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Most teams waste hours digging through docs, StackOverflow, and old Slack threads. CA: FCGLv72C18362Ent3BDNqToracaaM2sYukjTXS7hBAGS Support engineers answer the same questions again and again. So I built Slack Integrations 👇 An AI agent that lives directly inside your Slack workspace. • answers technical questions using your docs • runs on...

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18,339 views • 7 months ago •via X (Twitter)

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Another blow to Anthropic! They spent months building what's now fully open-source. Anthropic recently put Claude inside Slack, where you can tag it in a channel. It reads the thread, breaks the task into steps, and posts the result back. The problem is that it only runs Claude and only in the channels Anthropic supports. Running your own agent there is harder. The reasoning, tool calls, and state management are mostly handled by the framework. Connecting that agent to a messaging platform is not. Moreover, each platform has a different integration: - Slack renders messages with Block Kit - Teams uses Adaptive Cards - and each has its own SDK, auth flow, and delivery model. If an agent needs to run on three platforms, one must write three separate integrations against the same agent logic. That overhead explains why most custom agents never get deployed to Slack, and why the ones that do are usually a single vendor's hosted assistant. The alternative is to keep the agent in one place and add a per-platform adapter that translates its output into each platform's native format. The agent is written once, and each channel requires just another output target instead of a separate build. CopilotKit open-sourced this full implementation in the Channels SDK. Essentially, any agent that implements AG-UI can run in a messaging platform in a few lines of code, like Slack, Teams, Discord, WhatsApp, and many more. Because the agent runs inside the thread, it has that conversation's context, so it can summarize the discussion, open a ticket, or route to the right person. It works with any backend, so LangGraph, CrewAI, Mastra, Google ADK, or a plain HTTP agent can connect through an existing endpoint. The same message can render as a Block Kit in Slack and as Adaptive Cards in Teams. In practice, the model and orchestration stay the same; it requires no migration or rewrite. It also handles human-in-the-loop approvals, persistence, and transcripts that carry state across platforms, so a thread started in Teams can continue in Slack. CopilotKit is open-source, and AG-UI is supported across every major agent framework, including LangGraph, CrewAI, Mastra, and Google ADK. Here's the repo: (don't forget to star it ⭐) The agent running in Slack no longer has to be a vendor's. It can be the one you already built. The video below shows this in action. Thanks to CopilotKit for working with me on this launch.
0:30

Another blow to Anthropic! They spent months building what's now fully open-source. Anthropic recently put Claude inside Slack, where you can tag it in a channel. It reads the thread, breaks the task into steps, and posts the result back. The problem is that it only runs Claude and only in the channels Anthropic supports. Running your own agent there is harder. The reasoning, tool calls, and state management are mostly handled by the framework. Connecting that agent to a messaging platform is not. Moreover, each platform has a different integration: - Slack renders messages with Block Kit - Teams uses Adaptive Cards - and each has its own SDK, auth flow, and delivery model. If an agent needs to run on three platforms, one must write three separate integrations against the same agent logic. That overhead explains why most custom agents never get deployed to Slack, and why the ones that do are usually a single vendor's hosted assistant. The alternative is to keep the agent in one place and add a per-platform adapter that translates its output into each platform's native format. The agent is written once, and each channel requires just another output target instead of a separate build. CopilotKit open-sourced this full implementation in the Channels SDK. Essentially, any agent that implements AG-UI can run in a messaging platform in a few lines of code, like Slack, Teams, Discord, WhatsApp, and many more. Because the agent runs inside the thread, it has that conversation's context, so it can summarize the discussion, open a ticket, or route to the right person. It works with any backend, so LangGraph, CrewAI, Mastra, Google ADK, or a plain HTTP agent can connect through an existing endpoint. The same message can render as a Block Kit in Slack and as Adaptive Cards in Teams. In practice, the model and orchestration stay the same; it requires no migration or rewrite. It also handles human-in-the-loop approvals, persistence, and transcripts that carry state across platforms, so a thread started in Teams can continue in Slack. CopilotKit is open-source, and AG-UI is supported across every major agent framework, including LangGraph, CrewAI, Mastra, and Google ADK. Here's the repo: (don't forget to star it ⭐) The agent running in Slack no longer has to be a vendor's. It can be the one you already built. The video below shows this in action. Thanks to CopilotKit for working with me on this launch.

Akshay 🚀

243,930 views • 1 month ago

Startups have raised $1B+ to crack Enterprise AI search. Now someone open-sourced it for FREE. The video below shows a self-hosted Slack assistant that answers your questions by searching across all your company's tools in a single query. It's built on top of Airweave, an open-source context retrieval layer that makes all your tools searchable for Agents using semantic, keyword, and agentic search. Here's how the app works: - The app watches for questions in Slack. - It searches every connected tool at once using Airweave (Notion, GitHub, Jira, Linear, etc.) to find relevant context. - The Airweave engine ranks the results by relevance and returns references to the original docs. - An LLM generates the final response and sends it back to Slack with citations. The entire stack is self-hostable via Docker. Find the project in the replies!
0:26

Startups have raised $1B+ to crack Enterprise AI search. Now someone open-sourced it for FREE. The video below shows a self-hosted Slack assistant that answers your questions by searching across all your company's tools in a single query. It's built on top of Airweave, an open-source context retrieval layer that makes all your tools searchable for Agents using semantic, keyword, and agentic search. Here's how the app works: - The app watches for questions in Slack. - It searches every connected tool at once using Airweave (Notion, GitHub, Jira, Linear, etc.) to find relevant context. - The Airweave engine ranks the results by relevance and returns references to the original docs. - An LLM generates the final response and sends it back to Slack with citations. The entire stack is self-hostable via Docker. Find the project in the replies!

Avi Chawla

11,982 views • 5 months ago

There is a new self-hosted personal AI-Agent platform called PocketPaw. 100% open source. 'pip install pocketpaw' and in less than 60 seconds you own an AI agent. It connects to WhatsApp, Slack, Discord, and a local Web Dashboard. Capable of browsing the web, managing Gmail/Calendar, and executing code. Runs entirely on your machine using local Ollama models or Claude/OpenAI. Includes "Guardian AI" to review shell commands for security + 6 layers of defence. Watch the demo 👇
1:54

There is a new self-hosted personal AI-Agent platform called PocketPaw. 100% open source. 'pip install pocketpaw' and in less than 60 seconds you own an AI agent. It connects to WhatsApp, Slack, Discord, and a local Web Dashboard. Capable of browsing the web, managing Gmail/Calendar, and executing code. Runs entirely on your machine using local Ollama models or Claude/OpenAI. Includes "Guardian AI" to review shell commands for security + 6 layers of defence. Watch the demo 👇

AshutoshShrivastava

24,585 views • 7 months ago

AI agents are powerful. They're also isolated. Switch fixes that. Here's the thing about most AI agents: they work alone. They don't see what your team is discussing. They don't share context across conversations. Every new chat is a fresh start. If you've ever tried to actually integrate an agent into your team's workflow, you know the friction. Then I found 𝗦𝘄𝗶𝘁𝗰𝗵. It brings AI agents into Slack, Teams, Discord, and Telegram as named participants. They sit in the same rooms as your team, with the same context, history, and participants. What I actually like about it: → Agents join as real participants in channels — not bots you have to Mention every time → Each room carries its own context, history, and rules → Works with Claude Code, OpenAI, LangChain, Google ADK, and custom agents → Open source and self-hostable. Running in minutes. No migration. No lock-in. Just agents where your team already works. Knowledge lives in the room, not in the agent. Switch keeps people, agents, decisions, and work history together so context survives handoffs. It's from Flint AI. Open source. Runs in minutes. 👉 Human + agent teams, actually working together. 📌 Download 𝗦𝘄𝗶𝘁𝗰𝗵 Console → 🚀𝗦𝘄𝗶𝘁𝗰𝗵 is live on Product Hunt: 🔄 Repost if you've ever wished your AI agent could just join the Slack channel. — #AI #Agents #Slack #Teams #Switch #ProductHunt
0:22

AI agents are powerful. They're also isolated. Switch fixes that. Here's the thing about most AI agents: they work alone. They don't see what your team is discussing. They don't share context across conversations. Every new chat is a fresh start. If you've ever tried to actually integrate an agent into your team's workflow, you know the friction. Then I found 𝗦𝘄𝗶𝘁𝗰𝗵. It brings AI agents into Slack, Teams, Discord, and Telegram as named participants. They sit in the same rooms as your team, with the same context, history, and participants. What I actually like about it: → Agents join as real participants in channels — not bots you have to Mention every time → Each room carries its own context, history, and rules → Works with Claude Code, OpenAI, LangChain, Google ADK, and custom agents → Open source and self-hostable. Running in minutes. No migration. No lock-in. Just agents where your team already works. Knowledge lives in the room, not in the agent. Switch keeps people, agents, decisions, and work history together so context survives handoffs. It's from Flint AI. Open source. Runs in minutes. 👉 Human + agent teams, actually working together. 📌 Download 𝗦𝘄𝗶𝘁𝗰𝗵 Console → 🚀𝗦𝘄𝗶𝘁𝗰𝗵 is live on Product Hunt: 🔄 Repost if you've ever wished your AI agent could just join the Slack channel. — #AI #Agents #Slack #Teams #Switch #ProductHunt

Anuj

306,574 views • 10 days ago

What does work look like when you have more agent employees than teammates? Buzz is the closest answer I've seen. An open source app built on an open protocol, out of Block. I have no affiliation with any of it. It's close. It's not there yet, but it's close. Which is exactly why it's worth an afternoon: You love it and forget about Slack. You hate it and finally configure Slack the way you should have from the start. Either way you watched agent-native software get built in the open, by the people using it. There is no losing in this situation. The people who live in the future are the ones who can look around the corner. Go get your hands dirty.
3:05

What does work look like when you have more agent employees than teammates? Buzz is the closest answer I've seen. An open source app built on an open protocol, out of Block. I have no affiliation with any of it. It's close. It's not there yet, but it's close. Which is exactly why it's worth an afternoon: You love it and forget about Slack. You hate it and finally configure Slack the way you should have from the start. Either way you watched agent-native software get built in the open, by the people using it. There is no losing in this situation. The people who live in the future are the ones who can look around the corner. Go get your hands dirty.

The Startup Ideas Podcast (SIP) 🧃

16,314 views • 1 month ago

An engineer at Block (formerly Square) has an AI agent watching his screen all day. The engineer will discuss a feature with a colleague on Slack. A few hours later, the agent has already built the feature and opened a PR. This isn't some distant AI future. It's happening now, thanks to the work Block has done building their own internal (and open-source) AI agent called "Goose." I sat down with Dhanji Prasanna, CTO at Block, to understand how they're achieving what most companies are still trying to figure out. Engineers using Goose report saving 8-10 hours per week. Across the entire company—including support, legal, and risk teams—they're saving 20-25% of manual hours, which equates to over 100,000 hours per week (!!!). The most surprising finding: non-technical teams are the ones seeing the most productivity gains. Their enterprise risk management team built an entire self-service system, compressing weeks of work into hours. No waiting for Q2 roadmaps or internal apps teams. Dhanji walked me through their full transformation—from convincing Jack Dorsey with an "AI manifesto" to reorganizing from GMs to functional structure to shipping Goose as open source. The whole conversation is live now. Link in comments.
0:46

An engineer at Block (formerly Square) has an AI agent watching his screen all day. The engineer will discuss a feature with a colleague on Slack. A few hours later, the agent has already built the feature and opened a PR. This isn't some distant AI future. It's happening now, thanks to the work Block has done building their own internal (and open-source) AI agent called "Goose." I sat down with Dhanji Prasanna, CTO at Block, to understand how they're achieving what most companies are still trying to figure out. Engineers using Goose report saving 8-10 hours per week. Across the entire company—including support, legal, and risk teams—they're saving 20-25% of manual hours, which equates to over 100,000 hours per week (!!!). The most surprising finding: non-technical teams are the ones seeing the most productivity gains. Their enterprise risk management team built an entire self-service system, compressing weeks of work into hours. No waiting for Q2 roadmaps or internal apps teams. Dhanji walked me through their full transformation—from convincing Jack Dorsey with an "AI manifesto" to reorganizing from GMs to functional structure to shipping Goose as open source. The whole conversation is live now. Link in comments.

Lenny Rachitsky

503,125 views • 10 months ago

This guy closes $5K/month managed agent clients and his AI agent does the fulfillment. His agent Dewey builds the client's agent, onboards it into their Slack, and handles the customer support after. Nick Vasilescu watches client problems get solved from his phone while he's on a walk. He came back on the Build With AI podcast to walk through the entire system. Here's what I learned: 1. Agents building agents is here. Dewey built a $5K/month client's agent on Orgo and onboarded it into their Slack himself. 2. The company behind Hermes is hiring forward deployed engineers for enterprise. The SMB and mid-market layer beneath is up for grabs. 3. His agent has its own email, phone, and card. Dewey signed up for Higgsfield and paid for it himself. 4. Customer support runs without him. Dewey sits in iMessage group chats with clients and fixes issues on the fly. 5. The 80/20 stack: a harness (Hermes or OpenClaw), a model, an Orgo computer, Agent Mail, Agent Phone, Obsidian, Honcho for memory, Composio, Latitude. 6. packages the agent card, email, and phone for about $20/month. 7. Templatize once, deploy forever. Save your ideal stack as an Orgo template and one-click clone it for every client. 8. Nobody pays $5K/month for an agent that doesn't make them money. Build the client an agent, then help them resell it to THEIR customers. B2B2B never churns. 9. Skills come from a context dump. The client dumps everything into Slack and Dewey turns the discovery call transcript into skills. 10. Sell to real businesses, not startups. SMBs doing $1M to $2M minimum pay more and ask fewer questions. Nick put Dewey's entire build into a simple blueprint. Anyone can set this up and be texting their agent in under 2 minutes. Grab the blueprint (free) here: His 2 key takeaways: 1. Build is commoditized. The valuable skill is asking the right questions and knowing which tool to point the agent at. 2. Speed to value wins. The same day a client wires money, ship them something. Agent live by day two. Nick is living further in the future than almost anyone I know and round two did not disappoint. Go follow Nick Vasilescu. Full video below. (Also available on the Build With AI podcast wherever you get your pods)
42:49

This guy closes $5K/month managed agent clients and his AI agent does the fulfillment. His agent Dewey builds the client's agent, onboards it into their Slack, and handles the customer support after. Nick Vasilescu watches client problems get solved from his phone while he's on a walk. He came back on the Build With AI podcast to walk through the entire system. Here's what I learned: 1. Agents building agents is here. Dewey built a $5K/month client's agent on Orgo and onboarded it into their Slack himself. 2. The company behind Hermes is hiring forward deployed engineers for enterprise. The SMB and mid-market layer beneath is up for grabs. 3. His agent has its own email, phone, and card. Dewey signed up for Higgsfield and paid for it himself. 4. Customer support runs without him. Dewey sits in iMessage group chats with clients and fixes issues on the fly. 5. The 80/20 stack: a harness (Hermes or OpenClaw), a model, an Orgo computer, Agent Mail, Agent Phone, Obsidian, Honcho for memory, Composio, Latitude. 6. packages the agent card, email, and phone for about $20/month. 7. Templatize once, deploy forever. Save your ideal stack as an Orgo template and one-click clone it for every client. 8. Nobody pays $5K/month for an agent that doesn't make them money. Build the client an agent, then help them resell it to THEIR customers. B2B2B never churns. 9. Skills come from a context dump. The client dumps everything into Slack and Dewey turns the discovery call transcript into skills. 10. Sell to real businesses, not startups. SMBs doing $1M to $2M minimum pay more and ask fewer questions. Nick put Dewey's entire build into a simple blueprint. Anyone can set this up and be texting their agent in under 2 minutes. Grab the blueprint (free) here: His 2 key takeaways: 1. Build is commoditized. The valuable skill is asking the right questions and knowing which tool to point the agent at. 2. Speed to value wins. The same day a client wires money, ship them something. Agent live by day two. Nick is living further in the future than almost anyone I know and round two did not disappoint. Go follow Nick Vasilescu. Full video below. (Also available on the Build With AI podcast wherever you get your pods)

Corey Ganim

32,144 views • 1 month ago

This is insane. Pedro Franceschi (29 year old CEO of Brex, acquired by Capital One for $5.15B) decomposed his CEO job using OpenClaw. here's what he’s built: > signal ingestion pipeline screens his email, Slack, Google Docs, and WhatsApp... filters everything through specific programs and the 25 key people he cares about > Granola runs on every meeting, feeds transcripts into the pipeline, and auto generates action items > the system takes each to-do, pulls context from the original meeting, and drafts the follow-up... Slack, email, or text. Pedro just clicks approve. > a virtual recruiter named "Jim" lives in Slack with his own email... and taught himself to screen fabricated resumes without anyone coding that capability > a security layer called "Crab Trap" intercepts all agent network traffic through an LLM proxy... a second AI monitoring the first in real time this isn't some bullshit hype influencer demo. this is how a $5 billion company CEO actually operates right now. anyone telling you OpenClaw is useless? liars. a billion dollar company says otherwise. (full podcast link in the post below) 👇
9:19

This is insane. Pedro Franceschi (29 year old CEO of Brex, acquired by Capital One for $5.15B) decomposed his CEO job using OpenClaw. here's what he’s built: > signal ingestion pipeline screens his email, Slack, Google Docs, and WhatsApp... filters everything through specific programs and the 25 key people he cares about > Granola runs on every meeting, feeds transcripts into the pipeline, and auto generates action items > the system takes each to-do, pulls context from the original meeting, and drafts the follow-up... Slack, email, or text. Pedro just clicks approve. > a virtual recruiter named "Jim" lives in Slack with his own email... and taught himself to screen fabricated resumes without anyone coding that capability > a security layer called "Crab Trap" intercepts all agent network traffic through an LLM proxy... a second AI monitoring the first in real time this isn't some bullshit hype influencer demo. this is how a $5 billion company CEO actually operates right now. anyone telling you OpenClaw is useless? liars. a billion dollar company says otherwise. (full podcast link in the post below) 👇

klöss

477,928 views • 5 months ago

The reason your AI gives generic answers isn't the model. It's that your company's knowledge lives in 12 different tools and your agent can't see any of it. So here's how to build a context database your agents can actually use: 1) Pull it in: - Slack, email, meetings, docs, tickets - Auto-collected on a schedule into one place. 2) Clean it up. - An agent files what matters and drops the noise, so your brain doesn't bloat. 3) Store it as memory. - Folders of files your agents read from and write back to as they learn. 4) Point your agents at it. - Now they draft, build, and ship with full context instead of guessing. Same model. Same agent. The difference is whether it can understand your company or not. Build the database once. Everything downstream gets smarter.
8:17

The reason your AI gives generic answers isn't the model. It's that your company's knowledge lives in 12 different tools and your agent can't see any of it. So here's how to build a context database your agents can actually use: 1) Pull it in: - Slack, email, meetings, docs, tickets - Auto-collected on a schedule into one place. 2) Clean it up. - An agent files what matters and drops the noise, so your brain doesn't bloat. 3) Store it as memory. - Folders of files your agents read from and write back to as they learn. 4) Point your agents at it. - Now they draft, build, and ship with full context instead of guessing. Same model. Same agent. The difference is whether it can understand your company or not. Build the database once. Everything downstream gets smarter.

The Startup Ideas Podcast (SIP) 🧃

13,598 views • 3 months ago

New Course: ACP: Agent Communication Protocol Learn to build agents that communicate and collaborate across different frameworks using ACP in this short course built with IBM Research's BeeAI, and taught by Sandi Besen, AI Research Engineer & Ecosystem Lead at IBM, and Nicholas Renotte, Head of AI Developer Advocacy at IBM. Building a multi-agent system with agents built or used by different teams and organizations can become challenging. You may need to write custom integrations each time a team updates their agent design or changes their choice of agentic orchestration framework. The Agent Communication Protocol (ACP) is an open protocol that addresses this challenge by standardizing how agents communicate, using a unified RESTful interface that works across frameworks. In this protocol, you host an agent inside an ACP server, which handles requests from an ACP client and passes them to the appropriate agent. Using a standardized client-server interface allows multiple teams to reuse agents across projects. It also makes it easier to switch between frameworks, replace an agent with a new version, or update a multi-agent system without refactoring the entire system. In this course, you’ll learn to connect agents through ACP. You’ll understand the lifecycle of an ACP Agent and how it compares to other protocols, such as MCP (Model Context Protocol) and A2A (Agent-to-Agent). You’ll build ACP-compliant agents and implement both sequential and hierarchical workflows of multiple agents collaborating using ACP. Through hands-on exercises, you’ll build: - A RAG agent with CrewAI and wrap it inside an ACP server. - An ACP Client to make calls to the ACP server you created. - A sequential workflow that chains an ACP server, created with Smolagents, to the RAG agent. - A hierarchical workflow using a router agent that transforms user queries into tasks, delegated to agents available through ACP servers. - An agent that uses MCP to access tools and ACP to communicate with other agents. You’ll finish up by importing your ACP agents into the BeeAI platform, an open-source registry for discovering and sharing agents. ACP enables collaboration between agents across teams and organizations. By the end of this course, you’ll be able to build ACP agents and workflows that communicate and collaborate regardless of framework. Please sign up here:
2:49

New Course: ACP: Agent Communication Protocol Learn to build agents that communicate and collaborate across different frameworks using ACP in this short course built with IBM Research's BeeAI, and taught by Sandi Besen, AI Research Engineer & Ecosystem Lead at IBM, and Nicholas Renotte, Head of AI Developer Advocacy at IBM. Building a multi-agent system with agents built or used by different teams and organizations can become challenging. You may need to write custom integrations each time a team updates their agent design or changes their choice of agentic orchestration framework. The Agent Communication Protocol (ACP) is an open protocol that addresses this challenge by standardizing how agents communicate, using a unified RESTful interface that works across frameworks. In this protocol, you host an agent inside an ACP server, which handles requests from an ACP client and passes them to the appropriate agent. Using a standardized client-server interface allows multiple teams to reuse agents across projects. It also makes it easier to switch between frameworks, replace an agent with a new version, or update a multi-agent system without refactoring the entire system. In this course, you’ll learn to connect agents through ACP. You’ll understand the lifecycle of an ACP Agent and how it compares to other protocols, such as MCP (Model Context Protocol) and A2A (Agent-to-Agent). You’ll build ACP-compliant agents and implement both sequential and hierarchical workflows of multiple agents collaborating using ACP. Through hands-on exercises, you’ll build: - A RAG agent with CrewAI and wrap it inside an ACP server. - An ACP Client to make calls to the ACP server you created. - A sequential workflow that chains an ACP server, created with Smolagents, to the RAG agent. - A hierarchical workflow using a router agent that transforms user queries into tasks, delegated to agents available through ACP servers. - An agent that uses MCP to access tools and ACP to communicate with other agents. You’ll finish up by importing your ACP agents into the BeeAI platform, an open-source registry for discovering and sharing agents. ACP enables collaboration between agents across teams and organizations. By the end of this course, you’ll be able to build ACP agents and workflows that communicate and collaborate regardless of framework. Please sign up here:

Andrew Ng

105,343 views • 1 year ago

Your team already talks about your ads in Slack. So we built an agent that talks back. Meet Max: the AI media buyer that lives where your team already works. 1,200+ performance teams. $300M in annualized ad spend connected to Max. Max reads the whole thread, screenshots, CSVs, last week's brief, and runs your ads across Meta, Google, TikTok, Snapchat, Reddit, OpenAI Ads, and AppLovin. Ask Max in any thread: >"Why did CPA jump 30% yesterday?" >"Make me a 1:1 static from our best-performing creative." Every change drafted for you. Approved in one click. Never without it.
0:23

Your team already talks about your ads in Slack. So we built an agent that talks back. Meet Max: the AI media buyer that lives where your team already works. 1,200+ performance teams. $300M in annualized ad spend connected to Max. Max reads the whole thread, screenshots, CSVs, last week's brief, and runs your ads across Meta, Google, TikTok, Snapchat, Reddit, OpenAI Ads, and AppLovin. Ask Max in any thread: >"Why did CPA jump 30% yesterday?" >"Make me a 1:1 static from our best-performing creative." Every change drafted for you. Approved in one click. Never without it.

Creatify AI

869,456 views • 7 days ago

HERMES AGENT RUNS MONITORING, RESEARCH, LEAD DETECTION, AND COMPETITIVE ANALYSIS ON AUTOPILOT. AND KNOWS WHEN NOT TO SPEND YOUR TOKENS. the biggest unlock most people skip: Hermes cron jobs can decide ON THEIR OWN whether the LLM should wake up. WAKE AGENT — THE $0 GATE every cron job can run a Python script first. the script checks: did anything actually change? nothing changed: → script outputs {"wakeAgent": false} → LLM stays asleep → zero tokens spent something changed: → script outputs {"wakeAgent": true} → agent wakes up and handles it three gate patterns from official docs: → file-change: compare file mtime to last run. no change? sleep. → external-flag: another process drops a ready file. no flag? sleep. → HTTP-check: ping a URL, diff the response. same as last time? sleep. real example: monitor AWS costs every hour. script pulls current spend from AWS API. no spike? agent sleeps. zero cost. costs jump 40%? agent wakes, reports to Slack, takes action through Stripe MCP. you run 20 monitoring jobs a day. 18 of them find nothing. you pay for 2. NO AGENT — PURE SCRIPT, ZERO LLM some jobs don't need reasoning at all. TLS checks. uptime pings. disk alerts. heartbeats. hermes cron edit --no-agent --script check_health.py script runs. stdout goes straight to Telegram, Discord, or Slack. no LLM involved. flip any job between modes: hermes cron edit --agent # add LLM hermes cron edit --no-agent # remove LLM free monitoring that lives inside the same ecosystem as your agent. 4 MORE USE CASES THIS UNLOCKS: COMPETITIVE ANALYSIS weekly cron with script that diffs competitor pages. agent only analyzes actual changes. updates your tracking file and PRD skill automatically. PRD AS A SKILL save product requirements as a skill, not a document. skills load on demand into fresh context. documents drift. skills stay sharp. CONTENT REPURPOSING hand a video script to the agent. it drafts X and LinkedIn posts in your voice. writes to a review folder. you approve via Telegram. LEAD DETECTION webhook monitors inbox. agent spots potential leads. drafts responses using your business context. schedules meetings from your calendar. the pattern across all of these: scripts handle the mechanical work for free. the agent only spends tokens on reasoning that requires judgment. comment CRON and I'll send you 5 ready-to-paste cron configs with wakeAgent and no_agent patterns. full Hermes SOUL.MD guide 👇
10:37

HERMES AGENT RUNS MONITORING, RESEARCH, LEAD DETECTION, AND COMPETITIVE ANALYSIS ON AUTOPILOT. AND KNOWS WHEN NOT TO SPEND YOUR TOKENS. the biggest unlock most people skip: Hermes cron jobs can decide ON THEIR OWN whether the LLM should wake up. WAKE AGENT — THE $0 GATE every cron job can run a Python script first. the script checks: did anything actually change? nothing changed: → script outputs {"wakeAgent": false} → LLM stays asleep → zero tokens spent something changed: → script outputs {"wakeAgent": true} → agent wakes up and handles it three gate patterns from official docs: → file-change: compare file mtime to last run. no change? sleep. → external-flag: another process drops a ready file. no flag? sleep. → HTTP-check: ping a URL, diff the response. same as last time? sleep. real example: monitor AWS costs every hour. script pulls current spend from AWS API. no spike? agent sleeps. zero cost. costs jump 40%? agent wakes, reports to Slack, takes action through Stripe MCP. you run 20 monitoring jobs a day. 18 of them find nothing. you pay for 2. NO AGENT — PURE SCRIPT, ZERO LLM some jobs don't need reasoning at all. TLS checks. uptime pings. disk alerts. heartbeats. hermes cron edit --no-agent --script check_health.py script runs. stdout goes straight to Telegram, Discord, or Slack. no LLM involved. flip any job between modes: hermes cron edit --agent # add LLM hermes cron edit --no-agent # remove LLM free monitoring that lives inside the same ecosystem as your agent. 4 MORE USE CASES THIS UNLOCKS: COMPETITIVE ANALYSIS weekly cron with script that diffs competitor pages. agent only analyzes actual changes. updates your tracking file and PRD skill automatically. PRD AS A SKILL save product requirements as a skill, not a document. skills load on demand into fresh context. documents drift. skills stay sharp. CONTENT REPURPOSING hand a video script to the agent. it drafts X and LinkedIn posts in your voice. writes to a review folder. you approve via Telegram. LEAD DETECTION webhook monitors inbox. agent spots potential leads. drafts responses using your business context. schedules meetings from your calendar. the pattern across all of these: scripts handle the mechanical work for free. the agent only spends tokens on reasoning that requires judgment. comment CRON and I'll send you 5 ready-to-paste cron configs with wakeAgent and no_agent patterns. full Hermes SOUL.MD guide 👇

YanXbt

96,056 views • 3 months ago

I put Creao AI in a chair and made it explain itself. No code. no team. you describe the agent, it builds it and keeps it running. what people are actually building with it: ➡ Content creators: topic research, first drafts, one article repurposed into a week of posts ➡ Marketers: daily ad hooks, keywords and creatives ready by 9am ➡ Ecommerce: quote responder, inventory snapshots every 4 hours, DM answers in your voice ➡ Solopreneurs: the ops work you keep postponing, running on schedule ➡ Freelancers: proposals, onboarding docs, weekly client reports you never write again ➡ AI-first teams: scheduled agents wired into gmail, sheets, slack, notion free to start, no card. Everyone is building AI agent on CREAO get in and start building check the first comment to get bonus credits on CREAO 👇
1:58

I put Creao AI in a chair and made it explain itself. No code. no team. you describe the agent, it builds it and keeps it running. what people are actually building with it: ➡ Content creators: topic research, first drafts, one article repurposed into a week of posts ➡ Marketers: daily ad hooks, keywords and creatives ready by 9am ➡ Ecommerce: quote responder, inventory snapshots every 4 hours, DM answers in your voice ➡ Solopreneurs: the ops work you keep postponing, running on schedule ➡ Freelancers: proposals, onboarding docs, weekly client reports you never write again ➡ AI-first teams: scheduled agents wired into gmail, sheets, slack, notion free to start, no card. Everyone is building AI agent on CREAO get in and start building check the first comment to get bonus credits on CREAO 👇

SOP | AI Agent Builder

13,667 views • 2 months ago

Introducing Magnitude: your actually local agent 100% private and offline. No token costs, no API keys. Open source. Today's agents are local. The model isn't. Every prompt, every file, every secret gets sent straight to Anthropic and OpenAI. Magnitude is built around local models and runs the whole stack itself. The inference engine is part of the agent, so the models run inside it, right on your computer. It lives in your terminal, and setup is one command. Magnitude profiles your hardware and shows you which models fit, with the trade-offs between quality, speed, and memory. Pick one and start working. No painful config or server to babysit. Out of the box, it can use your shell, edit files, and run scripts. Add skills and it can work with Excel, PowerPoint, PDFs or Chrome. Use it for everyday work: - Analyze sensitive data - Manage private notes - Review code and logs - Search and organize files - Build docs or slides npm i -g @magnitudedev/cli GitHub:
0:35

Introducing Magnitude: your actually local agent 100% private and offline. No token costs, no API keys. Open source. Today's agents are local. The model isn't. Every prompt, every file, every secret gets sent straight to Anthropic and OpenAI. Magnitude is built around local models and runs the whole stack itself. The inference engine is part of the agent, so the models run inside it, right on your computer. It lives in your terminal, and setup is one command. Magnitude profiles your hardware and shows you which models fit, with the trade-offs between quality, speed, and memory. Pick one and start working. No painful config or server to babysit. Out of the box, it can use your shell, edit files, and run scripts. Add skills and it can work with Excel, PowerPoint, PDFs or Chrome. Use it for everyday work: - Analyze sensitive data - Manage private notes - Review code and logs - Search and organize files - Build docs or slides npm i -g @magnitudedev/cli GitHub:

Tom Greenwald

396,270 views • 1 month ago

Gusto cofounder Edward Kim says today is the most important day in Gusto's history since launch. They just shipped Gusto Cofounder, an AI agent that you talk to through texting and Slack that automates almost everything a small business does in its back office. He explains: "We start with all the problems that we're already helping our customers with — payroll, benefits, HR, scheduling — then we bring on a lot of the power of AI into it." "You tell us what your business processes are, starting with payroll, benefits, and all these things we're already doing for you. And Gusto Cofounder will basically run your business process for you." "You communicate with it through SMS and Slack. You don't really even interface with it through a website." "And we connect to all the systems that you're using outside of Gusto as well. Notion, QuickBooks, Google Workspace... Gusto Cofounder brings it all together into one place where it automates your business processes."
4:07

Gusto cofounder Edward Kim says today is the most important day in Gusto's history since launch. They just shipped Gusto Cofounder, an AI agent that you talk to through texting and Slack that automates almost everything a small business does in its back office. He explains: "We start with all the problems that we're already helping our customers with — payroll, benefits, HR, scheduling — then we bring on a lot of the power of AI into it." "You tell us what your business processes are, starting with payroll, benefits, and all these things we're already doing for you. And Gusto Cofounder will basically run your business process for you." "You communicate with it through SMS and Slack. You don't really even interface with it through a website." "And we connect to all the systems that you're using outside of Gusto as well. Notion, QuickBooks, Google Workspace... Gusto Cofounder brings it all together into one place where it automates your business processes."

TBPN

58,412 views • 3 months ago

We use OpenClaws to do all of our work at Every 📧. We have 25 full-time employees, so we’re one of the few companies in the world that has seen how work changes when everyone has their own personal agent in the company Slack. I chatted with Every 📧 COO Brandon (Brandon Gell) and Every 📧 head of platform Willie (Willie) to share what we’ve learned. We get into: - Why agents become mirrors of their owners, and how that influences how other people on the team interact with them - How a parallel AI org chart forms on its own. People have stopped tagging me on Slack with questions about Proof, the document editor I vibe coded, because they knew my agent R2-C2 can step in - The etiquette for human-agent collaboration is being invented in real time. Brandon's rule is that if there's an established process or documented answer, always ask the agent, not their human - Why everyone is a manager now, and why even experienced managers carry limiting beliefs about what their agents can do - This is a must-watch for anyone trying to understand how AI workers change daily operations, not just in theory, but inside a company that’s half-agent Watch below! Timestamps Introduction: How Brandon built Zosia, an AI agent to run his household: Brandon’s “aha” moment: What happened when everyone on the team got their own agent: How agents take on their owners' personalities, and why that matters inside an org: Why it’s important for agents to work in public: What we’re still figuring out when it comes to agent behavior, including memory gaps, group chat etiquette, and the "ant death spiral" problem: How we built Plus One, our hosted OpenClaw product: The cultural shift required to make agents work at scale:
49:42

We use OpenClaws to do all of our work at Every 📧. We have 25 full-time employees, so we’re one of the few companies in the world that has seen how work changes when everyone has their own personal agent in the company Slack. I chatted with Every 📧 COO Brandon (Brandon Gell) and Every 📧 head of platform Willie (Willie) to share what we’ve learned. We get into: - Why agents become mirrors of their owners, and how that influences how other people on the team interact with them - How a parallel AI org chart forms on its own. People have stopped tagging me on Slack with questions about Proof, the document editor I vibe coded, because they knew my agent R2-C2 can step in - The etiquette for human-agent collaboration is being invented in real time. Brandon's rule is that if there's an established process or documented answer, always ask the agent, not their human - Why everyone is a manager now, and why even experienced managers carry limiting beliefs about what their agents can do - This is a must-watch for anyone trying to understand how AI workers change daily operations, not just in theory, but inside a company that’s half-agent Watch below! Timestamps Introduction: How Brandon built Zosia, an AI agent to run his household: Brandon’s “aha” moment: What happened when everyone on the team got their own agent: How agents take on their owners' personalities, and why that matters inside an org: Why it’s important for agents to work in public: What we’re still figuring out when it comes to agent behavior, including memory gaps, group chat etiquette, and the "ant death spiral" problem: How we built Plus One, our hosted OpenClaw product: The cultural shift required to make agents work at scale:

Dan Shipper 📧

67,958 views • 5 months ago

Our team was recently pitched by a well-known AI SDR company. Two problems came up: 1. The company only offered AI SDRs for outbound, we needed a solution to assist us on inbound processes. 2. The software was too complex and overpriced relative to our expected usage. So, I took a couple of weekends to build an early version for what we wanted: - Simple UI that syncs with GMail - Custom AI labeling for emails - AI Agent that drafts replies via CrewAI - Custom knowledge base for the Agent v - Slack inquiry tool for the Agent so it can ask us for clarifications anytime it has knowledge gaps With the help of AI, it's never been easier or more fun to take your workflows and turn them into full web apps. If you're interested in a released version of this, link below.
2:31

Our team was recently pitched by a well-known AI SDR company. Two problems came up: 1. The company only offered AI SDRs for outbound, we needed a solution to assist us on inbound processes. 2. The software was too complex and overpriced relative to our expected usage. So, I took a couple of weekends to build an early version for what we wanted: - Simple UI that syncs with GMail - Custom AI labeling for emails - AI Agent that drafts replies via CrewAI - Custom knowledge base for the Agent v - Slack inquiry tool for the Agent so it can ask us for clarifications anytime it has knowledge gaps With the help of AI, it's never been easier or more fun to take your workflows and turn them into full web apps. If you're interested in a released version of this, link below.

Horacio Lopez

24,281 views • 1 year ago

This is insane. An AI agent can run every boring job in outbound. We spent the last 6 months building ours. Here are my 8 favorite agents to build: Replies get sorted before we open the inbox. Campaigns go live from one command. Weak inboxes pull themselves out before they hurt a domain. Here are the agents behind it: 1. Reply Agent Reads every reply and drafts the response. A human reviews, edits, and sends. 2. Mailbox Health Agent Watches inbox and domain health. It predicts when you need new mailboxes, then buys and warms them. 3. Campaign Optimizer Agent Checks every live campaign every 6 hours. If 500+ leads were emailed and replies are under 4%, it tests new copy, replaces inboxes replying under 1%, and shifts sending to better hours. 4. Lead Qualification Agent Scores each lead by company size, industry, and tech stack. It enriches the record, updates your CRM, and only loads qualified leads into campaigns. High-priority prospect? You get a Slack ping. 5. Meeting Booking Agent Finds meeting requests inside replies. It books the slot, writes prep notes using the lead's background, sends reminders, and logs the outcome. 6. Pipeline Progression Agent Tracks opens, clicks, and website visits. It moves the CRM stage, triggers the next sequence, and creates a task when a lead shows real intent. 7. Copywriting Agent Writes cold emails and follow-ups in the campaign's voice. 8. Analytics Agent Watches campaign metrics in real time and explains what to fix in plain language. We built ours with custom code. Smartlead's SmartAgents let you build agents like these from a plain-English prompt, inside the platform where your campaigns already run. If you repeat an outbound task more than twice a week, that is an agent you have not built yet. Which one would you build first?
0:45

This is insane. An AI agent can run every boring job in outbound. We spent the last 6 months building ours. Here are my 8 favorite agents to build: Replies get sorted before we open the inbox. Campaigns go live from one command. Weak inboxes pull themselves out before they hurt a domain. Here are the agents behind it: 1. Reply Agent Reads every reply and drafts the response. A human reviews, edits, and sends. 2. Mailbox Health Agent Watches inbox and domain health. It predicts when you need new mailboxes, then buys and warms them. 3. Campaign Optimizer Agent Checks every live campaign every 6 hours. If 500+ leads were emailed and replies are under 4%, it tests new copy, replaces inboxes replying under 1%, and shifts sending to better hours. 4. Lead Qualification Agent Scores each lead by company size, industry, and tech stack. It enriches the record, updates your CRM, and only loads qualified leads into campaigns. High-priority prospect? You get a Slack ping. 5. Meeting Booking Agent Finds meeting requests inside replies. It books the slot, writes prep notes using the lead's background, sends reminders, and logs the outcome. 6. Pipeline Progression Agent Tracks opens, clicks, and website visits. It moves the CRM stage, triggers the next sequence, and creates a task when a lead shows real intent. 7. Copywriting Agent Writes cold emails and follow-ups in the campaign's voice. 8. Analytics Agent Watches campaign metrics in real time and explains what to fix in plain language. We built ours with custom code. Smartlead's SmartAgents let you build agents like these from a plain-English prompt, inside the platform where your campaigns already run. If you repeat an outbound task more than twice a week, that is an agent you have not built yet. Which one would you build first?

Hosun Chung

156,625 views • 1 month ago