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This Chinese developer runs 9 agents on Claude Code under a GPT-5.5 orchestrator and they close 500 client tasks a month without a single assistant. His client work is closed without him, on a single laptop and only three subscriptions. The entire system lives on one MacBook Pro M4... with 128 GB of memory and subscriptions to Claude Code and GPT-5.5 cost him approximately $300 a month. There is no CRM, no team, no office only a terminal window with 9 parallel streams. The orchestrator works with a simple system prompt: «You are the orchestrator of a client inbox. Classify every incoming email into 4 categories: code, content, analysis, communication. Delegate to the corresponding worker agent. When the result is ready, check it for completeness, send it to the client on my behalf, and mark the task as closed. Do not ask clarifying questions.» And the orchestrator checks the inbox every 30 seconds, classifies fresh emails, and distributes them to 9 worker agents on Claude Code, each of whom is responsible for their own class of tasks. Here is an example of how one of them closes a request to refactor a client's auth module: Task: refactor user-auth module Broke the monolith into 3 files by responsibilities Added unit tests, coverage increased to 87% Renamed 4 functions to camelCase according to the style guide PR is ready for review, link below» And so about 50 cycles a day. By noon 25 tasks are closed, by dinner 50, and by the end of the month 500. On average, it takes about 7 minutes from the appearance of an email in the inbox to sending the result to the client. This is more than what a live team of 6 developers, copywriters and analysts working 8 hours a day closes. This is no longer an agency. This is a workstation where an orchestrator replaces a manager, and 9 worker agents replace the staff. The pipeline goes from inbox to closing 500 times a month without human participation at any step.show more

Blaze

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29,917 次观看 • 6 个月前 •via X (Twitter)

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This Chinese guy created agents in Claude Code for MCP servers and single-handedly serves 6 marketing agencies a month from one iPhone, earning $5,000 from each. Inside he runs a pipeline of 7 agents on Claude Sonnet 4.6 that every Monday pulls a scan of the tech stack from a selected agency, develops an MCP server for its ad accounts, and over the course of a week brings it to production code ready to connect to Claude Desktop. No DevOps, no senior developer, no project manager. Just a Mac Mini in a work corner, an iPhone in the pocket, and a single API key. And traditional dev shops keep 5 people on project rates for the same contract, while his entire P&L is tokens, dirt-cheap hosting on Cloudflare, and Calendly. 7 agents run under a shared orchestrator-router and burn about 5 million tokens a day, which in the API bill comes out to $540 a month. The Mac Mini itself sits at home and keeps the entire orchestrator running 24/7, and from the iPhone the owner connects to it through a secure remote terminal and sees the output of any session right on the smartphone screen, wherever he happens to be. His starting system prompt looks like this: "you run a solo shop for custom MCP servers for marketing agencies. you hand out read-only tasks to 6 sub-agents and own all commits and shipping yourself. sub-agents: // Hunter (finds marketing agencies of 15 to 60 people that have no MCP access to Google Ads, Meta Ads, TikTok Ads, and HubSpot) // Mapper (pulls their tech stack, identifies 3 to 5 integration pains, and simultaneously writes the technical spec for the server: which tools, resources, and prompts to export through MCP, which auth flow and rate limit) // Coder (generates an MCP server in Python through the MCP SDK, deploys 8 to 15 tools for ad accounts and CRM) // Validator (connects the server to Claude Desktop, runs real client API keys in a sandbox, and checks for compliance with the MCP spec) // Shipper (writes a README, integration guide, deployment manual, packages the server, and hosts it on Cloudflare Workers or pushes to the GitHub of the client) // Mobile (always online on the iPhone, books demo calls in Calendly, picks up hot fixes, and confirms contracts through a secure remote terminal to the Mac Mini). only 1 owner agent works on 1 contract, no overlaps. you pull the owner out of observation mode only when a deal goes above $7,500 or the test coverage of the server drops below 85%." This prompt gives the system an understanding of its role and the limits of intervention from the very first line. It knows it is supposed to find agencies on its own. It knows it is supposed to bring every MCP server to production on its own. It knows it connects the live owner only on large deals or when the tests do not converge. → The pipeline runs without breaks, day or night → Hunter goes through about 130 marketing agencies on LinkedIn and Clutch per day → Mapper rolls out 4 audit reports with the tech stack and a final spec for each → Coder writes 1 to 2 MCP servers per week in Python with 8 to 15 tools → Validator validates every server through Claude Desktop with real client API keys → Shipper rolls out the full documentation package and pushes the finished product to Cloudflare Workers or the GitHub of the client And only when a contract breaks $7,500 or test coverage drops below 85% does the orchestrator pull the owner from whatever he is doing. And when the owner at that moment is behind the wheel or at a meeting in a coworking space, the Mobile agent in his iPhone picks up 1 contract in progress: confirms a meeting with the agency CMO in Calendly, opens a live demo of the MCP server through a secure terminal to the Mac Mini, and writes the test result to the shared state. The owner just swipes "approve" and in 15 minutes joins the Zoom demo. The fresh system log from last Wednesday looks like this: "hunter report: 132 agencies checked on LinkedIn and Clutch, 19 without MCP integrations, 8 with active requests for AI tooling in job posts, 4 with an open Q4 budget. passing to mapper." "coder: MCP server for Northwave Performance Marketing built in Python, 11 tools for Google Ads, Meta Ads, and GA4, 320 lines of code. exported to /Users/dev/mcp-shop/clients/northwave/server.py. validator connecting to Claude Desktop." "validator: 11 tools passed validation through Claude Desktop, test coverage 92%, average latency 380 ms. passing to shipper." "eval flag: contract with Pacific Reach Agency at $8,200 exceeds the approved limit of $7,500. sending for manual review." In his work setup there is no cloud server, no external team, and not even a separate office. At home sits a Mac Mini with a sandbox at /Users/dev/mcp-shop, on top runs an MCP router with a single API key to Claude, and the same key is forwarded to a secure terminal on the iPhone. Out of everything I have seen this year, this is the cleanest solo shop for custom MCP servers for marketing agencies: $540 a month on the API, about $30,000 into the account, and between them 7 system prompts, 1 Mac Mini in a work corner, and 1 iPhone that never leaves the pocket.

This Chinese guy created agents in Claude Code for MCP servers and single-handedly serves 6 marketing agencies a month from one iPhone, earning $5,000 from each. Inside he runs a pipeline of 7 agents on Claude Sonnet 4.6 that every Monday pulls a scan of the tech stack from a selected agency, develops an MCP server for its ad accounts, and over the course of a week brings it to production code ready to connect to Claude Desktop. No DevOps, no senior developer, no project manager. Just a Mac Mini in a work corner, an iPhone in the pocket, and a single API key. And traditional dev shops keep 5 people on project rates for the same contract, while his entire P&L is tokens, dirt-cheap hosting on Cloudflare, and Calendly. 7 agents run under a shared orchestrator-router and burn about 5 million tokens a day, which in the API bill comes out to $540 a month. The Mac Mini itself sits at home and keeps the entire orchestrator running 24/7, and from the iPhone the owner connects to it through a secure remote terminal and sees the output of any session right on the smartphone screen, wherever he happens to be. His starting system prompt looks like this: "you run a solo shop for custom MCP servers for marketing agencies. you hand out read-only tasks to 6 sub-agents and own all commits and shipping yourself. sub-agents: // Hunter (finds marketing agencies of 15 to 60 people that have no MCP access to Google Ads, Meta Ads, TikTok Ads, and HubSpot) // Mapper (pulls their tech stack, identifies 3 to 5 integration pains, and simultaneously writes the technical spec for the server: which tools, resources, and prompts to export through MCP, which auth flow and rate limit) // Coder (generates an MCP server in Python through the MCP SDK, deploys 8 to 15 tools for ad accounts and CRM) // Validator (connects the server to Claude Desktop, runs real client API keys in a sandbox, and checks for compliance with the MCP spec) // Shipper (writes a README, integration guide, deployment manual, packages the server, and hosts it on Cloudflare Workers or pushes to the GitHub of the client) // Mobile (always online on the iPhone, books demo calls in Calendly, picks up hot fixes, and confirms contracts through a secure remote terminal to the Mac Mini). only 1 owner agent works on 1 contract, no overlaps. you pull the owner out of observation mode only when a deal goes above $7,500 or the test coverage of the server drops below 85%." This prompt gives the system an understanding of its role and the limits of intervention from the very first line. It knows it is supposed to find agencies on its own. It knows it is supposed to bring every MCP server to production on its own. It knows it connects the live owner only on large deals or when the tests do not converge. → The pipeline runs without breaks, day or night → Hunter goes through about 130 marketing agencies on LinkedIn and Clutch per day → Mapper rolls out 4 audit reports with the tech stack and a final spec for each → Coder writes 1 to 2 MCP servers per week in Python with 8 to 15 tools → Validator validates every server through Claude Desktop with real client API keys → Shipper rolls out the full documentation package and pushes the finished product to Cloudflare Workers or the GitHub of the client And only when a contract breaks $7,500 or test coverage drops below 85% does the orchestrator pull the owner from whatever he is doing. And when the owner at that moment is behind the wheel or at a meeting in a coworking space, the Mobile agent in his iPhone picks up 1 contract in progress: confirms a meeting with the agency CMO in Calendly, opens a live demo of the MCP server through a secure terminal to the Mac Mini, and writes the test result to the shared state. The owner just swipes "approve" and in 15 minutes joins the Zoom demo. The fresh system log from last Wednesday looks like this: "hunter report: 132 agencies checked on LinkedIn and Clutch, 19 without MCP integrations, 8 with active requests for AI tooling in job posts, 4 with an open Q4 budget. passing to mapper." "coder: MCP server for Northwave Performance Marketing built in Python, 11 tools for Google Ads, Meta Ads, and GA4, 320 lines of code. exported to /Users/dev/mcp-shop/clients/northwave/server.py. validator connecting to Claude Desktop." "validator: 11 tools passed validation through Claude Desktop, test coverage 92%, average latency 380 ms. passing to shipper." "eval flag: contract with Pacific Reach Agency at $8,200 exceeds the approved limit of $7,500. sending for manual review." In his work setup there is no cloud server, no external team, and not even a separate office. At home sits a Mac Mini with a sandbox at /Users/dev/mcp-shop, on top runs an MCP router with a single API key to Claude, and the same key is forwarded to a secure terminal on the iPhone. Out of everything I have seen this year, this is the cleanest solo shop for custom MCP servers for marketing agencies: $540 a month on the API, about $30,000 into the account, and between them 7 system prompts, 1 Mac Mini in a work corner, and 1 iPhone that never leaves the pocket.

Blaze

55,926 次观看 • 6 个月前

SPEC IS BECOMING THE PRODUCT An Anthropic engineer gave Claude a spec, pointed it to an Asana board and left for the weekend. Claude broke it into tickets and spun up a team of agents. The agents started picking up tasks on their own. No one told them to. They just did.

SPEC IS BECOMING THE PRODUCT An Anthropic engineer gave Claude a spec, pointed it to an Asana board and left for the weekend. Claude broke it into tickets and spun up a team of agents. The agents started picking up tasks on their own. No one told them to. They just did.

Shubham Saboo

285,777 次观看 • 8 个月前

This Chinese developer launched 6 agents under 1 orchestrator, and they run his UI design agency at $32,000 a month on their own. He built a system of 6 agents on Claude Sonnet 4.6 that single-handedly runs his agency for UI auditing and redesign for SaaS startups and e-commerce. No contractors, no project manager, and no team. Just him, a MacBook, and 1 API key. Traditional design agencies out of Shenzhen keep teams of 8 people on salaries for the same volume, while he keeps only API tokens. 6 agents work through a single orchestrator on Claude Code Router. Usage is about 4 million tokens a day, the average API bill is just $480 a month. All 6 go through MCP servers and write shared state to the file system, without shared state in memory and without race conditions. And here is the system prompt he gave the orchestrator before launch: "you are the orchestrator of a one-man UI agency. you delegate read-only research tasks to 5 sub-agents and own all writes. sub-agents: // Hunter (finds SaaS and e-commerce sites with outdated UI) // Auditor (runs each site through Lighthouse, accessibility, and design system checks) // Pitcher (writes cold outreach and redesign proposals with before/after screenshots) // Splitter (breaks accepted projects into typed milestones) // Designer (generates Figma mockups and Tailwind components) // Checker (runs evals on every artifact before it leaves the harness). you never let 2 sub-agents touch 1 file. you stop and request human approval only when an invoice exceeds $5,000 or when the design system eval score drops below 0.88." Meaning the system knows exactly what it is and within what boundaries it operates. It knows it is supposed to find clients on its own. It knows it is supposed to write proposals with screenshots and mockups without intervention. It knows the human only plugs in when the amounts go above $5,000 or when the design system eval does not converge. → The system runs 24 hours a day → Hunter finds about 200 sites with outdated UI a day → Auditor runs each one through Lighthouse and WCAG → Pitcher prepares about 28 personalized proposals with before/after screenshots → Splitter breaks 3 accepted projects per week into milestones → Designer generates mockups and components, Checker runs evals on every artifact And only when the invoice breaks $5,000 or the eval drops below 0.88 does the orchestrator wake the human. Here is what the system outputs in his log during 1 of the sessions: "hunter report, tuesday: 213 sites found, 31 with last redesign before 2020, 14 with Lighthouse score below 65, 6 with active redesign RFP. passing top 6 to auditor." "pitcher: 27 cold outreach sent with before/after screenshots, 5 replies, 3 discovery calls scheduled. passing to splitter." "designer: milestone 2 of Lotus Tea Co redesign complete. Figma frames exported to /Users/dev/agency/clients/lotus/v2. checker running design system evals." "eval flag: proposal for $6,800 exceeds the approved limit of $5,000. sending for manual review." He has no remote server. No separate backend. Just a local file sandbox in /Users/dev/agency, an MCP router, and an API key to Claude. Out of everything I have seen this year, this is the cleanest one-person UI design agency: $480 in, about $32,000 out, and between them 6 prompts and 1 file system.

This Chinese developer launched 6 agents under 1 orchestrator, and they run his UI design agency at $32,000 a month on their own. He built a system of 6 agents on Claude Sonnet 4.6 that single-handedly runs his agency for UI auditing and redesign for SaaS startups and e-commerce. No contractors, no project manager, and no team. Just him, a MacBook, and 1 API key. Traditional design agencies out of Shenzhen keep teams of 8 people on salaries for the same volume, while he keeps only API tokens. 6 agents work through a single orchestrator on Claude Code Router. Usage is about 4 million tokens a day, the average API bill is just $480 a month. All 6 go through MCP servers and write shared state to the file system, without shared state in memory and without race conditions. And here is the system prompt he gave the orchestrator before launch: "you are the orchestrator of a one-man UI agency. you delegate read-only research tasks to 5 sub-agents and own all writes. sub-agents: // Hunter (finds SaaS and e-commerce sites with outdated UI) // Auditor (runs each site through Lighthouse, accessibility, and design system checks) // Pitcher (writes cold outreach and redesign proposals with before/after screenshots) // Splitter (breaks accepted projects into typed milestones) // Designer (generates Figma mockups and Tailwind components) // Checker (runs evals on every artifact before it leaves the harness). you never let 2 sub-agents touch 1 file. you stop and request human approval only when an invoice exceeds $5,000 or when the design system eval score drops below 0.88." Meaning the system knows exactly what it is and within what boundaries it operates. It knows it is supposed to find clients on its own. It knows it is supposed to write proposals with screenshots and mockups without intervention. It knows the human only plugs in when the amounts go above $5,000 or when the design system eval does not converge. → The system runs 24 hours a day → Hunter finds about 200 sites with outdated UI a day → Auditor runs each one through Lighthouse and WCAG → Pitcher prepares about 28 personalized proposals with before/after screenshots → Splitter breaks 3 accepted projects per week into milestones → Designer generates mockups and components, Checker runs evals on every artifact And only when the invoice breaks $5,000 or the eval drops below 0.88 does the orchestrator wake the human. Here is what the system outputs in his log during 1 of the sessions: "hunter report, tuesday: 213 sites found, 31 with last redesign before 2020, 14 with Lighthouse score below 65, 6 with active redesign RFP. passing top 6 to auditor." "pitcher: 27 cold outreach sent with before/after screenshots, 5 replies, 3 discovery calls scheduled. passing to splitter." "designer: milestone 2 of Lotus Tea Co redesign complete. Figma frames exported to /Users/dev/agency/clients/lotus/v2. checker running design system evals." "eval flag: proposal for $6,800 exceeds the approved limit of $5,000. sending for manual review." He has no remote server. No separate backend. Just a local file sandbox in /Users/dev/agency, an MCP router, and an API key to Claude. Out of everything I have seen this year, this is the cleanest one-person UI design agency: $480 in, about $32,000 out, and between them 6 prompts and 1 file system.

Blaze

56,062 次观看 • 6 个月前

A 22-YEAR-OLD FROM LONDON CLOSED A DEAL ON AN AI AGENT TEAM WITH ZERO DEVELOPERS ON HER TEAM. CLIENT SIGNED THE CONTRACT. SYSTEM RUNS ITSELF. she is not a programmer. not technical. has no team. but she has four agents and one pipeline that does what others pay $370,000 a year for. agent 1 scrapes google maps and instagram while she sleeps. leads are already in the system by morning. agent 2 creates a personalized plan and mockup for every potential client. automatically. no human involved. agent 3 writes a personal outreach email for each lead and drops a ready draft directly into the client's gmail. agent 4 coordinates the work of all three. tracks the status of every lead. signals when a human is needed. the rest of the time - full autopilot. she did not write code. she made a proposal. negotiated the terms. got the contract signed. claude code did everything else. most businesses still keep people on tasks that require no decisions - only execution. lead generation. cold outreach. personalized mockups. emails. anything with a clear algorithm AI closes better. faster. without errors from exhaustion. and while a competitor waits for a reply from a junior - her system already sent its hundredth email today.

A 22-YEAR-OLD FROM LONDON CLOSED A DEAL ON AN AI AGENT TEAM WITH ZERO DEVELOPERS ON HER TEAM. CLIENT SIGNED THE CONTRACT. SYSTEM RUNS ITSELF. she is not a programmer. not technical. has no team. but she has four agents and one pipeline that does what others pay $370,000 a year for. agent 1 scrapes google maps and instagram while she sleeps. leads are already in the system by morning. agent 2 creates a personalized plan and mockup for every potential client. automatically. no human involved. agent 3 writes a personal outreach email for each lead and drops a ready draft directly into the client's gmail. agent 4 coordinates the work of all three. tracks the status of every lead. signals when a human is needed. the rest of the time - full autopilot. she did not write code. she made a proposal. negotiated the terms. got the contract signed. claude code did everything else. most businesses still keep people on tasks that require no decisions - only execution. lead generation. cold outreach. personalized mockups. emails. anything with a clear algorithm AI closes better. faster. without errors from exhaustion. and while a competitor waits for a reply from a junior - her system already sent its hundredth email today.

SITA

74,386 次观看 • 4 个月前

This Chinese guy created agents in Claude Code for landing pages and single-handedly serves 47 small businesses a month, taking $400 from each. He built a system of 7 agents on Claude Sonnet 4.6 that analyzes Google Maps in small towns, finds small businesses without websites there, and over 1 weekend takes each one to a finished mockup with video and cold message. No assistant, no sales team, no SDR. Just him, a MacBook, an iPhone, and 1 API key. And traditional web design agencies keep teams of 8 people on salary for the same order flow, while his expenses are only tokens and subscriptions to Lovable, Higgsfield, and Calendly. 7 agents work through 1 orchestrator on Claude Code Router. Usage is about 3 million tokens a day, the average API bill is about $480 a month. All 7 go through MCP servers and write shared state to the file system, without shared state in memory and without race conditions, and 1 of them lives right in the iPhone and picks up positive replies from the subway, a taxi, or on walks. And here is the system prompt he put into the orchestrator before launch: "You are the orchestrator of a solo agency that sells ready-made websites to local businesses. You delegate read-only tasks to 6 sub-agents and own all writes. sub-agents: // Scout (walks through Google Maps in selected cities, looks for narrow niches: 5+ years on the map, fewer than 50 reviews, no website or a website from 2014, but high ratings) // Diagnoser (for each lead writes a 50-word diagnosis, hero angle, tone matched to the industry, and a cold message under 70 words) // Builder (generates a landing page mockup in Lovable through MCP only for the top 5 leads per day, with the sharpest diagnoses and the biggest gap) // Filmer (pulls 5 screenshots of the mockup and through Higgsfield renders a 10-second vertical video 1080x1920 with a soft zoom) // Pitcher (sends a personalized cold message through the right channel for the niche: email to roofers, SMS to tradesmen, IG DM to salons, LinkedIn to realtors) // Checker (runs every message through evals for personalization, absence of AI markers and buzzwords before sending) // Mobile (lives in the iPhone, handles positive replies in real time, books Zoom calls in Calendly through MCP while the owner is on the go). You never let 2 sub-agents touch 1 lead. You stop and request approval from the human only when a deal exceeds $3,000 or the reply rate in a niche for the day drops below 12%." Meaning the system knows what it is and within what boundaries it is allowed to act. It knows it is supposed to find leads on its own. It knows it is supposed to take each one to a mockup, video, and cold message without intervention. It knows the human only steps in when a deal goes above $3,000 or the reply rate stops converging. → The system runs 24 hours a day → Scout goes through about 220 local businesses on Google Maps per day and leaves 30 new leads in the queue → Diagnoser outputs 30 structured diagnoses + briefs + cold messages per day → Builder assembles 3 to 5 finished landing pages in Lovable for the sharpest leads → Filmer renders a 10-second vertical video in Higgsfield for each one → Pitcher sends 30 personalized messages per day across 4 channels with a reply rate of about 14% → Checker runs every message through evals before sending And only when a deal breaks $3,000 or the reply rate for the day drops below 12% does the orchestrator wake the owner. And when the owner at that moment is sitting in the subway or a taxi, the Mobile agent in his iPhone picks up 1 move on its own: replies to a fresh positive reply from a dentist, books a Zoom through Calendly synced to the local time of the client, and puts the lead back in the queue. The owner only has to tap "approve" and in just 10 minutes join the call. Here is what the system writes in his log during 1 of the Saturdays: "scout report: 218 businesses checked in Austin, Denver, and Miami, 34 without a website, 19 with a website from 2014, 6 with an active redesign request in reviews. passing top 30 to diagnoser." "pitcher: 30 cold messages sent across 4 channels, 14 replies, 5 positive, 3 Zoom calls booked for Sunday. passing to closer." "builder: landing page for Westside Cosmetic Dentistry built in Lovable, 5 sections, mobile, soft beige. URL placed at /Users/dev/maps-agency/clients/westside/v1. filmer launching Higgsfield." "eval flag: deal with The Lotus Salon at $3,400 exceeds the approved limit of $3,000. sending for manual review." He has no server of his own and no separate backend. Just a local file sandbox at /Users/dev/maps-agency, an MCP router, 1 API key to Claude, and the same key forwarded to Claude Code on his iPhone. Out of everything I have seen this year, this is the cleanest one-person agency for selling websites to small businesses: $480 a month on the API, about $18,800 into the account, and between them 7 prompts, 1 file system, and 1 phone in the pocket.

This Chinese guy created agents in Claude Code for landing pages and single-handedly serves 47 small businesses a month, taking $400 from each. He built a system of 7 agents on Claude Sonnet 4.6 that analyzes Google Maps in small towns, finds small businesses without websites there, and over 1 weekend takes each one to a finished mockup with video and cold message. No assistant, no sales team, no SDR. Just him, a MacBook, an iPhone, and 1 API key. And traditional web design agencies keep teams of 8 people on salary for the same order flow, while his expenses are only tokens and subscriptions to Lovable, Higgsfield, and Calendly. 7 agents work through 1 orchestrator on Claude Code Router. Usage is about 3 million tokens a day, the average API bill is about $480 a month. All 7 go through MCP servers and write shared state to the file system, without shared state in memory and without race conditions, and 1 of them lives right in the iPhone and picks up positive replies from the subway, a taxi, or on walks. And here is the system prompt he put into the orchestrator before launch: "You are the orchestrator of a solo agency that sells ready-made websites to local businesses. You delegate read-only tasks to 6 sub-agents and own all writes. sub-agents: // Scout (walks through Google Maps in selected cities, looks for narrow niches: 5+ years on the map, fewer than 50 reviews, no website or a website from 2014, but high ratings) // Diagnoser (for each lead writes a 50-word diagnosis, hero angle, tone matched to the industry, and a cold message under 70 words) // Builder (generates a landing page mockup in Lovable through MCP only for the top 5 leads per day, with the sharpest diagnoses and the biggest gap) // Filmer (pulls 5 screenshots of the mockup and through Higgsfield renders a 10-second vertical video 1080x1920 with a soft zoom) // Pitcher (sends a personalized cold message through the right channel for the niche: email to roofers, SMS to tradesmen, IG DM to salons, LinkedIn to realtors) // Checker (runs every message through evals for personalization, absence of AI markers and buzzwords before sending) // Mobile (lives in the iPhone, handles positive replies in real time, books Zoom calls in Calendly through MCP while the owner is on the go). You never let 2 sub-agents touch 1 lead. You stop and request approval from the human only when a deal exceeds $3,000 or the reply rate in a niche for the day drops below 12%." Meaning the system knows what it is and within what boundaries it is allowed to act. It knows it is supposed to find leads on its own. It knows it is supposed to take each one to a mockup, video, and cold message without intervention. It knows the human only steps in when a deal goes above $3,000 or the reply rate stops converging. → The system runs 24 hours a day → Scout goes through about 220 local businesses on Google Maps per day and leaves 30 new leads in the queue → Diagnoser outputs 30 structured diagnoses + briefs + cold messages per day → Builder assembles 3 to 5 finished landing pages in Lovable for the sharpest leads → Filmer renders a 10-second vertical video in Higgsfield for each one → Pitcher sends 30 personalized messages per day across 4 channels with a reply rate of about 14% → Checker runs every message through evals before sending And only when a deal breaks $3,000 or the reply rate for the day drops below 12% does the orchestrator wake the owner. And when the owner at that moment is sitting in the subway or a taxi, the Mobile agent in his iPhone picks up 1 move on its own: replies to a fresh positive reply from a dentist, books a Zoom through Calendly synced to the local time of the client, and puts the lead back in the queue. The owner only has to tap "approve" and in just 10 minutes join the call. Here is what the system writes in his log during 1 of the Saturdays: "scout report: 218 businesses checked in Austin, Denver, and Miami, 34 without a website, 19 with a website from 2014, 6 with an active redesign request in reviews. passing top 30 to diagnoser." "pitcher: 30 cold messages sent across 4 channels, 14 replies, 5 positive, 3 Zoom calls booked for Sunday. passing to closer." "builder: landing page for Westside Cosmetic Dentistry built in Lovable, 5 sections, mobile, soft beige. URL placed at /Users/dev/maps-agency/clients/westside/v1. filmer launching Higgsfield." "eval flag: deal with The Lotus Salon at $3,400 exceeds the approved limit of $3,000. sending for manual review." He has no server of his own and no separate backend. Just a local file sandbox at /Users/dev/maps-agency, an MCP router, 1 API key to Claude, and the same key forwarded to Claude Code on his iPhone. Out of everything I have seen this year, this is the cleanest one-person agency for selling websites to small businesses: $480 a month on the API, about $18,800 into the account, and between them 7 prompts, 1 file system, and 1 phone in the pocket.

Blaze

2,724,147 次观看 • 6 个月前

It is becoming f*cking dangerous: AI agents mapped out a full codebase based on just one question Jev creator Diogo Almeida put together a playbook to sync up 80 agents over 540,000 lines of code, creating a visual flow of the system architecture Simply ask: "What takes place when a user uploads a file?" and follow the full circuit from the frontend and API down to background jobs, databases, and tests Every element on the layout connects straight to the underlying codebase. Claude Code agents map out the underlying code. Jev directs tasks to the right workers and strips away irrelevant data for each investigation phase: > which specific component handles this functionality > which files need to be analyzed > is another worker currently handling this route > does this part require a deeper look The final blueprint charted 170 user flows and tied them to 2,400 tests. On top of that, 26 open points remained clearly flagged for manual review How to use it: Hand over the playbook along with your repo to Claude Code, then submit this prompt: Generate a searchable blueprint of this repository Run an audit on entry points, services, background tasks, and databases. Assign separate modules to different agents Map every user scenario through the codebase. Link file locations and key references to each step Rely on Jev to manage the workflow and filter out context noise. Set up dedicated agents to double-check proposed links against the raw code Connect existing tests to their respective flows. Flag assumed logic and incomplete paths, then build a UI to navigate the map A good approach is to run a trial on a single user path first before scaling it up to the full repository This way, any incoming developer can review a feature, trace its dependencies, and locate test cases prior to making a single change Bookmark this

It is becoming f*cking dangerous: AI agents mapped out a full codebase based on just one question Jev creator Diogo Almeida put together a playbook to sync up 80 agents over 540,000 lines of code, creating a visual flow of the system architecture Simply ask: "What takes place when a user uploads a file?" and follow the full circuit from the frontend and API down to background jobs, databases, and tests Every element on the layout connects straight to the underlying codebase. Claude Code agents map out the underlying code. Jev directs tasks to the right workers and strips away irrelevant data for each investigation phase: > which specific component handles this functionality > which files need to be analyzed > is another worker currently handling this route > does this part require a deeper look The final blueprint charted 170 user flows and tied them to 2,400 tests. On top of that, 26 open points remained clearly flagged for manual review How to use it: Hand over the playbook along with your repo to Claude Code, then submit this prompt: Generate a searchable blueprint of this repository Run an audit on entry points, services, background tasks, and databases. Assign separate modules to different agents Map every user scenario through the codebase. Link file locations and key references to each step Rely on Jev to manage the workflow and filter out context noise. Set up dedicated agents to double-check proposed links against the raw code Connect existing tests to their respective flows. Flag assumed logic and incomplete paths, then build a UI to navigate the map A good approach is to run a trial on a single user path first before scaling it up to the full repository This way, any incoming developer can review a feature, trace its dependencies, and locate test cases prior to making a single change Bookmark this

Bober_smart

14,890 次观看 • 19 天前

Claude Code is more than a coding agent. It's more like a super smart orchestrator agent. Watch this evaluator loop agent I just built using sub agents and / commands. This is one of the fastest ways to build custom agentic workflows. Claude Code is no joke!

Claude Code is more than a coding agent. It's more like a super smart orchestrator agent. Watch this evaluator loop agent I just built using sub agents and / commands. This is one of the fastest ways to build custom agentic workflows. Claude Code is no joke!

elvis

138,470 次观看 • 1 年前

CLAUDE CODE + GROK + JEV - THE FILE WHERE THREE HEADS CLOSE A TICKET IN 9 MINUTES FOR $1.74 a month of this costs $208.80, the same 120 merges by hand are 300 hours and $28,500 ticket → plan → patch → tests → review → merge Claude Code on Opus 5.5 writes the patch, Grok reads it back and argues, Jev holds the gate and merges only on green tests three heads, and not one of them is allowed to push code to prod on its own 38 files read, 3 touched, +412 lines, 48 tests, and the first four failed, so the patch went back two minutes twenty later it was 48 of 48, and only then did Jev open the gate out of 120 patches Claude Code and Grok signed off on 78 straight away and had a fight about the other 22 those 22 arguments are the product, because a reviewer that never objects is just decoration the meter clocked $208.80 for the month, and to catch the human bill it would have to keep running for another 136 months and here is the brutal part of this file: 6 of the 94 merged tickets came back with bugs every one of them sat in the part of the code the test suite has never read, because coverage there is 61% two models in the same blind spot is not two opinions, it is one mistake signed twice save this and paste it into Claude Code - let it run your ticket through these three heads ↓

CLAUDE CODE + GROK + JEV - THE FILE WHERE THREE HEADS CLOSE A TICKET IN 9 MINUTES FOR $1.74 a month of this costs $208.80, the same 120 merges by hand are 300 hours and $28,500 ticket → plan → patch → tests → review → merge Claude Code on Opus 5.5 writes the patch, Grok reads it back and argues, Jev holds the gate and merges only on green tests three heads, and not one of them is allowed to push code to prod on its own 38 files read, 3 touched, +412 lines, 48 tests, and the first four failed, so the patch went back two minutes twenty later it was 48 of 48, and only then did Jev open the gate out of 120 patches Claude Code and Grok signed off on 78 straight away and had a fight about the other 22 those 22 arguments are the product, because a reviewer that never objects is just decoration the meter clocked $208.80 for the month, and to catch the human bill it would have to keep running for another 136 months and here is the brutal part of this file: 6 of the 94 merged tickets came back with bugs every one of them sat in the part of the code the test suite has never read, because coverage there is 61% two models in the same blind spot is not two opinions, it is one mistake signed twice save this and paste it into Claude Code - let it run your ticket through these three heads ↓

Sprytix

12,400 次观看 • 1 个月前

this is pure f*cking treasure 10 GitHub repos with 165K+ combined stars to build your own AI agent team that works 24/7 most people still use one AI chat for everything. these repos turn Claude into a whole team: one agent researches, one codes, one reviews, and a lead runs them all one resource per step, from your first 3 agents to a full swarm running Claude and Codex together (1/10) CrewAI the easiest way to assemble a team of agents: give them roles and tasks, then hit kickoff ▸ (2/10) LangGraph connect agents into a graph with checkpoints, so if something fails it resumes instead of starting over ▸ (3/10) wshobson/agents ready-made agents for Claude Code (backend, frontend, security, tests, devops) plus an orchestrator to run them ▸ (4/10) oh-my-team 12 specialist agents for Claude Code that split the work and merge the results ▸ (5/10) agent-swarm your own self-hosted AI company: one lead agent hires workers and runs them 24/7 ▸ (6/10) Open-Agents spawns many Claude agents in parallel, so 6 tasks run at once instead of one after another ▸ (7/10) swarms an enterprise-grade framework for when your team grows from 5 agents to 50 ▸ (8/10) Microsoft Agent Framework Microsoft's new framework, the AutoGen successor, for workflows, agents and runtime in one place ▸ (9/10) CLI Agent Orchestrator runs Claude, Codex and other CLIs side by side as one team, with a supervisor watching all of them ▸ (10/10) ensemble launches Codex and Claude together, so they build and ship as one ▸ the loop: assemble → plug in → run → scale → orchestrate pick one, click, let it run, and while everyone else is still typing prompts, your agents are already shipping ↓

this is pure f*cking treasure 10 GitHub repos with 165K+ combined stars to build your own AI agent team that works 24/7 most people still use one AI chat for everything. these repos turn Claude into a whole team: one agent researches, one codes, one reviews, and a lead runs them all one resource per step, from your first 3 agents to a full swarm running Claude and Codex together (1/10) CrewAI the easiest way to assemble a team of agents: give them roles and tasks, then hit kickoff ▸ (2/10) LangGraph connect agents into a graph with checkpoints, so if something fails it resumes instead of starting over ▸ (3/10) wshobson/agents ready-made agents for Claude Code (backend, frontend, security, tests, devops) plus an orchestrator to run them ▸ (4/10) oh-my-team 12 specialist agents for Claude Code that split the work and merge the results ▸ (5/10) agent-swarm your own self-hosted AI company: one lead agent hires workers and runs them 24/7 ▸ (6/10) Open-Agents spawns many Claude agents in parallel, so 6 tasks run at once instead of one after another ▸ (7/10) swarms an enterprise-grade framework for when your team grows from 5 agents to 50 ▸ (8/10) Microsoft Agent Framework Microsoft's new framework, the AutoGen successor, for workflows, agents and runtime in one place ▸ (9/10) CLI Agent Orchestrator runs Claude, Codex and other CLIs side by side as one team, with a supervisor watching all of them ▸ (10/10) ensemble launches Codex and Claude together, so they build and ship as one ▸ the loop: assemble → plug in → run → scale → orchestrate pick one, click, let it run, and while everyone else is still typing prompts, your agents are already shipping ↓

Noisy

14,563 次观看 • 20 天前

A Chinese developer created an agent system in Claude Code to sell landing pages to small businesses and, working completely solo, serves about 47 clients a month charging around $400 for each one. He built 7 agents on Claude Sonnet 4.6 capable of analyzing Google Maps in small cities, detecting businesses without websites or with totally outdated pages, and taking each opportunity all the way to a finished mockup, a promotional video, and a ready-to-send prospecting message. No assistants. No sales team. No SDRs. Just him, a MacBook, an iPhone, and an API key. While traditional agencies keep full teams to handle the same workflow, his only real costs are tokens and subscriptions to Lovable, Higgsfield, and Calendly. The 7 agents work coordinated by an orchestrator in Claude Code Router. The system consumes about 3 million tokens daily and the average API spend is just around $480 a month. They all work via MCP servers and share state using the file system, avoiding concurrency and shared memory issues. Even one of the agents lives directly on his iPhone and responds to leads while he's on the subway, in a taxi, or walking. This was the main prompt he set up: “You are the orchestrator of a solo agency that sells ready-made websites to local businesses…” The key is that the system perfectly understands what it is, what its limits are, and what goals it must achieve. It knows it has to find leads automatically. It knows it has to convert each opportunity into a landing page, a video, and a sales message without human intervention. And it knows exactly when to involve the owner. The system runs 24/7: Scout analyzes about 220 businesses daily and queues up 30 new leads. Diagnoser generates diagnostics and personalized messages for each lead. Builder creates between 3 and 5 complete landing pages for the best prospects. Filmer produces a 10-second vertical video for each proposal. Pitcher sends about 30 messages daily across 4 different channels with a response rate close to 14%. Checker automatically reviews all messages before sending them. Only when a deal exceeds $3,000 or the response rate drops below 12% does the system wake the owner. And if at that moment he's on the subway or in a taxi, the Mobile agent automatically responds to the interested lead, schedules a call in Calendly, and returns the lead to the queue. The owner just has to hit “approve” and jump into the meeting. Some real system logs: “218 businesses analyzed in Austin, Denver, and Miami. 34 without websites, 19 with 2014-era sites, and 6 with reviews requesting a redesign.” “30 messages sent. 14 responses. 5 positive. 3 Zooms scheduled.” “Landing page created for a dental clinic. Responsive. 5 sections. Video rendering.” “$3,400 agreement exceeds approved limit. Sending for manual review.” And the craziest part is that he has no dedicated servers or backend. Just a local sandbox, an MCP router, a Claude API key, and that same key connected to his iPhone. Of everything I've seen this year, it's probably the cleanest and most efficient example of a one-person automated agency: $480 a month on APIs. $18,800 in revenue. 7 prompts. A file system. And a phone in his pocket. Save this before it's too late.

A Chinese developer created an agent system in Claude Code to sell landing pages to small businesses and, working completely solo, serves about 47 clients a month charging around $400 for each one. He built 7 agents on Claude Sonnet 4.6 capable of analyzing Google Maps in small cities, detecting businesses without websites or with totally outdated pages, and taking each opportunity all the way to a finished mockup, a promotional video, and a ready-to-send prospecting message. No assistants. No sales team. No SDRs. Just him, a MacBook, an iPhone, and an API key. While traditional agencies keep full teams to handle the same workflow, his only real costs are tokens and subscriptions to Lovable, Higgsfield, and Calendly. The 7 agents work coordinated by an orchestrator in Claude Code Router. The system consumes about 3 million tokens daily and the average API spend is just around $480 a month. They all work via MCP servers and share state using the file system, avoiding concurrency and shared memory issues. Even one of the agents lives directly on his iPhone and responds to leads while he's on the subway, in a taxi, or walking. This was the main prompt he set up: “You are the orchestrator of a solo agency that sells ready-made websites to local businesses…” The key is that the system perfectly understands what it is, what its limits are, and what goals it must achieve. It knows it has to find leads automatically. It knows it has to convert each opportunity into a landing page, a video, and a sales message without human intervention. And it knows exactly when to involve the owner. The system runs 24/7: Scout analyzes about 220 businesses daily and queues up 30 new leads. Diagnoser generates diagnostics and personalized messages for each lead. Builder creates between 3 and 5 complete landing pages for the best prospects. Filmer produces a 10-second vertical video for each proposal. Pitcher sends about 30 messages daily across 4 different channels with a response rate close to 14%. Checker automatically reviews all messages before sending them. Only when a deal exceeds $3,000 or the response rate drops below 12% does the system wake the owner. And if at that moment he's on the subway or in a taxi, the Mobile agent automatically responds to the interested lead, schedules a call in Calendly, and returns the lead to the queue. The owner just has to hit “approve” and jump into the meeting. Some real system logs: “218 businesses analyzed in Austin, Denver, and Miami. 34 without websites, 19 with 2014-era sites, and 6 with reviews requesting a redesign.” “30 messages sent. 14 responses. 5 positive. 3 Zooms scheduled.” “Landing page created for a dental clinic. Responsive. 5 sections. Video rendering.” “$3,400 agreement exceeds approved limit. Sending for manual review.” And the craziest part is that he has no dedicated servers or backend. Just a local sandbox, an MCP router, a Claude API key, and that same key connected to his iPhone. Of everything I've seen this year, it's probably the cleanest and most efficient example of a one-person automated agency: $480 a month on APIs. $18,800 in revenue. 7 prompts. A file system. And a phone in his pocket. Save this before it's too late.

Marre

24,953 次观看 • 3 个月前

ANTHROPIC LEAKED A SETUP WHERE ONE FILE STARTS 7 LOOPS THAT TURN A PROMPT INTO A WORKING BUSINESS AI engineer drops in 1 file - the other 95% of the work closes automatically without them. spec → tasks → code → tests → docs → release → monitoring → back into the spec you don't write tickets anymore - you write the thing tickets get derived from, and Opus 5 cuts the spec into 6 tasks by itself. a task that fails its test comes back to itself, not to you - and runs a second lap without a single notification. 6 lanes of code run in parallel and none of them wait on you - the critic filters the diff before you ever open it. every new production bug becomes a permanent line in the eval set - the system never steps on it twice. monitoring isn't the end of the line, it's the start of the next spec - those back edges are what make it a graph instead of a conveyor. and one place is left where you say yes - release, 5 minutes instead of 5 hours. save this and paste it into Claude Code - and you get 7 loops that run without you ↓

ANTHROPIC LEAKED A SETUP WHERE ONE FILE STARTS 7 LOOPS THAT TURN A PROMPT INTO A WORKING BUSINESS AI engineer drops in 1 file - the other 95% of the work closes automatically without them. spec → tasks → code → tests → docs → release → monitoring → back into the spec you don't write tickets anymore - you write the thing tickets get derived from, and Opus 5 cuts the spec into 6 tasks by itself. a task that fails its test comes back to itself, not to you - and runs a second lap without a single notification. 6 lanes of code run in parallel and none of them wait on you - the critic filters the diff before you ever open it. every new production bug becomes a permanent line in the eval set - the system never steps on it twice. monitoring isn't the end of the line, it's the start of the next spec - those back edges are what make it a graph instead of a conveyor. and one place is left where you say yes - release, 5 minutes instead of 5 hours. save this and paste it into Claude Code - and you get 7 loops that run without you ↓

Sprytix

66,333 次观看 • 2 个月前

This guy replaced an $8,000 survey crew with one drone and a pipeline on Claude Code that digitizes a whole site in a single trip. Inside it is not one drone but a whole pipeline of 5 modules on Claude Code each with its own job all answering to a single orchestrator. And he built them himself with no team. He draws one line on the map from the controller and the whole site starts turning into data at once. Pilot flies the DJI Matrice 350 RTK along the route and reaches where a crew drags tripods: the parking lot and the roofs and the grading. Scanner on the Zenmuse L2 records the geometry with a laser down to the centimeter. Builder in DJI Terra stitches the point cloud into a digital twin. Analyst on Claude builds what the client needs out of the finished model: a volume report and a progress diff and a tour behind a link. Mobile lives in his iPhone and hands the developer a link to the model while he drives to the next site. The site is ready as a file in about an hour and the developer rotates it in the browser himself and measures distances. No crew. No tripods. No week on site. Just him and a pickup and a drone and one API key. The whole processing pipeline lives in a folder at /Users/dev/site-capture. But he did not stop at a one time scan. The pipeline raises him by voice only when a flight misses a patch or when a sag on site goes past tolerance. And this is not made up: the DJI Matrice 350 RTK and the Zenmuse L2 are off the shelf enterprise hardware. The specs are one search away and the L2 really does write around 240,000 points per second. A crew for the same job charges $8,000 and three trips plus a week of desk work. His cost is tokens and subscriptions: one battery charge per flight and around $300 a month to host the finished models. In the end he draws one line on the map and an operator that does not exist scans the site and calculates the volumes and builds the report while he never leaves his truck. There is a huge market of everyone who needs to measure construction sites and warehouses and roofs and parking lots over and over while they still send out a photographer with a camera and wait a week. And he built this whole pipeline himself: one drone and one scanner and Claude Code that turns a flight into a file the developer pays for.

This guy replaced an $8,000 survey crew with one drone and a pipeline on Claude Code that digitizes a whole site in a single trip. Inside it is not one drone but a whole pipeline of 5 modules on Claude Code each with its own job all answering to a single orchestrator. And he built them himself with no team. He draws one line on the map from the controller and the whole site starts turning into data at once. Pilot flies the DJI Matrice 350 RTK along the route and reaches where a crew drags tripods: the parking lot and the roofs and the grading. Scanner on the Zenmuse L2 records the geometry with a laser down to the centimeter. Builder in DJI Terra stitches the point cloud into a digital twin. Analyst on Claude builds what the client needs out of the finished model: a volume report and a progress diff and a tour behind a link. Mobile lives in his iPhone and hands the developer a link to the model while he drives to the next site. The site is ready as a file in about an hour and the developer rotates it in the browser himself and measures distances. No crew. No tripods. No week on site. Just him and a pickup and a drone and one API key. The whole processing pipeline lives in a folder at /Users/dev/site-capture. But he did not stop at a one time scan. The pipeline raises him by voice only when a flight misses a patch or when a sag on site goes past tolerance. And this is not made up: the DJI Matrice 350 RTK and the Zenmuse L2 are off the shelf enterprise hardware. The specs are one search away and the L2 really does write around 240,000 points per second. A crew for the same job charges $8,000 and three trips plus a week of desk work. His cost is tokens and subscriptions: one battery charge per flight and around $300 a month to host the finished models. In the end he draws one line on the map and an operator that does not exist scans the site and calculates the volumes and builds the report while he never leaves his truck. There is a huge market of everyone who needs to measure construction sites and warehouses and roofs and parking lots over and over while they still send out a photographer with a camera and wait a week. And he built this whole pipeline himself: one drone and one scanner and Claude Code that turns a flight into a file the developer pays for.

Blaze

10,793 次观看 • 4 个月前

A developer who says he's not a game dev built Riposte, a browser FPS, with Claude Code and three.js in about a month. → Weapons, characters and maps are all generated in code → No Blender, no imported models → Physics on props, cloth and water → Built to run on PC, phone and controller The cost breakdown is the useful part: → Started on the €20/month plan and kept hitting limits → Switched many tasks to Sonnet 5.5 to stretch it → Moved to the €90/month plan in October and never hit the limit → No extra credits bought One tip: Claude Code started spinning up parallel agents for everything, so he told it to stop in the project instructions. It's free to play in the browser. Reddit:

A developer who says he's not a game dev built Riposte, a browser FPS, with Claude Code and three.js in about a month. → Weapons, characters and maps are all generated in code → No Blender, no imported models → Physics on props, cloth and water → Built to run on PC, phone and controller The cost breakdown is the useful part: → Started on the €20/month plan and kept hitting limits → Switched many tasks to Sonnet 5.5 to stretch it → Moved to the €90/month plan in October and never hit the limit → No extra credits bought One tip: Claude Code started spinning up parallel agents for everything, so he told it to stop in the project instructions. It's free to play in the browser. Reddit:

0xMarioNawfal - The Roundtable Show

57,336 次观看 • 19 天前

This agent relies on a marketplace of 2.8M+ specialized agents to complete a task. This is how I imagine the future of personal agents. Here is how this works: 1. Agentverse is a marketplace of agents 2. Anyone can build and publish an agent there 3. ASI:One is a personal agent with access to the marketplace 4. To solve a task, ASI:One finds the best agent for the job In other words: You have an agent that can solve pretty much *anything* because it knows how to outsource the work to a network of specialized agents. And you don't need to do any setup to get this working. The way ranking agents in the marketplace works is pretty cool: Fetch.ai, the company behind it, created an AgentRank algorithm that works much like Google's PageRank. Every time an agent calls another, it forms a connection. Over time, these connections build into a graph of who relies on whom. An agent that is frequently called by other reputable agents scores higher and surfaces higher in results. The end result here is that your agent will consistently use the best agents in the marketplace to accomplish a goal. You should give this a try: You don't need to pay or even register to try these agents. Thanks to the team for partnering with me on this post.

This agent relies on a marketplace of 2.8M+ specialized agents to complete a task. This is how I imagine the future of personal agents. Here is how this works: 1. Agentverse is a marketplace of agents 2. Anyone can build and publish an agent there 3. ASI:One is a personal agent with access to the marketplace 4. To solve a task, ASI:One finds the best agent for the job In other words: You have an agent that can solve pretty much anything because it knows how to outsource the work to a network of specialized agents. And you don't need to do any setup to get this working. The way ranking agents in the marketplace works is pretty cool: Fetch.ai, the company behind it, created an AgentRank algorithm that works much like Google's PageRank. Every time an agent calls another, it forms a connection. Over time, these connections build into a graph of who relies on whom. An agent that is frequently called by other reputable agents scores higher and surfaces higher in results. The end result here is that your agent will consistently use the best agents in the marketplace to accomplish a goal. You should give this a try: You don't need to pay or even register to try these agents. Thanks to the team for partnering with me on this post.

Santiago

18,949 次观看 • 4 个月前

Anthropic just released a talk on building headless automation with Claude Code. Presented by Sid Bidasaria, Member of Technical Staff at Anthropic live at Code with Claude on May 22, 2025 in San Francisco. Here is what the talk covers. Headless mode lets you run Claude Code without a person actively typing prompts from inside an automated script. Instead of a live session, a script calls Claude with a pre-written instruction using the -p flag. This opens the door for Claude Code to become a piece of a much larger, automated process. In plain terms: Claude Code stops being a tool you use and starts being a service that runs on its own. What this unlocks: Scheduled tasks: Run Claude Code on a cron schedule without anyone at a keyboard. Fix linting errors across an entire codebase. Automatically. Overnight. CI/CD integration: Trigger Claude Code as a step in your build process. Open a PR. Claude reviews it, flags issues, and pushes fixes before a human ever looks at it. GitHub automation: A project manager comments "Claude fix this" on a GitHub issue. Claude reads the request, finds the code, writes the fix, and opens the PR. Multi-machine workflows: One orchestrator dispatches tasks to multiple Claude Code instances running in parallel across different repos simultaneously. When you combine headless mode, hooks, and GitHub Actions, development teams can automate tasks that usually eat up significant time freeing senior engineers to focus on architectural problems while Claude handles the repetitive ones. If you use Claude Code for anything beyond single sessions this talk is worth 20 minutes of your time.

Anthropic just released a talk on building headless automation with Claude Code. Presented by Sid Bidasaria, Member of Technical Staff at Anthropic live at Code with Claude on May 22, 2025 in San Francisco. Here is what the talk covers. Headless mode lets you run Claude Code without a person actively typing prompts from inside an automated script. Instead of a live session, a script calls Claude with a pre-written instruction using the -p flag. This opens the door for Claude Code to become a piece of a much larger, automated process. In plain terms: Claude Code stops being a tool you use and starts being a service that runs on its own. What this unlocks: Scheduled tasks: Run Claude Code on a cron schedule without anyone at a keyboard. Fix linting errors across an entire codebase. Automatically. Overnight. CI/CD integration: Trigger Claude Code as a step in your build process. Open a PR. Claude reviews it, flags issues, and pushes fixes before a human ever looks at it. GitHub automation: A project manager comments "Claude fix this" on a GitHub issue. Claude reads the request, finds the code, writes the fix, and opens the PR. Multi-machine workflows: One orchestrator dispatches tasks to multiple Claude Code instances running in parallel across different repos simultaneously. When you combine headless mode, hooks, and GitHub Actions, development teams can automate tasks that usually eat up significant time freeing senior engineers to focus on architectural problems while Claude handles the repetitive ones. If you use Claude Code for anything beyond single sessions this talk is worth 20 minutes of your time.

Elias

14,096 次观看 • 5 个月前

AI agents are becoming coworkers. And every coworker needs a computer. When someone joins a team, they get a desktop, files and apps of their own, ready for the job. Agents are starting to do real work too. Research, coding, client projects, reports, admin. So I give mine the same setup. With Orgo, every agent gets its own full computer in the cloud. A real desktop with a browser, terminal, files and apps. My PC stays mine. My agents work on theirs. Onboarding takes seconds: 1. Pick a template: Hermes, OpenClaw, Claude Code, Codex and more. 2. Choose the computer specs. 3. Launch. The agent is already installed. Connect a model and the tools for the job, and it's ready to work. Like any good teammate, it keeps going when I step away, even with my PC off. I can check in live and take over the desktop anytime. And when the team grows, I don't buy more hardware. Every new agent gets its own computer. So does every project and every client. Your agents are part of the team now. Give them a computer. Use the link below for a free 3-day Orgo trial:

AI agents are becoming coworkers. And every coworker needs a computer. When someone joins a team, they get a desktop, files and apps of their own, ready for the job. Agents are starting to do real work too. Research, coding, client projects, reports, admin. So I give mine the same setup. With Orgo, every agent gets its own full computer in the cloud. A real desktop with a browser, terminal, files and apps. My PC stays mine. My agents work on theirs. Onboarding takes seconds: 1. Pick a template: Hermes, OpenClaw, Claude Code, Codex and more. 2. Choose the computer specs. 3. Launch. The agent is already installed. Connect a model and the tools for the job, and it's ready to work. Like any good teammate, it keeps going when I step away, even with my PC off. I can check in live and take over the desktop anytime. And when the team grows, I don't buy more hardware. Every new agent gets its own computer. So does every project and every client. Your agents are part of the team now. Give them a computer. Use the link below for a free 3-day Orgo trial:

Hermes Release Watch

158,794 次观看 • 17 天前

AI AGENTS 101 (58 minute free masterclass) send this to anyone who wants to understand ai agents, claude skills, md files, how to get the most out of AI etc in plain english: 1. chat vs agents - chat models answer questions in a back and forth while agents take a goal, figure out the steps, and deliver a result 2. agents don’t stop after one response. they keep running until the task is actually finishedno babysitting required 3. everything runs on a loop. they gather context, decide what to do, take an action, then repeat until done 4. the loop is the system. they look at files, tools, and the internet. decide the next step. execute and then feed that back into the next step. over and over until completion 5. the model is just one piece. gpt, claude, gemini are the reasoning layer. the key is model + loop + tools + context 6. mcp is how agents use tools. it connects things like browser, code, apis, and your internal software. once connected, the agent decides when to use them to get the job done 7. context beats prompt all day. you don't need to write perfect prompts. load your agent with context about your business, style, and goals and then simple instructions work 8. claude.md or agents.md is the onboarding doc it tells the agent who it is, how to behave, what it knows, and what tools it can use. this gets loaded every time before it starts 9. memory.md is how it improves. agents don’t remember by default. this file stores preferences, corrections, and patterns you tell the agent to update it, and it gets better over time 10. skills + harnesses make it usable. skills are reusable tasks like writing, research, analysis the harness is the environment like claude code or openclaw that runs everything. basiclaly, different interfaces, same system underneath this episode with remy on The Startup Ideas Podcast (SIP) 🧃 was one of the clearest ways of understanding a lot of the core concepts of ai agents could be the best beginners course for ai agents 58 mins. all free. no advertisers. i just want to see you build cool stuff. im rooting for you. send to a friend watch

AI AGENTS 101 (58 minute free masterclass) send this to anyone who wants to understand ai agents, claude skills, md files, how to get the most out of AI etc in plain english: 1. chat vs agents - chat models answer questions in a back and forth while agents take a goal, figure out the steps, and deliver a result 2. agents don’t stop after one response. they keep running until the task is actually finishedno babysitting required 3. everything runs on a loop. they gather context, decide what to do, take an action, then repeat until done 4. the loop is the system. they look at files, tools, and the internet. decide the next step. execute and then feed that back into the next step. over and over until completion 5. the model is just one piece. gpt, claude, gemini are the reasoning layer. the key is model + loop + tools + context 6. mcp is how agents use tools. it connects things like browser, code, apis, and your internal software. once connected, the agent decides when to use them to get the job done 7. context beats prompt all day. you don't need to write perfect prompts. load your agent with context about your business, style, and goals and then simple instructions work 8. claude.md or agents.md is the onboarding doc it tells the agent who it is, how to behave, what it knows, and what tools it can use. this gets loaded every time before it starts 9. memory.md is how it improves. agents don’t remember by default. this file stores preferences, corrections, and patterns you tell the agent to update it, and it gets better over time 10. skills + harnesses make it usable. skills are reusable tasks like writing, research, analysis the harness is the environment like claude code or openclaw that runs everything. basiclaly, different interfaces, same system underneath this episode with remy on The Startup Ideas Podcast (SIP) 🧃 was one of the clearest ways of understanding a lot of the core concepts of ai agents could be the best beginners course for ai agents 58 mins. all free. no advertisers. i just want to see you build cool stuff. im rooting for you. send to a friend watch

GREG ISENBERG

378,296 次观看 • 7 个月前

I built Orchestrator with , a proof of concept for how we envision the future of software engineering. In the future, engineers will manage swarms of agents that execute their plans in parallel. Orchestrator is a step towards this: it takes an input prompt and creates a plan executed by multiple AI IDEs (in this case, Cursor) working together. Here are three examples of what Orchestrator can build 👇

I built Orchestrator with , a proof of concept for how we envision the future of software engineering. In the future, engineers will manage swarms of agents that execute their plans in parallel. Orchestrator is a step towards this: it takes an input prompt and creates a plan executed by multiple AI IDEs (in this case, Cursor) working together. Here are three examples of what Orchestrator can build 👇

James Zhou

11,619 次观看 • 1 年前

ANTHROPIC LEAKED A FILE WHERE 5 AGENTS WITH JEV BUILD A $1.2M A YEAR SYSTEM IN 30 MINUTES you pay $3.70 and walk away with an asset that can do $1.2M a year repo → data model → API → UI → evals no layer starts until the one under it is closed, and that edge is what turns five scripts into a system the operator on Haiku 4.5 brings up the repo and CI, the architect on Opus 5 lays down the data model and migrations two builders on Sonnet 5 run API and UI in parallel, and the reviewer on Opus 5.5 can keep finished code out of prod the evals were sitting in the file before the first line of code existed at minute zero there was not a single file, by the end it is a working product with payments and tests five agents cover in half an hour what a human team bills for a quarter JEV hands one line between layers instead of the whole history, so the next agent never re-reads the project and the run costs $3.70 instead of $370 the build takes 30 minutes, the first customers show up weeks later save this and paste it into Claude Code - let it assemble those 5 layers for you ↓

ANTHROPIC LEAKED A FILE WHERE 5 AGENTS WITH JEV BUILD A $1.2M A YEAR SYSTEM IN 30 MINUTES you pay $3.70 and walk away with an asset that can do $1.2M a year repo → data model → API → UI → evals no layer starts until the one under it is closed, and that edge is what turns five scripts into a system the operator on Haiku 4.5 brings up the repo and CI, the architect on Opus 5 lays down the data model and migrations two builders on Sonnet 5 run API and UI in parallel, and the reviewer on Opus 5.5 can keep finished code out of prod the evals were sitting in the file before the first line of code existed at minute zero there was not a single file, by the end it is a working product with payments and tests five agents cover in half an hour what a human team bills for a quarter JEV hands one line between layers instead of the whole history, so the next agent never re-reads the project and the run costs $3.70 instead of $370 the build takes 30 minutes, the first customers show up weeks later save this and paste it into Claude Code - let it assemble those 5 layers for you ↓

Sprytix

14,336 次观看 • 1 个月前

I'm making $8,000 a month building Claude skills for other people's businesses. I call it the AI Concierge. I have 5 clients paying $1,200 to $2,000 a month each. My effective rate is about $1,200 an hour. Every engagement runs on one framework I call AOA: Audit, Optimize, Automate. Here's the entire model: 1) Most people skip straight to Automate. That's the fun part. It's also how you end up automating a broken process. 2) Audit first. The client shares their screen and walks the process in painful detail while I take notes in a Google Doc and interrupt with a million questions. 3) Capture the plumbing, not just the steps. Who owns each step, which tool it runs in, where the data lives, and whether that tool can connect to Claude. 4) Hand the audit doc to Claude and ask it to find a better way. Then push back. Claude's first synopsis is usually partly wrong, and the client catches it. 5) Cut the fat. Most processes are 15 steps and only need to be 11. Most clients use 3 tools where 2 would do. 6) One home per fact. One client stored customer data in ClickUp and the CRM. We killed the ClickUp copy. Duplicates are just human error waiting to happen. 7) Have Claude write the build plan. It splits the process into skills, scaffolds each one, and lists next steps. Then you lock the doc. 8) A skill is a markdown file plus reference files. A reference file is just an example of what good looks like. Claude builds all of it. 9) Build one skill at a time from the build plan. If the audit was done right, Claude one shots it. Test on a real run, let Claude interview you, iterate. Version 2 is usually the keeper. 10) Draft first, never auto send. A speed to lead skill drafts replies for review on day one. It earns auto send later. Two things that make this work: 1) Audit and Optimize are 80% of the work. Automate is the 20%. You have to earn the right to build. 2) Skills do one thing well. Split at every point a human or a wait interrupts the chain, then let an orchestrator run them back to back. Full step-by-step playbook for starting a profitable AI Concierge business (free): Full breakdown below. Enjoy!

I'm making $8,000 a month building Claude skills for other people's businesses. I call it the AI Concierge. I have 5 clients paying $1,200 to $2,000 a month each. My effective rate is about $1,200 an hour. Every engagement runs on one framework I call AOA: Audit, Optimize, Automate. Here's the entire model: 1) Most people skip straight to Automate. That's the fun part. It's also how you end up automating a broken process. 2) Audit first. The client shares their screen and walks the process in painful detail while I take notes in a Google Doc and interrupt with a million questions. 3) Capture the plumbing, not just the steps. Who owns each step, which tool it runs in, where the data lives, and whether that tool can connect to Claude. 4) Hand the audit doc to Claude and ask it to find a better way. Then push back. Claude's first synopsis is usually partly wrong, and the client catches it. 5) Cut the fat. Most processes are 15 steps and only need to be 11. Most clients use 3 tools where 2 would do. 6) One home per fact. One client stored customer data in ClickUp and the CRM. We killed the ClickUp copy. Duplicates are just human error waiting to happen. 7) Have Claude write the build plan. It splits the process into skills, scaffolds each one, and lists next steps. Then you lock the doc. 8) A skill is a markdown file plus reference files. A reference file is just an example of what good looks like. Claude builds all of it. 9) Build one skill at a time from the build plan. If the audit was done right, Claude one shots it. Test on a real run, let Claude interview you, iterate. Version 2 is usually the keeper. 10) Draft first, never auto send. A speed to lead skill drafts replies for review on day one. It earns auto send later. Two things that make this work: 1) Audit and Optimize are 80% of the work. Automate is the 20%. You have to earn the right to build. 2) Skills do one thing well. Split at every point a human or a wait interrupts the chain, then let an orchestrator run them back to back. Full step-by-step playbook for starting a profitable AI Concierge business (free): Full breakdown below. Enjoy!

Corey Ganim

71,752 次观看 • 1 个月前