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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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Karthik

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18,339 görüntüleme • 5 ay önce •via X (Twitter)

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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

11,927 görüntüleme • 4 ay önce

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 görüntüleme • 5 ay önce

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

502,844 görüntüleme • 8 ay önce

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

476,673 görüntüleme • 3 ay önce

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) 🧃

13,486 görüntüleme • 1 ay önce

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 görüntüleme • 1 yıl önce

🚀 Less managing, more creating. Building app usually means: • Hours lost debugging • Constantly juggling tools • Non-stop AI hand-holding Replit’s new Agent 3 changes that entirely → ⁠ We just tried out Agent 3, and it’s impressive! ⁠ It’s not another ordinary AI coding assistant. It’s a full-on collaborator that: • Runs autonomously for up to 200 minutes, building your app from start to finish. • Tests its own work in a real browser and fixes bugs automatically, saving hours of manual debugging. • Lets you build other agents and automations (think Slack bots, Telegram reminders, email summaries). ⁠ Here's why Agent 3 is different: Unlike traditional AI tools where you constantly babysit and prompt, Agent 3 understands your idea and takes charge, freeing you to focus on creativity, strategy, or simply grabbing coffee while your app builds itself. And to show how easy it is, we built a complete waitlist app in less than an hour, from idea to almost finished product. No babysitting. No endless tweaking. ⁠ Replit ⠕ is calling this “Autonomy for All,” and after seeing Agent 3 in action, it’s easy to see how it can bring millions more creators online. ✅ Faster builds ✅ Less frustration ✅ More polished, reliable apps With Agent 3, app-building really does feel less like wrestling with software and more like collaborating with a teammate. ⁠ Try Replit Agent 3 yourself today…👆 Get $10 credit when you purchase Replit Core using our link above!
2:38

🚀 Less managing, more creating. Building app usually means: • Hours lost debugging • Constantly juggling tools • Non-stop AI hand-holding Replit’s new Agent 3 changes that entirely → ⁠ We just tried out Agent 3, and it’s impressive! ⁠ It’s not another ordinary AI coding assistant. It’s a full-on collaborator that: • Runs autonomously for up to 200 minutes, building your app from start to finish. • Tests its own work in a real browser and fixes bugs automatically, saving hours of manual debugging. • Lets you build other agents and automations (think Slack bots, Telegram reminders, email summaries). ⁠ Here's why Agent 3 is different: Unlike traditional AI tools where you constantly babysit and prompt, Agent 3 understands your idea and takes charge, freeing you to focus on creativity, strategy, or simply grabbing coffee while your app builds itself. And to show how easy it is, we built a complete waitlist app in less than an hour, from idea to almost finished product. No babysitting. No endless tweaking. ⁠ Replit ⠕ is calling this “Autonomy for All,” and after seeing Agent 3 in action, it’s easy to see how it can bring millions more creators online. ✅ Faster builds ✅ Less frustration ✅ More polished, reliable apps With Agent 3, app-building really does feel less like wrestling with software and more like collaborating with a teammate. ⁠ Try Replit Agent 3 yourself today…👆 Get $10 credit when you purchase Replit Core using our link above!

There's An AI For That

40,330 görüntüleme • 10 ay önce

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

95,587 görüntüleme • 1 ay önce

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 görüntüleme • 19 gün önce

Data teams spend weeks on simple requests. (This AI answers them in minutes.) Most data analysis is repetitive manual tasks. Data teams spend more time on setup than actual analysis. The workflow usually looks like this: → Run some exploratory data analysis in a local Jupyter notebook or environment → Pull data from multiple disconnected sources → Write code from scratch for every analysis → Export static charts that stakeholders can't explore (or wrestle with legacy BI to create a dashboard) → Manually send updates via email or Slack when data changes → Start over for each new request Most teams accept this as "how data analysis works." While business decisions wait for insights. That's where Fabi changes the entire approach. It's a powerful, AI-native platform built for teams that want to boost productivity and supercharge their data workflows. Instead of working on separate tools and manual processes, you collaborate on analysis that automatically delivers insights where teams work. Here's what makes Fabi different: AI-Native Analysis Environment ↳ SQL and Python work together with AI assistance that handles coding and debugging automatically. Smart Automation Workflows ↳ Automatically send AI-powered reports and summaries right where business works in Slack, email, and spreadsheets. Universal Data Integration ↳ Analyze data from files, Google Sheets, Airtable, plus your data warehouse and databases in one place. Collaborative Data Apps ↳ Create interactive dashboards that stakeholders can explore and ask follow-up questions directly. What you can do with Fabi that legacy BI can't: ➟ Send AI-generated insights directly to Slack channels ➟ Automatically email data summaries to stakeholders ➟ Analyze uploaded files without complex ETL processes ➟ Collaborate on analysis like Google Docs for data ➟ Build workflows that push insights to spreadsheets Perfect for teams that want to move beyond the constraints of legacy and increase their impact. Teams using Fabi see immediate results: ✓ Insights delivered in minutes instead of days ✓ Reduced context switching between tools ✓ Stakeholders explore data independently ✓ Workflows automated to save hours of manual work From analysis to automated delivery - all in one AI-native environment. 📌 Try Fabi today: 👉 Follow Fabi.ai and marc for Fabi updates. 🔄 Repost to help other teams streamline data analysis #DataAnalysis #ModernBI #DataOps #InteractiveDashboards #FabiPartnership #SponsoredByFabi
5:34

Data teams spend weeks on simple requests. (This AI answers them in minutes.) Most data analysis is repetitive manual tasks. Data teams spend more time on setup than actual analysis. The workflow usually looks like this: → Run some exploratory data analysis in a local Jupyter notebook or environment → Pull data from multiple disconnected sources → Write code from scratch for every analysis → Export static charts that stakeholders can't explore (or wrestle with legacy BI to create a dashboard) → Manually send updates via email or Slack when data changes → Start over for each new request Most teams accept this as "how data analysis works." While business decisions wait for insights. That's where Fabi changes the entire approach. It's a powerful, AI-native platform built for teams that want to boost productivity and supercharge their data workflows. Instead of working on separate tools and manual processes, you collaborate on analysis that automatically delivers insights where teams work. Here's what makes Fabi different: AI-Native Analysis Environment ↳ SQL and Python work together with AI assistance that handles coding and debugging automatically. Smart Automation Workflows ↳ Automatically send AI-powered reports and summaries right where business works in Slack, email, and spreadsheets. Universal Data Integration ↳ Analyze data from files, Google Sheets, Airtable, plus your data warehouse and databases in one place. Collaborative Data Apps ↳ Create interactive dashboards that stakeholders can explore and ask follow-up questions directly. What you can do with Fabi that legacy BI can't: ➟ Send AI-generated insights directly to Slack channels ➟ Automatically email data summaries to stakeholders ➟ Analyze uploaded files without complex ETL processes ➟ Collaborate on analysis like Google Docs for data ➟ Build workflows that push insights to spreadsheets Perfect for teams that want to move beyond the constraints of legacy and increase their impact. Teams using Fabi see immediate results: ✓ Insights delivered in minutes instead of days ✓ Reduced context switching between tools ✓ Stakeholders explore data independently ✓ Workflows automated to save hours of manual work From analysis to automated delivery - all in one AI-native environment. 📌 Try Fabi today: 👉 Follow Fabi.ai and marc for Fabi updates. 🔄 Repost to help other teams streamline data analysis #DataAnalysis #ModernBI #DataOps #InteractiveDashboards #FabiPartnership #SponsoredByFabi

Andrew Bolis

36,504 görüntüleme • 10 ay önce

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 görüntüleme • 1 ay önce

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 görüntüleme • 3 ay önce

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 görüntüleme • 1 yıl önce

Most AI Agents can Talk..... Very few can actually do anything. You can spin up an agent in minutes… but the second you want it to: 🔴 Create a GitHub issue ⚪ Update Notion 🟡 Send a Slack message 🔵 Log actions properly 🟣 Handle auth securely You’re suddenly building integrations for weeks. This is why Merge Agent Handler caught my attention. It gives your AI agent real tool access across thousands of platforms through one integration, but the part I actually care about is this: Production-ready security + full audit logs out of the box. You can see every tool call. Every API request. Every response. So your agent isn’t just “connected”..... it’s monitored and controlled. If you’re building agents that need to take real actions, this is worth looking at: Video shows it in action 👇
0:22

Most AI Agents can Talk..... Very few can actually do anything. You can spin up an agent in minutes… but the second you want it to: 🔴 Create a GitHub issue ⚪ Update Notion 🟡 Send a Slack message 🔵 Log actions properly 🟣 Handle auth securely You’re suddenly building integrations for weeks. This is why Merge Agent Handler caught my attention. It gives your AI agent real tool access across thousands of platforms through one integration, but the part I actually care about is this: Production-ready security + full audit logs out of the box. You can see every tool call. Every API request. Every response. So your agent isn’t just “connected”..... it’s monitored and controlled. If you’re building agents that need to take real actions, this is worth looking at: Video shows it in action 👇

Kaitee

49,963 görüntüleme • 4 ay önce

From idea → live AI agent in under 2 minutes. Just built an agent inside Airia, and honestly… this platform feels like cheating (in a good way). Here’s the workflow I set up - no code, no stress: 1️⃣ Create Agent Opened a blank agent → dragged in the steps I needed. 2️⃣ Pick the Model Chose my preferred LLM instantly - Airia lets you mix OpenAI, Anthropic, Gemini, Mistral, anything. 3️⃣ Connect Data Uploaded a file + linked my workspace apps. Salesforce, Slack, PDFs… all plug in seamlessly. 4️⃣ Run It Live Prompted it to generate a full summary from my uploaded doc. The response was clean, structured, and fast. 5️⃣ Publish & Share Hit Publish → Submit to Community so anyone can use it. (You can access 2,500+ ready-made agents there too.) If you’ve been wanting to build automated workflows without juggling 10 different tools, Airia is genuinely the easiest way to do it. 👉 Try the free trial: 👉 Explore the Community: #Airia #AgenticAI #AIAgents #AIOrchestration #AIWorkflow #MachineLearning
6:35

From idea → live AI agent in under 2 minutes. Just built an agent inside Airia, and honestly… this platform feels like cheating (in a good way). Here’s the workflow I set up - no code, no stress: 1️⃣ Create Agent Opened a blank agent → dragged in the steps I needed. 2️⃣ Pick the Model Chose my preferred LLM instantly - Airia lets you mix OpenAI, Anthropic, Gemini, Mistral, anything. 3️⃣ Connect Data Uploaded a file + linked my workspace apps. Salesforce, Slack, PDFs… all plug in seamlessly. 4️⃣ Run It Live Prompted it to generate a full summary from my uploaded doc. The response was clean, structured, and fast. 5️⃣ Publish & Share Hit Publish → Submit to Community so anyone can use it. (You can access 2,500+ ready-made agents there too.) If you’ve been wanting to build automated workflows without juggling 10 different tools, Airia is genuinely the easiest way to do it. 👉 Try the free trial: 👉 Explore the Community: #Airia #AgenticAI #AIAgents #AIOrchestration #AIWorkflow #MachineLearning

Parul Gautam

70,392 görüntüleme • 8 ay önce

An agency added $133,752 in new ARR in 30 days. No new hires. Just work that wasn't getting done before. We run an AI-focused X account testing tools and workflows that actually save teams real time, not just add more steps. Most AI tools stop after giving you an answer. We wanted to see what happened when you gave one actual work to do. We added Viktor to our Slack workspace and connected him to our existing tools. Instead of bouncing between apps, we handed him work directly inside the workspace our team already uses. He started producing real outputs, not just suggestions. We asked him to summarize this week's AI news. He came back with a structured briefing: biggest stories, why they matter, key takeaways, and a thread-ready digest, all posted directly into Slack. It felt less like chatting with an AI and more like handing work to a teammate. We also ran a workflow that normally means switching between several apps and manually stitching everything together. Viktor handled it in the same place our team already collaborates. Reports, summaries, deliverables, done. That's what makes him different. The value isn't better answers. It's an AI employee who executes work that would otherwise sit on someone's to-do list. Nico Torres, Founder of Authority Makers, saw it in month one: $133,752 in new ARR from outbound work Viktor ran that previously had no one running it. Tell me I'm wrong, but this is what a non-human hire actually looks like in practice. Get started free at Viktor. $100 in credits included, no card. #AIemployee #AIWorkflows #ProductivityTools Paid Partnership
0:24

An agency added $133,752 in new ARR in 30 days. No new hires. Just work that wasn't getting done before. We run an AI-focused X account testing tools and workflows that actually save teams real time, not just add more steps. Most AI tools stop after giving you an answer. We wanted to see what happened when you gave one actual work to do. We added Viktor to our Slack workspace and connected him to our existing tools. Instead of bouncing between apps, we handed him work directly inside the workspace our team already uses. He started producing real outputs, not just suggestions. We asked him to summarize this week's AI news. He came back with a structured briefing: biggest stories, why they matter, key takeaways, and a thread-ready digest, all posted directly into Slack. It felt less like chatting with an AI and more like handing work to a teammate. We also ran a workflow that normally means switching between several apps and manually stitching everything together. Viktor handled it in the same place our team already collaborates. Reports, summaries, deliverables, done. That's what makes him different. The value isn't better answers. It's an AI employee who executes work that would otherwise sit on someone's to-do list. Nico Torres, Founder of Authority Makers, saw it in month one: $133,752 in new ARR from outbound work Viktor ran that previously had no one running it. Tell me I'm wrong, but this is what a non-human hire actually looks like in practice. Get started free at Viktor. $100 in credits included, no card. #AIemployee #AIWorkflows #ProductivityTools Paid Partnership

Farlane

100,860 görüntüleme • 4 gün önce

Still spending hours on repetitive work that should’ve been automated by now? Most teams waste valuable time creating documents from scratch, rewriting the same emails, reformatting data, and manually producing content that could be generated in seconds — with the right tools. Meet |Context — The AI Office Suite built to transform how work gets done. 🚀 Not just where you work — but what you work on. Context doesn’t just assist you. It acts like a teammate: learning, reasoning, writing, researching, and adapting to your workflow. Here’s how transforms your workday: ✅ Unified Workspace: Connect over 300 tools like Slack, Google Docs, Salesforce, and Excel in one place, and streamline your entire workflow. ✅ AI-Powered Automation: Let AI handle repetitive tasks, generate content, and update documents in real time, saving you hours every week. ✅ Real-Time Collaboration: Collaborate seamlessly with your team on shared workspaces, making edits, updates, and feedback simple and quick. ✅ Top-Tier Security: With enterprise-grade security, including SOC 2, GDPR compliance, and AES-256 encryption, your data stays safe and secure. ✅ Endless Customization: Build custom templates, automate research, and tailor workflows to meet the unique needs of your business. Ready to level up your workflow? Try it now👉 #ContextAI #Automation #AIWorkspace #Productivity #SmartWork #NoCodeTools #AIIntegration #Teamworkrder
0:45

Still spending hours on repetitive work that should’ve been automated by now? Most teams waste valuable time creating documents from scratch, rewriting the same emails, reformatting data, and manually producing content that could be generated in seconds — with the right tools. Meet |Context — The AI Office Suite built to transform how work gets done. 🚀 Not just where you work — but what you work on. Context doesn’t just assist you. It acts like a teammate: learning, reasoning, writing, researching, and adapting to your workflow. Here’s how transforms your workday: ✅ Unified Workspace: Connect over 300 tools like Slack, Google Docs, Salesforce, and Excel in one place, and streamline your entire workflow. ✅ AI-Powered Automation: Let AI handle repetitive tasks, generate content, and update documents in real time, saving you hours every week. ✅ Real-Time Collaboration: Collaborate seamlessly with your team on shared workspaces, making edits, updates, and feedback simple and quick. ✅ Top-Tier Security: With enterprise-grade security, including SOC 2, GDPR compliance, and AES-256 encryption, your data stays safe and secure. ✅ Endless Customization: Build custom templates, automate research, and tailor workflows to meet the unique needs of your business. Ready to level up your workflow? Try it now👉 #ContextAI #Automation #AIWorkspace #Productivity #SmartWork #NoCodeTools #AIIntegration #Teamworkrder

Manish Kumar Shah

33,734 görüntüleme • 1 yıl önce

Introducing the Knowledge Agent Template Vercel dropped the Chat SDK so we built a full template on top of it that you can deploy in one click. Here's what's inside: • Add your sources: GitHub repos, YouTube, etc. directly from the UI • File-based agent using grep/find/cat • Powered by Vercel Workflow + Sandbox • Multi-platform: web chat, GitHub bot, Discord bot, and more • Comes with skills to edit your project, add sources, create agents Deploy, add a source, ask questions. Your knowledge base is live and always in sync. This is just scratching the surface. There's way more packed in this template, and I'll be breaking down the features all week. Links in the below 👇
0:29

Introducing the Knowledge Agent Template Vercel dropped the Chat SDK so we built a full template on top of it that you can deploy in one click. Here's what's inside: • Add your sources: GitHub repos, YouTube, etc. directly from the UI • File-based agent using grep/find/cat • Powered by Vercel Workflow + Sandbox • Multi-platform: web chat, GitHub bot, Discord bot, and more • Comes with skills to edit your project, add sources, create agents Deploy, add a source, ask questions. Your knowledge base is live and always in sync. This is just scratching the surface. There's way more packed in this template, and I'll be breaking down the features all week. Links in the below 👇

Hugo

54,481 görüntüleme • 4 ay önce

do you understand what Claude just did? > you can now tell Claude to use your entire computer. open apps. navigate your browser. fill spreadsheets. anything you’d do sitting at your desk > uses your connected apps first… Slack, Calendar, integrations. when there’s no connector, it asks permission to open the app on your screen directly > assign a task from your phone. walk away. come back to finished work on your computer > tell it once to scan your email every morning or pull a report every Friday and it just handles it > macOS only right now. research preview in Cowork and Claude Code > not long ago you needed a custom framework just to get Claude running on a loop. now it’s fully native → dispatch from your phone… native → scheduled recurring tasks… native → remote monitoring… native → multi-agent coordination… native → persistent memory across sessions… native every feature that made agentic frameworks valuable is becoming a platform feature for Claude the AI race just got way more interesting
0:20

do you understand what Claude just did? > you can now tell Claude to use your entire computer. open apps. navigate your browser. fill spreadsheets. anything you’d do sitting at your desk > uses your connected apps first… Slack, Calendar, integrations. when there’s no connector, it asks permission to open the app on your screen directly > assign a task from your phone. walk away. come back to finished work on your computer > tell it once to scan your email every morning or pull a report every Friday and it just handles it > macOS only right now. research preview in Cowork and Claude Code > not long ago you needed a custom framework just to get Claude running on a loop. now it’s fully native → dispatch from your phone… native → scheduled recurring tasks… native → remote monitoring… native → multi-agent coordination… native → persistent memory across sessions… native every feature that made agentic frameworks valuable is becoming a platform feature for Claude the AI race just got way more interesting

klöss

463,336 görüntüleme • 4 ay önce