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Original: Lenny Rachitsky · 02/03/2026

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Summary

This episode features Jesse Genet discussing her use of OpenClaw agents for managing various aspects of her life, and Chintan Turakhia sharing how Coinbase scaled AI across its engineering team.

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Key Insights

“Treat your AI agent like a new hire, not an extension of yourself.” — Jesse Genet on managing AI agents effectively.
“Organize speed runs to kickstart widespread AI adoption.” — Chintan Turakhia on strategies for AI integration at Coinbase.
“Photos are the most underrated input for AI agents.” — Jesse Genet discussing the simplicity of using photos for AI workflows.

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Topics

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Full Article

Every Monday, host Claire Vo shares a 30- to 45-minute episode with a new guest demoing a practical, impactful way theyve learned to use AI in their work or life. No pontificatingjust specific and actionable advice. 5 OpenClaw agents run my home, finances, and code | Jesse GenetBrought to you by: OptimizelyYour AI agent orchestration platform for marketing and digital teamsJesse Genet is a homeschooling parent and entrepreneur who operates five specialized OpenClaw agents, each on its own Mac Mini, to manage homeschool curriculum, family finances, scheduling, development projects, and household operations. She treats each agent like a new hire: defined role, scoped access, decision log, and progressive trust. In this episode she shares how she photographs curriculum books to auto-generate lesson plans, builds custom apps with zero prior terminal experience, partitions sensitive data across machines, and bridges the digital and physical world by inventorying every toy and supply in her house.Detailed workflow walkthroughs from this episode: How I AI: Jesse Genets 5 OpenClaw Agents for Homeschooling, App Building, and Physical Inventories: https://www.chatprd.ai/how-i-ai/jesse-genets-5-openclaw-agents-for-homeschooling-app-building-and-physical-inventories Automate Homeschool Lesson Planning and Material Creation with an AI Agent: https://www.chatprd.ai/how-i-ai/workflows/automate-homeschool-lesson-planning-and-material-creation-with-an-ai-agent Build a Custom Slop-Free Kids TV App Without Coding Experience: https://www.chatprd.ai/how-i-ai/workflows/build-a-custom-slop-free-kids-tv-app-without-coding-experience Create an AI-Powered Inventory of Your Physical Items: https://www.chatprd.ai/how-i-ai/workflows/create-an-ai-powered-inventory-of-your-physical-itemsBiggest takeaways:Treat your AI agent like a new hire, not an extension of yourself. Jesses entire agent management philosophy comes from her experience hiring employees. She gives agents their own identities, separate data access, and communication channelsnever full access to her email or accounts. Progressive trust is the model: start limited, expand as the agent proves reliable.Physical partitioning is a real security strategy. Running each agent on its own Mac Mini sounds extreme, but it solves a real problem: preventing one agent from accidentally leaking sensitive data through another agents communication channel. The finance agent can read bank statements but cant text anyone. The scheduling agent can text, but has no financial data. This is a practical framework anyone managing multiple agents should think through.Photos are the most underrated input for AI agents. Jesses core workflow is shockingly simple: take a photo, send it to the agent, get structured output. She photographs lesson activities, book pages, physical supplies, and curriculum materials. The agent handles all the heavy lifting of logging, categorizing, and connecting that information. No typing, no structured inputjust photos.You dont need to be technical to build real software with a coding agent. Jesse had never opened a terminal before six months ago. With Cole, her coding agent, she built a custom kids TV app, iterated over four days, and deployed it to a Google TV Streamer. Her approach: describe what you want, push back when the agent says something isnt possible, and keep going.Inventory your physical world so AI can reach into it. One of Jesses most powerful moves was photographing all her educational supplies and having Sylvie build an inventory. Now when she asks for a lesson plan, the agent can say, Also, pull out the tracing board from the cupboard. This bridges the gap between the digital agent and the physical world in a way thats immediately useful.Use decision files to prevent agents from relitigating settled questions. Jesse maintains a decisions file in Obsidian that each agent knows about. When she makes a final call, she flags it, and the agent knows not to revisit it. This is a simple, powerful pattern for anyone dealing with agents that keep second-guessing or re-asking about things youve already resolved. Listen now on YouTube | Spotify | Apple PodcastsHow Coinbase scaled AI to 1,000+ engineers | Chintan TurakhiaBrought to you by:WorkOSMake your app enterprise-ready todayRovoAI that knows your businessChintan Turakhia leads engineering at Coinbase, where hes helped turn a 1,000-plus-person org into an AI-native team. In this episode, Chintan shares how Coinbase got 100 engineers to ship 75 PRs in 15 minutes, cut PR review time from 150 hours to 15, and built internal agents that turn user feedback into shipped features in minutes. Claire and Chintan dive into the speed run tactic that broke through skepticism, how to identify and replicate AI power users, and why the best engineering leaders are getting more hands-on with code in the AI era.Detailed workflow walkthroughs from this episode: How I AI: Chintan Turakhias Playbook for AI Adoption at Coinbase: https://www.chatprd.ai/how-i-ai/playbook-for-ai-engineering-adoption-at-coinbase Use ChatGPT to Become Your Own Personal Wine Sommelier: https://www.chatprd.ai/how-i-ai/workflows/use-chatgpt-to-become-your-own-personal-wine-sommelier Build an Automated User Feedback to Pull Request Pipeline: https://www.chatprd.ai/how-i-ai/workflows/build-an-automated-user-feedback-to-pull-request-pipeline Create a Data-Driven AI Adoption Playbook Using Cursor: https://www.chatprd.ai/how-i-ai/workflows/create-a-data-driven-ai-adoption-playbook-using-cursorBiggest takeaways:Organize speed runs to kickstart widespread AI adoption. Coinbase gathered 100 engineers for a 15-minute session where everyone used AI tools to push 75 PRs simultaneously. The high success rate of merged PRs proved AIs effectiveness at scale and transformed the teams mindset about what they could accomplish.Show, dont tell, when driving AI adoption in engineering teams. Chintan spent months personally using Cursor daily, discovering use cases and techniques before asking his team to adopt it. By tackling real bugs and showing concrete wins, he built credibility that made adoption natural rather than forced.Focus AI adoption on eliminating soul-sucking work first. Rather than tackling complex engineering challenges, Chintan targeted the tedious tasks engineers hate: writing unit tests, fixing linting issues, and managing Git commands. Engineers naturally want to build better things faster, and removing friction from their workflow created immediate buy-in.Create public channels to showcase AI wins. Coinbase established a Cursor Wins Slack channel where engineers shared their successes. This visibility created organic FOMO and peer learning as engineers saw colleagues shipping more code with less effort.Measure the entire feedback-to-feature cycle time, not just lines of code. Coinbase reduced PR review times from 150 hours to 15 hours and built systems to capture user feedback and convert it to shipped features in minutes rather than weeks. This end-to-end acceleration creates a virtuous cycle where faster shipping leads to more user feedback and continuous improvement.Engineering leaders should be writing more code, not less. Chintans calendar is now nearly empty, as AI has eliminated coordination overhead and meetings. Instead, he spends more time writing code, fixing bugs, and exploring technical approaches.Use AI to analyze AI adoption patterns. Chintan used Cursor itself to analyze Cursor usage data, identifying natural user cohorts from inactive to super-users. This meta-application of AI allowed him to create targeted guidance for each segment, helping engineers progress from light usage to power usage. The analysis revealed that agent-heavy users were 16x more productive with AI than other users, providing concrete data to drive further adoption. Listen now on YouTube | Spotify | Apple PodcastsIf youre enjoying these episodes, reply and let me know what youd love to learn more about: AI workflows, hiring, growth, product strategyanything.Catch you next week,LennyP.S. Want every new episode delivered the moment it drops? Hit Follow on your favorite podcast app.

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Originally published at https://www.lennysnewsletter.com/p/this-week-on-how-i-ai-5-openclaw.