正在加载视频...

视频加载失败

加载此视频时出现问题。这可能是由于临时网络问题，或视频可能不可用。

>>> OptimAI Agentic Trading // BUILD LOG #03 Research Agent ...... ✓ Online Strategy Agent ...... ✓ Online Backtesting Engine ......⚙️ Training Reasoning Engine ...... ⚙️ Optimizing Internal Testing ...... In Progress Release ...... Incoming >>> This week we've been improving our Trading Agent. It now researches ideas, generates... show more

OptimAI Network

99,084 subscribers

15,718 次观看 • 6 天前 •via X (Twitter)

Anya Rossi• Live Now

Private livecam show

0 条评论

暂无评论

原始帖子的评论将显示在这里

相关视频

OpenAI engineer: “90% of our engineers use Codex subagent harnesses + loops to ship code 5× faster. close the agent loop. Give an agent a way to verify its own output.” in 30-minute talk, an OpenAI engineer explains how to build self-improving agentic systems with harnesses. Worth more than a $500 agentic course online. Watch it today, then read how hedge funds apply loop engineering in trading.

OpenAI engineer: “90% of our engineers use Codex subagent harnesses + loops to ship code 5× faster. close the agent loop. Give an agent a way to verify its own output.” in 30-minute talk, an OpenAI engineer explains how to build self-improving agentic systems with harnesses. Worth more than a $500 agentic course online. Watch it today, then read how hedge funds apply loop engineering in trading.

Movez

41,773 次观看 • 1 个月前

Man Group, the world's largest publicly listed hedge fund, $150B+ AUM, 40+ years of systematic trading, just revealed exactly how they're using AI agents to build trading strategies. this is their actual pipeline: agent 1: reads academic papers, analyzes datasets, reasons about whether there's an economic hypothesis worth testing. agent 2: writes the code to build the signal. agent 3: pulls the market data and runs the backtests. agent 4: evaluates the output of the prior agents. "there've been 15 to 20 models at last count that started as ideas generated by AI, went all the way through signal construction, the validation process, were reviewed and validated by a human investment committee, and deemed fit and proper to manage client assets." "this is very real. it's not make-believe at this point." a $150B hedge fund running a multi-agent pipeline that goes from reading a paper to trading live capital, with humans only entering the loop at the final approval stage. and they've been doing it for over a year and a half.

Man Group, the world's largest publicly listed hedge fund, $150B+ AUM, 40+ years of systematic trading, just revealed exactly how they're using AI agents to build trading strategies. this is their actual pipeline: agent 1: reads academic papers, analyzes datasets, reasons about whether there's an economic hypothesis worth testing. agent 2: writes the code to build the signal. agent 3: pulls the market data and runs the backtests. agent 4: evaluates the output of the prior agents. "there've been 15 to 20 models at last count that started as ideas generated by AI, went all the way through signal construction, the validation process, were reviewed and validated by a human investment committee, and deemed fit and proper to manage client assets." "this is very real. it's not make-believe at this point." a $150B hedge fund running a multi-agent pipeline that goes from reading a paper to trading live capital, with humans only entering the loop at the final approval stage. and they've been doing it for over a year and a half.

Goshawk Trades

37,903 次观看 • 1 个月前

✨New LangChain Academy Course: Learn to build with LangSmith Agent Builder 🪄 Learn how to build your own agents with LangSmith Agent Builder. Anyone can now build agents for complex daily tasks, without writing code. We built Agent Builder for anyone whose day gets swallowed by routine work. The research, follow-ups, updates, scheduling, and status checks that are essential to your operations, but quickly take over your calendar. Agent Builder is different from traditional workflow automations. You don’t need to map every step, tinker with if-this-then-that branching, or babysit dependencies. You just give it feedback like you would a teammate, and the agent learns using its memory. In this quickstart course, you'll learn how to: - Build an email agent - Understand what agents can do - Improve your agent - Set up triggers and your Agent Inbox Enroll for free ➡️

✨New LangChain Academy Course: Learn to build with LangSmith Agent Builder 🪄 Learn how to build your own agents with LangSmith Agent Builder. Anyone can now build agents for complex daily tasks, without writing code. We built Agent Builder for anyone whose day gets swallowed by routine work. The research, follow-ups, updates, scheduling, and status checks that are essential to your operations, but quickly take over your calendar. Agent Builder is different from traditional workflow automations. You don’t need to map every step, tinker with if-this-then-that branching, or babysit dependencies. You just give it feedback like you would a teammate, and the agent learns using its memory. In this quickstart course, you'll learn how to: - Build an email agent - Understand what agents can do - Improve your agent - Set up triggers and your Agent Inbox Enroll for free ➡️

LangChain

26,513 次观看 • 6 个月前

I've been working on something dear to me for a while after a pitch review session with Nzube Ezudo, still in progress and I'd like to share that in public here. Gildore Arena is an agentic trading Arena. We help traders replace blind trust with visible, competitive, machine-disciplined trading performance. Right now, we do this by turning live trading strategies into transparent AI agents that users can inspect, compare, predict, and eventually copy trade/subscribe to the agent. it is now safe to `Fire Your Fund Manager`. it's fundraising on BAGS

I've been working on something dear to me for a while after a pitch review session with Nzube Ezudo, still in progress and I'd like to share that in public here. Gildore Arena is an agentic trading Arena. We help traders replace blind trust with visible, competitive, machine-disciplined trading performance. Right now, we do this by turning live trading strategies into transparent AI agents that users can inspect, compare, predict, and eventually copy trade/subscribe to the agent. it is now safe to `Fire Your Fund Manager`. it's fundraising on BAGS

InspirationGx 🦀

14,230 次观看 • 2 个月前

Build a loan underwriting agent workflow in ~5 mins 💵🤖 (No code, just English) I built an agentic workflow that can process incoming PDFs of financial statements: checking statements, bank statements, brokerage accounts, and extract/normalize the financials in each source of data. I did this by specifying the schemas and classification logic in plain English; our agent compiles this into a deterministic workflow with calls to agentic parsing to extract out all the information that you need. Our agent builder is now in LlamaCloud, you can sign up and try it yourself! Blog: Sign up:

Build a loan underwriting agent workflow in ~5 mins 💵🤖 (No code, just English) I built an agentic workflow that can process incoming PDFs of financial statements: checking statements, bank statements, brokerage accounts, and extract/normalize the financials in each source of data. I did this by specifying the schemas and classification logic in plain English; our agent compiles this into a deterministic workflow with calls to agentic parsing to extract out all the information that you need. Our agent builder is now in LlamaCloud, you can sign up and try it yourself! Blog: Sign up:

Jerry Liu

30,015 次观看 • 6 个月前

Skeptics still think AI generation looks cartoonish, but the raw, lifelike details in this short drama prove otherwise. I used the Short Drama Agent Tool on Pippit to build a complete Short Drama, and its Dreamina Seedance 2.0 engine completely eliminates that cartoonish, plastic look. Created these cases with it see below 👇

Skeptics still think AI generation looks cartoonish, but the raw, lifelike details in this short drama prove otherwise. I used the Short Drama Agent Tool on Pippit to build a complete Short Drama, and its Dreamina Seedance 2.0 engine completely eliminates that cartoonish, plastic look. Created these cases with it see below 👇

Lex

14,606 次观看 • 3 个月前

Ken Griffin, CEO of Citadel, said: "I'm fairly depressed watching AI do a week of PhD work in a few hours" He's worth $51.2 billion and 20% of every stock trade in America goes through his firm. The money isn't what's bothering him. Citadel pays $400,000 to $650,000 a year for that work. New trading AI agent does it now, on a free trial, for people who have never written a line of code. It went live this summer and most of your timeline still hasn't noticed. You type one sentence in plain English. AI agents loop takes it from there: > an AI agent turns your sentence into a real trading strategy > backtests it across 5 years of data in about 12 seconds > scores it 0 to 100 and shows you the exact spot where it leaks > kills the versions that only look good on paper > rebuilds what's left and runs the loop again > stress-tests the survivor on years it has never seen, then puts it live on your exchange in about 90 seconds That list is the entire job description of a junior quant. At a desk, one properly tested idea burns about $87,500 in salary time, and most of what they build is dead by week six. Two clicks and this AI agent is testing your own idea. Costs you nothing and it runs the whole thing by itself: Griffin gets to be depressed about it. He already owns the desk. You just got handed one. Bookmark & read full story of how they got an AI to build trading strategies and kill its own bad ones is in the article below.

Ken Griffin, CEO of Citadel, said: "I'm fairly depressed watching AI do a week of PhD work in a few hours" He's worth $51.2 billion and 20% of every stock trade in America goes through his firm. The money isn't what's bothering him. Citadel pays $400,000 to $650,000 a year for that work. New trading AI agent does it now, on a free trial, for people who have never written a line of code. It went live this summer and most of your timeline still hasn't noticed. You type one sentence in plain English. AI agents loop takes it from there: > an AI agent turns your sentence into a real trading strategy > backtests it across 5 years of data in about 12 seconds > scores it 0 to 100 and shows you the exact spot where it leaks > kills the versions that only look good on paper > rebuilds what's left and runs the loop again > stress-tests the survivor on years it has never seen, then puts it live on your exchange in about 90 seconds That list is the entire job description of a junior quant. At a desk, one properly tested idea burns about $87,500 in salary time, and most of what they build is dead by week six. Two clicks and this AI agent is testing your own idea. Costs you nothing and it runs the whole thing by itself: Griffin gets to be depressed about it. He already owns the desk. You just got handed one. Bookmark & read full story of how they got an AI to build trading strategies and kill its own bad ones is in the article below.

cvxv666

222,320 次观看 • 8 天前

Learn to build your own voice-activated AI assistant that can execute tasks like gathering recent AI news from the web, scripting out a podcast, and using tools to put all that into a multi-speaker podcast. See our new short course: "Building Live Voice Agents with Google’s ADK (Agent Development Kit),” taught by Google’s Lavi Nigam and Sita Lakshmi Sangameswaran. ADK provides modular components that make it easy to build and debug agents. It also includes a built-in web interface for tracing agentic reasoning. This course illustrates these concepts via building a live voice agent that can chain actions to complete a complex task like creating a podcast. This requires maintaining context, implementing guardrails, reasoning, and handling audio streaming, while keeping latency low. You’ll learn to: - Build voice agents that listen, reason, and respond - Guide your agent to follow a specific workflow to accomplish a task - Coordinate specialized agents to build an agentic podcast workflow that researches topics and produces multi-speaker audio - Understand how to deploy an agent into production Even if you’re not yet building voice systems, you'll find understanding how realtime agents stream data and maintain reliability useful for designing modern agentic applications. Please join here:

Learn to build your own voice-activated AI assistant that can execute tasks like gathering recent AI news from the web, scripting out a podcast, and using tools to put all that into a multi-speaker podcast. See our new short course: "Building Live Voice Agents with Google’s ADK (Agent Development Kit),” taught by Google’s Lavi Nigam and Sita Lakshmi Sangameswaran. ADK provides modular components that make it easy to build and debug agents. It also includes a built-in web interface for tracing agentic reasoning. This course illustrates these concepts via building a live voice agent that can chain actions to complete a complex task like creating a podcast. This requires maintaining context, implementing guardrails, reasoning, and handling audio streaming, while keeping latency low. You’ll learn to: - Build voice agents that listen, reason, and respond - Guide your agent to follow a specific workflow to accomplish a task - Coordinate specialized agents to build an agentic podcast workflow that researches topics and produces multi-speaker audio - Understand how to deploy an agent into production Even if you’re not yet building voice systems, you'll find understanding how realtime agents stream data and maintain reliability useful for designing modern agentic applications. Please join here:

Andrew Ng

118,063 次观看 • 10 个月前

2 Cursor agents in separate tabs chat and plan the most interesting app ever and build it too! collaboratively All you need is 2 rules, THAT IS IT! here is how: create 2 rule files set to "Manual" agent-1 .mdc: --- You are agent-1 you will be chatting with agent-2 to design and build the most interesting python app ever you will write to agent_1.txt file and read from agent_2.txt file if you are waiting for a new response write a cli command to wait for 5 seconds and check again you will repeat this untill the full app is built you start the conversation --- agent-2 .mdc: --- You are agent-2 you will be chatting with agent-1 to design and build the most interesting python app ever you will write to agent_2.txt file and read from agent_1.txt file if you are waiting for a new response write a cli command to wait for 5 seconds and check again you will repeat this untill the full app is built agent-1 will start the convo --- create a new agent tab, you should have 2 tabs assign agent 1 its rule and agent 2 its rule type "begin" for agent 1 and enter type "begin" for agent 2 and enter That is it! and then watch them go to work! --- Want to level up your Cursor game? I’ve created a 45-chapter course on mastering Cursor. Check it out via the link in my bio! each chapter is short and independent and designed to get your started quickly featuring 26 hours of content where we build interesting apps and ideas from scratch in each chapter. ---

2 Cursor agents in separate tabs chat and plan the most interesting app ever and build it too! collaboratively All you need is 2 rules, THAT IS IT! here is how: create 2 rule files set to "Manual" agent-1 .mdc: --- You are agent-1 you will be chatting with agent-2 to design and build the most interesting python app ever you will write to agent_1.txt file and read from agent_2.txt file if you are waiting for a new response write a cli command to wait for 5 seconds and check again you will repeat this untill the full app is built you start the conversation --- agent-2 .mdc: --- You are agent-2 you will be chatting with agent-1 to design and build the most interesting python app ever you will write to agent_2.txt file and read from agent_1.txt file if you are waiting for a new response write a cli command to wait for 5 seconds and check again you will repeat this untill the full app is built agent-1 will start the convo --- create a new agent tab, you should have 2 tabs assign agent 1 its rule and agent 2 its rule type "begin" for agent 1 and enter type "begin" for agent 2 and enter That is it! and then watch them go to work! --- Want to level up your Cursor game? I’ve created a 45-chapter course on mastering Cursor. Check it out via the link in my bio! each chapter is short and independent and designed to get your started quickly featuring 26 hours of content where we build interesting apps and ideas from scratch in each chapter. ---

echo.hive

88,611 次观看 • 1 年前

This story about how Zuck pushed Aditya Agarwal to his extreme & made him build the search engine in the early days is awesome! Aditya was one of the early engg in Facebook & in week 2 of joining he was still figuring out what to do,none were giving any directions. Mark walked upto him & asked him to build a search engine -just him he didn't mean a team. Because people want to search for others through the search engine in Facebook & this is crucial. Aditya was pretty skeptical about himself as he didn't know how to build one. He suggested hiring someone from Google or Yahoo to build this out, Mark was like: "Dude if I can build Facebook you can build a damn search engine" And he actually ended up doing it! Still now he shares this lesson from this defining moment of his life to every founders - Anything could be done if you'd put your mind into it.

This story about how Zuck pushed Aditya Agarwal to his extreme & made him build the search engine in the early days is awesome! Aditya was one of the early engg in Facebook & in week 2 of joining he was still figuring out what to do,none were giving any directions. Mark walked upto him & asked him to build a search engine -just him he didn't mean a team. Because people want to search for others through the search engine in Facebook & this is crucial. Aditya was pretty skeptical about himself as he didn't know how to build one. He suggested hiring someone from Google or Yahoo to build this out, Mark was like: "Dude if I can build Facebook you can build a damn search engine" And he actually ended up doing it! Still now he shares this lesson from this defining moment of his life to every founders - Anything could be done if you'd put your mind into it.

Aish

630,651 次观看 • 2 年前

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!

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 次观看 • 1 年前

Gliquid 💦 Public Beta out now —— also available as a Farcaster Frame Liquid has evolved to become the platform for curating DeFi strategies. We also provide a framework for AI Agents to create and monetize their expertise while allowing users to discover and participate in these strategies with minimal friction. We created the first Liquid Agent to test run this and we’ll make the DeFi agent launchpad accessible to everyone when it’s ready. Couple things to note: - on the current interface, the only agent deploying strategies is Yuki , the flagship Liquid agent. Expect more interesting strategies next week - Claiming of yields and rewards not working yet. It’s a priority and we plan to get it out next week. - we initially had a cron setup for when the agent deploys a strategy but we’ll be switching to a prompting interface we are building internally. - Mobile app still coming pending Apple approval.

Gliquid 💦 Public Beta out now —— also available as a Farcaster Frame Liquid has evolved to become the platform for curating DeFi strategies. We also provide a framework for AI Agents to create and monetize their expertise while allowing users to discover and participate in these strategies with minimal friction. We created the first Liquid Agent to test run this and we’ll make the DeFi agent launchpad accessible to everyone when it’s ready. Couple things to note: - on the current interface, the only agent deploying strategies is Yuki , the flagship Liquid agent. Expect more interesting strategies next week - Claiming of yields and rewards not working yet. It’s a priority and we plan to get it out next week. - we initially had a cron setup for when the agent deploys a strategy but we’ll be switching to a prompting interface we are building internally. - Mobile app still coming pending Apple approval.

njokuscript.um

13,518 次观看 • 1 年前

A 29-year-old sales consultant from China quit his job and now makes in 2 weeks what his boss earns all year. $306,000 profit last month. He replaced an entire quant team with Claude and 6 AI agents. Built his own ETH price simulation engine. Generating $15,000+ per day on autopilot. I reverse-engineered his system. One Claude prompt. 90 minutes. Fully autonomous. Giving this free for 24 hours. To get it: 1. Comment 'AGENT' 2. Like and retweet this 3. Follow Marry Evan so I can DM you His wallet: 0x06dc51826bc524d9a83770e7de9dd7e005b0452 on Polymarket. Almost nobody is watching. What the 6-agent swarm actually does: → Each agent validates its own trading decisions independently → Collects data 24/7 across markets → Runs continuous ETH price simulations in MiroFish engine → Memorizes every pattern, market reaction, trading signal → Detects market inefficiencies in real-time → Executes when edge appears → No human input required Not prediction. Pure math exploiting market lag. The coverage and speed beat top-tier trading teams. Every trade is a perfect cycle. Every dollar is extracted from pricing gaps that disappear in seconds. The system does not guess the future. It reads the numbers correctly and takes the money before markets reprice. The edge exists right now. It won't in 6 months when everyone runs similar systems. You only need: Claude + a device + 1 hour to deploy. Save this post. Build the agent swarm this week. Start with $100. Scale on evidence.

A 29-year-old sales consultant from China quit his job and now makes in 2 weeks what his boss earns all year. $306,000 profit last month. He replaced an entire quant team with Claude and 6 AI agents. Built his own ETH price simulation engine. Generating $15,000+ per day on autopilot. I reverse-engineered his system. One Claude prompt. 90 minutes. Fully autonomous. Giving this free for 24 hours. To get it: 1. Comment 'AGENT' 2. Like and retweet this 3. Follow Marry Evan so I can DM you His wallet: 0x06dc51826bc524d9a83770e7de9dd7e005b0452 on Polymarket. Almost nobody is watching. What the 6-agent swarm actually does: → Each agent validates its own trading decisions independently → Collects data 24/7 across markets → Runs continuous ETH price simulations in MiroFish engine → Memorizes every pattern, market reaction, trading signal → Detects market inefficiencies in real-time → Executes when edge appears → No human input required Not prediction. Pure math exploiting market lag. The coverage and speed beat top-tier trading teams. Every trade is a perfect cycle. Every dollar is extracted from pricing gaps that disappear in seconds. The system does not guess the future. It reads the numbers correctly and takes the money before markets reprice. The edge exists right now. It won't in 6 months when everyone runs similar systems. You only need: Claude + a device + 1 hour to deploy. Save this post. Build the agent swarm this week. Start with $100. Scale on evidence.

Marry Evan

19,080 次观看 • 2 个月前

A 29-year-old sales consultant from China quit his job. Now making 2 weeks what his boss earns all year. $306,000 profit last month. He replaced an entire quant team with Claude and 6 AI agents. Built his own ETH price simulation engine. Generating $15,000+ per day on autopilot. I reverse-engineered his system. One Claude prompt. 90 minutes. Fully autonomous. Giving this free for 24 hours. To get it: 1. Comment AGENT 2. Like and retweet this 3. Follow Himanshu Kumar so I can DM you His wallet: 0x06dc51826bc524d9a83770e7de9dd7e005b0452 on Polymarket. Almost nobody is watching. What the 6-agent swarm actually does: → Each agent validates its own trading decisions independently → Collects data 24/7 across markets → Runs continuous ETH price simulations in MiroFish engine → Memorizes every pattern, market reaction, trading signal → Detects market inefficiencies in real-time → Executes when edge appears → No human input required Not prediction. Pure math exploiting market lag. The coverage and speed beat top-tier trading teams. Every trade is a perfect cycle. Every dollar is extracted from pricing gaps that disappear in seconds. The system does not guess the future. It reads the numbers correctly and takes the money before markets reprice. The edge exists right now. It won't in 6 months when everyone runs similar systems. You only need: Claude + a device + 1 hour to deploy. Save this post. Build the agent swarm this week. Start with $100. Scale on evidence.

A 29-year-old sales consultant from China quit his job. Now making 2 weeks what his boss earns all year. $306,000 profit last month. He replaced an entire quant team with Claude and 6 AI agents. Built his own ETH price simulation engine. Generating $15,000+ per day on autopilot. I reverse-engineered his system. One Claude prompt. 90 minutes. Fully autonomous. Giving this free for 24 hours. To get it: 1. Comment AGENT 2. Like and retweet this 3. Follow Himanshu Kumar so I can DM you His wallet: 0x06dc51826bc524d9a83770e7de9dd7e005b0452 on Polymarket. Almost nobody is watching. What the 6-agent swarm actually does: → Each agent validates its own trading decisions independently → Collects data 24/7 across markets → Runs continuous ETH price simulations in MiroFish engine → Memorizes every pattern, market reaction, trading signal → Detects market inefficiencies in real-time → Executes when edge appears → No human input required Not prediction. Pure math exploiting market lag. The coverage and speed beat top-tier trading teams. Every trade is a perfect cycle. Every dollar is extracted from pricing gaps that disappear in seconds. The system does not guess the future. It reads the numbers correctly and takes the money before markets reprice. The edge exists right now. It won't in 6 months when everyone runs similar systems. You only need: Claude + a device + 1 hour to deploy. Save this post. Build the agent swarm this week. Start with $100. Scale on evidence.

Himanshu Kumar

12,314 次观看 • 2 个月前

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

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

Hosun Chung

156,050 次观看 • 19 天前

It’s SBC Week — and we’re launching something big! The Agent Social Network just leveled up. Introducing Multi-Agent Groups: organize multiple AI agents into a collaborative team to handle complex, high-frequency tasks. We’ve now surpassed 300K paid users! ⚡ The first use case of Multi-Agent Groups: Smart Trading 🎥 Watch how users group a Market Screener, Token Swapper, and Auto Trader to execute end-to-end trading strategies — all inside a group chat with real friends and agents. This is the next level of collaborative intelligence. 💡 What’s new: – Developers contribute single agents; ChainOpera’s AI Agent Social Network (ASN) orchestrates them into powerful multi-agent groups – Significantly increased user engagement & retention – Developer participation is surging 📈 Milestone: – 300K+ paid users 👥 The community is actively co-building agent groups across Trading, Marketing, Productivity, E-commerce, and DeFi — creating a powerful dev-user growth flywheel 🌀 We can’t wait to see even more powerful multi-agent groups emerge! ✨ Try: — earn via Prompt-to-Earn (feedback/data) ✨ Build: — develop and publish agents to earn points & tokens #AI #AgentNetwork #MultiAgent #SmartTrading #ChainOperaAI #Web3 #AgentEconomy

It’s SBC Week — and we’re launching something big! The Agent Social Network just leveled up. Introducing Multi-Agent Groups: organize multiple AI agents into a collaborative team to handle complex, high-frequency tasks. We’ve now surpassed 300K paid users! ⚡ The first use case of Multi-Agent Groups: Smart Trading 🎥 Watch how users group a Market Screener, Token Swapper, and Auto Trader to execute end-to-end trading strategies — all inside a group chat with real friends and agents. This is the next level of collaborative intelligence. 💡 What’s new: – Developers contribute single agents; ChainOpera’s AI Agent Social Network (ASN) orchestrates them into powerful multi-agent groups – Significantly increased user engagement & retention – Developer participation is surging 📈 Milestone: – 300K+ paid users 👥 The community is actively co-building agent groups across Trading, Marketing, Productivity, E-commerce, and DeFi — creating a powerful dev-user growth flywheel 🌀 We can’t wait to see even more powerful multi-agent groups emerge! ✨ Try: — earn via Prompt-to-Earn (feedback/data) ✨ Build: — develop and publish agents to earn points & tokens #AI #AgentNetwork #MultiAgent #SmartTrading #ChainOperaAI #Web3 #AgentEconomy

ChainOpera AI

26,280 次观看 • 1 年前