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This is my take on the perfect AI assistant. A Rust-based agentic operating system designed to scale for large Slack and Discord communities. The channel is the ambassador to the human. Branches think. Workers execute. Nothing ever blocks. Meet Spacebot 🟣 The biggest issue with OpenClaw is when it's... doing work, it can't talk to you. Spacebot's architecture fixes this by design the conversation layer never touches tools. It delegates thinking to branches and heavy tasks to workers, so it's always responsive even with 100 people talking at once. Dump your memory files, notes, documents and chat histories into a folder — Spacebot turns them into structured memories automatically. Eight typed memory categories, graph associations, hybrid search. Not markdown files. Not vibes in a vector database. Built-in OpenCode workers for deep coding sessions. Browser automation. Brave web search. Cron jobs. A skill system compatible with your existing OpenClaw skills. And a gorgeous control UI at The cortex oversees the whole system — auditing memories, actioning goals and todos. You teach your Spacebot by talking to it. Structure and speed over config files and markdown. Self-hosting is a single Rust binary. Or one-click cloud deploy at This is for teams, communities, and personal assistants. It will blow you away. ⭐️show more

Jamie Pine

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169,525 Aufrufe • vor 6 Monaten •via X (Twitter)

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Finally, a proper chat UI for Hermes Agent (open-source)! Hermes ships an official dashboard, but it's primarily built for management, and its chat is just a terminal piped into a browser tab. Hermes Web UI is an open-source chat-first alternative. It's self-hosted and points at your existing ~/.hermes state, so there's nothing new to configure. - It's a native web chat, not a terminal in a tab - Sessions group by date with a context ring - Kanban renders the agent's task board - Spaces manages your workspaces - Skills panel lists the full catalog - Tasks panel shows cron jobs - Insights show usage and activity - Memory shows MEMORY and SOUL files - Logs tails the agent, gateway, and error logs The whole setup runs 100% locally, binds to localhost by default, and you reach it over an SSH tunnel or Tailscale from your phone. I have shared the Hermes Web UI GitHub repo in the replies. Do note that it's a community project, not official, so expect occasional rough edges (concurrent profile runs are blocked for now). To dive deeper into Hermes Agent, my co-founder wrote a full masterclass about it, covering the learning loop, the memory tiers, self-evolving skills, GEPA, and running multiple isolated agents. Read it below.

Avi Chawla

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Obsidian CEO actually released the skill that lets your AI agent run your entire vault for you. It's called obsidian-skills. Install it into Claude Code, Codex, or OpenCode, and your agent can read, create, search, and organize notes directly, plus handle Markdown, Bases, and Canvas files without you touching a thing. This is the missing piece for an AI second brain. Your notes live in Obsidian, and now the agent actually knows how to work them. Want the full picture? We broke down how to build a complete AI second brain from scratch in the article below.

Obsidian CEO actually released the skill that lets your AI agent run your entire vault for you. It's called obsidian-skills. Install it into Claude Code, Codex, or OpenCode, and your agent can read, create, search, and organize notes directly, plus handle Markdown, Bases, and Canvas files without you touching a thing. This is the missing piece for an AI second brain. Your notes live in Obsidian, and now the agent actually knows how to work them. Want the full picture? We broke down how to build a complete AI second brain from scratch in the article below.

Simplifying AI

68,025 Aufrufe • vor 11 Tagen

ANTHROPIC KILLED THE VECTOR DATABASE AND REPLACED IT WITH A FOLDER no embeddings. no black-box retrieval. no special memory tools. the agent just reads and writes a folder the insight: your agent already knows how to use a filesystem. so Anthropic made long-term memory a directory of small markdown files, mounted straight into the agent's sandbox at /mnt/memory the agent manages it with the tools it already has, bash, read, write, edit, grep, and Anthropic auto-injects a note into the system prompt so the agent always knows the memory is there and where to look the result: > every change is an immutable version: full audit trail, point-in-time recovery, redaction > read-only or read-write per store, so many agents can safely share one memory > workspace-scoped and persistent, memory that survives every session end most teams bolt on a vector DB and pray retrieval works. Anthropic went the opposite way: compiled knowledge as plain, human-readable files you can open, diff, and edit yourself and there's a second layer. Anthropic's "Dreaming" runs offline, reviews past sessions and the store, and rewrites it into a cleaner, higher-signal version. the agent literally consolidates memory in its sleep. bookmark this. the most production-grade agent memory right now is just a well-organized folder, and Anthropic proved it.

ANTHROPIC KILLED THE VECTOR DATABASE AND REPLACED IT WITH A FOLDER no embeddings. no black-box retrieval. no special memory tools. the agent just reads and writes a folder the insight: your agent already knows how to use a filesystem. so Anthropic made long-term memory a directory of small markdown files, mounted straight into the agent's sandbox at /mnt/memory the agent manages it with the tools it already has, bash, read, write, edit, grep, and Anthropic auto-injects a note into the system prompt so the agent always knows the memory is there and where to look the result: > every change is an immutable version: full audit trail, point-in-time recovery, redaction > read-only or read-write per store, so many agents can safely share one memory > workspace-scoped and persistent, memory that survives every session end most teams bolt on a vector DB and pray retrieval works. Anthropic went the opposite way: compiled knowledge as plain, human-readable files you can open, diff, and edit yourself and there's a second layer. Anthropic's "Dreaming" runs offline, reviews past sessions and the store, and rewrites it into a cleaner, higher-signal version. the agent literally consolidates memory in its sleep. bookmark this. the most production-grade agent memory right now is just a well-organized folder, and Anthropic proved it.

NO1ennn

13,346 Aufrufe • vor 8 Tagen

If you do not set up your OpenClaw correctly, it's going to SUCK It will do tasks poorly, not remember anything, and not be proactive Here are the 5 things you need to do right away to turn your OpenClaw into AGI (demos of each in the video below) 1. Brain dump: your OpenClaw won't know how to accomplish your goals for you, if it doesn't know your goals If you haven't yet, you want to tell your OpenClaw: • Your interests • Your career • Your goals • Your ambitions • Anything personal Do this, and your Claw will have the context to be SUPER powerful for you 2. Connect your tools Your OpenClaw can basically use almost every tool you use on your computer You just need to ask it to Ask your OpenClaw to connect to any tools you use daily, and it will just figure out how to do it. Then create a skill for it I have it check my Things 3 todo list every morning and complete any tasks it can 3. Build a Mission Control Your mission control is just a hub for your OpenClaw to build custom tooling Ask your OpenClaw to build a Mission Control using NextJS Then anytime your bot doesn't have the tool available to do a task, have it build it in your Mission Control 4. Mission Statement Your OpenClaw needs a mission statement This is the one sentence statement that will be the north star for every single thing it does It should be based on your goals and ambitions. My Claw's mission statement is "“An autonomous organization of AI agents that does work for me and produces value 24/7” Make your own and have your Claw put it at the top of your Mission Control. Now every task your Claw does will take you closer to this statement 5. Make it proactive Your OpenClaw won't be proactive unless you set those expectations with it Tell your Claw you want it to do a task every night at 2am that brings you one step closer to your mission statement Now every morning you'll wake up and it will do something that surprises you and helps bring you closer to your goals Do these 5 things and your OpenClaw will be 10000x more powerful

If you do not set up your OpenClaw correctly, it's going to SUCK It will do tasks poorly, not remember anything, and not be proactive Here are the 5 things you need to do right away to turn your OpenClaw into AGI (demos of each in the video below) 1. Brain dump: your OpenClaw won't know how to accomplish your goals for you, if it doesn't know your goals If you haven't yet, you want to tell your OpenClaw: • Your interests • Your career • Your goals • Your ambitions • Anything personal Do this, and your Claw will have the context to be SUPER powerful for you 2. Connect your tools Your OpenClaw can basically use almost every tool you use on your computer You just need to ask it to Ask your OpenClaw to connect to any tools you use daily, and it will just figure out how to do it. Then create a skill for it I have it check my Things 3 todo list every morning and complete any tasks it can 3. Build a Mission Control Your mission control is just a hub for your OpenClaw to build custom tooling Ask your OpenClaw to build a Mission Control using NextJS Then anytime your bot doesn't have the tool available to do a task, have it build it in your Mission Control 4. Mission Statement Your OpenClaw needs a mission statement This is the one sentence statement that will be the north star for every single thing it does It should be based on your goals and ambitions. My Claw's mission statement is "“An autonomous organization of AI agents that does work for me and produces value 24/7” Make your own and have your Claw put it at the top of your Mission Control. Now every task your Claw does will take you closer to this statement 5. Make it proactive Your OpenClaw won't be proactive unless you set those expectations with it Tell your Claw you want it to do a task every night at 2am that brings you one step closer to your mission statement Now every morning you'll wake up and it will do something that surprises you and helps bring you closer to your goals Do these 5 things and your OpenClaw will be 10000x more powerful

Alex Finn

321,282 Aufrufe • vor 5 Monaten

Karpathy found a way to reduce token consumption by 90% The problem is that the LLM re-reads the same files over and over again, loses context between documents, and provides less accurate answers as a result The solution is called Wiki Layer the LLM cleans, structures, and links all your data once, after which it never works with raw files again Three folders `raw/` for originals, `wiki/` for a clean knowledge base in Markdown, and files with rules for the agent Result up to 90% token savings on repeat queries, automatic links between documents, and a visual knowledge graph in Obsidian Everything stays on your local machine nothing goes to the cloud

Karpathy found a way to reduce token consumption by 90% The problem is that the LLM re-reads the same files over and over again, loses context between documents, and provides less accurate answers as a result The solution is called Wiki Layer the LLM cleans, structures, and links all your data once, after which it never works with raw files again Three folders `raw/` for originals, `wiki/` for a clean knowledge base in Markdown, and files with rules for the agent Result up to 90% token savings on repeat queries, automatic links between documents, and a visual knowledge graph in Obsidian Everything stays on your local machine nothing goes to the cloud

Asteri

1,094,858 Aufrufe • vor 2 Monaten

This Obsidian and Claude architecture charges nothing and removes the maintenance entirely. Obsidian stores everything as plain Markdown files. Claude Code points at the vault directory. No API integrations. No paid connectors. No configuration beyond a folder path. The gap between project management systems that get abandoned the moment life gets busy and a system that maintains itself from the notes you were already writing is not discipline or organizational skill. It is the architecture described in this article, and the afternoon it takes to build five markdown components that never need a status update meeting again.

This Obsidian and Claude architecture charges nothing and removes the maintenance entirely. Obsidian stores everything as plain Markdown files. Claude Code points at the vault directory. No API integrations. No paid connectors. No configuration beyond a folder path. The gap between project management systems that get abandoned the moment life gets busy and a system that maintains itself from the notes you were already writing is not discipline or organizational skill. It is the architecture described in this article, and the afternoon it takes to build five markdown components that never need a status update meeting again.

NeilXbt

32,130 Aufrufe • vor 2 Monaten

Friends this is not the time to fear ai 🤖 this is the time to be curious and play with it and understand it … please 🙏 don’t put your head in the sand … there is so much you can’t control when it comes to ai 🤖 and how it will play out … but there is a lot you can control on how you work with it and use it to build something for you and your family !!! Pls If you’re finally ready to not be scared of ai pls leave a 🤖 emoji in the the chat

Friends this is not the time to fear ai 🤖 this is the time to be curious and play with it and understand it … please 🙏 don’t put your head in the sand … there is so much you can’t control when it comes to ai 🤖 and how it will play out … but there is a lot you can control on how you work with it and use it to build something for you and your family !!! Pls If you’re finally ready to not be scared of ai pls leave a 🤖 emoji in the the chat

Gary Vaynerchuk

15,342 Aufrufe • vor 9 Monaten

THIS GUY BUILT A COMMAND CENTER FOR MANAGING AI AGENT TEAMS he was running 20+ agents through openclaw. all from terminal tabs and Discord. 12 tabs open. still losing track of who's doing what. which cron failed. so he built a dashboard that gives you one place to see everything: > org map showing the full agent hierarchy, who reports to who and what tools each agent has > chat with any agent through a real UI instead of bouncing between terminals > kanban board for tracking tasks across all agents > cron monitor with pipeline visualizations so you can see which jobs are failing > memory browser to read through all agent knowledge files in one place you can even point it at your openclaw workspace and it auto-discovers every agent from their config files. zero setup. AND ITS OPEN SOURCE TOO.

THIS GUY BUILT A COMMAND CENTER FOR MANAGING AI AGENT TEAMS he was running 20+ agents through openclaw. all from terminal tabs and Discord. 12 tabs open. still losing track of who's doing what. which cron failed. so he built a dashboard that gives you one place to see everything: > org map showing the full agent hierarchy, who reports to who and what tools each agent has > chat with any agent through a real UI instead of bouncing between terminals > kanban board for tracking tasks across all agents > cron monitor with pipeline visualizations so you can see which jobs are failing > memory browser to read through all agent knowledge files in one place you can even point it at your openclaw workspace and it auto-discovers every agent from their config files. zero setup. AND ITS OPEN SOURCE TOO.

Om Patel

46,780 Aufrufe • vor 4 Monaten

THE ULTIMATE GUIDE TO OPENCLAW (1hr free masterclass) 1. fix memory so it compounds add MEMORY.md + daily logs. instruct it to promote important learnings into MEMORY.md because this is what makes it improve over time 2. set up personalization early identity.md, user.md, soul.md. write these properly or everything feels generic. this is what makes it sound like you and understand your world 3. structure your workspace properly most setups break because the foundation is messy. folders, files, and roles need to be clean or everything downstream degrades 4. create a troubleshooting baseline make a separate claude/chatgpt project just for openclaw. download the openclaw docs (context7) and load them in. when things break, it checks docs instead of guessing this alone fixes most issues!! 5. configure models and fallbacks set primary model to GPT 5.4 and add fallbacks across providers. this is what keeps tasks running instead of failing mid-way 6. turn repeat work into skills install summarize skill early. anything you do 2–3 times → turn into a skill. this is how it starts executing real workflows 7. connect tools with clear rules add browser + search (brave api). use managed browser for automation. use chrome relay only when login is neededthis avoids flaky behavior 8. use heartbeat to keep it alive add rules to check memory + cron healthif jobs are stale, force-run themthis prevents silent failures 9. use cron to schedule real work set daily and weekly tasksreports, follow-ups, content workflowsthis is where it starts acting without you 10. lock down security properly move secrets to a separate env file outside workspace. set strict permissions (folder 700, file 600). use allowlists for telegram access. don’t expose your gateway publicly 11. understand what openclaw actually is it’s a system that remembers, acts, and improves. basically, closer to an employee than a tool this ep of The Startup Ideas Podcast (SIP) 🧃 is now out w/ Moritz Kremb it's literally a full 1hr free course to take you from from “i installed openclaw”to “this thing is actually working for me” most people are one step away from openclaw working they installed it, they tried it and it didn’t click this ep will make it click all free, no advertisers, i just want to see you build your ideas with ideas with this ultimate guide to openclaw watch

THE ULTIMATE GUIDE TO OPENCLAW (1hr free masterclass) 1. fix memory so it compounds add MEMORY.md + daily logs. instruct it to promote important learnings into MEMORY.md because this is what makes it improve over time 2. set up personalization early identity.md, user.md, soul.md. write these properly or everything feels generic. this is what makes it sound like you and understand your world 3. structure your workspace properly most setups break because the foundation is messy. folders, files, and roles need to be clean or everything downstream degrades 4. create a troubleshooting baseline make a separate claude/chatgpt project just for openclaw. download the openclaw docs (context7) and load them in. when things break, it checks docs instead of guessing this alone fixes most issues!! 5. configure models and fallbacks set primary model to GPT 5.4 and add fallbacks across providers. this is what keeps tasks running instead of failing mid-way 6. turn repeat work into skills install summarize skill early. anything you do 2–3 times → turn into a skill. this is how it starts executing real workflows 7. connect tools with clear rules add browser + search (brave api). use managed browser for automation. use chrome relay only when login is neededthis avoids flaky behavior 8. use heartbeat to keep it alive add rules to check memory + cron healthif jobs are stale, force-run themthis prevents silent failures 9. use cron to schedule real work set daily and weekly tasksreports, follow-ups, content workflowsthis is where it starts acting without you 10. lock down security properly move secrets to a separate env file outside workspace. set strict permissions (folder 700, file 600). use allowlists for telegram access. don’t expose your gateway publicly 11. understand what openclaw actually is it’s a system that remembers, acts, and improves. basically, closer to an employee than a tool this ep of The Startup Ideas Podcast (SIP) 🧃 is now out w/ Moritz Kremb it's literally a full 1hr free course to take you from from “i installed openclaw”to “this thing is actually working for me” most people are one step away from openclaw working they installed it, they tried it and it didn’t click this ep will make it click all free, no advertisers, i just want to see you build your ideas with ideas with this ultimate guide to openclaw watch

GREG ISENBERG

217,873 Aufrufe • vor 5 Monaten

OpenClaw just got a desktop app. And nobody's talking about it. It's called Claw X. It's free. And it changes everything about how you use AI agents. Here's the problem it solves: Running OpenClaw required command line. That's like owning a Ferrari but the only way to drive it is reading a manual written in Japanese. Claw X fixes that with a clean desktop UI. One-click install. No terminal. No commands. No tech skills. Here's what you get inside: A dashboard showing your agent's activity, skills, and scheduled tasks all in one place. One-click connections to Telegram, Discord, and WhatsApp. Visual settings for switching AI providers — Anthropic, OpenAI, Google, or even free local models via Ollama. Scheduled task management without touching a single config file. And the setup takes 2 minutes. Just paste the GitHub link into OpenClaw and say "install this." It sets itself up. You've been avoiding OpenClaw because the setup looked complicated. That excuse is

OpenClaw just got a desktop app. And nobody's talking about it. It's called Claw X. It's free. And it changes everything about how you use AI agents. Here's the problem it solves: Running OpenClaw required command line. That's like owning a Ferrari but the only way to drive it is reading a manual written in Japanese. Claw X fixes that with a clean desktop UI. One-click install. No terminal. No commands. No tech skills. Here's what you get inside: A dashboard showing your agent's activity, skills, and scheduled tasks all in one place. One-click connections to Telegram, Discord, and WhatsApp. Visual settings for switching AI providers — Anthropic, OpenAI, Google, or even free local models via Ollama. Scheduled task management without touching a single config file. And the setup takes 2 minutes. Just paste the GitHub link into OpenClaw and say "install this." It sets itself up. You've been avoiding OpenClaw because the setup looked complicated. That excuse is

Julian Goldie SEO

94,513 Aufrufe • vor 5 Monaten

Memory for OpenClaw is now Native! Our first OpenClaw Memory Skill was a massive success: 30k+ downloads in a week and 500k+ organic impressions overnight for launch post. But we knew memory needed to be native. On March 21, OpenClaw merged PR #50848, allowing us to go beyond the skill layer and integrate directly into the agent’s context assembly flow. We try to make OpenClaw a truly 24/7 employee capable of complex workflows. The technical setup isn’t the hardest part but the real challenge is giving it a "brain" that remembers exact project details, past decisions, and team changes over time. The Native Memory Plugin is now live on NPM & ClawHub. Here is what it brings to your OpenClaw agents: 👉 Native Integration: Automatically manages a Three-Layer Memory architecture (Context Tree, Workspace Memory, Daily Memory). 👉 Git-like Stateful Memory: Organizes memory into a semantic hierarchy of human-readable, diffable Markdown files. You always get updated knowledge and can actually see and fix what your agent learns. 👉 Top Market Accuracy: Achieves an industry-leading 92.2% retrieval accuracy (LoCoMo & LongMemEval benchmarks), maintaining 90% accuracy even with cheap, lightweight models. 👉 Local-first & Portable: Local-by-default, fully portable for multi-agent teams. 👉 Super Easy Setup

Memory for OpenClaw is now Native! Our first OpenClaw Memory Skill was a massive success: 30k+ downloads in a week and 500k+ organic impressions overnight for launch post. But we knew memory needed to be native. On March 21, OpenClaw merged PR #50848, allowing us to go beyond the skill layer and integrate directly into the agent’s context assembly flow. We try to make OpenClaw a truly 24/7 employee capable of complex workflows. The technical setup isn’t the hardest part but the real challenge is giving it a "brain" that remembers exact project details, past decisions, and team changes over time. The Native Memory Plugin is now live on NPM & ClawHub. Here is what it brings to your OpenClaw agents: 👉 Native Integration: Automatically manages a Three-Layer Memory architecture (Context Tree, Workspace Memory, Daily Memory). 👉 Git-like Stateful Memory: Organizes memory into a semantic hierarchy of human-readable, diffable Markdown files. You always get updated knowledge and can actually see and fix what your agent learns. 👉 Top Market Accuracy: Achieves an industry-leading 92.2% retrieval accuracy (LoCoMo & LongMemEval benchmarks), maintaining 90% accuracy even with cheap, lightweight models. 👉 Local-first & Portable: Local-by-default, fully portable for multi-agent teams. 👉 Super Easy Setup

andy nguyen

169,136 Aufrufe • vor 4 Monaten

It takes 5 minutes to create a world-class web research agent that runs 24/7 and returns structured search results. 1. You can limit the sources it's allowed to use 2. You can specify the goals it needs to complete 3. You can design the format structure of the results 4. You can configure how much computing power it will use I show you how to set up a Web Search Agent in the video. A few benefits here: 1. This is great for complex research tasks 2. It's really easy to add this like a LEGO piece to a more complex workflow 3. Create an agent once, and reuse it with different inputs 4. The agent adapts to your use case as you work with it By the way, Nimble published a whole library of templates and research agents you can reuse and modify. You can start sending queries to one of these existing agents, and it will begin to adapt to Go here to try it out: Thanks to the Nimble team for partnering with me on this post.

It takes 5 minutes to create a world-class web research agent that runs 24/7 and returns structured search results. 1. You can limit the sources it's allowed to use 2. You can specify the goals it needs to complete 3. You can design the format structure of the results 4. You can configure how much computing power it will use I show you how to set up a Web Search Agent in the video. A few benefits here: 1. This is great for complex research tasks 2. It's really easy to add this like a LEGO piece to a more complex workflow 3. Create an agent once, and reuse it with different inputs 4. The agent adapts to your use case as you work with it By the way, Nimble published a whole library of templates and research agents you can reuse and modify. You can start sending queries to one of these existing agents, and it will begin to adapt to Go here to try it out: Thanks to the Nimble team for partnering with me on this post.

Santiago

20,943 Aufrufe • vor 13 Tagen

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.

Oliver Henry

110,125 Aufrufe • vor 5 Monaten

This is one of the coolest open-source AI agent projects I've seen in a while: 'Understand Anything' It's a plugin for Claude Code, Codex, OpenCode etc. that analyzes your codebase and turns it into a knowledge base that you can interact with. It explains the codebase to you, rather than showing you the structure. It seems like it's designed for code but I opened my Obsidian vault of podcast highlights in Claude Code, then ran /understand. The result is a knowledge graph that I can search of highlights from 888 podcast episodes and 144K lines of markdown text.

This is one of the coolest open-source AI agent projects I've seen in a while: 'Understand Anything' It's a plugin for Claude Code, Codex, OpenCode etc. that analyzes your codebase and turns it into a knowledge base that you can interact with. It explains the codebase to you, rather than showing you the structure. It seems like it's designed for code but I opened my Obsidian vault of podcast highlights in Claude Code, then ran /understand. The result is a knowledge graph that I can search of highlights from 888 podcast episodes and 144K lines of markdown text.

Dan McAteer

174,267 Aufrufe • vor 1 Monat

Big moment for Postgres! AI agents broke the idea of what a database is supposed to do. Traditional databases were built for humans, and Agents broke that model. - They branch endlessly. - They run ten experiments at once. - They need isolation, context, memory, structured reasoning, and safe sandboxes. Letting agents touch production systems is terrifying because the old model of Postgres was never built for this kind of behavior. Agentic Postgres is an agent-ready version of Postgres by TimescaleDB (by Tiger Data) that solves this. I think it is one of the biggest upgrades to the Agent stack this year and Tiger Data is working with me on this post to share what they did. Some key features: > It instantly creates branches of an entire database, which is perfect for parallel agent evals, safe experiments, migrations, or isolated testing. Forks take seconds and cost almost nothing. > It comes with a built-in MCP server, which agents can use to get schema guidance, best practices, and safe, structured access to Postgres. This is also helpful to run migrations with a real understanding. > It comes with actual hybrid search (vector search and BM25), so Agents can retrieve data directly inside the database. > The database is Memory native. This gives a persistent context for Agents to evolve. This is one of the first times I have seen Postgres feel ready for the AI native era.

Big moment for Postgres! AI agents broke the idea of what a database is supposed to do. Traditional databases were built for humans, and Agents broke that model. - They branch endlessly. - They run ten experiments at once. - They need isolation, context, memory, structured reasoning, and safe sandboxes. Letting agents touch production systems is terrifying because the old model of Postgres was never built for this kind of behavior. Agentic Postgres is an agent-ready version of Postgres by TimescaleDB (by Tiger Data) that solves this. I think it is one of the biggest upgrades to the Agent stack this year and Tiger Data is working with me on this post to share what they did. Some key features: > It instantly creates branches of an entire database, which is perfect for parallel agent evals, safe experiments, migrations, or isolated testing. Forks take seconds and cost almost nothing. > It comes with a built-in MCP server, which agents can use to get schema guidance, best practices, and safe, structured access to Postgres. This is also helpful to run migrations with a real understanding. > It comes with actual hybrid search (vector search and BM25), so Agents can retrieve data directly inside the database. > The database is Memory native. This gives a persistent context for Agents to evolve. This is one of the first times I have seen Postgres feel ready for the AI native era.

Avi Chawla

94,290 Aufrufe • vor 9 Monaten

OpenClaw just got a plugin nobody is talking about. And it can replace a whole operations team. It is called Paperclip. It lets you build a full AI company on your own computer. You get an AI CEO. You get a marketing team. You get a tech team. They have real memory so they never forget a task. They wake up every day and do the work for you. You do not have to manage them or pay them a salary. Your data stays safe because it runs on your hardware. Here is how to start today. Go to GitHub and download OpenClaw. Install the Paperclip dashboard inside it. Pick one task and hire your first AI worker.

OpenClaw just got a plugin nobody is talking about. And it can replace a whole operations team. It is called Paperclip. It lets you build a full AI company on your own computer. You get an AI CEO. You get a marketing team. You get a tech team. They have real memory so they never forget a task. They wake up every day and do the work for you. You do not have to manage them or pay them a salary. Your data stays safe because it runs on your hardware. Here is how to start today. Go to GitHub and download OpenClaw. Install the Paperclip dashboard inside it. Pick one task and hire your first AI worker.

Julian Goldie SEO

33,889 Aufrufe • vor 5 Monaten

THIS GUY BUILT AN AUTONOMOUS AI AGENT OUT OF CLAUDE CODE + OBSIDIAN and this is way more interesting than another “use AI to take notes” demo the trick is simple: Obsidian is not the writing app here. it becomes the agent’s memory, task board, and context folder. Claude Code is not just answering prompts. it reads the vault, edits files, follows instructions, and keeps moving through the work like a junior operator with a filesystem. the reusable setup looks like this: 1. create an Obsidian vault for one project 2. keep goals, rules, tasks, decisions, and references as markdown files 3. point Claude Code at the folder 4. give it a clear operating loop: read context → choose next task → execute → write back what changed 5. use the notes as persistent memory instead of re-explaining the project every chat that’s the part people miss. the “agent” is not magic. it’s the boring combination of: - local files - explicit rules - task state - write access - a model that can run through the repo/vault Obsidian makes the memory human-readable. Claude Code makes the memory executable. that combo is why the video worked: it turns a notes app into an operating surface for actual work. best use cases: - content systems - research vaults - coding projects - client ops docs - personal knowledge bases that need actions, not just storage the caveat: if your vault is messy, your agent becomes messy too. folders, naming, “done” criteria, and forbidden actions matter more than the prompt. but once the structure is clean, this is one of the easiest ways to build an agent that remembers what happened yesterday without paying for a full custom app.

THIS GUY BUILT AN AUTONOMOUS AI AGENT OUT OF CLAUDE CODE + OBSIDIAN and this is way more interesting than another “use AI to take notes” demo the trick is simple: Obsidian is not the writing app here. it becomes the agent’s memory, task board, and context folder. Claude Code is not just answering prompts. it reads the vault, edits files, follows instructions, and keeps moving through the work like a junior operator with a filesystem. the reusable setup looks like this: 1. create an Obsidian vault for one project 2. keep goals, rules, tasks, decisions, and references as markdown files 3. point Claude Code at the folder 4. give it a clear operating loop: read context → choose next task → execute → write back what changed 5. use the notes as persistent memory instead of re-explaining the project every chat that’s the part people miss. the “agent” is not magic. it’s the boring combination of: - local files - explicit rules - task state - write access - a model that can run through the repo/vault Obsidian makes the memory human-readable. Claude Code makes the memory executable. that combo is why the video worked: it turns a notes app into an operating surface for actual work. best use cases: - content systems - research vaults - coding projects - client ops docs - personal knowledge bases that need actions, not just storage the caveat: if your vault is messy, your agent becomes messy too. folders, naming, “done” criteria, and forbidden actions matter more than the prompt. but once the structure is clean, this is one of the easiest ways to build an agent that remembers what happened yesterday without paying for a full custom app.

kocer

30,403 Aufrufe • vor 1 Monat

Once you learn these three things, you can build nearly anything yourself. Skills, chains, and plugins. Learn them once and you can automate a real part of your week yourself. Here's the system: A skill is a standard operating procedure. It's one long, reusable prompt that does one thing well, like writing a newsletter, setting up a PPC campaign, triaging your inbox, or drafting a note to the board. You write it once and reuse it forever. The trick is to feed it your real work. For my writing skill, I gave Claude posts I admire and my own exported analytics with the winners marked, so it could see the patterns. Show it what good looks like and it nails your voice. Skip that step and it guesses. A chain connects skills into an automation. One skill's output feeds the next, in order. A copywriting skill writes the messaging, then hands it to a PPC-setup skill that builds the campaign. That's an automation, and you built it without writing code. A plugin is the harness that holds it all. It bundles your skills and chains into one thing you share with your team. Instead of sending 15 separate prompts around, you send one plugin, and it knows when to use each skill on its own. Skill, chain, plugin. It's one step up the ladder, and once you're up there it's easy. Now the worked example. I call it the Daily Driver. It's five skills chained together: email triage, a writer, Slack triage, a thinking partner, and a setup skill that connects my tools and personalizes everything. I chained them into one morning brief and scheduled it to run at 9am and message me the list. Two things made the biggest difference. Give it memory. I made plain docs for About Me, my Brand Voice, and my working preferences. I had Claude interview me and saved each answer as a file. Now every output sounds like me and knows my projects and my team. Connect your tools. The plugin reads my inbox, Slack, and calendar through MCP connectors for Gmail, Slack, and Google Calendar. That's what turns "summarize my morning" into a real brief instead of an empty wish. This is the highest-ROI thing an operator can set up this week. I run my YouTube channel, podcast, community, and newsletter on plugins I built myself, all on the $100 Max plan with no engineer in sight. I made a full walkthrough that shows the whole build start to finish. Want the Daily Driver plugin to start from? Comment PLUGIN below and I'll send it to you. #AI #automation #ClaudeCode

JJ Englert

53,969 Aufrufe • vor 2 Monaten

Meta's chief AI officer says a swarm of agents out-produced a team of 100 engineers, and the machinery behind it is markdown files and cron jobs: "Like I think we've seen internally in Meta cases where if you can develop the right agentic loop and you have the right eval or the right metric for the agents to optimize, you can have a swarm of agents accomplish more than like a team of 100 engineers." "Very, very handily. Actually, very, very easily." "And so I think figuring out what the world looks like with lots of these agentic coordination problems, I think that is one of the most interesting problems today." "It just comes down to skills, markdown files, cron jobs." "I think it's always funny how mundane everything is once you really dig into it." If he is right, writing the metric now matters more than writing the code, and one judgment call about that metric stands in for 100 engineers' worth of output. Wang says Meta has already run this internally. - Alexandr Wang (Alexandr Wang), Chief AI Officer at Meta, with Garry Tan (Garry Tan) at Y Combinator (Y Combinator) Startup School 2026.

Meta's chief AI officer says a swarm of agents out-produced a team of 100 engineers, and the machinery behind it is markdown files and cron jobs: "Like I think we've seen internally in Meta cases where if you can develop the right agentic loop and you have the right eval or the right metric for the agents to optimize, you can have a swarm of agents accomplish more than like a team of 100 engineers." "Very, very handily. Actually, very, very easily." "And so I think figuring out what the world looks like with lots of these agentic coordination problems, I think that is one of the most interesting problems today." "It just comes down to skills, markdown files, cron jobs." "I think it's always funny how mundane everything is once you really dig into it." If he is right, writing the metric now matters more than writing the code, and one judgment call about that metric stands in for 100 engineers' worth of output. Wang says Meta has already run this internally. - Alexandr Wang (Alexandr Wang), Chief AI Officer at Meta, with Garry Tan (Garry Tan) at Y Combinator (Y Combinator) Startup School 2026.

Karl Mehta

26,779 Aufrufe • vor 12 Tagen