用开源工具OpenClaw实现自动化赚钱,47天赚1247美元的实操经验

本文介绍了作者如何利用开源自动化工具OpenClaw,在47天内赚取1247美元的案例。OpenClaw通过智能分配任务给不同的AI模型,实现了高效且低成本的自动化操作。

在项目初期,API调用费用较高,每月约579美元,限制了盈利空间。经过优化,成本降低至约73美元,节省了87%的支出。这样每次任务的成本从0.08美元降至0.009美元,提升了11倍的成本效益。

OpenClaw通过模型路由技术,将复杂任务拆分分配给不同价位的模型,结合专门的推理工具,减少了对高价模型的依赖,降低了整体开销。通过合理配置,既保证了结果质量,也控制了费用。

赚钱场景主要是开发微型SaaS工具,例如社交媒体定时发布器,自动分析受众活跃时间,在Twitter、LinkedIn和Facebook上精准发布内容,帮助客户节省时间和人力成本。

操作步骤包括:
1. 学习OpenClaw的基本用法和API配置。
2. 设计可重复使用的自动化技能模块,比如定时发布。
3. 优化模型调用,设置路由规则降低费用。
4. 通过小规模客户测试并收集反馈。
5. 扩展功能,打造可持续收费的产品。

总结来看,结合合理的技术架构和成本优化,利用开源工具打造自动化微型服务,是AI创业中相对低门槛且可复制的赚钱路径。

Anthropic的AI代理市场实测:69人用100美元预算完成186笔真实交易

Anthropic公司做了一个名为Project Deal的实验,创建了一个由AI代理买卖双方组成的分类市场。69名员工各自拥有100美元预算,AI代理代表他们在Slack频道内进行商品买卖谈判,无需人工干预。

实验持续一周,完成了186笔交易,交易总价值超过4000美元,商品种类丰富,从滑雪板到乒乓球塑料袋应有尽有。此次测试分为四个市场,其中一个完全使用Anthropic的旗舰模型Claude Opus 4.5,其他使用性能较低的模型做对比。

结果显示,使用更高级模型的AI代理每人多完成约两笔交易,且卖出的价格平均比低阶模型高出3.64美元,说明AI模型的质量直接影响交易效率和收益。

参与者对交易公平性的评价中等,绝大多数愿意再次参与,近半数表示愿意为类似服务付费。实验还发现,激进报价和合作式谈判在经济结果上差别不大。

赚钱场景可以应用于自动化二手商品交易、企业采购谈判、在线拍卖等领域。企业或创业者可基于此搭建AI代理市场,减少人工谈判成本,提高交易量和价格优化。

具体操作步骤:
1. 选择合适的AI模型并调整预算和激励机制。
2. 搭建交易平台,确保AI代理能独立沟通和决策。
3. 设计透明的交易流程和纠纷处理机制。
4. 持续监控模型表现,迭代升级代理智能。
5. 通过小范围试点,收集市场反馈后扩大规模。

整体来看,AI代理市场已具备真实商业价值,合理运用可带来可观的交易额与利润。

AI助力副业新机遇:10种轻松利用AI提升效率的兼职项目详解

随着AI技术普及,许多传统兼职工作被大幅简化,允许个人用更少时间赚取更高收入。这里总结了10个AI显著提升效率的副业项目,涵盖多个领域。

例如,邮件收件箱整理专员(时薪20-40美元)可用AI自动分类邮件、识别重点内容、草拟回复,极大减少手动操作。播客节目笔记写手(时薪25-75美元)借助AI转录音频、提炼重点并添加时间标记,提升产出速度和质量。

知识库编写员、在线社区管理员和内容重塑专员等角色,均利用AI工具自动生成或整理内容,节省大量时间。在线约会资料顾问则通过AI分析用户语音和风格,优化个人介绍,提高成功率。

赚钱场景广泛,适合自由职业者、远程工作者及希望兼职增加收入的人群。AI工具降低了入门门槛,即使没有专业背景,也能快速上手并提供专业服务。

具体操作步骤如下:
1. 选定感兴趣且有市场需求的兼职方向。
2. 学习并掌握对应的AI工具使用方法。
3. 在自由职业平台或社交媒体发布服务信息,吸引客户。
4. 利用AI加速工作流程,保证交付质量。
5. 收集客户反馈,不断优化服务内容与效率。

总体来看,AI不仅帮助兼职者提高效率,扩大服务能力,还能通过技术手段提升收入潜力。合理结合自身优势和AI工具,未来几年内,这类副业将成为重要的收入来源。

AI for Non-Profits: Automating Grant Sections with Strategic Prompts

For small non-profit grant writers, crafting compelling organizational backgrounds and problem statements is a critical, yet repetitive, bottleneck. They are time-consuming to rewrite from scratch for every application, yet too important to copy-paste verbatim. AI automation, powered by precise instructions—prompts—transforms this task from a chore into a strategic advantage.

Beyond Generic AI: The Art of the Strategic Prompt

Generic AI queries yield generic, unusable text. The key is providing structured, specific context. Instead of “write a background,” instruct the AI with a clear role and scope: “You are a strategic grant writing consultant specializing in [Your Sector]. Synthesize the following information into a 250-word section.” This frames the AI’s approach.

Your Content Library as AI Fuel

Effective prompts feed AI your unique organizational data. Create a library with your Mission & Vision Statements, Founding Story (e.g., “Founded in 2010 by community nurses…”), Key Milestones, Core Programs, Leadership Credentials, and Previous Relevant Successes (e.g., “Our pilot reduced A1C levels by 15%”). This library becomes the source material for all AI-generated drafts.

Prompt Engineering for Flawless Drafts

Combine your library with specific instructions for each section. For an Organizational Background, prompt: “Synthesize the following: [Mission], [Founding Story], [Key Milestones], [Core Programs], [Leadership Credentials]. Structure into two concise paragraphs: 1) Mission, history, growth. 2) Core competencies and proof of effectiveness. Use a professional yet passionate tone.”

For a Problem Statement, ensure funder alignment: “Define the problem from the perspective of [Target Population]. The funder’s RFP goal is [improving third-grade literacy]. Keep the focus consistently on *their* experience. Use a tone that is urgent, factual, and compelling, but not sensationalist. Keep to 150 words.” Add guardrails: “Do not use jargon. Do not make unsubstantiated claims.”

From Draft to Perfection: The Human-in-the-Loop

The AI produces a strong, tailored first draft in seconds. Your expertise is then applied to refine. If the draft lacks impact, instruct: “Revise to incorporate this client quote and lead with the most startling statistic.” If it’s vague, command: “Add more specific data on program reach.” This iterative process saves hours of initial writing, freeing you for high-level strategy and narrative polish.

For a comprehensive guide with detailed workflows, templates, and additional strategies, see my e-book: AI for Small Non-Profit Grant Writers: How to Automate Funder Research Alignment and Grant Proposal Section Drafting from Past Submissions.

AI for Specialty Food Producers: Automating FDA Label Compliance & Ingredient Sourcing Alerts

For small-scale specialty food producers, a single ingredient change from a supplier can trigger a compliance crisis. A new batch of “Brand X Organic Raw Apple Cider Vinegar – 5% Acidity” might have a different acidity level or a new processing aid. Suddenly, your nutrition facts are wrong, your “Organic” claim is at risk, and your packaging is non-compliant. This sourcing nightmare is a constant threat to your brand integrity and legal standing.

AI automation is now a practical shield against this risk. The strategy moves you from reactive firefighting to proactive control by digitizing and monitoring your supply chain.

Step 1: Build Your Digital Ingredient Dossier

Start by creating a master record for every ingredient. This dossier must include the Supplier Name & Contact, the Current Specification Sheet Link or PDF (non-negotiable), and all Key Compliance Flags: allergens, Organic Cert. ID, Non-GMO status, and vegan declarations. This centralized digital hub is the foundation for AI to work effectively.

Step 2: Deploy AI-Powered Spec Sheet Monitoring

Integrate AI tools to continuously monitor supplier portals or analyze emailed spec sheets. The AI scans for any change in the documentation. When a change is detected—like a new sub-ingredient or altered nutritional macro—it triggers an automatic, prioritized alert. This gives you a critical time advantage.

Step 3: Execute the Human-in-the-Loop Triage Protocol

When an alert arrives, follow a decisive protocol. First, ASSESS the exact impact: Does this affect my ingredient statement, nutrition facts, or claims (Organic, Vegan, etc.)? Your Immediate Action is to quarantine any non-compliant new ingredient.

Next, CALCULATE your inventory runway. How much old, compliant ingredient do you have versus new stock? This dictates your timeline. Then, COMMUNICATE your decision internally and, if required, to customers or retailers.

Finally, DECIDE on your Packaging Action. Can you use old packaging with a sticker correction, or is a full reprint necessary? This structured response, guided by AI-generated data, turns chaos into a managed operational procedure.

For a comprehensive guide with detailed workflows, templates, and additional strategies, see my e-book: AI for Small-Scale Specialty Food Producers: How to Automate FDA/Nutrition Label Generation and Ingredient Sourcing Alerts.

AI Automation for Independent Music Teachers: Auto-Generating Materials

As an independent music teacher, your time is precious. AI automation can reclaim hours spent on administrative tasks, specifically in generating lesson materials. By systematizing the creation of handouts, practice sheets, and repertoire lists, you enhance consistency while deepening student engagement.

Automating Concept Handout Creation

When a student struggles with a recurring conceptual gap—like rhythm subdivision or breath support—AI can generate a targeted explanatory handout. Use a Triple-Prompt Structure: first, ask AI to explain the concept in detail; second, use an “Explain It Simply” prompt for a student-friendly version; third, request practical exercises. From your student’s Dynamic Profile, insert specific details. After generating the handout, save it as a master template in a “Studio Handouts” folder for future use. In the lesson, introduce it together and attach it to the practice sheet.

Streamlining Repertoire Planning

Every 3-6 months, schedule a 5-minute “What’s Next?” chat. Gather the student’s current interests and favorite pieces. Then, use a Repertoire List Generator prompt with these inputs. AI will produce a curated list of 5-6 appropriate options. Your critical role: review the list, remove inappropriate suggestions, and add 1-2 personal recommendations. Present the list and let the student choose—this agency boosts motivation. Finally, add the chosen piece(s) to your master lesson plan template for that student.

Generating Personalized Practice Sheets

The weekly practice sheet is vital for progress. Pull up the student’s Dynamic Profile for latest notes. Ask AI to generate the sheet based on the lesson’s focus and goals. The critical step: scan and personalize. Add one handwritten note or a friendly emoji to maintain human connection. Save the sheet as a PDF with a clear filename: `[StudentName]_PracticeSheet_[YYYY-MM-DD].pdf`. Then, email it directly or upload it to your student portal (e.g., Google Classroom, Music Teachers Helper). This ensures accountability and clear direction.

These automated workflows turn generic materials into personalized, scalable tools. AI handles the heavy lifting of generation, while you inject the expertise and connection that makes teaching effective. The result is more focused lesson time and structured student progress.

For a comprehensive guide with detailed workflows, templates, and additional strategies, see my e-book: AI for Independent Music Teachers: How to Automate Lesson Plan Creation and Student Progress Tracking.

AI Automation for Southeast Asia Cross-Border Sellers: Conquering Six Customs Regimes

The ASEAN Documentation Maze

Expanding across Singapore, Malaysia, Indonesia, Thailand, Vietnam, and the Philippines means navigating six distinct customs environments. Manual HS code classification and document preparation is slow, error-prone, and scales poorly. A single mistake triggers delays, fines, or seized shipments. AI automation transforms this complex burden into a competitive, streamlined advantage.

AI-Powered HS Code Precision

Accurate Harmonized System (HS) codes are the foundation. AI tools like ChatGPT, trained on your product catalog and regional tariff databases, can analyze item descriptions and images to suggest the most likely codes for each market. This is not a blind guess; it’s a consistent, audit-ready process. You can build validation workflows in Notion or Airtable to have team members confirm AI suggestions, creating a self-improving system that learns from corrections.

Automating Multi-Country Customs Documents

Once codes are set, AI automation generates the necessary declarations. Platforms like Zapier or Make can connect your e-commerce or ERP system to document templates. A new order from Thailand automatically triggers the creation of a Thai Customs Declaration and Commercial Invoice, populated with the correct AI-validated HS code, value, and origin. This data flow ensures 100% consistency and eliminates manual copy-paste errors across thousands of shipments.

Building Your Compliance Workflow

Start by centralizing your product data and approved HS codes in a tool like Notion. Use Make to create a scenario where a new product addition kicks off an AI classification review. Integrate Submittable or Fluxx workflows for internal compliance approvals. The goal is a seamless pipeline: from product upload to AI-assisted code assignment, to human review, to automated document generation for any of the six target countries.

From Complexity to Competitive Edge

Automating this core logistics function does more than prevent errors. It drastically speeds up shipping times, improves customer satisfaction, and frees your team to focus on growth, not paperwork. It also builds a resilient, scalable compliance framework as you add more markets. In the fast-paced ASEAN cross-border trade, this operational efficiency is a direct differentiator.

For a comprehensive guide with detailed workflows, templates, and additional strategies, see my e-book: AI for Southeast Asia Cross-Border Sellers: Automating HS Code Classification and Multi-Country Customs Documentation.

How AI Automation Ensures Style Consistency for Professional Self-Publishers

For professional authors, your book’s presentation is a direct extension of your brand. Inconsistent formatting across Kindle, print, and ePub files dilutes that brand, breaking the reader’s trust. Every time a customer adjusts to a new font or spacing, their cognitive load increases, pulling them out of your narrative. AI-assisted formatting directly addresses this by automating the precise translation of your design intent into every format.

The Core Challenge: One Vision, Three Formats

Each output has unique technical constraints. A print PDF requires absolute positioning, embedded fonts like Garamond at 24pt, and CMYK color. An ePub file uses semantic HTML and full CSS3 for true reflowability, specifying fonts in `em` units. Amazon’s KPF format has limited CSS, relying on scaled Kindle-specific fonts. Manually ensuring consistency here is error-prone and time-consuming.

AI as Your Formatting Conductor

AI automation works through intelligent metadata mapping. You define a style once—for example, a chapter title as Garamond, bold, 24pt, centered—and the AI maps it correctly everywhere. It generates the correct HTML tag (<h3>), creates the precise CSS for ePub (font-family: "Garamond", serif; font-size: 1.5em;), selects the closest Kindle font, and sets the exact 24pt size for print. This ensures your H1 (book title), H2 (part), H3 (chapter), and H4 (section) hierarchy is perfectly preserved.

Unifying Every Element

Consistency extends beyond body text and headings to every special element. AI tools systematize your blockquote style (is it indented or italic?), caption style (smaller, centered font), code block formatting (monospace with shading), and footnote presentation. This meticulous attention ensures a unified reader experience, whether someone is reading a print copy or a reflowable ePub on their phone.

Protecting Your Reputation

The stakes are high. Formatting inconsistencies are a common catalyst for damaging 1-star reviews that cite a “cheap” look or note frustrating differences from the print version. AI automation mitigates this review risk by eliminating human error in the translation process. It guarantees that the spacing after your paragraphs, your first-line indents, and your exact color palette are faithfully reproduced, fulfilling your brand promise on every platform.

For a comprehensive guide with detailed workflows, templates, and additional strategies, see my e-book: AI-Assisted E-book Formatting for Self-Publishers.

Building Your Digital Lumberyard: How AI Automates Material Lists and Quotes

For handyman professionals, the back-office work of quoting can eat up more time than the job itself. AI automation is now transforming this tedious process, turning client photos directly into accurate job scopes and material lists. The foundation of this system is a custom digital lumberyard—a centralized database of every part, piece, and product you use.

Start by building your master material list. For each item, log the Item Name (e.g., “2×4 x 8′ – Pressure Treated”), a simple Internal SKU (like LUM-2×4-8PT), and its Category (Lumber, Fasteners, etc.). Include critical Description/Specs, Unit of Measure, and a linked Supplier Record with contact and delivery details. Populate this list with your top 50 materials and current costs from key suppliers.

Next, create Template Jobs for your most common projects, like “Repair 10ft of Wood Fence Section.” Each template is a pre-defined assembly list pulling items from your digital lumberyard. For the fence repair, it would auto-populate with: LUM-2×4-8PT for rails, LUM-1x6x6-PT for pickets, and specific fasteners like FST-DeckScrew-3in. The Total Calculated Material Cost updates automatically as prices or quantities change.

This is where AI integrates. When a client sends a photo of a damaged fence, AI vision software analyzes it to identify scope and materials. Your system matches this to the “Fence Repair” template. It then generates a precise material list and quote by pulling real-time costs and SKUs from your database. You simply review and send a professional quote in minutes, not hours.

To launch, build 5-10 common project templates. Document your new workflow: Photo -> AI Scope Analysis -> Template Match -> AI-Generated List -> Final Review -> Send Quote. This system eliminates guesswork, ensures consistency, and dramatically speeds up your sales cycle, letting you focus on the skilled work you do best.

For a comprehensive guide with detailed workflows, templates, and additional strategies, see my e-book: AI for Handyman Businesses: How to Automate Job Quote Generation and Material Lists from Client Photos.

Building Your Custom AI Extraction Pipeline: A Step-by-Step Python Tutorial for Academics

For niche academic researchers, systematic literature reviews are crucial yet time-consuming. Generic AI tools often fail to grasp your field’s specific jargon and data needs. The solution? A custom, automated extraction pipeline built in Python. This tutorial outlines the step-by-step process to create one.

Step 1: Define and Annotate Your “Gold Set”

Start by defining variables: list every data point (e.g., “sample size,” “assay type”) in precise, operationalized terms. Next, gather sample texts: collect 10-20 PDFs representing the full variety of your corpus. Then, perform manual annotation: extract the defined variables from these samples to create your verified “gold set” of correct data. This set is your benchmark for training and testing.

Step 2: Build and Test Core Extraction Functions

Now, build & test core functions. Write one Python function per variable. Use libraries like `PyPDF2` or `pdfplumber` for text extraction, and `spaCy` or `regex` for parsing. Test each function rigorously against your gold set. For instance, a function might locate a “Results” section and extract a numerical sample size using a regular expression.

Step 3: Implement Quality Control Logic

Automation requires oversight. Add flagging logic to your code. Create rules to mark ambiguous extractions—like a “sample size” value of “N/A” or an outlier number—for your manual review. This ensures the pipeline doesn’t silently propagate errors.

Step 4: Refine, Validate, and Scale

Iteration is key. Refine heuristics based on failure analysis. Debug complex logic flows using tools like PythonTutor. Then, audit & validate: spot-check a random sample (e.g., 20%) of the machine’s extractions against manual checks to calculate accuracy and identify remaining edge cases. Finally, run at scale: process your full corpus with your validated, robust pipeline.

This custom approach gives you control, transparency, and precision tailored to your research niche, saving countless hours while maintaining scholarly rigor.

For a comprehensive guide with detailed workflows, templates, and additional strategies, see my e-book: AI for Niche Academic Researchers: How to Automate Systematic Literature Review Screening and Data Extraction.

How AI Automation Builds Your Product Database for Smoother Imports

For niche importers, managing product data across countless shipments is a manual, error-prone nightmare. Inconsistent HS codes or descriptions trigger customs delays and financial risk. The solution is a centralized Product Database, powered by AI automation, acting as your Single Source of Truth (SSoT).

Instead of re-entering data for each order, you log a product once. Core fields include your Internal SKU (e.g., ART-BRUSH-RD02), a Marketing Name, and critical compliance data: the correct HS Code (e.g., 8202.10.0000 for hand saws) and its official HS Code Description. Crucially, you must record the true Country of Origin (where manufactured, like China), not just the shipping port.

Assign one person as the “owner” to edit core compliance fields. This control ensures accuracy and creates an audit trail for customs. Input the precise Duty Rate (e.g., 3.8% for US imports from China for your code) from official databases like the USITC’s HTS. Add a detailed Material Composition breakdown (e.g., “Blade: High-Carbon Steel; Handle: Japanese White Oak”) to support classification.

This structured database directly feeds your AI automation tools. AI can use this clean data to generate perfect commercial invoices and customs declarations instantly, ensuring consistency. Furthermore, set up automated formula columns. A Landed Cost Calculator can sum: (Unit Cost + Unit Shipping) + (Duty Rate * Declared Value) + Fees, revealing true profitability before you ship.

Include Package Dimensions & Weight per unit to automate freight quotes. The result? You eliminate re-work, mitigate compliance risk, and gain instant financial clarity. Your database becomes the intelligent engine for all logistics and documentation, saving time and protecting your business.

For a comprehensive guide with detailed workflows, templates, and additional strategies, see my e-book: AI for Niche Physical Product Importers: How to Automate Customs Documentation and HS Code Risk Assessment.

Automating Quarterly Data Aggregation: How AI Connects Portfolios, Performance, and Benchmarks for RIAs

For independent financial advisors, the quarterly review process is a time-consuming necessity. Manually aggregating portfolio data, calculating performance, and aligning it with client-specific benchmarks eats hours that could be spent on high-value planning and relationships. AI automation now offers a precise, scalable solution to transform this chore into a streamlined, error-free operation.

The Core Workflow: From Manual to Automated

The goal is to create a system where a script automatically fetches current holdings from your custodian’s API, calculates time-weighted returns (TWR), and pulls performance for the benchmarks defined in each client’s Investment Policy Statement (IPS). For example, an IPS mandate of “60% S&P 500 / 40% Agg Bond” becomes a direct input. The script reads this policy from your CRM, uses the corresponding tickers (e.g., SPY, AGG), and seamlessly integrates their quarterly performance into the client’s data file.

Tangible Benefits for Your Practice

This automation delivers immediate professional advantages. First, it ensures Enhanced Consistency & Accuracy, eliminating fat-finger errors in data entry and complex manual calculations. Second, it enables a Massive Time Recovery, shrinking hours of work per client down to minutes of system monitoring and validation. To maintain trust, conduct a Sample Audit: manually calculate the TWR for 1-2 clients each quarter to validate the script’s output. This practice safeguards quality while preserving 95% of your saved time.

Your Actionable Setup Checklist

Implementation is methodical. Start by identifying your primary custodian’s API documentation and applying for developer access. Next, structure your client data by storing their specific benchmark tickers directly in your CRM for the script to reference. The automation process then follows three key steps: 1. Read the client’s policy portfolio from your CRM or IPS database. 2. Pull current holdings and transaction data via the custodian API. 3. Fetch benchmark performance and auto-generate a structured data output for each review.

Structured Output for Seamless Reporting

The final output is a clean, organized data set—not a finished report, but the perfected foundation for one. A typical structured output includes client name, quarter dates, portfolio TWR, benchmark component performance (e.g., SPY: +8.2%, AGG: -1.5%), and the calculated policy benchmark return. This data feeds directly into your client communication tools or report-drafting AI, allowing you to focus on insight and narrative, not number-crunching.

For a comprehensive guide with detailed workflows, templates, and additional strategies, see my e-book: AI for Independent Financial Advisors (RIAs): How to Automate Investment Policy Statement (IPS) Creation and Quarterly Client Review Report Drafting.