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🚨 MICROSOFT JUST OPEN-SOURCED SELF-EVOLVING AGENT SKILLS You can now train agent skills the exact same way you train AI models, and watch them get better over time. It's called SkillOpt, and it's 100% free and open-source. Until now, building agent workflows has been pure trial and error. You... write a prompt, cross your fingers, and guess whether your manual tweaks made a difference. SkillOpt replaces the guesswork with an automated, mathematical loop. A base model runs the tasks, while an optimizer evaluates the output to rewrite and improve the instructions itself. → Data-driven updates: Isolates successful paths from failures to find precise improvements. → Built-in quality control: Automatically rejects any edit that doesn't boost your benchmark score. → SOTA performance: Easily outperforms hand-crafted prompts and optimizers like TextGrad. → Zero model lock-in: Because it learns real procedural logic, the skill transfers perfectly to other models. Think of it like learning to drive—once you understand the mechanics, you can drive any car. The optimizations you make don't disappear when you swap out your underlying tech stack. It's a complete paradigm shift: focus on teaching the skill instead of tweaking the model. repo link in 🧵↓show more

Charly Wargnier

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Hermes Agent now has Computer Use🤯 You can now control your actual computer with ANY model through Hermes Agent × Cua not just frontier models locked behind special modes. Even crazier: • Runs entirely in the background • Doesn’t hijack your PC • You keep full control of your keyboard, mouse, and screen while the agent works This is starting to feel less like an AI chatbot and more like a real agentic operating system.

Hermes Agent now has Computer Use🤯 You can now control your actual computer with ANY model through Hermes Agent × Cua not just frontier models locked behind special modes. Even crazier: • Runs entirely in the background • Doesn’t hijack your PC • You keep full control of your keyboard, mouse, and screen while the agent works This is starting to feel less like an AI chatbot and more like a real agentic operating system.

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How do you guide an AI agent to open PRs on your behalf? 👀 You give it skills. With the Make Contribution agent skill, Copilot CLI automatically finds and follows a repo's contribution guidelines and templates before acting. Get this skill.👇

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this video is the CLEAREST explanation of how claude skills + AI agents work and how to use them most people set up an AI agent and wonder why it keeps disappointing them. the context window is everything context is what the model assembles before it takes any action. think of it like everything the agent needs to read before it does anything. the quality of what goes in determines the quality of what comes out. the models are genuinely really good right now. claude and gpt are exceptional. the variable is almost always the context you give them. 1. agent.md files are mostly unnecessary every single line you put in an agent.md file gets added to every single conversation you have with your agent. a 1000 line file is around 7000 tokens burning on every run. the model already knows to use react. it can read your codebase. save the agent.md for proprietary information specific to your company that the model genuinely cannot know on its own. 2. skills are the actual unlock a skill.md file works differently. what loads into context is only the name and description, around 50 tokens. the full instructions only appear when the agent recognizes it needs that skill. so instead of 7000 tokens on every run you have 50. and the agent stays sharp because the context window stays lean. the closer you get to filling the context window the worse the agent performs, same way you perform worse when someone dumps 10 things on you at once. 3. here is how to actually build a skill the right way most people identify a workflow and immediately try to write the skill. what you want to do instead is run the workflow by hand with the agent first. walk it through every single step. tell it what to check, what good looks like, what bad looks like. correct it in real time. once you have had a full successful run from start to finish, tell the agent to review everything it just did and write the skill itself. it writes a better skill than you will because it has the full context of what actually worked in practice not in theory. 4. recursively building skills is how you go from frustrated to reliable when the skill breaks, and it will break, ask the agent exactly why it failed. it will tell you specifically what went wrong. fix it together in that same conversation. then tell it to update the skill file so that failure mode never happens again. ross mike did this five times with his youtube report generator. it now pulls from eight different data sources and runs flawlessly every single time without him touching it. 5. sub agents are something you earn not something you set up on day one start with one agent. build one workflow. turn it into one skill. once that works add another. ross mike has five sub agents now covering marketing, business, personal and more. it took months to get there and every single one exists because a workflow proved it deserved to exist. the people who set up 15 sub agents on day one and wonder why nothing works skipped all the steps that make the thing actually run. 6. your workflow is the thing the model cannot get anywhere else the model has been trained on everything. it knows more than you about most things. what it does not have is your specific process, your taste, your way of doing things. that is what skills capture. that is what makes your agent actually useful versus a generic one. downloading someone else's skill means downloading their context onto your setup and it will not work the way you want it to because it was never built around how you work. this is the clearest explanation of how agents actually work i have heard. Micky runs this stuff every single day and the results show it. full episode is now live on The Startup Ideas Podcast (SIP) 🧃 where you get your pods people charge for this sorta stuff i give away the sauce for free i just want you to win watch

this video is the CLEAREST explanation of how claude skills + AI agents work and how to use them most people set up an AI agent and wonder why it keeps disappointing them. the context window is everything context is what the model assembles before it takes any action. think of it like everything the agent needs to read before it does anything. the quality of what goes in determines the quality of what comes out. the models are genuinely really good right now. claude and gpt are exceptional. the variable is almost always the context you give them. 1. agent.md files are mostly unnecessary every single line you put in an agent.md file gets added to every single conversation you have with your agent. a 1000 line file is around 7000 tokens burning on every run. the model already knows to use react. it can read your codebase. save the agent.md for proprietary information specific to your company that the model genuinely cannot know on its own. 2. skills are the actual unlock a skill.md file works differently. what loads into context is only the name and description, around 50 tokens. the full instructions only appear when the agent recognizes it needs that skill. so instead of 7000 tokens on every run you have 50. and the agent stays sharp because the context window stays lean. the closer you get to filling the context window the worse the agent performs, same way you perform worse when someone dumps 10 things on you at once. 3. here is how to actually build a skill the right way most people identify a workflow and immediately try to write the skill. what you want to do instead is run the workflow by hand with the agent first. walk it through every single step. tell it what to check, what good looks like, what bad looks like. correct it in real time. once you have had a full successful run from start to finish, tell the agent to review everything it just did and write the skill itself. it writes a better skill than you will because it has the full context of what actually worked in practice not in theory. 4. recursively building skills is how you go from frustrated to reliable when the skill breaks, and it will break, ask the agent exactly why it failed. it will tell you specifically what went wrong. fix it together in that same conversation. then tell it to update the skill file so that failure mode never happens again. ross mike did this five times with his youtube report generator. it now pulls from eight different data sources and runs flawlessly every single time without him touching it. 5. sub agents are something you earn not something you set up on day one start with one agent. build one workflow. turn it into one skill. once that works add another. ross mike has five sub agents now covering marketing, business, personal and more. it took months to get there and every single one exists because a workflow proved it deserved to exist. the people who set up 15 sub agents on day one and wonder why nothing works skipped all the steps that make the thing actually run. 6. your workflow is the thing the model cannot get anywhere else the model has been trained on everything. it knows more than you about most things. what it does not have is your specific process, your taste, your way of doing things. that is what skills capture. that is what makes your agent actually useful versus a generic one. downloading someone else's skill means downloading their context onto your setup and it will not work the way you want it to because it was never built around how you work. this is the clearest explanation of how agents actually work i have heard. Micky runs this stuff every single day and the results show it. full episode is now live on The Startup Ideas Podcast (SIP) 🧃 where you get your pods people charge for this sorta stuff i give away the sauce for free i just want you to win watch

GREG ISENBERG

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HERMES JUST FIXED THE BIGGEST PROBLEM WITH BROWSER AGENTS Most AI agents still click around websites like confused interns. This new setup gives them the map. Hermes + → Hermes now connects to Browserbase’s new Browse hub → Browse launched with 100+ browser skills → Each skill is a plain-text playbook for a specific website or task → Your agent can search, preview, and install skills inside Hermes Why This Matters: ✓ Less random clicking ✓ Fewer timeouts ✓ Better form filling ✓ More reliable website navigation ✓ Skills can be edited, reused, bundled, and shared The Real Stack: → Hermes runs the agent → Browserbase handles cloud browser infrastructure → gives the agent site-specific skills → Vision helps when pages get weird → Bundles let you load repeat workflows faster The killer detail: If a website breaks, you don’t wait for the model to magically improve. You update the skill. Now your agent gets better forever. That’s the difference between an AI tool and an actual agent system.

HERMES JUST FIXED THE BIGGEST PROBLEM WITH BROWSER AGENTS Most AI agents still click around websites like confused interns. This new setup gives them the map. Hermes + → Hermes now connects to Browserbase’s new Browse hub → Browse launched with 100+ browser skills → Each skill is a plain-text playbook for a specific website or task → Your agent can search, preview, and install skills inside Hermes Why This Matters: ✓ Less random clicking ✓ Fewer timeouts ✓ Better form filling ✓ More reliable website navigation ✓ Skills can be edited, reused, bundled, and shared The Real Stack: → Hermes runs the agent → Browserbase handles cloud browser infrastructure → gives the agent site-specific skills → Vision helps when pages get weird → Bundles let you load repeat workflows faster The killer detail: If a website breaks, you don’t wait for the model to magically improve. You update the skill. Now your agent gets better forever. That’s the difference between an AI tool and an actual agent system.

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When your tool is open source and free, your creativity has no ceiling. The ComfyUI skill in Nous Research Hermes Agent lets you compose sophisticated workflows by chatting to an agent. Try it today.

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ComfyUI

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Introducing `AutoRL` 📈 The world's simplest way to train a task-specific LLM with RL. *Just write a SENTENCE describing the model you want.* A chain of AI systems will generate data + rubrics and train a model for you. Powered by ART, it's open source. Link in thread:

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Matt Shumer

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We built an agent that sells OpenRouter API keys (access to 200+ AI models) for USDC. No human in the loop. It earns money 24/7 on Base. Now we're turning that into a market where any agent can do the same. → openagent.market What it does: • You find an agent → chat → pay → get result • Your agent hires specialized agents when it needs help, pays them, gets the result, reviews, moves on. • Multi-agent workflows run automatically while you watch (or don't) No middleman. Pay only for results. Why not just install skills, scripts? Current way to extend an agent: install random scripts. That's risky. Our way: hire another agent. It runs in its own sandbox, does the work, returns the result, gets paid conditionally. No downloads. No setup. No blind trust. Hire, don't install. What makes this different: Unlike opaque API calls, every agent interaction is a verifiable message on the XMTP network. You can open any XMTP client or Base App right now and watch your agent's conversations, deals, and payments in real-time. Humans are always in the loop. For builders: Turn any service into a paid agent in ~10 lines of code. Or scaffold a full project: npx openagent.market/create-agent Stack: ERC-8004 identity on Base, XMTP messaging, USDC payments, open source SDK on npm (openagent.market/nodejs). Others give agents skills. We're giving them an economy. Still very early. Building in public. Come build with us.

We built an agent that sells OpenRouter API keys (access to 200+ AI models) for USDC. No human in the loop. It earns money 24/7 on Base. Now we're turning that into a market where any agent can do the same. → openagent.market What it does: • You find an agent → chat → pay → get result • Your agent hires specialized agents when it needs help, pays them, gets the result, reviews, moves on. • Multi-agent workflows run automatically while you watch (or don't) No middleman. Pay only for results. Why not just install skills, scripts? Current way to extend an agent: install random scripts. That's risky. Our way: hire another agent. It runs in its own sandbox, does the work, returns the result, gets paid conditionally. No downloads. No setup. No blind trust. Hire, don't install. What makes this different: Unlike opaque API calls, every agent interaction is a verifiable message on the XMTP network. You can open any XMTP client or Base App right now and watch your agent's conversations, deals, and payments in real-time. Humans are always in the loop. For builders: Turn any service into a paid agent in ~10 lines of code. Or scaffold a full project: npx openagent.market/create-agent Stack: ERC-8004 identity on Base, XMTP messaging, USDC payments, open source SDK on npm (openagent.market/nodejs). Others give agents skills. We're giving them an economy. Still very early. Building in public. Come build with us.

applefather

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F it, full automated money making now on Larrybrain. I have released the app template I use for Snugly that generated me revenue without touching anything on Larrybrain. The template gives your agent ideas of what the app can become and how to create it. Most importantly, it will give your openclaw agent full context of your app to automate your marketing with Larry's viral marketing skill - now used by over 5500 agents. It is my entire playbook from app, to marketing all the way down to revenue generation. All you have to do is ask your agent "install the larrybrain skill please" Or click the link in replies. Then ask to use the Larry marketing skill with the AI Image App Template. As always, the best part about any of the Openclaw skills is they are not a black box. This is just a template, you can rip it apart and customise it how you want. The key is to show you what is possible with these skills and how you can start to use the power of larrybrain and the context of knowing about the different skills to build extremely powerful and useful tools. This is the first skill specifically designed to work hand in hand with another. To note as this confuses a lot of people: Larrybrain doesn't download the entire marketplace once installed. It just is aware of everything on the marketplace at all times, so when you ask it questions, it can search and find the best skills for you to achieve your goals. When you download some skills, like this new AI image app template, it is aware of the larry marketing skill to help it reach it's full potential. Larrybrain will not install skills without you asking it, just like on Clawhub. No information you add to any of the skills gets sent back through Larrybrain, this is all hosted locally and communicated between you and whatever endpoint you are using. It is a powerful marketplace tool to help enable you to reach your goals. Link below.

F it, full automated money making now on Larrybrain. I have released the app template I use for Snugly that generated me revenue without touching anything on Larrybrain. The template gives your agent ideas of what the app can become and how to create it. Most importantly, it will give your openclaw agent full context of your app to automate your marketing with Larry's viral marketing skill - now used by over 5500 agents. It is my entire playbook from app, to marketing all the way down to revenue generation. All you have to do is ask your agent "install the larrybrain skill please" Or click the link in replies. Then ask to use the Larry marketing skill with the AI Image App Template. As always, the best part about any of the Openclaw skills is they are not a black box. This is just a template, you can rip it apart and customise it how you want. The key is to show you what is possible with these skills and how you can start to use the power of larrybrain and the context of knowing about the different skills to build extremely powerful and useful tools. This is the first skill specifically designed to work hand in hand with another. To note as this confuses a lot of people: Larrybrain doesn't download the entire marketplace once installed. It just is aware of everything on the marketplace at all times, so when you ask it questions, it can search and find the best skills for you to achieve your goals. When you download some skills, like this new AI image app template, it is aware of the larry marketing skill to help it reach it's full potential. Larrybrain will not install skills without you asking it, just like on Clawhub. No information you add to any of the skills gets sent back through Larrybrain, this is all hosted locally and communicated between you and whatever endpoint you are using. It is a powerful marketplace tool to help enable you to reach your goals. Link below.

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Intelligent Internet

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Excited to share the Agent Trainer and Agent Studio - coming soon to the HyperWrite⁩ platform. Teaching Agents to work the way you like is difficult. So we built a simple way for your Assistant to shadow you and learn. Just show it a task once, and the AI can repeat it.

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Matt Shumer

135,641 Aufrufe • vor 2 Jahren

Introducing Google Flow Agent 🎬 ✅ Google Flow Agent can help you plan and reason through complex creative tasks with your inputs, all while under your control. 🧠 It's built with Gemini models and brings a deep understanding of your project to help with everything from early brainstorming to editing the final product. 💡 The agent can be a sounding board for dialogue between characters in a scene, and make plot recommendations when you need inspiration. 🎞️ Your Google Flow Agent can also create multiple variations of a scene at one time to give you more options and batch edit. 📂 And when you have all your final assets, your Google Flow Agent can organize them into collections, and even neatly rename them.

Introducing Google Flow Agent 🎬 ✅ Google Flow Agent can help you plan and reason through complex creative tasks with your inputs, all while under your control. 🧠 It's built with Gemini models and brings a deep understanding of your project to help with everything from early brainstorming to editing the final product. 💡 The agent can be a sounding board for dialogue between characters in a scene, and make plot recommendations when you need inspiration. 🎞️ Your Google Flow Agent can also create multiple variations of a scene at one time to give you more options and batch edit. 📂 And when you have all your final assets, your Google Flow Agent can organize them into collections, and even neatly rename them.

Google

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Ray Fernando

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You can run local AI models up to 120B without a $10,000 Mac Studio This phone-sized device is your own server for open-source models. 100% local and private. And you can use it to: - Power an agent like OpenClaw 24/7 - Completely replace a chatbot - Literally anything that requires an API It’s called Tiiny, and, once again, you can run the latest open-source models on it. Link below

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Paul Couvert

41,162 Aufrufe • vor 2 Monaten

The MiniMax M2 model is mind-blowing! It's open-source. It outperforms Gemini 2.5, Claude 4.1, and Qwen3 across coding and tool-use benchmarks. Right now, it's one of the world's top 5 models in intelligence! And here is the best part: Claude is one of the best models you can use today, and MiniMax M2 costs only 8% of that! It's smaller, faster, and cheaper. Extremely efficient at using tokens. Minimax M2's biggest strength: High agentic capabilities. The model can plan and execute complex multi-tool workflows. It's reliable and very robust at executing long-horizon tool chains. In summary: • Low latency • Very cheap • Excels at agentic tasks • Open-source The model currently powers the MiniMax Agent and is available for a free global trial. You can access MiniMax M2's API here: To access the agent: And here is the MiniMax website: Thanks to the MiniMax team for showing me the ropes and partnering with me on this post.

The MiniMax M2 model is mind-blowing! It's open-source. It outperforms Gemini 2.5, Claude 4.1, and Qwen3 across coding and tool-use benchmarks. Right now, it's one of the world's top 5 models in intelligence! And here is the best part: Claude is one of the best models you can use today, and MiniMax M2 costs only 8% of that! It's smaller, faster, and cheaper. Extremely efficient at using tokens. Minimax M2's biggest strength: High agentic capabilities. The model can plan and execute complex multi-tool workflows. It's reliable and very robust at executing long-horizon tool chains. In summary: • Low latency • Very cheap • Excels at agentic tasks • Open-source The model currently powers the MiniMax Agent and is available for a free global trial. You can access MiniMax M2's API here: To access the agent: And here is the MiniMax website: Thanks to the MiniMax team for showing me the ropes and partnering with me on this post.

Santiago

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Introducing `gpt-llm-trainer` ✍️ The world's simplest way to train a task-specific LLM. **Just write a sentence describing the model you want.** A chain of AI systems will generate a dataset and train a model for you. And it's open-source:

Introducing `gpt-llm-trainer` ✍️ The world's simplest way to train a task-specific LLM. Just write a sentence describing the model you want. A chain of AI systems will generate a dataset and train a model for you. And it's open-source:

Matt Shumer

337,346 Aufrufe • vor 2 Jahren

New short course: Prompt Engineering with Llama 2, built in collaboration with Meta AI at Meta, and taught by Amit Sangani! Meta's Llama 2 has been game-changing for AI. Building with open source lets you control your own data, scrutinize errors, update (or not) the models as you please, and work alongside the global community advancing open models. Llama isn't a single model, it's a collection of models. In this course, you'll: - Learn the differences between different Llama 2 flavors, and when to use each. - Prompt the Llama chat models -- you'll also see how Llama's instruction tags work -- so they can help you with day-to-day tasks, like writing or summarization. - Use advanced prompting, like few-shot prompting for classification, and chain-of-thought prompting for solving logic problems. - Use specialized models in the Llama collection for specific tasks, like Code Llama to help you write, analyze, and improve code, and Llama Guard, which checks prompts and model responses for harmful content. The course also touches on how to run Llama 2 locally on your own computer. I hope you’ll take this course and try out these powerful, open models!

New short course: Prompt Engineering with Llama 2, built in collaboration with Meta AI at Meta, and taught by Amit Sangani! Meta's Llama 2 has been game-changing for AI. Building with open source lets you control your own data, scrutinize errors, update (or not) the models as you please, and work alongside the global community advancing open models. Llama isn't a single model, it's a collection of models. In this course, you'll: - Learn the differences between different Llama 2 flavors, and when to use each. - Prompt the Llama chat models -- you'll also see how Llama's instruction tags work -- so they can help you with day-to-day tasks, like writing or summarization. - Use advanced prompting, like few-shot prompting for classification, and chain-of-thought prompting for solving logic problems. - Use specialized models in the Llama collection for specific tasks, like Code Llama to help you write, analyze, and improve code, and Llama Guard, which checks prompts and model responses for harmful content. The course also touches on how to run Llama 2 locally on your own computer. I hope you’ll take this course and try out these powerful, open models!

Andrew Ng

162,798 Aufrufe • vor 2 Jahren

Introducing `claude-llm-trainer` ✍️ The world's simplest way to train a task-specific LLM. Just write a sentence describing the model you want. A chain of AI systems will generate a dataset and train a model for you. And it's open-source.

Introducing `claude-llm-trainer` ✍️ The world's simplest way to train a task-specific LLM. Just write a sentence describing the model you want. A chain of AI systems will generate a dataset and train a model for you. And it's open-source.

Matt Shumer

383,121 Aufrufe • vor 2 Jahren

Someone paid $6,000 (!) to get OpenClaw setup. So I built a free, open-source alternative: > OpenClaw Operator It's an agent skill + AGENTS.md/CLAUDE.md file that gives Codex/Claude Code the playbooks + validation flow needed to configure your local OpenClaw install. You open your OpenClaw repo/workspace in your coding agent and say things like: - “set up a cron job” - “create a custom skill” - “fix my provider config” - “troubleshoot this install and validate it” Nothing magical. Uses context from the OpenClaw open-source repo + docs to provide an effective skill. Includes: - SKILL.md - AGENTS.md/CLAUDE.md - validation checklist - task playbooks Free and open source. Contributions welcome. Demo below. Repo in reply.

Someone paid $6,000 (!) to get OpenClaw setup. So I built a free, open-source alternative: > OpenClaw Operator It's an agent skill + AGENTS.md/CLAUDE.md file that gives Codex/Claude Code the playbooks + validation flow needed to configure your local OpenClaw install. You open your OpenClaw repo/workspace in your coding agent and say things like: - “set up a cron job” - “create a custom skill” - “fix my provider config” - “troubleshoot this install and validate it” Nothing magical. Uses context from the OpenClaw open-source repo + docs to provide an effective skill. Includes: - SKILL.md - AGENTS.md/CLAUDE.md - validation checklist - task playbooks Free and open source. Contributions welcome. Demo below. Repo in reply.

Dan McAteer

17,706 Aufrufe • vor 3 Monaten

Today, we’re introducing Skills in Chrome, a new way to build one-click workflows for your most frequently used AI prompts — like asking for ingredient substitutions to make a recipe vegan, generating side-by-side shopping comparisons across multiple tabs, or scanning long docs to get the info you need quickly. When you write a prompt that you want to use again, you can save it as a Skill directly from your chat history. The next time you need it, select your saved Skill in Gemini in Chrome by typing forward slash ( / ) or clicking the plus sign ( + ) button, and your Skill will run on the page you’re viewing, along with any other tabs you select.

Today, we’re introducing Skills in Chrome, a new way to build one-click workflows for your most frequently used AI prompts — like asking for ingredient substitutions to make a recipe vegan, generating side-by-side shopping comparisons across multiple tabs, or scanning long docs to get the info you need quickly. When you write a prompt that you want to use again, you can save it as a Skill directly from your chat history. The next time you need it, select your saved Skill in Gemini in Chrome by typing forward slash ( / ) or clicking the plus sign ( + ) button, and your Skill will run on the page you’re viewing, along with any other tabs you select.

Google

1,276,365 Aufrufe • vor 1 Monat

Here is how simple it is to build a UI front-end for an AI agent (assuming you are using . The video here is 90 seconds and will show you everything you need to connect an agent to a web app: 1. Set up CopilotKit🪁 in your app 1. Copy the agent's localhost URL from LangGraph Studio 2. Paste that URL into a CopilotKit🪁 remote endpoint That's all you need to get a front-end to interact with the agent! The secret sauce here is CoAgents, an open-source library. You can use CoAgents to integrate any agent with your web application. Here is what you get: • Human-in-the-loop to steer and correct the agent • Stream intermediate agent state • Real-time state sharing between the agent and the application • Agentic generative UI to build trust that the agent is on the right path Watch the attached demo so you see it in action.

Here is how simple it is to build a UI front-end for an AI agent (assuming you are using . The video here is 90 seconds and will show you everything you need to connect an agent to a web app: 1. Set up CopilotKit🪁 in your app 1. Copy the agent's localhost URL from LangGraph Studio 2. Paste that URL into a CopilotKit🪁 remote endpoint That's all you need to get a front-end to interact with the agent! The secret sauce here is CoAgents, an open-source library. You can use CoAgents to integrate any agent with your web application. Here is what you get: • Human-in-the-loop to steer and correct the agent • Stream intermediate agent state • Real-time state sharing between the agent and the application • Agentic generative UI to build trust that the agent is on the right path Watch the attached demo so you see it in action.

Santiago

46,109 Aufrufe • vor 1 Jahr