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Making $$$ with Loop Engineering
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Making $$$ with Loop Engineering

How to Use Engineering Loops to Run Your Business

Introduction to Engineering Loops

  • The concept of engineering loops has gained popularity on social media, particularly Twitter, and is presented as a method for enhancing business operations.
  • A tutorial is introduced that explains how loops can be implemented in business using cloud code or Codeex, aiming to attract customers and improve SEO continuously.

Learning Outcomes

  • Listeners will learn how to automate their businesses using loops, which have recently become popular for product development and other applications.
  • The discussion emphasizes the potential of loops not just for product building but also for customer acquisition and operational efficiency.

Commitment to Practical Examples

  • Ellie commits to providing clear explanations and practical examples of implementing loops in business operations by the end of the episode.
  • The conversation will cover both high-level concepts and practical applications, including significant improvements in SEO through loop implementation.

Historical Context of Loops

  • Loop engineering became widely recognized about a month prior due to discussions from notable figures like Boris from Claude Code.
  • A humorous tweet suggests that software should autonomously achieve product-market fit without prompting, highlighting skepticism around loop engineering's practicality.

Conceptual Framework

  • The idea of running a business as a continuous loop is likened to principles from the Lean Startup methodology: build, measure, learn.
  • This iterative process applies not only at the product level but across various aspects of business operations such as SEO performance tracking.

Application in Business Operations

  • Familiarity with lean manufacturing principles illustrates how constant iteration leads to improved products; this concept translates well into startup methodologies.
  • Eric Ries' Lean Startup book draws parallels between Toyota's efficient assembly line processes and modern startup practices focused on rapid iteration.

Implementing AI Agents in Loop Engineering

Steps Involved in Loop Engineering with AI

  • The process involves defining steps similar to those in Lean Startup: build (develop an AI solution), verify (test functionality), and iterate based on feedback.
  • Establishing stop conditions ensures that AI does not run indefinitely; it must converge on specific results or goals during its iterations.

Real-world Examples of Loop Implementation

  • An example includes managing an inbox with an AI agent that categorizes emails effectively through continuous evaluation metrics (eval scores).
  • Setting targets for these evaluations allows the agent to adjust its approach until it meets desired accuracy levels consistently.

SEO Applications Using Engineering Loops

Continuous Improvement Through SEO Loops

  • Running monthly SEO improvement loops can help businesses climb search rankings over time without needing constant manual intervention.
  • This approach mirrors traditional methods where agencies would analyze performance over time; however, automation reduces reliance on external experts.

Experimentation with Results Tracking

  • Businesses can experiment with different strategies while monitoring objective metrics like Google ranking positions—allowing quick adjustments if results are negative.

Setting Up Your Own SEO Loop

Tools Required for Implementation

  • To set up an effective SEO loop, connecting tools like Google Search Console via API is essential for tracking performance data accurately.
  • Other useful tools include DataForSEO APIs which provide competitive analysis insights necessary for optimizing content strategy.

Automation Process

  • Automating tasks within your website’s backend allows ongoing adjustments based on real-time data analysis without requiring constant human oversight.
  • Keeping records of changes made during each iteration helps evaluate what strategies worked best over time.

Conclusion: Future Implications

  • By leveraging these techniques today, businesses can create self-sustaining systems capable of improving their online presence significantly over time.

Understanding AI Loops and Their Applications

Introduction to AI Loops

  • Discussion on automation features in AI tools like Claude and Cursor, emphasizing the concept of "loops" that allow systems to pick up where they left off periodically.
  • Mention of a conversation with Ross Mike, who expresses skepticism about the hype surrounding loops, suggesting that token providers may benefit more than users.

Cost-Benefit Analysis of SEO Loops

  • Inquiry into the value of clicks and customers in relation to costs incurred by running loops; emphasizes the importance of stopping loops based on financial thresholds.
  • Agreement with Mike's perspective on cost concerns, citing high expenses associated with AI credits (e.g., $1.3 million monthly at OpenAI).
  • Assertion that implementing an SEO loop is relatively inexpensive compared to hiring an agency, estimating costs as low as $5 per run.

Monthly Updates and Monitoring

  • Explanation of running SEO loops monthly without deep complexity; suggests setting up notifications for updates via Slack for oversight.
  • Highlights potential savings for users on higher subscription plans due to access to thousands of tokens.

Exploring Other Types of Loops

Facebook Ad Loop

  • Introduction to a Facebook ad loop where AI generates ads and optimizes performance based on data analysis.
  • Comparison between human-run ad agencies and AI capabilities in experimenting with different ad variants.

Challenges in Content Generation

  • Acknowledgment that while some AI-generated content can be effective, it may not always match human quality, particularly in video or graphic creation.

Optimization Strategies

  • Discussion on how AI can easily modify copy for Google ads or other text-based advertising formats.

Integrating Human Creativity with AI Efficiency

  • Emphasis on combining human creativity with AI optimization for better ad performance without needing extensive budgets.

Volume Strategy in Advertising

  • Insight into the volume game in advertising—testing various narratives and hooks to identify successful strategies.

Product Feedback Loop Concept

Ultimate Business Automation Idea

  • Proposal for a product feedback loop where an AI continuously learns from user feedback and analytics to improve itself autonomously.

Metrics for Success

  • Suggestion that success metrics could vary (e.g., NPS, retention), allowing flexibility in evaluating feature effectiveness.

Distinguishing Between Bug Fixes and Feature Development

  • Recommendation to separate bug tracking from feature development within feedback loops for clearer objectives.

Future Implications of Self-Building AIs

Theoretical Business Models

  • Speculation about future companies utilizing self-building AIs capable of responding dynamically to market needs based on user interactions.

Risks Involved

  • Caution against fully relying on such systems due to inherent risks but acknowledges potential innovations emerging from this approach.

Conclusion: Expanding Loop Applications Across Businesses

Limitless Potential

  • Final thoughts suggest every aspect of business could potentially utilize looping mechanisms driven by AI insights.
Video description

I sit down with Elie Steinbock to unpack loop engineering and how to run a business on loops. We start with the roots of the idea in the lean startup and Toyota's manufacturing, then move into practical, copy-ready workflows for SEO, Facebook ads, and product feedback. Elie walks through a live Google Search Console example on Draft Fantasy and shows how to set up an SEO loop that runs once a month for years. The core promise for listeners: hand repeatable business work to an AI agent that measures an objective metric and improves over time. By the end, you know how loops work and how to launch your first one today. Timestamps 00:00 – Intro and episode promise 02:54 – What is Loop Engineering 06:51 – Loops with AI agents: build and verify 11:17 – Example of Loop: SEO as an objective-metric loop 15:29 – Setting up the SEO loop and tools 25:27 – Cost and token economics 29:05 – The Paid ads loop 33:10 – The product feedback loop 36:25 – A minimal viable loop for every channel 39:21 – Closing Thoughts Key Points * Loop engineering means giving an agent a task, an objective metric, and a stop condition so it improves on a schedule. * The lean startup and Toyota's build-measure-learn cycle map directly onto AI agents. * An SEO loop connects to Google Search Console and Data for SEO, then pushes rankings up month over month. * These loops run cheaply — often a few dollars per monthly run — which beats the cost of an agency. * The same pattern extends to Facebook ads, and a product feedback loop stands as the ultimate version. * Start small with a minimal viable loop tied to a clear metric like impressions or ten likes. Numbered Section Summaries * The Promise of Running a Business on Loops I open by asking Elie what listeners will walk away with, and he frames the whole episode: use loops to automate SEO, ads, and more. We agree the aim is clear, copyable workflows people can launch today. * Where Loop Engineering Comes From Elie traces the recent buzz to Boris from Claude Code and Peter Steinberger, plus a joking tweet from his friend Dimitro about software that builds itself. He grounds it in the lean startup's build-measure-learn cycle, which itself grew from Toyota's lean manufacturing. * Loops With AI Agents: Build and Verify Elie explains the agent version: a build step paired with a verify step and a clear stop condition. He uses Inbox Zero's evals as an example, where the agent keeps adjusting the prompt or model until accuracy passes 90%. * The SEO Loop We dig into SEO as the flagship example, where Google ranking serves as a clean, objective metric. Elie describes a loop that runs once a month, learns from the last run via a markdown memory file, and steadily climbs the rankings. * Setting It Up on Real Data Elie shows his Draft Fantasy Search Console, connects the agent to Google Search Console and Data for SEO, and runs the loop live in Codex. He shares the Atom Eve prompt as a deeper template people can copy. * Cost and Token Economics I raise Ross Mike's skepticism about loop buzz and token spend, and Elie makes the case that an SEO loop stays cheap — often under five dollars per monthly run. He adds that Max-plan users have plenty of headroom, while tight budgets suit cheaper open models like GLM 5.2. * Ads, Product Feedback, and the Ultimate Loop We move to a Facebook ads loop that tests copy and creative variants, favoring a mix of human hooks and AI optimization. Then Elie describes the product feedback loop — reading customer feedback, analytics, and logs to prioritize and ship — as the closest thing to a business that builds itself. * Starting Small We close on the minimal viable loop: begin with one channel and a modest, verifiable metric like impressions or ten likes, then let it compound. Elie and I agree that every part of a business could sit on a loop, and starting one today makes for a low-risk experiment. The #1 tool to find startup ideas/trends - https://www.ideabrowser.com/ LCA helps Fortune 500s and fast-growing startups build their future - from Warner Music to Fortnite to Dropbox. We turn 'what if' into reality with AI, apps, and next-gen products https://latecheckout.agency/ The Vibe Marketer - Resources for people into vibe marketing/marketing with AI: https://www.thevibemarketer.com/ FIND ME ON SOCIAL X/Twitter: https://twitter.com/gregisenberg Instagram: https://instagram.com/gregisenberg/ LinkedIn: https://www.linkedin.com/in/gisenberg/ FIND ELIE ON SOCIAL Youtube: https://www.youtube.com/elie2222 X/Twitter: https://x.com/elie2222