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NVIDIA just released a repo that scans AI agent skills for security risks BEFORE you run them, which matters more than people realize now that everyone's installing random tools, skills, and MCPs from GitHub. I break down that one AND 4 other GitHub repos trending over the last 30...

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GREG ISENBERG

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44,648 Aufrufe • vor 2 Tagen •via X (Twitter)

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GitHub + Cursor is just INSANE GitHub has 28 million open-source projects that anyone can fork and build a new product So I built a new product: Repo to Docs The Agent will scan entire GitHub repo, analyze all files and create a knowledge base (docs) for AI coding tools: ↓

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i soft launched EveAgents it's an open-source registry of eve agents based on the Vercel framework that you can use for integrations like Slack, Discord, GitHub etc use this command to list them all: > npx @bergside/eveagents list and install using: > npx @bergside/eveagents install seo-growth-analyst the agents aren't super wow right now, but it's a start and we can all make them better by contributing to the GitHub repo which is MIT licensed: thank you

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Google open-sourced MCP Toolbox for Databases. I gave it access to everything else. For context, Google's MCP Toolbox for Databases is an open-source server that lets AI agents securely query structured databases like PostgreSQL and MySQL through the MCP protocol However, most enterprise knowledge doesn't actually live in databases. It's scattered across emails, Slack threads, GitHub repos, Salesforce records, customer reviews, and internal docs. So Agents can't see any of it, which means they're working with a fraction of the context they need. I fixed that using MindsDB. It acts as a universal SQL layer that sits on top of all your data sources: structured, semi-structured, and unstructured. This means you can query Salesforce, Gmail, GitHub, S3 files, Jira, and 200+ more sources using SQL syntax. The clever part is how it connects to the MCP Toolbox. MindsDB exposes everything through MySQL, so from the Agent's perspective, it's just running SQL and getting context back. It doesn't know or care that the data came from five different sources behind the scenes. This setup unlocks some powerful capabilities: → One SQL interface for dozens of enterprise sources → Cross-datasource joins (combine GitHub and CRM data in a single query) → Built-in ML capabilities for working with unstructured data → Simple MCP tools that now have massively expanded reach In the video below, the Agent queries GitHub data and a customer review database in one SQL query. So what used to require ETL pipelines and weeks of engineering effort now happens instantly. At the end of the day, AI agents are only as useful as the data they can access. This gives them a lot more to work with. I have shared the GitHub repo in the replies, where you can find more details about this.
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Google open-sourced MCP Toolbox for Databases. I gave it access to everything else. For context, Google's MCP Toolbox for Databases is an open-source server that lets AI agents securely query structured databases like PostgreSQL and MySQL through the MCP protocol However, most enterprise knowledge doesn't actually live in databases. It's scattered across emails, Slack threads, GitHub repos, Salesforce records, customer reviews, and internal docs. So Agents can't see any of it, which means they're working with a fraction of the context they need. I fixed that using MindsDB. It acts as a universal SQL layer that sits on top of all your data sources: structured, semi-structured, and unstructured. This means you can query Salesforce, Gmail, GitHub, S3 files, Jira, and 200+ more sources using SQL syntax. The clever part is how it connects to the MCP Toolbox. MindsDB exposes everything through MySQL, so from the Agent's perspective, it's just running SQL and getting context back. It doesn't know or care that the data came from five different sources behind the scenes. This setup unlocks some powerful capabilities: → One SQL interface for dozens of enterprise sources → Cross-datasource joins (combine GitHub and CRM data in a single query) → Built-in ML capabilities for working with unstructured data → Simple MCP tools that now have massively expanded reach In the video below, the Agent queries GitHub data and a customer review database in one SQL query. So what used to require ETL pipelines and weeks of engineering effort now happens instantly. At the end of the day, AI agents are only as useful as the data they can access. This gives them a lot more to work with. I have shared the GitHub repo in the replies, where you can find more details about this.

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These days you can access AI agents that execute commands, browse the web, and coordinate with other agents. But you should always make sure they're safe to use! 👀 Season 4 of GitHub's Secure Code Game lets you test your knowledge by hacking one yourself. Get started in minutes right from your browser with GitHub Codespaces (for free!). 👇
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These days you can access AI agents that execute commands, browse the web, and coordinate with other agents. But you should always make sure they're safe to use! 👀 Season 4 of GitHub's Secure Code Game lets you test your knowledge by hacking one yourself. Get started in minutes right from your browser with GitHub Codespaces (for free!). 👇

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this is f**king gold someone just open-sourced a GitHub repo with 2,700+ stars which gave out the entire "loop-engineering" framework for ai agents it lets you: →allows you to manage an entire team of 1000 agents →run auto-research by Andrej Karpathy →Agent-to-agent PR comment loop just you, your AI agents, and full control. this is where one-person businesses are headed. save this for later.

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I wish I found this sooner. 🫡 This open-source collection of Agentic UI Skills teaches AI coding agents how to build interfaces that feel polished, not just functional. You'll find specialized skills for things like: → Better User Interface → Better Typography → Better Colors → Better Layout → Better Accessibility ....& more Instead of relying on generic UI patterns, your AI agent can load the right design-engineering skill for the task at hand. If you're building modern frontends with AI coding agents, this is definitely worth bookmarking :) Source 🔗: github(DOT)com/jakubkrehel/skills Follow me for more amazing AI, Coding & Web Dev insights 💎

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5 open-source AI repos that blew up on github this week → 1. MinerU (opendatalab) - ~69k★ turns any PDF or office doc into clean, LLM-ready markdown - the fix for garbage RAG inputs 2. voicebox (jamiepine) - ~35k★ open-source AI voice studio - clone a voice, dictate, create. the local ElevenLabs 3. ai-website-cloner-template (JCodesMore) - ~22k★ clone any website with one command using your AI coding agent - great for learning and prototyping 4. Anthropic-Cybersecurity-Skills (mukul975) - ~21k★ 800+ ready security skills for AI agents, mapped to MITRE ATT&CK and NIST - works with claude code, cursor, codex 5. agent-native (builderio) - ~3k★ a framework for building apps designed for AI agents from day one, not bolted on after one of these saves you a paid tool. bookmark before you forge

self.dll

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this is f**king insane. Jack Dorsey just open sourced a free tool for running a one-person business. the GitHub repo already has 14.4K stars. It lets you: → run your own server → keep chat, search, Git, and automation in one place → add AI agents that work like teammates → decide exactly what each agent can do no monthly subscriptions. just you, your AI agents, and full control. this is where one-person businesses are headed. save this for later.
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this is f**king insane. Jack Dorsey just open sourced a free tool for running a one-person business. the GitHub repo already has 14.4K stars. It lets you: → run your own server → keep chat, search, Git, and automation in one place → add AI agents that work like teammates → decide exactly what each agent can do no monthly subscriptions. just you, your AI agents, and full control. this is where one-person businesses are headed. save this for later.

MIKE

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32:26

Send this to ANYONE on your team using AI agents with Claude/Codex skills: If I were you, I'd put your BEST AI skills in a GitHub repo, turn that repo into a PLUGIN, and have your team INSTALL it in Claude/Codex with auto update on. Why? 1. Everyone gets the same AI SOPs instead of 10 versions floating around Slack 2. When one person improves a skill, the whole team gets the better version. 3. New hires can start with your best workflows instead of a blank AI setup (this is a BIG deal). 4. If someone breaks a skill, you can roll it back with version control. 5. Personal skills can stay personal, while team skills become shared company infrastructure (and an asset!). 6. Your best AI processes stay with the company when someone leaves. 7. You can chain skills together for bigger workflows, like titles -> thumbnails -> descriptions -> YouTube publish. 8. You can track which skills are actually being used and delete the ones collecting dust. 9. You get less slop because the agent has real instructions, examples, taste, and process. 10. Your team moves from single-player AI to multiplayer AI. (thanks to AI with Remy | Learn AI for coming onto The Startup Ideas Podcast (SIP) 🧃) Watch full breakdown here (clearly explained): I don't know why I didn't do this before. The more I think about it, the more obvious it feels: your AI workflows should be version controlled company assets. It kinda feels like the difference between “we use AI” and “we actually operate with AI". Enjoy.

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Scrum is a cancer. There's nothing more bureaucratic, dysfunctional, and slow than a team that just wants to build software, but they are being forced to do it using Scrum. If you are stuck with this, here is a recommendation that will change your life: Go to connect your email, GitHub account, and the software you use to manage projects, and build a workflow that will brief you before every standup. Dimension is pretty incredible. You can literally write a prompt and have Dimension build an agent that will do the work in the background for you. Example: "Go to Linear and GitHub and write a summary of the tasks I completed last week. Organize them based on priority and effort. Send me an email with the results." Schedule this workflow to run 10 minutes before your Scrum meetings, and you'll get an email right before you need it. You can have AI now do all sorts of tasks for you: 1. Brief you before a meeting 2. Reply or handle cold emails 3. Take care of support messages 4. Research tasks for you And all of this can happen unprompted, in the background. Really awesome job from the team behind Dimension! Thanks to them for supporting my work by collaborating with me on this post.

Santiago

56,384 Aufrufe • vor 9 Monaten

gm! If you missed yesterday's space, here is the clip that you can listen explaining why Agent NFTs are important and future of NFTs. Also here is the TL;DR Agentic NFTs as productive assets. An NFT can own an AI agent's shared memory, tools, websites, and products it has built. Selling the NFT transfers the entire business/agent state to the new owner. ERC-8257 for tool-gating. CodinCowboy and ryan is working on the standard where agents register tools on-chain and access is gated by NFT ownership. That component that tells an agent "you need this NFT to use this tool" creating a market for exclusive tools. Use case: anyone can publish a tool and restrict it (e.g., "only Normies agents can call this"), letting tool value flow back to the gating NFT. Normies community fit. Normies API has served ~500M requests in 3 months, with 100+ community-built tools/games. ERC-8257 will let them build gated games, rewards, and skills exclusively for Normie agent holders. Why Normies is "agent-ready"? - Because everything is fully on-chain, metadata, ERCs, binding transaction. So the project is highly composable. My take on this topic: So far holding an NFT giving access to community, discord and merch. What we are doing with Normies is to give access to a business, tools, skills that agents can use effectively and be part of the economy layer of agentic future. Imagine someone builds a tool that does really 100% successful trading and only gates that skill to Normie Agents, and at some point you will only need a Normie NFT which has binding with the agent and access all these skills, tools. Future is now, Normies are the builders.
6:08

gm! If you missed yesterday's space, here is the clip that you can listen explaining why Agent NFTs are important and future of NFTs. Also here is the TL;DR Agentic NFTs as productive assets. An NFT can own an AI agent's shared memory, tools, websites, and products it has built. Selling the NFT transfers the entire business/agent state to the new owner. ERC-8257 for tool-gating. CodinCowboy and ryan is working on the standard where agents register tools on-chain and access is gated by NFT ownership. That component that tells an agent "you need this NFT to use this tool" creating a market for exclusive tools. Use case: anyone can publish a tool and restrict it (e.g., "only Normies agents can call this"), letting tool value flow back to the gating NFT. Normies community fit. Normies API has served ~500M requests in 3 months, with 100+ community-built tools/games. ERC-8257 will let them build gated games, rewards, and skills exclusively for Normie agent holders. Why Normies is "agent-ready"? - Because everything is fully on-chain, metadata, ERCs, binding transaction. So the project is highly composable. My take on this topic: So far holding an NFT giving access to community, discord and merch. What we are doing with Normies is to give access to a business, tools, skills that agents can use effectively and be part of the economy layer of agentic future. Imagine someone builds a tool that does really 100% successful trading and only gates that skill to Normie Agents, and at some point you will only need a Normie NFT which has binding with the agent and access all these skills, tools. Future is now, Normies are the builders.

serc

14,066 Aufrufe • vor 3 Monaten

Introducing Ferix - the skill issue killer. Ferix is built around Vercel's ‘npx skills’ command which Guillermo Rauch announced yesterday. Skills are stored in repositories on GitHub which means finding the skills you need for your project isn’t intuitive and you have to go searching. Ferix fixes this problem and acts as a central hub for all skill repositories. You an add a repository in 5 seconds and easily view it’s contents. So far I’ve added Better Auth, Expo & Vercel's agent skills but any user can add a supported repository. Let me know what you think and anything that can be improved! Autumn skills coming soon👀
1:06

Introducing Ferix - the skill issue killer. Ferix is built around Vercel's ‘npx skills’ command which Guillermo Rauch announced yesterday. Skills are stored in repositories on GitHub which means finding the skills you need for your project isn’t intuitive and you have to go searching. Ferix fixes this problem and acts as a central hub for all skill repositories. You an add a repository in 5 seconds and easily view it’s contents. So far I’ve added Better Auth, Expo & Vercel's agent skills but any user can add a supported repository. Let me know what you think and anything that can be improved! Autumn skills coming soon👀

Charlie Lamb

35,704 Aufrufe • vor 7 Monaten

Anthropic's Claude Ai Agents Team just Educated how to build production AI agents in under 30 mins. For Free. From the engineers who built the stack. CANCEL Your Weekend Plans, and Learn to Build AI Agents Today. Bookmark it. Watch it. Build your first production agent this weekend. $5,000/month. $7,000/month. $12,000/month. People are building agents for clients and charging $$$ as Beginners. You're still stuck in the thinking about AI phase. This video fixes that tonight. Follow Himanshu Kumar for more high-signal content that actually moves your AI engineering career forward. ↓ Ivan Nardini runs Developer Relations for AI at Google Cloud. He just gave away the entire production agent stack in 30 minutes. This is the talk that separates people deploying AI agents that actually scale from people whose agents break the moment they leave localhost. Here's everything inside. I break down a production AI video like this every week. Follow Himanshu Kumar. ↓ The 4-part agent stack that actually scales. Most devs are duct-taping frameworks together and calling it an "AI agent." Ivan lays out the real stack: Agent Development Kit (ADK): open-source, code-first framework for building, evaluating, and deploying agents. Supports Claude models through Vertex AI directly. Model Context Protocol (MCP): lets your agent talk to any tool or data source with one standard. Vertex AI Agent Engine: managed platform for deploying, monitoring, and scaling agents in production. No DevOps headaches. Agent-to-Agent Protocol: open protocol so agents built on different frameworks can actually work together. This is the stack replacing every hacky agent setup in production right now. Full MCP + Claude breakdowns drop weekly on Himanshu Kumar. ↓ Building your first real agent. Ivan builds a birthday planner agent live. LLM Agent class. Name it. Define instructions. Pick the model. He uses Claude 3.7 Sonnet. You could use Opus 4.7 for better reasoning. Full agent built in minutes. Not weeks. Watch the build once and you'll never structure an agent the wrong way again. I post agent architectures people pay $500 courses to learn. Himanshu Kumar. ↓ Multi-agent systems without the chaos. Single agents are easy. Multi-agent systems are where 99% of builders fail. Ivan extends the birthday planner by: Adding a calendar service through MCP tools Creating an orchestrator agent to route requests between agents Handling state and context across agent handoffs This is production multi-agent architecture. Clean. Scalable. Debuggable. Most tutorials hand-wave this part. This one shows you every step. Multi-agent orchestration content drops weekly on Himanshu Kumar. ↓ Deployment without the DevOps nightmare. This is where most AI projects die. You build a cool agent locally. It works. You try to deploy it. Everything breaks. Vertex AI Agent Engine fixes this: Minimal code deployment Automatic monitoring of latency, CPU, and memory Built-in observability and logging No infrastructure setup needed You provide config and requirements. The platform handles the rest. This is how agents actually get to production. Deployment guides for Claude agents post every week. Himanshu Kumar. ↓ Agent-to-Agent Protocol: the future nobody's talking about. Most people don't know this exists yet. The A2A Protocol lets agents built in different frameworks communicate seamlessly. Your Claude agent. My LangChain agent. Someone else's CrewAI agent. All talking to each other. All solving parts of the same problem. All without custom integration code. This is the infrastructure layer of the coming AI economy. Getting in early on A2A Protocol is like getting in early on HTTP in 1995. A2A deep dive coming soon. Himanshu Kumar. ↓ 30 minutes from the team shipping this in production. You'll learn more from this than from 6 months of YouTube tutorials made by people who've never deployed an agent past localhost. People who watch this understand production AI agents at the architect level. People who skip it keep hacking together frameworks that break every time an API updates. Save the video. Watch it tonight. Build a real agent this weekend. Follow Himanshu Kumar for more high-signal content that actually moves your AI engineering career forward.
30:12

Anthropic's Claude Ai Agents Team just Educated how to build production AI agents in under 30 mins. For Free. From the engineers who built the stack. CANCEL Your Weekend Plans, and Learn to Build AI Agents Today. Bookmark it. Watch it. Build your first production agent this weekend. $5,000/month. $7,000/month. $12,000/month. People are building agents for clients and charging $$$ as Beginners. You're still stuck in the thinking about AI phase. This video fixes that tonight. Follow Himanshu Kumar for more high-signal content that actually moves your AI engineering career forward. ↓ Ivan Nardini runs Developer Relations for AI at Google Cloud. He just gave away the entire production agent stack in 30 minutes. This is the talk that separates people deploying AI agents that actually scale from people whose agents break the moment they leave localhost. Here's everything inside. I break down a production AI video like this every week. Follow Himanshu Kumar. ↓ The 4-part agent stack that actually scales. Most devs are duct-taping frameworks together and calling it an "AI agent." Ivan lays out the real stack: Agent Development Kit (ADK): open-source, code-first framework for building, evaluating, and deploying agents. Supports Claude models through Vertex AI directly. Model Context Protocol (MCP): lets your agent talk to any tool or data source with one standard. Vertex AI Agent Engine: managed platform for deploying, monitoring, and scaling agents in production. No DevOps headaches. Agent-to-Agent Protocol: open protocol so agents built on different frameworks can actually work together. This is the stack replacing every hacky agent setup in production right now. Full MCP + Claude breakdowns drop weekly on Himanshu Kumar. ↓ Building your first real agent. Ivan builds a birthday planner agent live. LLM Agent class. Name it. Define instructions. Pick the model. He uses Claude 3.7 Sonnet. You could use Opus 4.7 for better reasoning. Full agent built in minutes. Not weeks. Watch the build once and you'll never structure an agent the wrong way again. I post agent architectures people pay $500 courses to learn. Himanshu Kumar. ↓ Multi-agent systems without the chaos. Single agents are easy. Multi-agent systems are where 99% of builders fail. Ivan extends the birthday planner by: Adding a calendar service through MCP tools Creating an orchestrator agent to route requests between agents Handling state and context across agent handoffs This is production multi-agent architecture. Clean. Scalable. Debuggable. Most tutorials hand-wave this part. This one shows you every step. Multi-agent orchestration content drops weekly on Himanshu Kumar. ↓ Deployment without the DevOps nightmare. This is where most AI projects die. You build a cool agent locally. It works. You try to deploy it. Everything breaks. Vertex AI Agent Engine fixes this: Minimal code deployment Automatic monitoring of latency, CPU, and memory Built-in observability and logging No infrastructure setup needed You provide config and requirements. The platform handles the rest. This is how agents actually get to production. Deployment guides for Claude agents post every week. Himanshu Kumar. ↓ Agent-to-Agent Protocol: the future nobody's talking about. Most people don't know this exists yet. The A2A Protocol lets agents built in different frameworks communicate seamlessly. Your Claude agent. My LangChain agent. Someone else's CrewAI agent. All talking to each other. All solving parts of the same problem. All without custom integration code. This is the infrastructure layer of the coming AI economy. Getting in early on A2A Protocol is like getting in early on HTTP in 1995. A2A deep dive coming soon. Himanshu Kumar. ↓ 30 minutes from the team shipping this in production. You'll learn more from this than from 6 months of YouTube tutorials made by people who've never deployed an agent past localhost. People who watch this understand production AI agents at the architect level. People who skip it keep hacking together frameworks that break every time an API updates. Save the video. Watch it tonight. Build a real agent this weekend. Follow Himanshu Kumar for more high-signal content that actually moves your AI engineering career forward.

Himanshu Kumar

228,904 Aufrufe • vor 4 Monaten