The Do-Nothing Playbook

Finally, a business strategy you can commit to.

A 20-page guide to running a real business with AI agents, no employees, and a suspiciously short workday.

Version 1.0. Published by do-nothing.ai. Not affiliated with any hustle culture. Strongly opposed to it, actually.

Before We Start

This is not a motivational guide. There is no 5 AM routine. No cold shower. No vision board.

This is a business manual for people who understand that effort is not the product. Output is the product. And AI agents can produce a lot of output.

If you are looking for someone to tell you to grind harder, you have the wrong document. If you want to know how to run a profitable business with a stack of AI agents doing most of the work while you handle the decisions, read on.

The model is real. The leverage is real. The 4-hour-ish workday is not guaranteed, but it is more achievable now than it has ever been.

Let's get to it.

Chapter 1: The Premise

Why "Doing Nothing" Is Actually the Strategy

For most of business history, the formula was simple. More output required more people. More people required more management. More management required more you. The ceiling was always the same thing: how much could one person coordinate before it fell apart.

AI changed the denominator.

Not in a "ChatGPT will write your emails" way. In a structural way. The question for a solo founder is no longer "how much can I personally do?" The question is "how well have I configured the agents handling execution?"

This is different from productivity hacks. Productivity hacks help you do more in the same time. AI agents change what "you" means in the equation.

Here is the distinction that matters:

The old model. You do the work. You hit your personal capacity ceiling at maybe 50 to 60 hours a week. Revenue scales linearly with your time until it stops.

The new model. Agents do the execution. You handle decisions, quality control, and strategy. Your output multiplies by a factor that would have required 5 to 10 employees in 2019.

The work that remains for you is the work that actually requires judgment. Which customers to prioritize. Which content strategy to pursue. When the agent got it wrong and what the correct answer is. What to build next.

Everything else? Configured, delegated, automated.

This is the Do-Nothing framework. Do less of the work that does not compound. Do more of the thinking that does.

Chapter 2: Pick Your Business Model

The Four Models That Actually Work

Not all businesses suit a solo AI operation equally. The ones that work share a common trait: the repetitive execution layer can run on agents, and the revenue does not depend on your presence every hour.

Here are the four that work.

Model 1: Productized Service with Agent Execution

What it is. You sell a defined deliverable. SEO content packages. Research reports. Lead generation campaigns. Automated outreach sequences. Clients know what they get. You know what you deliver. Agents handle the production.

Why it works. The business sells a predictable output. Pricing is clear. Agents handle the repeatable work. You review, refine, and manage the client relationship.

Who it is for. People with a specific skill or domain expertise who want to sell outputs, not time.

Income range. $5,000 to $30,000 per month solo, depending on niche and client tier.

The agent stack. Content production, research, outreach, and client reporting all delegate cleanly. You keep the client calls and final QA.

Model 2: Content Site with Affiliate and Sponsorship Revenue

What it is. A content site on a specific topic. Agents produce the content at scale. The site earns from affiliate links, display ads, and eventually sponsorships from companies that want your audience.

Why it works. The economics are entirely decoupled from your time once the content engine is running. A site with 200 solid guides and 30,000 monthly visitors earns while you sleep.

Who it is for. People willing to invest 3 to 6 months before significant revenue arrives. Patience is the main qualification.

Income range. $1,000 to $50,000 per month depending on traffic, niche, and monetization. The ceiling is genuinely high.

The agent stack. Content agents produce guides. Automation handles internal linking and publishing. You set topics, review output, and manage the content calendar.

do-nothing.ai runs exactly this model.

Model 3: SaaS with Agent Operations

What it is. A software product with a recurring subscription. Agents handle customer support, content marketing, and user onboarding. The product itself may also be AI-powered.

Why it works. Recurring revenue, not project-based income. The business compounds. Customer support and content, which kill solo founders at scale, are delegated to agents.

Who it is for. Technical founders who can build (or hire someone to build once) and then operate with minimal ongoing development.

Income range. Highly variable. $500 to $50,000 per month and beyond. Takes longer to reach scale but compounds harder.

The agent stack. Support agent for tier-one tickets. Content agent for blog and documentation. Onboarding agent for new users. You handle product decisions and anything that escalates.

Model 4: Agency of One

What it is. An agency that delivers results at agency quality without agency headcount. You take on client projects. Agents handle execution. You manage quality and client relationships.

Why it works. Clients pay agency rates. Your costs are token costs, not salaries. The margin is significantly higher than a traditional agency.

Who it is for. People with agency experience who understand client relationships and know what quality looks like.

Income range. $10,000 to $60,000 per month at full capacity.

The agent stack. Depends on the agency type. Content agencies run content agents. Outreach agencies run outreach agents. SEO agencies run content and research agents. The pattern is consistent.

How to Pick

Use this filter:

Pick one. Get it to $3,000 per month before adding a second model. The single biggest mistake in this game is diversifying before you have traction.

Chapter 3: Your AI Team

The Org Chart Is Software

An AI team is not a chatbot. It is not asking Claude to write an email for you.

An AI team is a set of specialized agents, each assigned to a specific business function, running with defined tools and instructions that match the standards of that role. The content agent publishes three guides a week. The support agent handles tier-one customer questions. The outreach agent drafts personalized sequences. These agents do not chat. They operate.

The difference:

Using AI. You prompt. It responds. You do something with the response.

AI team. The function runs. You review the output and approve what ships.

Here are the six functions that delegate cleanly:

1. Content and SEO

What the agent does. Drafts guides based on keyword targets, adds internal links, formats for both human readers and AI parsing, and flags gaps in the content library.

What you do. Set the keyword targets, review draft quality, approve what publishes.

Tool setup. Claude Opus 4.6 or Sonnet 4.6 for writing. Ahrefs or similar for keyword data. Your CMS or file system for publishing.

Time saved. 15 to 20 hours per week compared to doing it yourself.

2. Customer Support

What the agent does. Handles tier-one tickets. Answers standard questions. Routes complex issues to you. Drafts responses to escalations for your review.

What you do. Handle true escalations and policy decisions. Review the agent's routing logic quarterly.

Tool setup. Claude Haiku 4.5 for high-volume ticket handling (it's cheaper and fast enough for support). Your help desk or email client connected via Make.com or n8n.

When not to use it. High-stakes or highly emotional customer situations. Legal complaints. Refund decisions above a threshold you set.

3. Outbound Sales and Lead Research

What the agent does. Researches leads, drafts personalized outreach sequences, tracks responses, and surfaces warm replies for you.

What you do. Define the ICP (ideal customer profile), review the sequences, handle warm replies.

Tool setup. Claude Sonnet 4.6 for research and drafting. Apollo or Clay for lead data. Make.com for sequence automation.

Note. The agent drafts. You review before sending. An AI writing email that goes out with your name on it without your eyes on it is a risk not worth taking.

4. Financial Monitoring

What the agent does. Pulls revenue data, flags anomalies, generates weekly summaries, tracks expenses against budget.

What you do. Review the summaries. Make decisions.

Tool setup. Claude Haiku 4.5 connected to your financial data via API or export. Runs weekly.

5. Research and Intelligence

What the agent does. Monitors competitors, surfaces relevant news, synthesizes research on topics you specify, generates briefs.

What you do. Define what you want to monitor. Consume the briefs.

Tool setup. Claude with web search access. Runs on a schedule.

6. Social and Distribution

What the agent does. Repurposes content into social formats, drafts posts, schedules distribution.

What you do. Review before publishing. (Seriously. Review before publishing.)

Tool setup. Claude Sonnet 4.6 for drafting. Buffer or direct API for scheduling.

Building the Team in Order

Do not try to automate everything at once.

Month 1: Set up your most time-consuming function. If content is where your time goes, start there. If support is the bottleneck, start there.

Month 2: Add a second function. Refine the first.

Month 3 and beyond: Each agent you add has a learning curve. Instructions get better. Output improves. The system compounds.

By month 4, if you are doing this right, you should have more capacity than a three-person team at a fraction of the cost.

Chapter 4: The Delegation Framework

How to Hand Off Real Work to an Agent

Delegation to AI agents is a skill. It is not about finding magic words. It is about understanding what agents need to succeed, and structuring your instructions accordingly.

Most bad agent outputs are not model failures. They are instruction failures.

Step 1: Check If the Task Is Delegatable

Before writing a single line of instructions, ask three questions.

Is the goal verifiable? Can you look at the output and know whether it is correct? A guide that hits a specific keyword, a report with the right data, a list that meets defined criteria: these are verifiable. "Write something interesting" is not.

Is the input well-defined? Agents work from context. If the task requires implicit knowledge they do not have, they will fill the gap with something that sounds plausible and is wrong.

Is the cost of failure acceptable? Agents make mistakes. The right question is: what happens when this one does? Low-stakes tasks (drafting, summarizing, generating options) have low failure cost. High-stakes actions (publishing, sending emails, deleting records) need human checkpoints.

If the answer to all three is yes, delegate it.

Step 2: Write an Instruction That Actually Works

A good agent instruction has five elements:

Role. What is this agent? Not "you are a helpful assistant." Specific: "You are a content editor at a resource site for AI founders. Your job is to write practical, specific guides in a dry, direct voice."

Context. What does the agent need to know about the situation? What is the site about? Who is the audience? What has already been done?

Task. Exactly what to do. Not "write a guide about AI tools." "Write a 1,500-word guide targeting the keyword 'ai agent for small business.' Cover what an agent does, what to automate first, three specific tools with pricing, and a realistic timeline."

Output format. How should the result be structured? Markdown? JSON? A list? A table? Specify it. Agents will invent a format if you do not.

Constraints. What not to do. No em dashes. No lists of five things that are really just one thing repeated five times. No vague intros that bury the point.

The single biggest improvement most people can make is adding a constraints section to their instructions.

Step 3: Run a Test Before You Automate

Before you wire any task into a workflow, run it manually three times. Review each output. Identify the failure patterns.

Common patterns:

Too generic. The agent produces technically correct output that could apply to anything. Fix: add more specific context and examples of what "specific" looks like.

Wrong scope. The agent either overdelivers (writes 3,000 words when you asked for 800) or underdelivers (produces a skeleton when you need a complete guide). Fix: specify length and completeness expectations explicitly.

Format drift. The output format changes between runs. Fix: include an exact template in the instructions, not just a description of what you want.

Run the test. Identify the failure. Fix the instruction. Re-test. Repeat until the pass rate is above 80 percent before automating.

Step 4: Build in a Human Checkpoint

Decide in advance what the agent can ship directly and what requires your review.

The rule that works for most operations:

Ship without review: Drafts for your own review queue. Internal data summaries. Research briefs. Research only.

Ship after quick review: Content that goes on the site. Outreach emails. Anything a customer or prospect will read.

Always require full review: Financial decisions. Legal correspondence. Any action that is difficult to reverse.

This is not distrust of the model. It is appropriate quality control for the stakes involved.

What to Do When an Agent Gets It Wrong

It will happen. The question is whether it is a one-off or a pattern.

One-off error. Flag it, correct it, move on. Do not rebuild your entire system because of one bad output.

Pattern error. Update the instructions. Add a constraint or clarification. Run the test again.

Systematic failure. The task may not be well-suited for AI delegation yet. Break it into smaller steps. Consider whether the input is well-defined enough.

The goal is not perfect agent output on the first try. The goal is a system where error rates are low enough and catches are reliable enough that the output is trustworthy at scale.

Chapter 5: Tools and Stack

What You Actually Need (And What You Can Skip)

The 2026 solopreneur AI stack is leaner than the listicles suggest. Here is the actual minimum viable setup.

The Core Stack

Total infrastructure cost before LLM API usage: $10 to $100 per month depending on traffic and plan tiers.

LLM API costs vary by usage volume. A content-heavy operation running 20 to 30 guides per month typically runs $30 to $150 in API costs.

LLM Selection Logic

Do not pick one model and use it everywhere. Match the model to the task.

Claude Opus 4.6. Use for reasoning-heavy tasks. Complex writing. Planning. Code architecture. Anything where the output quality directly affects revenue or reputation.

Claude Haiku 4.5. Use for high-volume, low-stakes tasks. Categorization. Summarization. Data extraction. Routing. It is 5 to 10x cheaper than Opus. Use it for the work that does not need the best model.

Claude Sonnet 4.6. The middle ground. Solid quality at a fraction of Opus pricing. Good for outreach drafts, social content, and anything where "very good" is sufficient and "great" is not required.

The Automation Layer

Make.com vs n8n. Make.com is easier to learn and has more connectors. n8n is self-hostable and has no per-operation pricing at scale. Start with Make.com. Switch to n8n if you are running more than 10,000 operations per month and cost becomes a factor.

What to automate. Content publishing pipelines. Inbound lead routing. Support ticket classification. Weekly reporting. Anything that happens on a schedule and does not require real-time judgment.

What not to automate. Your first pass at client communication. Anything with legal implications. Decisions that change the direction of the business.

What You Can Skip

Expensive agent orchestration platforms. If you are running a solo operation, you do not need an enterprise agent orchestration suite. The Anthropic API and Make.com handle 90 percent of what these platforms offer at a fraction of the cost.

Multiple AI writing tools. Pick one and learn its instructions well. Switching between Claude, GPT-4, and Gemini depending on the task sounds smart. In practice, it means none of your instructions are optimized and the output quality is inconsistent.

Social media schedulers with AI built in. The AI component is usually a thin wrapper on an existing model. Use your primary LLM and a simple scheduler separately.

Chapter 6: Pricing Your Work

Why Doing Less Lets You Charge More

Most solopreneurs selling AI-powered services make the same mistake: they price like it is 2019, when the work took ten times longer.

AI changes your cost structure. Your time per deliverable drops. Your quality ceiling rises. Your capacity multiplies. None of that is a reason to charge less.

The value you deliver to the client has not changed. Your margin has.

The Four Models

Hourly. Simple. Low friction to sell. Also: punishes you for being efficient. If AI makes you twice as fast and you bill by the hour, you just cut your own revenue in half. Use hourly only for consulting sessions and advisory calls, not production work.

Project-based. Flat fee for a defined deliverable. This is where AI-powered solopreneurs win. Your time drops. Your price stays. Margin improves. Clients pay for the outcome, not your hours.

Retainer. Monthly fee for an ongoing relationship. Predictable revenue. The right clients are loyal. The wrong ones expand scope infinitely. Define exactly what is included before you sign anything.

Value-based. Price based on what the outcome is worth to the client, not what it costs you to produce. A lead generation campaign that closes $200,000 in deals is worth more than the hours it took. Value-based pricing is the ceiling. Get there when your track record justifies it.

Real Price Ranges (2026)

These are not aspirational. They are market rates for competent operators in 2026.

The Margin Math

Here is the honest version:

You take on a content client at $2,500 per month for 10 guides. Before AI: 6 hours per guide equals 60 hours of your time. At $40 per hour (effective), that is $2,400 in time cost on a $2,500 contract. You net $100. You are employed.

After AI: 45 minutes of reviewing and editing per guide. 7.5 hours total. $375 in time cost. $150 in API costs. You net roughly $2,000 on the same contract.

Same client. Same deliverable. Very different outcome for you.

This is why AI-powered solopreneurs do not compete on price. The margin improvement does not need to be passed to the client to win deals.

When to Raise Your Prices

When your calendar is more than 70 percent full. When you turn down work because you do not have capacity. When clients accept your proposals immediately without negotiating.

Those are all signals that you are underpriced.

The move: raise prices on the next client, not the current ones. See if it changes your conversion rate. If it does not, you found your new floor.

Chapter 7: Building for Passive Revenue

Income That Does Not Require You Every Day

Passive income is not passive. What it means in practice: income that does not require proportional time. You build a system. The system earns.

AI makes this possible at a much smaller scale than it used to require. You do not need a team, a warehouse, or a venture round.

Here are the models that actually work.

1. Content Site With Affiliate and Sponsorship Revenue

You build a content site on a specific topic. Agents produce content at scale. The site earns from affiliate links, display ads, and eventually sponsorships.

Honest timeline. Three to six months before meaningful traffic. Six to twelve months before meaningful revenue. Not a quick play. A compounding play.

What agents handle. Content production, internal linking, formatting. You set topics and review quality.

Revenue ceiling. No real ceiling. Sites with 50,000 to 100,000 monthly visitors in commercial niches earn $10,000 to $80,000 per month from a combination of sources.

The trap to avoid. Publishing volume without quality control. AI can produce garbage at scale just as easily as quality at scale. Your editorial judgment is the variable that determines which one you get.

2. Digital Products

One thing you build once. Sold indefinitely.

Options that work with an AI-assisted creation process:

Guides and playbooks. Detailed, specific, useful documentation on a niche topic. Price range: $19 to $197. This document, for example.

Prompt libraries. Curated, tested prompts for a specific use case. Price range: $17 to $97.

Templates. Agent instructions, workflow blueprints, system documentation. Price range: $27 to $147.

Courses. More labor to create. Higher price point. More credibility required. Price range: $197 to $997.

What agents handle. Drafting the content. Research. Formatting. You provide the expertise, the angle, and quality control.

3. Sponsorships

If you build an audience, companies will pay to reach it.

The requirement is an audience. The minimum viable audience for sponsorship conversations: 5,000 monthly unique visitors to a specific-enough site, or 2,000 engaged email subscribers, or 10,000 followers in a niche.

At that scale, relevant companies in your niche will pay $200 to $2,000 per month for a placement, depending on the audience quality and niche CPM.

At 50,000 monthly visitors, you are having conversations with companies about $2,000 to $10,000 per month for a featured spot.

The agency model for sponsorships. Build the audience first. Worry about monetization second. Trying to sell sponsorships before you have the numbers is a poor use of time and damages your credibility.

4. SaaS Subscriptions

The best passive revenue model if you can build the product (or have it built).

Revenue recurs monthly. Your marginal cost per user is tiny. The business compounds.

The constraint: someone has to build the software. If you can build it, this is the highest-ceiling model available. If you cannot, the cost of having it built is often the barrier.

The AI-native SaaS. Products that use AI to deliver a specialized result for a specific job type. A niche AI assistant. An automated reporting tool. An AI-powered data product for an underserved industry. These are buildable as solo operations if the product scope is narrow enough.

Chapter 8: 16 Business Ideas Worth Starting

These are not theoretical. Each idea runs primarily on AI. If the same business worked just as well in 2019 without AI, it is not on this list.

1. AI Research Boutique. Deliver structured intelligence reports on competitors, markets, and regulations. Clients get analyst-quality work. You run one or two agents and review the output. Revenue: $300 to $2,000 per report.

2. Automated Newsletter. A weekly or daily newsletter on a specific topic. Agents source and summarize. You edit and add your take. Monetize with sponsorships and paid tiers. Revenue: $2,000 to $20,000 per month at scale.

3. Lead Generation Agency. AI agents research leads, score them, and draft personalized outreach. You review before sending and handle warm replies. Revenue: $3,000 to $15,000 per month per client.

4. AI-Powered SEO Agency. Content, technical audits, and internal linking recommendations. Agents produce the volume. You set strategy and review quality. Revenue: $2,000 to $8,000 per client per month.

5. Workflow Automation Consultant. You build Make.com or n8n flows for clients. Agents document the builds and generate client-facing instructions. Revenue: $3,000 to $15,000 per project.

6. Niche Content Site. Pick a topic. Build authority. Monetize with affiliates, ads, and sponsorships. Revenue: $1,000 to $50,000 per month depending on traffic.

7. AI Scriptwriter for Creators. YouTubers and podcasters need scripts. Agents draft based on the creator's angle. You edit. Revenue: $500 to $3,000 per script or $2,000 to $8,000 per month on retainer.

8. Automated Competitive Intelligence. Weekly briefs on what a client's competitors are doing. Agents monitor and summarize. You contextualize. Revenue: $1,000 to $4,000 per month.

9. AI Support Agent Setup. You configure and deploy customer support agents for small businesses. Ongoing retainer for maintenance. Revenue: $1,500 to $5,000 per setup plus $500 to $1,500 per month maintenance.

10. Personal Finance Analyzer. An AI tool that connects to financial data, categorizes spending, and surfaces insights. Subscription product. Revenue: $15 to $49 per month per user.

11. Job Application Optimizer. AI that tailors resumes and cover letters for specific job postings. One-off or subscription. Revenue: $29 to $99 per application or $49 to $99 per month.

12. AI Email Manager. Configures AI to triage, draft responses, and flag urgent items for a client's inbox. Revenue: $500 to $2,000 per month.

13. Document Intelligence Tool. Upload any document. AI extracts, summarizes, and answers questions. Subscription product for a specific vertical (legal, real estate, finance). Revenue: $49 to $199 per month per user.

14. Social Content Repurposing Agency. Take client's long-form content and repurpose into social formats. Agents handle the formats. You set standards. Revenue: $1,500 to $5,000 per month.

15. AI Hiring Assistant. Screens resumes, generates interview questions, and summarizes candidate fit. Sold to SMBs as a per-hire product. Revenue: $200 to $500 per hire.

16. Niche AI Aggregator. Aggregate news, tools, and resources for a specific industry. Agents surface content. You curate. Revenue: sponsorships, affiliate, and paid newsletter tiers. Revenue: $500 to $10,000 per month at scale.

Chapter 9: The Week in the Life

What "Doing Nothing" Actually Looks Like as a Daily Practice

Here is a realistic week for someone running a productized content service on this model.

Monday. Review the content agent's output from the weekend. Approve three guides for publishing. Set the keyword targets for next week's batch. 90 minutes.

Tuesday. Client emails and a call. Support escalations (two this week, both handled in 15 minutes). Review outreach sequence the agent drafted. Approve and send. 2 hours.

Wednesday. Strategic work. Competitive research brief from the intelligence agent. Review it. Identify two new content angles. Update the content agent's instructions based on a quality pattern you noticed. 1.5 hours.

Thursday. Financial review. Weekly summary from the finance agent. Invoicing. One business development conversation. 2 hours.

Friday. Light day. Review this week's publishing output. Flag anything that needs editing. Check analytics. Spend the rest of the afternoon thinking about what to build next quarter. 1 hour.

Total: approximately 8 to 10 hours of actual work across the week.

The agents handled:

• 8 content guides drafted and formatted
• 47 support tickets (3 escalated to you, handled in 15 minutes)
• 200 outreach sequences researched and drafted
• Weekly competitive intelligence brief
• Financial summary

This is not theoretical. It is the operating model of people running this stack correctly.

Chapter 10: The Exit

Three Ways This Ends Well

Most business books do not talk about the end. This one will.

There are three good exits from a well-run AI-native solo business.

Exit 1: Passive Income Maintenance Mode

You hit a revenue level you are happy with. The systems are stable. You reduce your active hours to 3 to 5 per week and let the business run.

This is not selling. This is converting a business into an asset that generates income with minimal ongoing input. It requires that the systems are genuinely robust, that the business does not depend on your personal brand or relationships, and that the revenue is defensible.

Achievable for content sites and productized services with strong documentation and clean agent infrastructure.

Exit 2: Sell the Business

AI-native businesses with recurring revenue and documented systems sell. The buyer gets an asset that produces revenue with minimal operator involvement.

Multiples depend on the business type and revenue consistency. SaaS businesses trade at 3 to 6x annual recurring revenue. Content sites trade at 25 to 40x monthly revenue. Service businesses with documented systems trade at 1.5 to 3x annual profit.

For a business doing $10,000 per month in net profit, that is a potential sale price of $150,000 to $480,000 depending on type and buyer.

Build the documentation. Keep the systems clean. Make it acquirable from day one.

Exit 3: Productize What You Built

You build an AI operation for yourself. It works. You package the process, the stack, the instructions, and the systems into a product other people pay for.

The meta-play: do this well enough that people want to learn how you did it. That is the origin of every successful course, every playbook, every community in this space.

This works if you have the numbers to back it up. "I scaled to $20,000 per month in 6 months" is a credible story. "Here is how AI can theoretically scale your revenue" is not.

Appendix: The Quick-Start Checklist

Week 1. Pick a business model from Chapter 2. Choose the one where your existing skills create the most leverage.

Week 2. Identify the single highest-time function in your business or planned business. Set up one agent for that function. Run it manually before automating.

Week 3. Get three clients or customers. Validate that the model works at small scale before scaling the automation.

Month 2. Add a second agent function. Start building the content or distribution layer.

Month 3. Review your margin, your time, and your revenue. Make the next investment decision from data, not theory.

Month 6. You should know whether this model is working for your specific situation. If it is, double down. If it is not, the instructions in this playbook are the most likely failure point. Fix them.

Last Page

You have read the whole thing. Good.

Now do the one thing most people skip: start with one model, one agent, and one client.

The stack in this guide is not complicated. The tools are available. The models are good enough. The only variable that actually matters is whether you configure it and run it or keep reading about configuring and running it.

This is, appropriately, the business strategy you can commit to. Doing less of what does not compound. Doing more of what does.

The rest is agents.

The Do-Nothing Playbook is published by do-nothing.ai. The site is a resource hub for people building AI-powered businesses. One person, a bunch of agents, and a suspiciously reasonable workweek.

Find more guides, tools, and resources at do-nothing.ai.

If this was useful, send it to someone still grinding.