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Understanding Cortex Agent: The autonomous DeFi execution agent on Solana 🧵 We all know how crypto hits us daily. Constantly watching markets, debating setups in your head, executing trades, trying to optimize everything manually. It eats your time fast and that time has a real cost. Now think about... this. What if all of that was handled by a multi-agent AI system with four specialist analysts, two adversarial researchers, and a dedicated execution layer? All synced with real-time data, updating every second. No need to micromanage every move. No burnout from doing everything yourself. There’s a project just starting to build, and we’re still early enough to follow every step of its progress. There’s an event from Superteam Earn for those who create the best thread about Cortex Agent. Hope you can enjoy mine, let me introduce it below 👇show more

Royale

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Your engineering team is about to snap. And your AI coding agent is making it worse. Introducing CodeRabbit Agent for Slack 🎉 A second brain for engineering teams. Because your tribal knowledge lives in Slack threads nobody can find. At CodeRabbit, we review millions of PRs every week and know how ace engineering teams operate on the planet. The same three things slow every team we see: - Context and decisions that live elsewhere. - Lack of a team-level durable knowledge base - And a trust layer that gives a safety net to your teams Built for Agentic SDLC workflows, CodeRabbit Agent for Slack solves all three problems in one shot while enabling teams to collaborate in real-time with the Agent. CodeRabbit Agent is Slack-native and builds your team’s operating context from every thread, every decision, every conversation your team has ever had. Make your team’s context compound!

Your engineering team is about to snap. And your AI coding agent is making it worse. Introducing CodeRabbit Agent for Slack 🎉 A second brain for engineering teams. Because your tribal knowledge lives in Slack threads nobody can find. At CodeRabbit, we review millions of PRs every week and know how ace engineering teams operate on the planet. The same three things slow every team we see: - Context and decisions that live elsewhere. - Lack of a team-level durable knowledge base - And a trust layer that gives a safety net to your teams Built for Agentic SDLC workflows, CodeRabbit Agent for Slack solves all three problems in one shot while enabling teams to collaborate in real-time with the Agent. CodeRabbit Agent is Slack-native and builds your team’s operating context from every thread, every decision, every conversation your team has ever had. Make your team’s context compound!

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Today, Sierra is releasing Ghostwriter, our agent for building agents. With Ghostwriter, you can create an AI agent for your customer experience — one that can chat, pick up the phone, speak dozens of languages, take action on your systems of record, and be protected with industry-leading guardrails — simply by having a conversation. No clicking, no forms, no menus. Codex and Claude Code have transformed how we build software, making it possible for software engineers to orchestrate and review the work rather than doing all the work themselves. We think the same transformation will happen for all software. Rather than every enterprise app having a web app for humans and an API for automation, every software platform’s UI will be an agent that can do the work on your behalf. I recorded a demo of my building and optimizing an agent with Ghostwriter so you can see how powerful and easy it is to use. It’s completely changed the way our early adopters build agents, and it’s changed the way I think about the software industry. Let me know what you think, and, if you’re interested in trying it out at your business, please reach out directly.

Today, Sierra is releasing Ghostwriter, our agent for building agents. With Ghostwriter, you can create an AI agent for your customer experience — one that can chat, pick up the phone, speak dozens of languages, take action on your systems of record, and be protected with industry-leading guardrails — simply by having a conversation. No clicking, no forms, no menus. Codex and Claude Code have transformed how we build software, making it possible for software engineers to orchestrate and review the work rather than doing all the work themselves. We think the same transformation will happen for all software. Rather than every enterprise app having a web app for humans and an API for automation, every software platform’s UI will be an agent that can do the work on your behalf. I recorded a demo of my building and optimizing an agent with Ghostwriter so you can see how powerful and easy it is to use. It’s completely changed the way our early adopters build agents, and it’s changed the way I think about the software industry. Let me know what you think, and, if you’re interested in trying it out at your business, please reach out directly.

Bret Taylor

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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

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LOVE was made entirely inside invideo. Not with one agent. I built a multi-agent system — a director, and around it casting, locations, storyboard, then one agent per main scene, all working at the same time. In the breakdown I go through the things that struck me most: — Expert agents talking to each other — Infinite memory — Slate editor — And a Project Analyst at the end, interviewing every other agent on what to change next time There's a lot more to say. I'll take it one thing at a time in the next posts. Invideo Agent Two isn't just an agent. It's a collaborator you shape into what you need. Full breakdown ↓

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Satya Nadella reveals his coding agent pulls every hyperscaler and neoclouds' SEC filings into a dashboard that refreshes every day for real-time ROIC "And this is the other aspect of it, which is the enterprise context combined with the world's context. In fact, I go to the SEC filings of every cloud provider, hyperscaler, each of these neoclouds. It's in real time." "I have a data runner in Fabric that brings all that data, puts it into a semantic model that then gets read by my coding agent and then surfaces it as a dashboard. And every day it's fresh." "So I have the entirety of every SEC filing that goes out there, plus all of my internal analysis constantly coming together, giving me real-time ROIC by layer."

Satya Nadella reveals his coding agent pulls every hyperscaler and neoclouds' SEC filings into a dashboard that refreshes every day for real-time ROIC "And this is the other aspect of it, which is the enterprise context combined with the world's context. In fact, I go to the SEC filings of every cloud provider, hyperscaler, each of these neoclouds. It's in real time." "I have a data runner in Fabric that brings all that data, puts it into a semantic model that then gets read by my coding agent and then surfaces it as a dashboard. And every day it's fresh." "So I have the entirety of every SEC filing that goes out there, plus all of my internal analysis constantly coming together, giving me real-time ROIC by layer."

Fireside Alpha

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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.

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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 Aufrufe • vor 1 Jahr

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AMIKO

24,112 Aufrufe • vor 10 Monaten

you let your agent keep notes, because the alternative was explaining yourself every morning it stopped asking, and it started repeating things that had stopped being true every guide on this covers what to save and where to put it, private for one agent, shared for the team not one of them covers how a fact leaves the agent writes its own memory while it works, so a guess it made on a tired tuesday gets stored in the same shape as something you told it, and after that the two are indistinguishable i counted mine this morning: forty files, six of them about decisions we reversed months ago, all forty read into every session since you did not give it a memory you gave it a record with no way to be corrected

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Lagosaur

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Dan Shipper

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Google

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Is your job actually safe from AI? I’ve been thinking about this a lot. To get some real answers, I'm using Frank AI Researcher to let you talk to an AI agent yourself. It takes 5 minutes. You tell the agent what you do every day and what tech you use. Then, it gives you a "future-proof score" to show how much of your work can be automated right now. I really want to see what you guys find out. Link to talk to the agent:👇

AI Panda

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MERRY MAN.🧠 I’ve been quietly building Cerebro’s own privacy focused AI agent. Instead of relying on someone else’s agent layer, we wanted to build our own and prove the Cerebro thesis from the inside. Your research. Your code. Your data. Your workflows. Kept private. But Merry Man is also about something we think the last wave of crypto AI agents was missing: Consumption. An agent that’s actually being used needs inference. It needs compute. And that creates a real destination for the inference economy we’ve been building around Cerebro. We don’t just want an AI agent with a token attached to it. We want to show what happens when the agent actually participates in the economy underneath it. The countdown has started. More of Merry Man will be revealed every day. Let's keep going.🧠

Cerebro

49,366 Aufrufe • vor 22 Tagen

M E S S I E R | Aver AI It's a real pleasure to collaborate with Aver AI, a true builder's project that has delivered on its promise by integrating the P2P Exchange into its on-chain #AI agent. This integration allows anyone to buy or sell tokens directly on the P2P Exchange via its on-chain AI #agent, while also providing real-time market analysis, technical insights, and optimized strategy execution. Check out the live showcase below of the first-ever transaction using the AverAI agent and see just how easy it is!

M E S S I E R | Aver AI It's a real pleasure to collaborate with Aver AI, a true builder's project that has delivered on its promise by integrating the P2P Exchange into its on-chain #AI agent. This integration allows anyone to buy or sell tokens directly on the P2P Exchange via its on-chain AI #agent, while also providing real-time market analysis, technical insights, and optimized strategy execution. Check out the live showcase below of the first-ever transaction using the AverAI agent and see just how easy it is!

MESSIER | M87

26,236 Aufrufe • vor 1 Jahr

In two years, every new tech company will run on a CRM you can vibe code to fit your business. This CRM will not be built from scratch on a coding platform though. It will be built on top of managed infrastructure with complete data capture, indices designed for LLMs to understand the whole picture, clean APIs, curated UI frameworks designed for selling, enterprise-grade security, and come with 24/7 support. You’ll instruct the agent using natural language and it will write the code + run it for you. That’s what we’re building at Lightfield and today we’re announcing step two of our plan - code execution. You can now ask your agent to build programs, artifacts, and run complex analysis instantly. It does this by writing and running Python in a high performance sandbox using full customer memory — including every email, meeting, and note that Lightfield has captured — and reasoning across every relationship to deliver high quality work. Ask your agent to build a competitive battle card before a call tomorrow. It pulls positioning, objections, and win/loss patterns from real conversations. Ask it to flag every open deal where your champion's engagement has dropped or sentiment has shifted. It reads across every conversation and tells you where to focus. Ask it to build a pipeline review with charts and graphs for your board. It produces the whole thing in minutes. Here’s what we did with it this week: → We asked our agent to grade our sales team on discovery, rapport, and closing. It gave a structured scorecard with specific examples from real conversations. → Our GTM team asked the agent to build a plan to expand one of our enterprise customers. It pulled competitive threats, upsell paths, stakeholder mapping, and a phased execution plan — in minutes. → We used it to find every feature request from the last quarter that our engineering team has since shipped, and draft a personalized follow-up to each customer using their original words. It closed loops across dozens of accounts that would have taken days to track down manually This is the first step towards building any custom GTM workflow in natural language on top of what Lightfield knows about your business - a world model built from every single interaction your team has had with customers.

In two years, every new tech company will run on a CRM you can vibe code to fit your business. This CRM will not be built from scratch on a coding platform though. It will be built on top of managed infrastructure with complete data capture, indices designed for LLMs to understand the whole picture, clean APIs, curated UI frameworks designed for selling, enterprise-grade security, and come with 24/7 support. You’ll instruct the agent using natural language and it will write the code + run it for you. That’s what we’re building at Lightfield and today we’re announcing step two of our plan - code execution. You can now ask your agent to build programs, artifacts, and run complex analysis instantly. It does this by writing and running Python in a high performance sandbox using full customer memory — including every email, meeting, and note that Lightfield has captured — and reasoning across every relationship to deliver high quality work. Ask your agent to build a competitive battle card before a call tomorrow. It pulls positioning, objections, and win/loss patterns from real conversations. Ask it to flag every open deal where your champion's engagement has dropped or sentiment has shifted. It reads across every conversation and tells you where to focus. Ask it to build a pipeline review with charts and graphs for your board. It produces the whole thing in minutes. Here’s what we did with it this week: → We asked our agent to grade our sales team on discovery, rapport, and closing. It gave a structured scorecard with specific examples from real conversations. → Our GTM team asked the agent to build a plan to expand one of our enterprise customers. It pulled competitive threats, upsell paths, stakeholder mapping, and a phased execution plan — in minutes. → We used it to find every feature request from the last quarter that our engineering team has since shipped, and draft a personalized follow-up to each customer using their original words. It closed loops across dozens of accounts that would have taken days to track down manually This is the first step towards building any custom GTM workflow in natural language on top of what Lightfield knows about your business - a world model built from every single interaction your team has had with customers.

Keith Peiris

27,634 Aufrufe • vor 7 Monaten

DeFi will become far more important than most people realise. And not because more people start using it. Because the users stop being people at all. When you think about it DeFi is actually better suited to machines than it is to us. An agent doesn't need a website, or buttons, or a front end. You can strip all of it away and just let the agent plug straight into the protocol, do the job, and leave. Now play it forward… An AI agent taking out a flash loan at 3 am, another paying half a cent for a bit of data to a different agent, and one moving its treasury across three chains in a second because a swap suddenly got cheaper somewhere. Genuinely hard to wrap your head around… but this is the user base DeFi ends up serving. This is the Economic Singularity I've been telling you about at the ground level, where the financial system is being rebuilt for users with no faces, no emotions, and no need for downtime.

DeFi will become far more important than most people realise. And not because more people start using it. Because the users stop being people at all. When you think about it DeFi is actually better suited to machines than it is to us. An agent doesn't need a website, or buttons, or a front end. You can strip all of it away and just let the agent plug straight into the protocol, do the job, and leave. Now play it forward… An AI agent taking out a flash loan at 3 am, another paying half a cent for a bit of data to a different agent, and one moving its treasury across three chains in a second because a swap suddenly got cheaper somewhere. Genuinely hard to wrap your head around… but this is the user base DeFi ends up serving. This is the Economic Singularity I've been telling you about at the ground level, where the financial system is being rebuilt for users with no faces, no emotions, and no need for downtime.

Raoul Pal

85,297 Aufrufe • vor 2 Monaten