7:03

How I Actually Make Money... A lot of people assume I made all my $$ being on YT, crypto, influencing AKA big loud exciting stuff. In reality I made it all doing what you see below ( this is a demo video for HYROS). For nearly a decade I've just sat around and figured how to make media buyers more revenue from their ads by improving their attribution. I'm not a crypto expert or some YT expert. I am an attribution and software sales expert. No one really knows me for this tho. Because it isn't loud. It's not sexy. Most people reading this don't even know what a Facebook ad attribution software is. I've gone into a remote office with around 10 other leadership team mates and built tracking tools based on my talks with customers...then sold the tracking tools... This has generated 150 million+ in networth for me. I just wanted to point this out because people usually assume the LOUD things online are making money. When in reality its just waking up and grinding out solutions for customers...for a very long time...and being the best at it. I'm on YT/here for fun. When I'm serious about generating money, this is what I do. Stop being loud. Focus on a customer and then focus on solving their problems better than anyone else and then drive them to your product with extreme efficiency. This is where the big $$ is made. <3

Alex Becker 🍊🏆🥇

190,555 görüntüleme • 2 yıl önce

I've been going through the videos I have with Ciera, and I'd like to share this one with you all. This was at the end of April, we'd been staying in hospital awaiting MRI results, and the next day was an EEG scan and blood testing. It was an incredibly anxious and stressful time for myself and the people close to us. However, my darling girl kept a smile on her face and showed me and the nursing staff what an angel she is. Here she is doing one of her favourite things, assisted clapping! Ciera cannot walk or talk, and she will most likely stay that way for a while, but I am determined to help her as much as I can, and work with her so she reaches her full potential. And of course, make sure she keeps smiling. I love you so much, Ciera Grace. 🩷
0:54

I've been going through the videos I have with Ciera, and I'd like to share this one with you all. This was at the end of April, we'd been staying in hospital awaiting MRI results, and the next day was an EEG scan and blood testing. It was an incredibly anxious and stressful time for myself and the people close to us. However, my darling girl kept a smile on her face and showed me and the nursing staff what an angel she is. Here she is doing one of her favourite things, assisted clapping! Ciera cannot walk or talk, and she will most likely stay that way for a while, but I am determined to help her as much as I can, and work with her so she reaches her full potential. And of course, make sure she keeps smiling. I love you so much, Ciera Grace. 🩷

Celine against The Machine

27,082 görüntüleme • 3 ay önce

Loop engineering is great until something breaks. Here is how I improve the reliability of my agentic loops. I use human-in-the-loop (HITL). It's easy and extremely effective. Anyone can build this. My setup: I recorded a quick demo of how it all works. I shared recently that I now use more voice agents to build and communicate with agents. I also use them to verify. I hate the idea of being tied down to my computer or in a Slack channel to communicate with my agents. Here is what I have done to streamline communication with my agents. All my Claude and Codex agent sessions now use the Dial MCP server. It has a bunch of tools and provisions my agents with their own number that can place calls as native tools, with voice, SMS, and iMessage behind one interface. As my loops/automations work on PRs and new features, my agents escalate decisions to me via a short phone call. This is extremely useful when I am on the road or away from my desk. If you want to try this with Claude Code or Codex, paste this into your agent and get started right away: "Get yourself a Dial phone number and call me. Say hello and that setup is working, then hang up. Follow Natan Voitenkov and team are building something special here. Go check them out. Give your agent a phone number now: ($5 free credit)
5:02

Loop engineering is great until something breaks. Here is how I improve the reliability of my agentic loops. I use human-in-the-loop (HITL). It's easy and extremely effective. Anyone can build this. My setup: I recorded a quick demo of how it all works. I shared recently that I now use more voice agents to build and communicate with agents. I also use them to verify. I hate the idea of being tied down to my computer or in a Slack channel to communicate with my agents. Here is what I have done to streamline communication with my agents. All my Claude and Codex agent sessions now use the Dial MCP server. It has a bunch of tools and provisions my agents with their own number that can place calls as native tools, with voice, SMS, and iMessage behind one interface. As my loops/automations work on PRs and new features, my agents escalate decisions to me via a short phone call. This is extremely useful when I am on the road or away from my desk. If you want to try this with Claude Code or Codex, paste this into your agent and get started right away: "Get yourself a Dial phone number and call me. Say hello and that setup is working, then hang up. Follow Natan Voitenkov and team are building something special here. Go check them out. Give your agent a phone number now: ($5 free credit)

elvis

15,614 görüntüleme • 3 ay önce

MCP is mind-blowing. Here is a genius setup that will earn you a promotion with your manager. I have multiple data sources: data in S3, MySQL, and local files. Using Spice (an open-source project), I can co-locate all of that data behind a single SQL endpoint and augment it using MCP. Before, accessing data was slow, and I had to make multiple requests to get anything. After using Spice, I can query the data much faster (since it's mirrored locally), and I don't have to worry about where the data is coming from. Spice acts as an MCP server, so I can connect to it from my IDE or the Claude Desktop app and have access to all the data. But even more cool than that: Spice also acts as an MCP client, so it can connect to other MCP servers. This is where things get really interesting! Watch the video. I'm querying multiple data sources from my IDE and using the Google Maps MCP server for driving directions.
14:14

MCP is mind-blowing. Here is a genius setup that will earn you a promotion with your manager. I have multiple data sources: data in S3, MySQL, and local files. Using Spice (an open-source project), I can co-locate all of that data behind a single SQL endpoint and augment it using MCP. Before, accessing data was slow, and I had to make multiple requests to get anything. After using Spice, I can query the data much faster (since it's mirrored locally), and I don't have to worry about where the data is coming from. Spice acts as an MCP server, so I can connect to it from my IDE or the Claude Desktop app and have access to all the data. But even more cool than that: Spice also acts as an MCP client, so it can connect to other MCP servers. This is where things get really interesting! Watch the video. I'm querying multiple data sources from my IDE and using the Google Maps MCP server for driving directions.

Santiago

142,696 görüntüleme • 1 yıl önce

single most useful thing for my local ai setup that made life easy is my 2x DGX Spark becoming one serving box for every device i own here is how i do it: > 1. the two sparks run one vLLM server together with one endpoint, i use dgx sparks, you can use any nodes that can run an llm > 2. install tailscale to have all your nodes and machines on the same tailnet, the endpoint lives there so every device reaches it from anywhere and no port is open to the internet > 3. that one tailnet endpoint with the exposed port is your base url for everything, it speaks OpenAI chat, OpenAI Responses and Anthropic Messages, so any chat app, coding agent or phone bot you point at it talks to the same model > 4. the server also answers to the name "local", so clients can ask for "local" instead of a model name and when i swap the model on the servers nothing on the devices needs a config change. right now it's serving GLM 5.3-Flash, next week it can be something else this video below is the whole thing running end to end, orange dots are requests going in, white and green are tokens coming back it takes however many requests you throw at it, your config decides how many run at once and the rest wait their turn, when several run together each request's tok/s drops a bit while the box moves more tokens in total. mine is set to one at a time right now, so when every device fires together they queue up this setup really improved my quality of life with local ai, if you get stuck anywhere setting it up leave a comment and i'll help you
1:04

single most useful thing for my local ai setup that made life easy is my 2x DGX Spark becoming one serving box for every device i own here is how i do it: > 1. the two sparks run one vLLM server together with one endpoint, i use dgx sparks, you can use any nodes that can run an llm > 2. install tailscale to have all your nodes and machines on the same tailnet, the endpoint lives there so every device reaches it from anywhere and no port is open to the internet > 3. that one tailnet endpoint with the exposed port is your base url for everything, it speaks OpenAI chat, OpenAI Responses and Anthropic Messages, so any chat app, coding agent or phone bot you point at it talks to the same model > 4. the server also answers to the name "local", so clients can ask for "local" instead of a model name and when i swap the model on the servers nothing on the devices needs a config change. right now it's serving GLM 5.3-Flash, next week it can be something else this video below is the whole thing running end to end, orange dots are requests going in, white and green are tokens coming back it takes however many requests you throw at it, your config decides how many run at once and the rest wait their turn, when several run together each request's tok/s drops a bit while the box moves more tokens in total. mine is set to one at a time right now, so when every device fires together they queue up this setup really improved my quality of life with local ai, if you get stuck anywhere setting it up leave a comment and i'll help you

Sudo su

32,379 görüntüleme • 9 gün önce

New short course on Serverless LLM apps with Amazon Bedrock, taught by Amazon Web Services' Mike G Chambers! A serverless architecture enables you to quickly deploy your applications without needing to set up and manage compute servers to run your applications on, the maintenance of which can be another full-time job. In this course, you’ll learn how to do this by using an event-driven architecture to build complex AI workflows. Mike illustrate these concepts by building a cool application that automatically detects incoming customer inquiries, transcribes them with ASR (automatic speech recognition), summarizes them with an LLM using Bedrock, and deploys serverless with AWS Lambda. I hope this course makes it much easier for you to build and deploy LLM applications requiring multi-step AI workflows. Please sign up here:
3:10

New short course on Serverless LLM apps with Amazon Bedrock, taught by Amazon Web Services' Mike G Chambers! A serverless architecture enables you to quickly deploy your applications without needing to set up and manage compute servers to run your applications on, the maintenance of which can be another full-time job. In this course, you’ll learn how to do this by using an event-driven architecture to build complex AI workflows. Mike illustrate these concepts by building a cool application that automatically detects incoming customer inquiries, transcribes them with ASR (automatic speech recognition), summarizes them with an LLM using Bedrock, and deploys serverless with AWS Lambda. I hope this course makes it much easier for you to build and deploy LLM applications requiring multi-step AI workflows. Please sign up here:

Andrew Ng

108,171 görüntüleme • 2 yıl önce

Someone finds a local business online and reaches out to book an appointment, request a quote, or ask a question. If nobody responds, they may move on to a competitor. That missed lead is a problem you can get paid to solve. In this video, I build the entire system from scratch: how to find the right market, build a website with AI, capture leads, follow up automatically, and track what is working, all in one place. Then I show you what to charge for it. This is the setup I would build if I were starting a marketing agency today. I’m a partner with HighLevel, and I’m using their platform to build it. Whether you want to work with dentists, gyms, salons, home service companies, or other local businesses, you can adapt this system to help them turn more inquiries into customers. Get the snapshot, the free course, and a 30-day trial through my HighLevel partner link:
14:59

Someone finds a local business online and reaches out to book an appointment, request a quote, or ask a question. If nobody responds, they may move on to a competitor. That missed lead is a problem you can get paid to solve. In this video, I build the entire system from scratch: how to find the right market, build a website with AI, capture leads, follow up automatically, and track what is working, all in one place. Then I show you what to charge for it. This is the setup I would build if I were starting a marketing agency today. I’m a partner with HighLevel, and I’m using their platform to build it. Whether you want to work with dentists, gyms, salons, home service companies, or other local businesses, you can adapt this system to help them turn more inquiries into customers. Get the snapshot, the free course, and a 30-day trial through my HighLevel partner link:

Neil Patel

11,415 görüntüleme • 28 gün önce

If your MCP server has dozens of tools, it’s probably built wrong. You need tools that are specific and clear for each use case—but you also can’t have too many. This creates an almost impossible tradeoff that most companies don’t know how to solve. That’s why I interviewed my friend Alex Rattray (Alex Rattray), the founder and CEO of Stainless. Stainless builds APIs, SDKs, and MCP servers for companies like OpenAI and Anthropic. Alex has spent years mastering how to make software talk to software, and he came on the show to share what he knows. I had him on Every 📧’s AI & I to talk about MCP and the future of the AI-native internet. We get into: • Design MCP servers to be lean and precise. Alex’s best practices for building reliable MCP servers start with keeping the toolset small, giving each tool a precise name and description, and minimizing the inputs and outputs the model has to handle. At Stainless, they also often add a JSON filter on top to strip out unnecessary data. • Make complex APIs manageable with dynamic mode. To solve the problem of how an AI figures out which tool to use in larger APIs, Stainless switches to “dynamic mode,” where the model gets only three tools: List the endpoints, pick one and learn about it, and then execute it. • MCP servers as business copilots. At Stainless, Alex uses MCP servers to connect tools like Notion and HubSpot, so he can ask questions like, “Which customers signed up last week?” The system queries multiple databases and returns a summary that would’ve otherwise taken multiple logins and searches. • Create a “brain” for your company with Claude Code. Alex built a shared company brain at Stainless by keeping Claude Code running on his system and asking it to save useful inputs—like customer feedback and SQL queries—into GitHub. Over time, this creates a curated archive his team can query easily. • The future of MCP is code execution. Instead of giving models hundreds of tools, Alex believes the most powerful setup will be a simple code execution tool and a doc search tool. The AI writes code against an API’s SDK, runs it on a server, and checks the docs when it gets stuck. This is a must-watch for anyone who wants to understand MCP—and learn how to use them as a competitive edge. Watch below! Timestamps: Introduction: 00:01:14 Why Alex likes running barefoot: 00:02:54 APIs and MCP, the connectors of the new internet: 00:05:09 Why MCP servers are hard to get right: 00:10:53 Design principles for reliable MCP servers: 00:20:07 Scaling MCP servers for large APIs: 00:23:50 Using MCP for business ops at Stainless: 00:25:14 Building a company brain with Claude Code: 00:28:12 Where MCP goes from here: 00:33:59 Alex’s take on the security model for MCP: 00:41:10
51:39

If your MCP server has dozens of tools, it’s probably built wrong. You need tools that are specific and clear for each use case—but you also can’t have too many. This creates an almost impossible tradeoff that most companies don’t know how to solve. That’s why I interviewed my friend Alex Rattray (Alex Rattray), the founder and CEO of Stainless. Stainless builds APIs, SDKs, and MCP servers for companies like OpenAI and Anthropic. Alex has spent years mastering how to make software talk to software, and he came on the show to share what he knows. I had him on Every 📧’s AI & I to talk about MCP and the future of the AI-native internet. We get into: • Design MCP servers to be lean and precise. Alex’s best practices for building reliable MCP servers start with keeping the toolset small, giving each tool a precise name and description, and minimizing the inputs and outputs the model has to handle. At Stainless, they also often add a JSON filter on top to strip out unnecessary data. • Make complex APIs manageable with dynamic mode. To solve the problem of how an AI figures out which tool to use in larger APIs, Stainless switches to “dynamic mode,” where the model gets only three tools: List the endpoints, pick one and learn about it, and then execute it. • MCP servers as business copilots. At Stainless, Alex uses MCP servers to connect tools like Notion and HubSpot, so he can ask questions like, “Which customers signed up last week?” The system queries multiple databases and returns a summary that would’ve otherwise taken multiple logins and searches. • Create a “brain” for your company with Claude Code. Alex built a shared company brain at Stainless by keeping Claude Code running on his system and asking it to save useful inputs—like customer feedback and SQL queries—into GitHub. Over time, this creates a curated archive his team can query easily. • The future of MCP is code execution. Instead of giving models hundreds of tools, Alex believes the most powerful setup will be a simple code execution tool and a doc search tool. The AI writes code against an API’s SDK, runs it on a server, and checks the docs when it gets stuck. This is a must-watch for anyone who wants to understand MCP—and learn how to use them as a competitive edge. Watch below! Timestamps: Introduction: 00:01:14 Why Alex likes running barefoot: 00:02:54 APIs and MCP, the connectors of the new internet: 00:05:09 Why MCP servers are hard to get right: 00:10:53 Design principles for reliable MCP servers: 00:20:07 Scaling MCP servers for large APIs: 00:23:50 Using MCP for business ops at Stainless: 00:25:14 Building a company brain with Claude Code: 00:28:12 Where MCP goes from here: 00:33:59 Alex’s take on the security model for MCP: 00:41:10

Dan Shipper 📧

15,645 görüntüleme • 1 yıl önce

Today we're launching Jitsu MCP. This is an MCP server that lets your agent run your entire event pipeline directly in Claude Code, Claude Desktop, Cursor, or VS Code. Here's how it works: Add the server: claude mcp add --transport http jitsu Run this prompt: "Route my web stream into ClickHouse, but only purchase and signup events. Send a test event, check Live Events, and if anything errors, find the function in the path and fix it." Approve the OAuth prompt once, and all 26 tools are live Your agent wires the streams, watches Live Events, and rewrites a function when one of them throws. It runs the fix against a real event before shipping it, so nothing is persisted until it works. Every change lands in the audit log tagged mcp, so you can tell what the agent did from what a person did. It's all MIT licensed, and it runs against your own deployment. Jitsu is one of the fastest growing open-source CDPs with 5,000+ GitHub stars. Capturing events is unlimited and free, and delivery is free up to 200k events a month. Comment Jitsu and I'll DM you the exact prompts I run in Claude Code to build an event pipeline end to end.
0:51

Today we're launching Jitsu MCP. This is an MCP server that lets your agent run your entire event pipeline directly in Claude Code, Claude Desktop, Cursor, or VS Code. Here's how it works: Add the server: claude mcp add --transport http jitsu Run this prompt: "Route my web stream into ClickHouse, but only purchase and signup events. Send a test event, check Live Events, and if anything errors, find the function in the path and fix it." Approve the OAuth prompt once, and all 26 tools are live Your agent wires the streams, watches Live Events, and rewrites a function when one of them throws. It runs the fix against a real event before shipping it, so nothing is persisted until it works. Every change lands in the audit log tagged mcp, so you can tell what the agent did from what a person did. It's all MIT licensed, and it runs against your own deployment. Jitsu is one of the fastest growing open-source CDPs with 5,000+ GitHub stars. Capturing events is unlimited and free, and delivery is free up to 200k events a month. Comment Jitsu and I'll DM you the exact prompts I run in Claude Code to build an event pipeline end to end.

Soham Mehta

282,005 görüntüleme • 1 ay önce

I installed a new Visual Studio Code extension to help me write code. But this extension doesn't get in your way. It doesn't try to write code for you. It only gives you hints and recommendations about your code. I like that. Like a second pair of eyes, looking over my shoulder and telling me how I can do things better. The extension is Traycer. You can install it directly from inside Visual Studio Code. It's free forever for anyone working on open-source projects (and if you aren't, you can use it for free until the end of June.) I recorded a super-duper-quick video to show you how this extension works. I know code generation is a huge thing, but I really appreciate tools that help me do what I do better. Thanks to the Traycer team for helping me with the extension and collaborating with me on this post!
2:56

I installed a new Visual Studio Code extension to help me write code. But this extension doesn't get in your way. It doesn't try to write code for you. It only gives you hints and recommendations about your code. I like that. Like a second pair of eyes, looking over my shoulder and telling me how I can do things better. The extension is Traycer. You can install it directly from inside Visual Studio Code. It's free forever for anyone working on open-source projects (and if you aren't, you can use it for free until the end of June.) I recorded a super-duper-quick video to show you how this extension works. I know code generation is a huge thing, but I really appreciate tools that help me do what I do better. Thanks to the Traycer team for helping me with the extension and collaborating with me on this post!

Santiago

101,068 görüntüleme • 2 yıl önce

Caitlin on what she learned through injury this season: “I think when I get a few years down here in my career, I think this is a moment and a season I’ll look back on and be like, this why I am who I am today… It was hard sitting on the bench, I’m not going to lie, but I think it taught myself how to be a really good leader and how to be a really good teammate… when you’re on the sidelines and you don’t have your jersey on, you can observe and really learn about people… how can I show up every single day and pour into them and be there for them… but also going forward down the road, if I have a teammate that’s injured… I know I will always show up for them and be there for them and check in on them, because it is an incredibly isolating thing, and you don’t realize that until you go through it… I think all of that is probably what I’ve leaned most.” 🎥: Tony East
1:47

Caitlin on what she learned through injury this season: “I think when I get a few years down here in my career, I think this is a moment and a season I’ll look back on and be like, this why I am who I am today… It was hard sitting on the bench, I’m not going to lie, but I think it taught myself how to be a really good leader and how to be a really good teammate… when you’re on the sidelines and you don’t have your jersey on, you can observe and really learn about people… how can I show up every single day and pour into them and be there for them… but also going forward down the road, if I have a teammate that’s injured… I know I will always show up for them and be there for them and check in on them, because it is an incredibly isolating thing, and you don’t realize that until you go through it… I think all of that is probably what I’ve leaned most.” 🎥: Tony East

Jess

80,019 görüntüleme • 1 yıl önce