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Most AI support bots aren't actually solving problems. They're assigning homework. Customer: "I need an invoice." AI: "Go to Billing → Settings → Invoices." Customer: 😑 That's not resolution. That's directions. The new shift happening in AI support isn't better answers. It's agents that can actually finish the job....

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Aaron Edwards

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396,250 просмотров • 2 месяцев назад •via X (Twitter)

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Announcing Chatbase Backstage: a control center for managing your AI customer support agent. Talk to your agent like a teammate to understand what's driving support tickets, update how it responds, deploy new Procedures, and manage workflows in plain English. Backstage is available on iMessage, Slack, and the Chatbase dashboard, so you can manage your customer experience from wherever you work.
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Announcing Chatbase Backstage: a control center for managing your AI customer support agent. Talk to your agent like a teammate to understand what's driving support tickets, update how it responds, deploy new Procedures, and manage workflows in plain English. Backstage is available on iMessage, Slack, and the Chatbase dashboard, so you can manage your customer experience from wherever you work.

Yasser

705,374 просмотров • 8 дней назад

Sam Altman says the next AI safety problem is not a chatbot being wrong. It is an agent taking actions you cannot audit. "We are going to have AI systems clicking around the internet." "This is the most interesting and consequential safety challenge we have yet faced." Because once an agent has access to your systems, information, and computer, mistakes stop looking like bad answers. They become actions. "You will not use our agents if you do not trust that they are not going to empty your bank account or delete your data." That is the shift: Chatbots could be vague. Agents need an audit trail. The next bottleneck is trust, verification, governance, permissions, provenance, and human override. This is exactly why we are convening the AI Assurance & Governance Summit at Stanford on Oct 1. If your company is deploying AI into high-stakes workflows, this is the room to be in:
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Sam Altman says the next AI safety problem is not a chatbot being wrong. It is an agent taking actions you cannot audit. "We are going to have AI systems clicking around the internet." "This is the most interesting and consequential safety challenge we have yet faced." Because once an agent has access to your systems, information, and computer, mistakes stop looking like bad answers. They become actions. "You will not use our agents if you do not trust that they are not going to empty your bank account or delete your data." That is the shift: Chatbots could be vague. Agents need an audit trail. The next bottleneck is trust, verification, governance, permissions, provenance, and human override. This is exactly why we are convening the AI Assurance & Governance Summit at Stanford on Oct 1. If your company is deploying AI into high-stakes workflows, this is the room to be in:

Karl Mehta

31,208 просмотров • 2 месяцев назад

Postman is going all-in on AI! Their AI Agent Builder is live (and it's pretty powerful!) Postman's API Network has 100,000+ APIs and they just released a suite of tools for building AI Agents that can automatically discover and connect to any of these APIs. Think about it for a second: You can now build an AI agent that searches the network, discovers an API, processes its documentation, connects to it, and evaluates whether everything worked at the end of the process. This is a huge deal. Every person I know uses the Postman client to work with APIs. Now, they will have access to the AI Agent Builder, a complete suite for designing, building, testing, and deploying agents. I've built a few simple agents myself, and it's tough. The ability to discover and use an API automatically is really exciting (and a huge time saver if it works as promised!) In collaboration with the Postman team, who is sponsoring this post, I recorded a video with my thoughts about the Postman AI Agent Builder.
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Postman is going all-in on AI! Their AI Agent Builder is live (and it's pretty powerful!) Postman's API Network has 100,000+ APIs and they just released a suite of tools for building AI Agents that can automatically discover and connect to any of these APIs. Think about it for a second: You can now build an AI agent that searches the network, discovers an API, processes its documentation, connects to it, and evaluates whether everything worked at the end of the process. This is a huge deal. Every person I know uses the Postman client to work with APIs. Now, they will have access to the AI Agent Builder, a complete suite for designing, building, testing, and deploying agents. I've built a few simple agents myself, and it's tough. The ability to discover and use an API automatically is really exciting (and a huge time saver if it works as promised!) In collaboration with the Postman team, who is sponsoring this post, I recorded a video with my thoughts about the Postman AI Agent Builder.

Santiago

230,647 просмотров • 1 год назад

🚨 THIS IS ACTUALLY INSANE Your AI agent can have access to the web. But if it can't reliably read what’s actually on the page, that access is almost useless. We looked at Firecrawl as the web layer for AI agents and the numbers are hard to ignore. The setup is simple: Give it a URL, search query, or website. Firecrawl handles the ugly part — crawling, scraping, rendering, extracting, and turning web content into something an AI model can actually use. The headline numbers: → 173,000+ GitHub stars → Search, scrape and interact with the web at scale → Supports web pages, PDFs, DOCX and other content → Structured data extraction for AI workflows → MCP support for connecting it directly to AI agents The workflow looks like this: Search → Scrape → Crawl → Extract → Feed the agent Three things stand out: 1. Scraping becomes an infrastructure layer Instead of maintaining your own pile of HTTP clients, parsers, browser automation and retry logic, you can treat web access as an API. 2. Agents get more than raw HTML The goal isn't just downloading a webpage. It's turning messy web content into clean context that an LLM can reason over. 3. The same layer works across different agent workflows Research agents. RAG pipelines. AI search. Competitive intelligence. Web-data extraction. The interesting shift: AI agents don't just need better models. They need better access to the information those models are supposed to reason about. Firecrawl is building that layer. Save this repo.
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🚨 THIS IS ACTUALLY INSANE Your AI agent can have access to the web. But if it can't reliably read what’s actually on the page, that access is almost useless. We looked at Firecrawl as the web layer for AI agents and the numbers are hard to ignore. The setup is simple: Give it a URL, search query, or website. Firecrawl handles the ugly part — crawling, scraping, rendering, extracting, and turning web content into something an AI model can actually use. The headline numbers: → 173,000+ GitHub stars → Search, scrape and interact with the web at scale → Supports web pages, PDFs, DOCX and other content → Structured data extraction for AI workflows → MCP support for connecting it directly to AI agents The workflow looks like this: Search → Scrape → Crawl → Extract → Feed the agent Three things stand out: 1. Scraping becomes an infrastructure layer Instead of maintaining your own pile of HTTP clients, parsers, browser automation and retry logic, you can treat web access as an API. 2. Agents get more than raw HTML The goal isn't just downloading a webpage. It's turning messy web content into clean context that an LLM can reason over. 3. The same layer works across different agent workflows Research agents. RAG pipelines. AI search. Competitive intelligence. Web-data extraction. The interesting shift: AI agents don't just need better models. They need better access to the information those models are supposed to reason about. Firecrawl is building that layer. Save this repo.

Vikas gupta

18,053 просмотров • 4 дней назад

Google Brain founder, Andrew Ng: "One of the most important skills of the future is the ability to tell a computer exactly what you want so it can do it for you." In this talk, he explains why the biggest opportunity isn't just using AI. It's learning how to turn work into agentic workflows, break tasks into steps, give agents the right tools and context, add evals, and make the whole system reliable. That's the difference between prompting an AI and engineering an agent that actually works. Watch it today, then read the full guide in the article below.
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Google Brain founder, Andrew Ng: "One of the most important skills of the future is the ability to tell a computer exactly what you want so it can do it for you." In this talk, he explains why the biggest opportunity isn't just using AI. It's learning how to turn work into agentic workflows, break tasks into steps, give agents the right tools and context, add evals, and make the whole system reliable. That's the difference between prompting an AI and engineering an agent that actually works. Watch it today, then read the full guide in the article below.

Rahul

14,431 просмотров • 1 месяц назад

CEOs are wasting millions on AI. Here's why. 💸 CEOs are pouring millions into AI, but simply swapping a human for an AI agent in the middle of a broken process won't change your business. True AI transformation requires an end-to-end workflow redesign. Don't just use AI to make the old way faster—use it to build a completely new experience. Ready to build AI workflows that actually move the needle? DeepLearning.AI offers free short courses to help you master agentic workflows and the latest AI tools. Start learning today: Subscribe to The Batch newsletter for weekly AI insights:
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CEOs are wasting millions on AI. Here's why. 💸 CEOs are pouring millions into AI, but simply swapping a human for an AI agent in the middle of a broken process won't change your business. True AI transformation requires an end-to-end workflow redesign. Don't just use AI to make the old way faster—use it to build a completely new experience. Ready to build AI workflows that actually move the needle? DeepLearning.AI offers free short courses to help you master agentic workflows and the latest AI tools. Start learning today: Subscribe to The Batch newsletter for weekly AI insights:

DeepLearning.AI

11,551 просмотров • 5 месяцев назад

AI customer service agents have arrived. Air AI is a conversational AI that can perform full 5-40 minute long sales and customer service calls. And it sounds completely replicable to humans. Details: -Can autonomously perform actions across 5,000 unique applications -Adaptable to various roles such as sales development representative, customer service agent, account executive, and more, based on creative input -Currently being utilized on live calls, serving real people and businesses daily -Companies can request beta access to deploy an AI sales or customer service team Unlike humans, AI does not take days off and never gets tired. The harsh reality is that some human jobs, such as customer service agents, will need help to compete against AI. This is both scary and incredible and the same time. What do you think?
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AI customer service agents have arrived. Air AI is a conversational AI that can perform full 5-40 minute long sales and customer service calls. And it sounds completely replicable to humans. Details: -Can autonomously perform actions across 5,000 unique applications -Adaptable to various roles such as sales development representative, customer service agent, account executive, and more, based on creative input -Currently being utilized on live calls, serving real people and businesses daily -Companies can request beta access to deploy an AI sales or customer service team Unlike humans, AI does not take days off and never gets tired. The harsh reality is that some human jobs, such as customer service agents, will need help to compete against AI. This is both scary and incredible and the same time. What do you think?

Rowan Cheung

171,129 просмотров • 3 лет назад

Most companies don't know what their customers are actually frustrated about. So I built an AI agent called **Customer Pain Point Miner**. Type a company name, and it automatically finds: • Top customer complaints • Features users keep requesting • Recurring frustrations • Product improvement opportunities It scans reviews, Reddit threads, forums, and blogs, then turns thousands of discussions into a sourced report. No surveys. No guessing. Just real customer feedback from across the web. Built it with Nimble Their APIs made it surprisingly easy to search, extract, and structure live web data into something an AI agent can actually reason about. The best roadmap isn't hidden in another brainstorming session. It's already sitting in your customers' conversations.
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Most companies don't know what their customers are actually frustrated about. So I built an AI agent called **Customer Pain Point Miner**. Type a company name, and it automatically finds: • Top customer complaints • Features users keep requesting • Recurring frustrations • Product improvement opportunities It scans reviews, Reddit threads, forums, and blogs, then turns thousands of discussions into a sourced report. No surveys. No guessing. Just real customer feedback from across the web. Built it with Nimble Their APIs made it surprisingly easy to search, extract, and structure live web data into something an AI agent can actually reason about. The best roadmap isn't hidden in another brainstorming session. It's already sitting in your customers' conversations.

Vipin Gautam (Viipin I Gautam)

96,162 просмотров • 2 месяцев назад

CONTEXT ENGINEERING > PROMPT ENGINEERING Everyone is obsessed with writing better prompts. The next generation of AI builders is focused on context engineering instead. • Prompt engineering shapes the question. Context engineering shapes everything the AI sees. • Great prompts can't save an agent missing critical context, memory, or tools. • AI failures often come from missing information, not weak models. • Context engineering combines prompts, memory, RAG, state management, and tool access into one system. What powers effective AI agents? → Memory: Remembers preferences, past interactions, and ongoing tasks. → State Management: Tracks progress across multi-step workflows. → RAG: Retrieves only the most relevant information when needed. → Tools: Connects AI to APIs, databases, code execution, and real-world actions. → Dynamic Prompts: Enriches instructions with live context at runtime. The key insight: Prompt Engineering = Better Questions Context Engineering = Better Systems The future of AI isn't building smarter prompts. It's building smarter environments for AI to think, remember, retrieve, and act.
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CONTEXT ENGINEERING > PROMPT ENGINEERING Everyone is obsessed with writing better prompts. The next generation of AI builders is focused on context engineering instead. • Prompt engineering shapes the question. Context engineering shapes everything the AI sees. • Great prompts can't save an agent missing critical context, memory, or tools. • AI failures often come from missing information, not weak models. • Context engineering combines prompts, memory, RAG, state management, and tool access into one system. What powers effective AI agents? → Memory: Remembers preferences, past interactions, and ongoing tasks. → State Management: Tracks progress across multi-step workflows. → RAG: Retrieves only the most relevant information when needed. → Tools: Connects AI to APIs, databases, code execution, and real-world actions. → Dynamic Prompts: Enriches instructions with live context at runtime. The key insight: Prompt Engineering = Better Questions Context Engineering = Better Systems The future of AI isn't building smarter prompts. It's building smarter environments for AI to think, remember, retrieve, and act.

Dami-Defi

10,127 просмотров • 2 месяцев назад

Something that stunned me about is they've moved away from the FDE playbook that's become the default for fast growing AI startups. Instead they've built AI to covert plain English from the customer into Python code to make the product work for their use cases i.e. an AI FDE. It's a huge technical feat and is how they can onboard enterprises in weeks vs months.
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Something that stunned me about is they've moved away from the FDE playbook that's become the default for fast growing AI startups. Instead they've built AI to covert plain English from the customer into Python code to make the product work for their use cases i.e. an AI FDE. It's a huge technical feat and is how they can onboard enterprises in weeks vs months.

Harj Taggar

654,557 просмотров • 10 месяцев назад

New short course: Serverless Agentic Workflows with Amazon Bedrock. Learn to build and deploy serverless agents in this course created with Amazon Web Services and taught by Mike G Chambers, a Senior Developer Advocate at AWS specializing in GenAI. (Disclosure: I serve on Amazon's board.) Generative AI applications are becoming more complex, sophisticated, and agentic. Agentic applications have workloads that can be hard to predict in advance -- for example, what tools will it decide to call? -- and a serverless architecture helps you efficiently providing on-demand resources. This course teaches you to build and deploy a serverless agentic application. You’ll learn to create agents with tools, code execution, and guardrails, and build responsible agents for business use cases: - Build a customer service bot for a fictional tea mug business that can answering questions, retrieve information, and process orders. - Connect your customer service agent to a CRM to get customer info and log support tickets in real-time. - Explore how you invoke the agent, and see the trace to review the agent’s thought process and observation loop until it reaches its final output. - Attach a code interpreter to your agent, giving it the ability to perform accurate calculations by writing and running its own Python code. - Implement guardrails to prevent your agent from revealing sensitive information or using inappropriate language. By the end, you will have built a sophisticated AI agent capable of handling real-world customer support scenarios. Please sign up here!
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New short course: Serverless Agentic Workflows with Amazon Bedrock. Learn to build and deploy serverless agents in this course created with Amazon Web Services and taught by Mike G Chambers, a Senior Developer Advocate at AWS specializing in GenAI. (Disclosure: I serve on Amazon's board.) Generative AI applications are becoming more complex, sophisticated, and agentic. Agentic applications have workloads that can be hard to predict in advance -- for example, what tools will it decide to call? -- and a serverless architecture helps you efficiently providing on-demand resources. This course teaches you to build and deploy a serverless agentic application. You’ll learn to create agents with tools, code execution, and guardrails, and build responsible agents for business use cases: - Build a customer service bot for a fictional tea mug business that can answering questions, retrieve information, and process orders. - Connect your customer service agent to a CRM to get customer info and log support tickets in real-time. - Explore how you invoke the agent, and see the trace to review the agent’s thought process and observation loop until it reaches its final output. - Attach a code interpreter to your agent, giving it the ability to perform accurate calculations by writing and running its own Python code. - Implement guardrails to prevent your agent from revealing sensitive information or using inappropriate language. By the end, you will have built a sophisticated AI agent capable of handling real-world customer support scenarios. Please sign up here!

Andrew Ng

81,048 просмотров • 1 год назад

We just crossed $10M in ARR at Chatbase! 🎉 🎉 And today, we're launching Chatbase as the full harness for customer-facing AI agents. Similar to how Claude code is a harness for coding agents, Chatbase is the harness for customer experience agents. That means we give the model the context, tools, workflows, guardrails, and human-in-the-loop systems to be the best ambassador for your brand. It's going beyond just solving issues and is giving your customers the best experiences across every channel. This is a milestone I have been thinking about and obsessed with since day 1, and I am super excited to bring my vision for customer facing agents to life with Chatbase. Thank you to every one of our customers and to the amazing Chatbase team for getting us here! Next stop: $100M ARR
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We just crossed $10M in ARR at Chatbase! 🎉 🎉 And today, we're launching Chatbase as the full harness for customer-facing AI agents. Similar to how Claude code is a harness for coding agents, Chatbase is the harness for customer experience agents. That means we give the model the context, tools, workflows, guardrails, and human-in-the-loop systems to be the best ambassador for your brand. It's going beyond just solving issues and is giving your customers the best experiences across every channel. This is a milestone I have been thinking about and obsessed with since day 1, and I am super excited to bring my vision for customer facing agents to life with Chatbase. Thank you to every one of our customers and to the amazing Chatbase team for getting us here! Next stop: $100M ARR

Yasser

851,776 просмотров • 3 месяцев назад

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

Simplifying AI

68,025 просмотров • 1 месяц назад

NEW: Introducing Octane AI Agentic Commerce Quizzes - Increase sales with AI. What is it? A sales quiz AI agent that makes 1-1 personalized sales experiences for every single customer. In real time. Powered by our new AI model CORE-1. Examples: 📸 Want to ask your customer to take a selfie and your AI agent automatically recommends them a full outfit from your catalog? Octane AI agents can do that. 🪞 Want to have an AI agent hand pick out each product for a personalized skin care routine? Want them to upload a selfie to detect their skin tone? Octane AI agents can do can that. 📊 Want to create an incredibly detailed report with graphs and tables and graphics thats generated by AI for each customer? Octane AI agents can do that. We give you the building blocks and you can build anything. And you can build it fast because our AI will do the heavy lifting for you. This is v1 and a representation of where our commerce and quiz technology is headed. Available today to everyone at 🆕 What we are launching today: • Smart Quiz Builder: Have an AI agent plan out and build your Octane AI quiz for you. It can even write custom HTML for beautiful results pages and progress bars. • Smart Products: It can take forever to setup the recommendation logic for a quiz. For those of you who need help, simply add smart products to your Octane AI quiz and your very own AI agent will hand-pick products for each customer who takes your quiz. It’s amazing. • Smart Copy: Instead of showing everyone who takes your quiz the exact same copy, use AI to personalize the quiz for every single person who takes it. Explain why these specific products are perfect for specifically them. • Image Analyzer: Let your customers upload or take a photo during the quiz and have AI analyze it. You can use this for anything from skin tone detection to picking out outfits! • Shopping Assistant: An AI agent that lives on your store that can help your customers at the right time. We have been building quiz software for almost 10 years now and AI is enabling us to make quizzes even more powerful. This is just the v1 of what we will be releasing in this area. We are so excited to see what you create with these new agentic products. Get creative, we think you will be surprised at how many interesting experiences you can create with Octane AI now.
0:35

NEW: Introducing Octane AI Agentic Commerce Quizzes - Increase sales with AI. What is it? A sales quiz AI agent that makes 1-1 personalized sales experiences for every single customer. In real time. Powered by our new AI model CORE-1. Examples: 📸 Want to ask your customer to take a selfie and your AI agent automatically recommends them a full outfit from your catalog? Octane AI agents can do that. 🪞 Want to have an AI agent hand pick out each product for a personalized skin care routine? Want them to upload a selfie to detect their skin tone? Octane AI agents can do can that. 📊 Want to create an incredibly detailed report with graphs and tables and graphics thats generated by AI for each customer? Octane AI agents can do that. We give you the building blocks and you can build anything. And you can build it fast because our AI will do the heavy lifting for you. This is v1 and a representation of where our commerce and quiz technology is headed. Available today to everyone at 🆕 What we are launching today: • Smart Quiz Builder: Have an AI agent plan out and build your Octane AI quiz for you. It can even write custom HTML for beautiful results pages and progress bars. • Smart Products: It can take forever to setup the recommendation logic for a quiz. For those of you who need help, simply add smart products to your Octane AI quiz and your very own AI agent will hand-pick products for each customer who takes your quiz. It’s amazing. • Smart Copy: Instead of showing everyone who takes your quiz the exact same copy, use AI to personalize the quiz for every single person who takes it. Explain why these specific products are perfect for specifically them. • Image Analyzer: Let your customers upload or take a photo during the quiz and have AI analyze it. You can use this for anything from skin tone detection to picking out outfits! • Shopping Assistant: An AI agent that lives on your store that can help your customers at the right time. We have been building quiz software for almost 10 years now and AI is enabling us to make quizzes even more powerful. This is just the v1 of what we will be releasing in this area. We are so excited to see what you create with these new agentic products. Get creative, we think you will be surprised at how many interesting experiences you can create with Octane AI now.

Matt Schlicht

290,863 просмотров • 9 месяцев назад

What does it actually mean to be AI native? There was no clear guide on the internet for how to become AI native so we built the definitive one (60 min masterclass): 1. An AI native org has 3 layers: people for strategy and taste, agents for execution, and a shared context layer that makes the entire company readable to agents. 2. AI eats the middle of your work. You used to spend 80% of your day on execution. Now agents do that. Your job is the bookends: deciding what to do and judging whether it's good enough. 3. Everyone is a manager now. Your output is the output of your agents. If your agents produce garbage, that's on you. You set them up wrong. 4. Using ChatGPT doesn't make you AI native. That's like having a website and calling yourself a tech company lol. 5. No AI native org without AI native people. Most companies skip straight to the tools. That's why it fails. If your people don't understand how to manage agents, the tech doesn't matter. 6. Making your company "readable" to agents is the real work. Every process, every decision, every piece of knowledge needs to exist in a format an agent can consume. Most companies are nowhere close. 7. Speed without signal is just expensive chaos. You need the system to move fast AND know if you're moving in the right direction. 8. The skill chain is how agents get good at your specific workflows. Skills build on skills. The more you invest in them, the more your company compounds. 9. The moat is the system. People managing agents, agents reading from rich context, the whole thing getting smarter every week. That compounds. Your competitor can copy your tools. They can't copy your system. Full episode with Theo Tabah from LCA on The Startup Ideas Podcast (SIP) 🧃. This is the stuff we normally keep internal but all the sauce is yours. Theo Tabah is the brains behind advising the world's biggest companies on AI and building AI products. Your fav CEO's first call for figuring out AI. You are in for a treat Become AI native in under 60 minutes Watch
56:43

What does it actually mean to be AI native? There was no clear guide on the internet for how to become AI native so we built the definitive one (60 min masterclass): 1. An AI native org has 3 layers: people for strategy and taste, agents for execution, and a shared context layer that makes the entire company readable to agents. 2. AI eats the middle of your work. You used to spend 80% of your day on execution. Now agents do that. Your job is the bookends: deciding what to do and judging whether it's good enough. 3. Everyone is a manager now. Your output is the output of your agents. If your agents produce garbage, that's on you. You set them up wrong. 4. Using ChatGPT doesn't make you AI native. That's like having a website and calling yourself a tech company lol. 5. No AI native org without AI native people. Most companies skip straight to the tools. That's why it fails. If your people don't understand how to manage agents, the tech doesn't matter. 6. Making your company "readable" to agents is the real work. Every process, every decision, every piece of knowledge needs to exist in a format an agent can consume. Most companies are nowhere close. 7. Speed without signal is just expensive chaos. You need the system to move fast AND know if you're moving in the right direction. 8. The skill chain is how agents get good at your specific workflows. Skills build on skills. The more you invest in them, the more your company compounds. 9. The moat is the system. People managing agents, agents reading from rich context, the whole thing getting smarter every week. That compounds. Your competitor can copy your tools. They can't copy your system. Full episode with Theo Tabah from LCA on The Startup Ideas Podcast (SIP) 🧃. This is the stuff we normally keep internal but all the sauce is yours. Theo Tabah is the brains behind advising the world's biggest companies on AI and building AI products. Your fav CEO's first call for figuring out AI. You are in for a treat Become AI native in under 60 minutes Watch

GREG ISENBERG

84,760 просмотров • 3 месяцев назад

Just built a customer support agent because my VA is one scam allegation away from a mental breakdown. This AI slave handles every customer tantrum. Connects to email, pulls Shopify data, and responds in your brand voice minus the trauma. Follow, RT + Comment "support" for the workflow. (Need to do all 3 or my agent won't find your handle to send the DM, give it 20 mins) - Complete setup walkthrough + node-by-node breakdown - Auto-labels emails so you know which Karens to avoid - Pulls customer + order info from Shopify instantly - Responds with your exact brand tone (without the sarcasm) - Accesses brand identity through Pinecone vector memory - Never threatens to quit or asks for a raise Why pay humans to get emotionally destroyed by customers when AI can take the beating instead?
1:36

Just built a customer support agent because my VA is one scam allegation away from a mental breakdown. This AI slave handles every customer tantrum. Connects to email, pulls Shopify data, and responds in your brand voice minus the trauma. Follow, RT + Comment "support" for the workflow. (Need to do all 3 or my agent won't find your handle to send the DM, give it 20 mins) - Complete setup walkthrough + node-by-node breakdown - Auto-labels emails so you know which Karens to avoid - Pulls customer + order info from Shopify instantly - Responds with your exact brand tone (without the sarcasm) - Accesses brand identity through Pinecone vector memory - Never threatens to quit or asks for a raise Why pay humans to get emotionally destroyed by customers when AI can take the beating instead?

Nozz

38,095 просмотров • 1 год назад

🚨 this chinese guy makes over $1,000,000 a year… by building AI agents. no employees. no massive startup. he just keeps building. while most people are still asking ChatGPT random questions, he’s using Claude to build software that solves real problems. this is what people call vibe coding. he opens Claude and says: “build me an AI agent for real estate businesses that creates property videos.” Claude writes the code. builds the interface. adds subscriptions. helps deploy the app. within a day, he has a working product. then he starts building the next one. that’s the part most people don’t understand. he isn’t trying to build one billion-dollar company. he’s building dozens of AI agents, each solving one problem for one industry. → an AI agent for dentists → an AI agent for ecommerce brands → an AI agent for podcasters → an AI agent for real estate businesses each one automates work that people normally do by hand. each one is built with simple prompts. each one can become a real business. the crazy part? you don’t need to be a software engineer anymore. you need to know how to think like a builder. how to spot problems. how to explain solutions to AI. and how to ship. that’s exactly why i’m reading this article: “How to Actually Build Your First AI Agent.” because this is the skill that’s creating the next generation of builders. the people who learn to build AI agents today won’t just use AI. they’ll own the tools everyone else ends up paying for.
1:15

🚨 this chinese guy makes over $1,000,000 a year… by building AI agents. no employees. no massive startup. he just keeps building. while most people are still asking ChatGPT random questions, he’s using Claude to build software that solves real problems. this is what people call vibe coding. he opens Claude and says: “build me an AI agent for real estate businesses that creates property videos.” Claude writes the code. builds the interface. adds subscriptions. helps deploy the app. within a day, he has a working product. then he starts building the next one. that’s the part most people don’t understand. he isn’t trying to build one billion-dollar company. he’s building dozens of AI agents, each solving one problem for one industry. → an AI agent for dentists → an AI agent for ecommerce brands → an AI agent for podcasters → an AI agent for real estate businesses each one automates work that people normally do by hand. each one is built with simple prompts. each one can become a real business. the crazy part? you don’t need to be a software engineer anymore. you need to know how to think like a builder. how to spot problems. how to explain solutions to AI. and how to ship. that’s exactly why i’m reading this article: “How to Actually Build Your First AI Agent.” because this is the skill that’s creating the next generation of builders. the people who learn to build AI agents today won’t just use AI. they’ll own the tools everyone else ends up paying for.

MIKE

39,041 просмотров • 2 месяцев назад

EVERYONE HAS BEEN TALKING ABOUT AI AGENTS. BUT THE REAL UNLOCK ISN'T BETTER PROMPTS: IT'S BETTER LOOPS. Everyone is chasing the perfect prompt. The top AI engineers are chasing something else: loops. Instead of chatting back and forth with AI, they give it one goal and let it continuously improve its own work until the objective is met. Here's the framework: • Define the goal once • AI discovers the work required • Specialized agents tackle tasks in parallel • A verification agent checks the results • If the goal isn't met: loop again. If it is: deliver. The key ingredient is memory. Each cycle remembers what was completed, what failed, and what still needs work, so every iteration gets smarter instead of starting from scratch. There are two approaches: • Open loops: Maximum autonomy, but higher token costs as AI explores freely. • Closed loops: Clear boundaries, defined success metrics, lower costs, and far more practical for everyday workflows. Don't ask AI to complete a task. Build a system where AI can plan, execute, verify, remember, and repeat until the task is actually finished. Prompt engineering got us here. Loop engineering is what takes AI from assistant to autonomous teammate.
19:17

EVERYONE HAS BEEN TALKING ABOUT AI AGENTS. BUT THE REAL UNLOCK ISN'T BETTER PROMPTS: IT'S BETTER LOOPS. Everyone is chasing the perfect prompt. The top AI engineers are chasing something else: loops. Instead of chatting back and forth with AI, they give it one goal and let it continuously improve its own work until the objective is met. Here's the framework: • Define the goal once • AI discovers the work required • Specialized agents tackle tasks in parallel • A verification agent checks the results • If the goal isn't met: loop again. If it is: deliver. The key ingredient is memory. Each cycle remembers what was completed, what failed, and what still needs work, so every iteration gets smarter instead of starting from scratch. There are two approaches: • Open loops: Maximum autonomy, but higher token costs as AI explores freely. • Closed loops: Clear boundaries, defined success metrics, lower costs, and far more practical for everyday workflows. Don't ask AI to complete a task. Build a system where AI can plan, execute, verify, remember, and repeat until the task is actually finished. Prompt engineering got us here. Loop engineering is what takes AI from assistant to autonomous teammate.

Dami-Defi

13,354 просмотров • 2 месяцев назад

“There is no competitor to ServiceTitan until the demo I saw from you guys today.” I got that DM this week from an owner who runs three home services brands. He sent it a few hours after seeing what we’ve been building. We started building AI agents three years ago with a text agent that captured leads and booked jobs. As we kept building, we learned that the system underneath the agent determined how much work the agent could do. Traditional systems of record were built for people clicking through workflows and entering data. An agent sitting on top of one faces major constraints on how much work it can complete. We wanted agents to run workflows from beginning to end, so we built the FSM around them. Larry was our first AI Employee. Because he operates inside our system, he can answer the call, book the job and put it directly on the dispatch board. An AI sitting on top of a traditional system might answer the same call, but often has to hand the rest back to the office staff. In a busy business with thousands of customer interactions, the difference between completing half the work and all of it is massive. Then we started thinking about how AI could impact the entire operation. That led us to build Operator, an orchestrator agent that can work across the schedule, jobs, invoices, payments, customer records and years of conversations. Business owners can give it work in plain English and let it carry that work across the system. As customers have started using Operator, they’ve realized they can ask it to do almost anything. One customer had 2,010 rows of equipment sitting in a spreadsheet. Operator went through the entire file, identified 78 rows that were air filter sizes instead of equipment, and attached the remaining 1,931 units to the correct customer properties. Now when a tech shows up, they already know what equipment is at the house. You can tell Operator: “Here’s our equipment list. Put every unit on the right customer record.” Another owner wanted a price book built from his own history. Operator read 455 past invoices and turned 306 free-text line items into a catalog organized around the work his company performs. He asked: “Read our past invoices and turn them into a starter price list.” One HVAC owner got access before our demo call. He asked Operator who owed him money that day and had payment reminders going out before we got on the phone. This is where we think AI is headed. You tell it what you need done, and it works across your business until the job is finished. If you want to see it, DM me.
1:16

“There is no competitor to ServiceTitan until the demo I saw from you guys today.” I got that DM this week from an owner who runs three home services brands. He sent it a few hours after seeing what we’ve been building. We started building AI agents three years ago with a text agent that captured leads and booked jobs. As we kept building, we learned that the system underneath the agent determined how much work the agent could do. Traditional systems of record were built for people clicking through workflows and entering data. An agent sitting on top of one faces major constraints on how much work it can complete. We wanted agents to run workflows from beginning to end, so we built the FSM around them. Larry was our first AI Employee. Because he operates inside our system, he can answer the call, book the job and put it directly on the dispatch board. An AI sitting on top of a traditional system might answer the same call, but often has to hand the rest back to the office staff. In a busy business with thousands of customer interactions, the difference between completing half the work and all of it is massive. Then we started thinking about how AI could impact the entire operation. That led us to build Operator, an orchestrator agent that can work across the schedule, jobs, invoices, payments, customer records and years of conversations. Business owners can give it work in plain English and let it carry that work across the system. As customers have started using Operator, they’ve realized they can ask it to do almost anything. One customer had 2,010 rows of equipment sitting in a spreadsheet. Operator went through the entire file, identified 78 rows that were air filter sizes instead of equipment, and attached the remaining 1,931 units to the correct customer properties. Now when a tech shows up, they already know what equipment is at the house. You can tell Operator: “Here’s our equipment list. Put every unit on the right customer record.” Another owner wanted a price book built from his own history. Operator read 455 past invoices and turned 306 free-text line items into a catalog organized around the work his company performs. He asked: “Read our past invoices and turn them into a starter price list.” One HVAC owner got access before our demo call. He asked Operator who owed him money that day and had payment reminders going out before we got on the phone. This is where we think AI is headed. You tell it what you need done, and it works across your business until the job is finished. If you want to see it, DM me.

Eric Rea

16,801 просмотров • 19 дней назад