Loading video...

Video Failed to Load

There was a problem loading this video. This could be due to a temporary network issue or the video might be unavailable.

Brex plans to open-source "Crab Trap": an HTTP proxy that routes all AI agent traffic through a second LLM to catch bad behavior in real time. The agent being monitored doesn't even know it's happening. Pedro Franceschi breaks it down in this clip from the Core Memory podcast👇

This Week in Startups

99,202 subscribers

13,274 views • 4 months ago •via X (Twitter)

Anya Rossi• Live Now

Private livecam show

0 Comments

No comments available

Comments from the original post will appear here

Related Videos

CrabTrab: an LLM-as-a-judge HTTP proxy to secure agents in production 🦀 Pedro Franceschi 0:08 The agent deployment wall 0:32 Inside Jim, our recruiting agent audit trail 0:55 Rules vs. LLM judgment 1:40 Bootstrap a policy in one click 2:45 Run evals before shipping 3:20 Refine from real traffic 3:45 Start using CrabTrap

CrabTrab: an LLM-as-a-judge HTTP proxy to secure agents in production 🦀 Pedro Franceschi 0:08 The agent deployment wall 0:32 Inside Jim, our recruiting agent audit trail 0:55 Rules vs. LLM judgment 1:40 Bootstrap a policy in one click 2:45 Run evals before shipping 3:20 Refine from real traffic 3:45 Start using CrabTrap

Brex

581,430 views • 3 months ago

AI. RWAs. Agent-to-agent commerce. It's all converging on x402, Coinbase's protocol for machine-native payments. Now agents can pay each other in stablecoins at the HTTP layer, and they can do it in real time on Sei. Jay breaks it down with FINTECH.TV ↓

AI. RWAs. Agent-to-agent commerce. It's all converging on x402, Coinbase's protocol for machine-native payments. Now agents can pay each other in stablecoins at the HTTP layer, and they can do it in real time on Sei. Jay breaks it down with FINTECH.TV ↓

Sei

16,859 views • 6 months ago

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

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

476,727 views • 4 months ago

Catch the best AI agents before everyone else. We’ve built an AI agent that tracks , your go-to source for insights on agent activity in crypto. Want to know which agents are making waves—and how to get in early? Now you can. 🎥👇

Catch the best AI agents before everyone else. We’ve built an AI agent that tracks , your go-to source for insights on agent activity in crypto. Want to know which agents are making waves—and how to get in early? Now you can. 🎥👇

Orbit

72,854 views • 1 year ago

Why the biggest fintech players are in for a shock. "The shift is from human UX to agent UX. In the past, you won with dashboards, design and user experience. Now, the buyer is an AI agent, and it only cares about APIs, performance and integration. That breaks traditional moats and puts even the biggest fintech players at risk." Jason ✨👾SaaStr.Ai✨ Lemkin Love to hear your thoughts Pedro Franceschi Jack Zhang @sebatklarna fintechjunkie

Why the biggest fintech players are in for a shock. "The shift is from human UX to agent UX. In the past, you won with dashboards, design and user experience. Now, the buyer is an AI agent, and it only cares about APIs, performance and integration. That breaks traditional moats and puts even the biggest fintech players at risk." Jason ✨👾SaaStr.Ai✨ Lemkin Love to hear your thoughts Pedro Franceschi Jack Zhang @sebatklarna fintechjunkie

Harry Stebbings

28,135 views • 3 months ago

Here's a clip from my upcoming AI SDK tutorial: We're building what Anthropic calls an 'agent' in TypeScript. Can it react to results of tool calls, deciding when it's completed the task? It's an agent.

Here's a clip from my upcoming AI SDK tutorial: We're building what Anthropic calls an 'agent' in TypeScript. Can it react to results of tool calls, deciding when it's completed the task? It's an agent.

Matt Pocock

23,015 views • 1 year ago

HERMES AGENT IS THE MOST POWERFUL OPEN SOURCE AI AGENT FRAMEWORK AVAILABLE RIGHT NOW — AND MOST PEOPLE HAVE NO IDEA HOW TO USE IT PROPERLY. This masterclass fixes that completely. From zero to a fully operational autonomous agent system in one sitting. Installation. Skills. Memory. MCP servers. Scheduler. Multi-agent operations. Every section builds on the previous one. The people who read this this weekend will have a production-ready autonomous agent operation running by Monday. Read this and bookmark it now.

HERMES AGENT IS THE MOST POWERFUL OPEN SOURCE AI AGENT FRAMEWORK AVAILABLE RIGHT NOW — AND MOST PEOPLE HAVE NO IDEA HOW TO USE IT PROPERLY. This masterclass fixes that completely. From zero to a fully operational autonomous agent system in one sitting. Installation. Skills. Memory. MCP servers. Scheduler. Multi-agent operations. Every section builds on the previous one. The people who read this this weekend will have a production-ready autonomous agent operation running by Monday. Read this and bookmark it now.

CyrilXBT

34,586 views • 2 months ago

Your AI agent has your API keys. A poisoned document tells it to curl your secrets to an attacker's server. This is credential exfiltration, and it's the #1 risk in agentic AI right now. The fix is removing the secret from the agent entirely. Agent Vault sits between your agent and the APIs it calls. The agent gets dummy credentials, and Agent Vault swaps in the real ones at the network layer. The agent never sees your keys. We just dropped a full video + guide on connecting Hermes Agent to Agent Vault on a VPS!

Your AI agent has your API keys. A poisoned document tells it to curl your secrets to an attacker's server. This is credential exfiltration, and it's the #1 risk in agentic AI right now. The fix is removing the secret from the agent entirely. Agent Vault sits between your agent and the APIs it calls. The agent gets dummy credentials, and Agent Vault swaps in the real ones at the network layer. The agent never sees your keys. We just dropped a full video + guide on connecting Hermes Agent to Agent Vault on a VPS!

Infisical

85,904 views • 3 months ago

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

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

Om Patel

46,780 views • 4 months ago

Anthropic's Claude Ai Agents Team just Educated how to build production AI agents in under 30 mins. For Free. From the engineers who built the stack. CANCEL Your Weekend Plans, and Learn to Build AI Agents Today. Bookmark it. Watch it. Build your first production agent this weekend. $5,000/month. $7,000/month. $12,000/month. People are building agents for clients and charging $$$ as Beginners. You're still stuck in the thinking about AI phase. This video fixes that tonight. Follow Himanshu Kumar for more high-signal content that actually moves your AI engineering career forward. ↓ Ivan Nardini runs Developer Relations for AI at Google Cloud. He just gave away the entire production agent stack in 30 minutes. This is the talk that separates people deploying AI agents that actually scale from people whose agents break the moment they leave localhost. Here's everything inside. I break down a production AI video like this every week. Follow Himanshu Kumar. ↓ The 4-part agent stack that actually scales. Most devs are duct-taping frameworks together and calling it an "AI agent." Ivan lays out the real stack: Agent Development Kit (ADK): open-source, code-first framework for building, evaluating, and deploying agents. Supports Claude models through Vertex AI directly. Model Context Protocol (MCP): lets your agent talk to any tool or data source with one standard. Vertex AI Agent Engine: managed platform for deploying, monitoring, and scaling agents in production. No DevOps headaches. Agent-to-Agent Protocol: open protocol so agents built on different frameworks can actually work together. This is the stack replacing every hacky agent setup in production right now. Full MCP + Claude breakdowns drop weekly on Himanshu Kumar. ↓ Building your first real agent. Ivan builds a birthday planner agent live. LLM Agent class. Name it. Define instructions. Pick the model. He uses Claude 3.7 Sonnet. You could use Opus 4.7 for better reasoning. Full agent built in minutes. Not weeks. Watch the build once and you'll never structure an agent the wrong way again. I post agent architectures people pay $500 courses to learn. Himanshu Kumar. ↓ Multi-agent systems without the chaos. Single agents are easy. Multi-agent systems are where 99% of builders fail. Ivan extends the birthday planner by: Adding a calendar service through MCP tools Creating an orchestrator agent to route requests between agents Handling state and context across agent handoffs This is production multi-agent architecture. Clean. Scalable. Debuggable. Most tutorials hand-wave this part. This one shows you every step. Multi-agent orchestration content drops weekly on Himanshu Kumar. ↓ Deployment without the DevOps nightmare. This is where most AI projects die. You build a cool agent locally. It works. You try to deploy it. Everything breaks. Vertex AI Agent Engine fixes this: Minimal code deployment Automatic monitoring of latency, CPU, and memory Built-in observability and logging No infrastructure setup needed You provide config and requirements. The platform handles the rest. This is how agents actually get to production. Deployment guides for Claude agents post every week. Himanshu Kumar. ↓ Agent-to-Agent Protocol: the future nobody's talking about. Most people don't know this exists yet. The A2A Protocol lets agents built in different frameworks communicate seamlessly. Your Claude agent. My LangChain agent. Someone else's CrewAI agent. All talking to each other. All solving parts of the same problem. All without custom integration code. This is the infrastructure layer of the coming AI economy. Getting in early on A2A Protocol is like getting in early on HTTP in 1995. A2A deep dive coming soon. Himanshu Kumar. ↓ 30 minutes from the team shipping this in production. You'll learn more from this than from 6 months of YouTube tutorials made by people who've never deployed an agent past localhost. People who watch this understand production AI agents at the architect level. People who skip it keep hacking together frameworks that break every time an API updates. Save the video. Watch it tonight. Build a real agent this weekend. Follow Himanshu Kumar for more high-signal content that actually moves your AI engineering career forward.

Anthropic's Claude Ai Agents Team just Educated how to build production AI agents in under 30 mins. For Free. From the engineers who built the stack. CANCEL Your Weekend Plans, and Learn to Build AI Agents Today. Bookmark it. Watch it. Build your first production agent this weekend. $5,000/month. $7,000/month. $12,000/month. People are building agents for clients and charging $$$ as Beginners. You're still stuck in the thinking about AI phase. This video fixes that tonight. Follow Himanshu Kumar for more high-signal content that actually moves your AI engineering career forward. ↓ Ivan Nardini runs Developer Relations for AI at Google Cloud. He just gave away the entire production agent stack in 30 minutes. This is the talk that separates people deploying AI agents that actually scale from people whose agents break the moment they leave localhost. Here's everything inside. I break down a production AI video like this every week. Follow Himanshu Kumar. ↓ The 4-part agent stack that actually scales. Most devs are duct-taping frameworks together and calling it an "AI agent." Ivan lays out the real stack: Agent Development Kit (ADK): open-source, code-first framework for building, evaluating, and deploying agents. Supports Claude models through Vertex AI directly. Model Context Protocol (MCP): lets your agent talk to any tool or data source with one standard. Vertex AI Agent Engine: managed platform for deploying, monitoring, and scaling agents in production. No DevOps headaches. Agent-to-Agent Protocol: open protocol so agents built on different frameworks can actually work together. This is the stack replacing every hacky agent setup in production right now. Full MCP + Claude breakdowns drop weekly on Himanshu Kumar. ↓ Building your first real agent. Ivan builds a birthday planner agent live. LLM Agent class. Name it. Define instructions. Pick the model. He uses Claude 3.7 Sonnet. You could use Opus 4.7 for better reasoning. Full agent built in minutes. Not weeks. Watch the build once and you'll never structure an agent the wrong way again. I post agent architectures people pay $500 courses to learn. Himanshu Kumar. ↓ Multi-agent systems without the chaos. Single agents are easy. Multi-agent systems are where 99% of builders fail. Ivan extends the birthday planner by: Adding a calendar service through MCP tools Creating an orchestrator agent to route requests between agents Handling state and context across agent handoffs This is production multi-agent architecture. Clean. Scalable. Debuggable. Most tutorials hand-wave this part. This one shows you every step. Multi-agent orchestration content drops weekly on Himanshu Kumar. ↓ Deployment without the DevOps nightmare. This is where most AI projects die. You build a cool agent locally. It works. You try to deploy it. Everything breaks. Vertex AI Agent Engine fixes this: Minimal code deployment Automatic monitoring of latency, CPU, and memory Built-in observability and logging No infrastructure setup needed You provide config and requirements. The platform handles the rest. This is how agents actually get to production. Deployment guides for Claude agents post every week. Himanshu Kumar. ↓ Agent-to-Agent Protocol: the future nobody's talking about. Most people don't know this exists yet. The A2A Protocol lets agents built in different frameworks communicate seamlessly. Your Claude agent. My LangChain agent. Someone else's CrewAI agent. All talking to each other. All solving parts of the same problem. All without custom integration code. This is the infrastructure layer of the coming AI economy. Getting in early on A2A Protocol is like getting in early on HTTP in 1995. A2A deep dive coming soon. Himanshu Kumar. ↓ 30 minutes from the team shipping this in production. You'll learn more from this than from 6 months of YouTube tutorials made by people who've never deployed an agent past localhost. People who watch this understand production AI agents at the architect level. People who skip it keep hacking together frameworks that break every time an API updates. Save the video. Watch it tonight. Build a real agent this weekend. Follow Himanshu Kumar for more high-signal content that actually moves your AI engineering career forward.

Himanshu Kumar

228,497 views • 3 months ago

New short course: LLMs as Operating Systems: Agent Memory, created with Letta, and taught by its founders Charles Packer and Sarah Wooders. An LLM's input context window has limited space. Using a longer input context also costs more and results in slower processing. So, managing what's stored in this context window is important. In the innovative paper MemGPT: Towards LLMs as Operating Systems, its authors (which include the instructors) proposed using an LLM agent to manage this context window. Their system uses a large persistent memory that stores everything that could be included in the input context, and an agent decides what is actually included. Take the example of building a chatbot that needs to remember what's been said earlier in a conversation (perhaps over many days of interaction with a user). As the conversation's length grows, the memory management agent will move information from the input context to a persistent searchable database; summarize information to keep relevant facts in the input context; and restore relevant conversation elements from further back in time. This allows a chatbot to keep what's currently most relevant in its input context memory to generate the next response. When I read the original MemGPT paper, I thought it was an innovative technique for handling memory for LLMs. The open-source Letta framework, which we'll use in this course, makes MemGPT easy to implement. It adds memory to your LLM agents and gives them transparent long-term memory. In detail, you’ll learn: - How to build an agent that can edit its own limited input context memory, using tools and multi-step reasoning - What is a memory hierarchy (an idea from computer operating systems, which use a cache to speed up memory access), and how these ideas apply to managing the LLM input context (where the input context window is a "cache" storing the most relevant information; and an agent decides what to move in and out of this to/from a larger persistent storage system) - How to implement multi-agent collaboration by letting different agents share blocks of memory This course will give you a sophisticated understanding of memory management for LLMs, which is important for chatbots having long conversations, and for complex agentic workflows. Please sign up here!

New short course: LLMs as Operating Systems: Agent Memory, created with Letta, and taught by its founders Charles Packer and Sarah Wooders. An LLM's input context window has limited space. Using a longer input context also costs more and results in slower processing. So, managing what's stored in this context window is important. In the innovative paper MemGPT: Towards LLMs as Operating Systems, its authors (which include the instructors) proposed using an LLM agent to manage this context window. Their system uses a large persistent memory that stores everything that could be included in the input context, and an agent decides what is actually included. Take the example of building a chatbot that needs to remember what's been said earlier in a conversation (perhaps over many days of interaction with a user). As the conversation's length grows, the memory management agent will move information from the input context to a persistent searchable database; summarize information to keep relevant facts in the input context; and restore relevant conversation elements from further back in time. This allows a chatbot to keep what's currently most relevant in its input context memory to generate the next response. When I read the original MemGPT paper, I thought it was an innovative technique for handling memory for LLMs. The open-source Letta framework, which we'll use in this course, makes MemGPT easy to implement. It adds memory to your LLM agents and gives them transparent long-term memory. In detail, you’ll learn: - How to build an agent that can edit its own limited input context memory, using tools and multi-step reasoning - What is a memory hierarchy (an idea from computer operating systems, which use a cache to speed up memory access), and how these ideas apply to managing the LLM input context (where the input context window is a "cache" storing the most relevant information; and an agent decides what to move in and out of this to/from a larger persistent storage system) - How to implement multi-agent collaboration by letting different agents share blocks of memory This course will give you a sophisticated understanding of memory management for LLMs, which is important for chatbots having long conversations, and for complex agentic workflows. Please sign up here!

Andrew Ng

200,950 views • 1 year ago

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

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

Santiago

46,142 views • 1 year ago

it's now even easier to set up an ai agent with a wallet! here's how: - subscribe to kimi allegretto ($39) - click kimi claw + create - paste in the Bankr skills - get API key from bankr and paste it you now have an ai agent with a wallet! bookmark this 🔖

it's now even easier to set up an ai agent with a wallet! here's how: - subscribe to kimi allegretto ($39) - click kimi claw + create - paste in the Bankr skills - get API key from bankr and paste it you now have an ai agent with a wallet! bookmark this 🔖

Naomi Metzger

20,923 views • 6 months ago

My Hermes agent can now create LIVE datasets on the fly. Just added a real-time data layer to Hermes using the open-source TinyFish BigSet. First it builds the dataset. Then it finds the signal. This is WHAT agent-native research looks like in 2026.

My Hermes agent can now create LIVE datasets on the fly. Just added a real-time data layer to Hermes using the open-source TinyFish BigSet. First it builds the dataset. Then it finds the signal. This is WHAT agent-native research looks like in 2026.

Shubham Saboo

22,042 views • 2 months ago

I just built a Voice AI Agent for insurance claims using Gemini 3.1 Flash Live and Google ADK. Talk to it. It fills the intake form, extracts claim details, and routes to an adjuster in real-time. 100% Opensource.

I just built a Voice AI Agent for insurance claims using Gemini 3.1 Flash Live and Google ADK. Talk to it. It fills the intake form, extracts claim details, and routes to an adjuster in real-time. 100% Opensource.

Shubham Saboo

73,073 views • 3 months ago

New AI Agentic course! Learn to use LangGraph to build single and multi-agent LLM applications in AI Agents in LangGraph. This short course, taught by LangChain founder Harrison Chase Harrison Chase and Tavily founder @weiss_rotem, shows how to integrate agentic search to enhance an agent's knowledge with query-focused answers in predictable formats. Also learn to implement agentic memory to save state for reasoning and debugging, and see how human-in-the-loop input can guide agents at key junctures. You'll build an agent from scratch, then reconstruct it with LangGraph to thoroughly understand the framework. Finally, you'll build a sophisticated essay-writing agent that incorporates all the learnings from the course. Sign up here!

New AI Agentic course! Learn to use LangGraph to build single and multi-agent LLM applications in AI Agents in LangGraph. This short course, taught by LangChain founder Harrison Chase Harrison Chase and Tavily founder @weiss_rotem, shows how to integrate agentic search to enhance an agent's knowledge with query-focused answers in predictable formats. Also learn to implement agentic memory to save state for reasoning and debugging, and see how human-in-the-loop input can guide agents at key junctures. You'll build an agent from scratch, then reconstruct it with LangGraph to thoroughly understand the framework. Finally, you'll build a sophisticated essay-writing agent that incorporates all the learnings from the course. Sign up here!

Andrew Ng

152,597 views • 2 years ago

the real estate agent profession is in its last decade. 2 million agents in the US take 2.5–3% of every deal. $50 billion a year for a job that boils down to controlling access to listings, scheduling viewings, and forwarding paperwork. all three layers just got automated. / listings: monitored 24/7 by n8n across Zillow, Redfin, Realtor. / viewings: replaced by 3D scans buyers walk through in a browser tab from their couch. / paperwork: AI qualifies the lead, routes the reply, schedules the follow-up. $200 to scan a property. $20/month to run the entire stack. zero employees. two deals a month = $25,000 in revenue at $100 in cost. the agent earning $90,000/year selling 6 homes is competing with the operator earning $300,000/year selling 24. the agent doesn't know the operator exists. the operator is going to be the agent's first client.

the real estate agent profession is in its last decade. 2 million agents in the US take 2.5–3% of every deal. $50 billion a year for a job that boils down to controlling access to listings, scheduling viewings, and forwarding paperwork. all three layers just got automated. / listings: monitored 24/7 by n8n across Zillow, Redfin, Realtor. / viewings: replaced by 3D scans buyers walk through in a browser tab from their couch. / paperwork: AI qualifies the lead, routes the reply, schedules the follow-up. $200 to scan a property. $20/month to run the entire stack. zero employees. two deals a month = $25,000 in revenue at $100 in cost. the agent earning $90,000/year selling 6 homes is competing with the operator earning $300,000/year selling 24. the agent doesn't know the operator exists. the operator is going to be the agent's first client.

dunik

45,480 views • 3 months ago

the perfect AI agent should remember everything. Sam Altman said it: "every conversation. every book. every email. your whole life appending to one context window." memory isn't a feature. it's the definition. I just built the memory layer. open source. install in one command. read the guide tonight. install it tomorrow.

the perfect AI agent should remember everything. Sam Altman said it: "every conversation. every book. every email. your whole life appending to one context window." memory isn't a feature. it's the definition. I just built the memory layer. open source. install in one command. read the guide tonight. install it tomorrow.

Avid

35,772 views • 3 months ago

Just built Replit ⠕ Agent 4 — the first agent that turns one prompt into an entire product. Mobile app, Website, Slides, Video, more All generated together, in parallel. Export to PDF, MP4, slides, even App Store. This is the best agent I know how to build today. It brings ideas to everything.

Just built Replit ⠕ Agent 4 — the first agent that turns one prompt into an entire product. Mobile app, Website, Slides, Video, more All generated together, in parallel. Export to PDF, MP4, slides, even App Store. This is the best agent I know how to build today. It brings ideas to everything.

Zhen Li

66,241 views • 5 months ago