How I AI

• How Block’s custom AI agent supercharges every team, from sales to data to engineering | Jackie Brosamer & Brad Axen

  VP of engineering Jackie Brosamer and principal engineer Brad Axen join me to demo Goose, Block’s open-source AI agent that runs locally, plugs into your existing tools through model context protocol (MCP) servers, and peels away the rote parts of work so people can focus on insight and impact.This episode is packed with in-depth demos: starting with a messy farm-stand sales CSV, Goose analyzes the data, builds visualizations, and generates a shareable HTML report. We then spin up an MCP that lets Goose talk to Square’s dashboard for inventory management, vibe code an email MCP that can send payment links automatically, and unpack how environment setup, debugging, and tool orchestration get handled behind the scenes.What you’ll learn:A practical, repeatable workflow for turning any working script or function into a custom MCP—and exposing it to natural-language controlHow to transform messy CSVs into visualizations, HTML reports, and actionable business insights without needing a data science backgroundWays to hook Goose into live business systems (e.g. Square inventory, payments) so analysis flows directly into operational actionThe thinking behind Block’s decision to open-source GooseLessons from Block’s bottom-up meets top-down adoption modelWhy organizational transformation, not just picking the right LLM, will separate AI winners from laggards over the next few yearsHow to scale an internal MCP catalogThe organizational transformation required to fully leverage AI capabilities—Brought to you by:CodeRabbit—Cut code review time and bugs in half. Instantly.Lenny’s List—Hands-on AI education curated by Lenny and Claire—Where to find Jackie Brosamer:LinkedIn: https://www.linkedin.com/in/jbrosamer/—Where to find Brad Axen:LinkedIn: https://www.linkedin.com/in/bradleyaxen/—Where to find Claire Vo:ChatPRD: https://www.chatprd.ai/Website: https://clairevo.com/LinkedIn: https://www.linkedin.com/in/clairevo/X: https://x.com/clairevo—In this episode, we cover:(00:00) Introduction to Goose and its data analysis capabilities(02:27) How Block embraced AI across the organization(04:48) What Goose is and why Block open-sourced it(07:45) Demo: Analyzing farm-stand sales data with Goose(12:18) Creating shareable HTML reports from data analysis(14:15) Model context protocols (MCPs) that Goose uses(18:56) Demo: Using Square MCP to create a product catalog(23:35) Creating payment links from analyzed data(26:30) Demo: Building a custom email MCP(31:18) Testing the new email MCP with Goose(36:09) Debugging and fixing MCP code errors(38:44) Connecting workflows: sending payment links via email(41:30) Lightning round and final thoughts—Tools referenced:• Goose: https://block.github.io/goose/• Pandas: https://pandas.pydata.org/• Plotly: https://plotly.com/• Python: https://www.python.org/• ChatGPT: https://chat.openai.com/• Claude: https://claude.ai/• Cursor: https://www.cursor.com/• Mailgun: https://www.mailgun.com/—Other references:• Block: https://block.com/• Model context protocol (MCP): https://www.anthropic.com/news/model-context-protocol• GitHub: https://github.com/—Production and marketing by https://penname.co/. For inquiries about sponsoring the podcast, email [email protected].

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• Successfully coding with AI in large enterprises: Centralized rules, workflows for tech debt, and training your team | Zach Davis (Director of Engineering at LaunchDarkly)

  Zach Davis is a product-minded engineering leader and builder at heart, with over 12 years of experience building high‑performing teams and crafting developer tools at companies like Atlassian and LaunchDarkly. In this episode, he shares how he’s helping his 100-plus-person engineering team successfully adopt AI tools by creating centralized documentation, using agents to tackle technical debt, and improving hiring processes—all while maintaining high quality standards in a mature codebase.What you’ll learn:1. How to create a centralized rules system that works across multiple AI tools instead of duplicating documentation2. A systematic approach to using AI agents like Devin and Cursor to analyze and reduce test noise in large codebases3. How to leverage AI tools to document your codebase more effectively by extracting knowledge from existing sources4. Why “what’s good for humans is also good for LLMs” should guide your documentation strategy5. A custom GPT workflow for improving interview feedback quality and coaching interviewers6. How to approach tech debt reduction with AI by creating prioritized task lists that both humans and AI agents can work from—Brought to you by:WorkOS—Make your app enterprise-ready todayLenny’s List on Maven—Hands-on AI education curated by Lenny and Claire—Where to find Zach Davis:LaunchDarkly: https://www.launchdarkly.comLinkedIn: https://www.linkedin.com/in/zach-davis-28207195/—Where to find Claire Vo:ChatPRD: https://www.chatprd.ai/Website: https://clairevo.com/LinkedIn: https://www.linkedin.com/in/clairevo/X: https://x.com/clairevo—In this episode, we cover:(00:00) Introduction to Zach Davis(02:44) Overview of AI tools used at LaunchDarkly(04:00) The importance of having someone responsible for driving AI adoption(05:44) Why vibe coding isn’t acceptable for enterprise development(06:42) Making engineers successful with AI on their first attempt(07:55) Creating centralized documentation for both humans and AI agents(10:19) Using feature flagging rules to improve AI outputs(12:33) Advice for getting started with rules(14:28) Demo: Setting up Devin’s environment in a large codebase(24:33) Devin’s plan overview(27:55) Demo: Creating a prioritized tech debt reduction plan(36:40) Demo: Using AI to improve hiring processes and interview feedback(40:34) Summary of key approaches for integrating AI into engineering workflows(42:08) Lightning round and final thoughts—Tools referenced:• Cursor: https://www.cursor.com/• Devin: https://devin.ai/• ChatGPT: https://chat.openai.com/• Claude: https://claude.ai/• Windsurf: https://windsurf.com/• Lovable: https://lovable.dev/• v0: https://v0.dev/• ChatPRD: https://www.chatprd.ai/• Figma: https://www.figma.com/• GitHub Copilot: https://github.com/features/copilot—Other references:• Jest: https://jestjs.io/• Vitest: https://vitest.dev/• MCP: https://www.anthropic.com/news/model-context-protocol• Confluence: https://www.atlassian.com/software/confluence—Production and marketing by https://penname.co/. For inquiries about sponsoring the podcast, email [email protected].

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• How this PM streamlines 60k-page FDA submissions and saves millions with Claude, Streamlit, and clever AI workflows | Prerna Kaul

  Prerna Kaul is a product and platform leader who has spent over 14 years turning machine-learning research into consumer and B2B products at Amazon Alexa, AGI, Moderna, and now Panasonic Well. In today’s episode, she explains how she’s using AI to slash some of the most time-consuming, expensive tasks in life sciences—from generating 60,000-page FDA submissions to crafting communication frameworks that help product managers navigate complex stakeholder dynamics. Her innovations are saving millions of dollars and helping lifesaving treatments reach the market faster.What you’ll learn:How Prerna built an AI system that automates the creation of 60,000-page regulatory documents for the FDA—reducing a process that took 4 to 6 months and 20 specialists to just minutesA step-by-step system for detecting and redacting PHI (protected health information) in clinical trial data using ClaudeHow to build user-friendly interfaces for non-technical colleagues using Streamlit to democratize AI toolsHow to use Claude’s prompt generator to create powerful communication frameworks that help PMs navigate complex stakeholder situationsWhy transparency about AI costs is crucial for gaining organizational buy-in and tracking ROIA practical framework for approaching AI safety and ethics in highly regulated industries—Brought to you by:CodeRabbit—Cut code review time and bugs in half. Instantly: https://lovable.dev/Lovable—Build apps by simply chatting with AI: https://lovable.dev/—Where to find Prerna Kaul:LinkedIn: https://www.linkedin.com/in/prernakkaul/—Where to find Claire Vo:ChatPRD: https://www.chatprd.ai/Website: https://clairevo.com/LinkedIn: https://www.linkedin.com/in/clairevo/X: https://x.com/clairevo—In this episode, we cover:(00:00) Introduction to Prerna(03:01) The FDA submission challenge: 60,000 pages, months of work, millions in costs(05:20) Getting started in Claude: from prompt to production-ready prototype(10:13) How Claude selected the right models for medical entity recognition(12:04) Using Streamlit to create accessible UIs for non-technical users(16:04) Detecting and redacting PHI in unstructured clinical notes(18:44) Generating the Common Technical Document (CTD) for FDA submission(21:54) Tracking and displaying AI operation costs for stakeholder buy-in(24:38) Real-world impact on vaccine development timelines and costs(26:12) Creating an AI communication coach for product managers(30:22) Training Claude on classic literature and persuasion techniques(31:53) Analyzing a complex stakeholder scenario with multiple competing priorities(34:40) Getting personalized communication strategies inspired by tech leaders(35:40) Summarizing strategic approaches(38:26) Conclusion and final thoughts—Tools referenced:• Claude: https://claude.ai/• Streamlit: https://streamlit.io/• Anthropic Console: https://console.anthropic.com/• Claude Sonnet 4: https://www.anthropic.com/claude/sonnet—Other references:• Claude project chat (AI Product Management Stakeholder Challenges): https://claude.ai/share/caba4ab0-b28a-480c-8633-71920b12999e• XML: ⁠https://www.w3.org/XML/⁠• Python: ⁠https://www.python.org/⁠• RegEx: ⁠https://regex101.com/• Moderna: https://www.modernatx.com/• FDA: https://www.fda.gov/• Project Gutenberg: https://www.gutenberg.org/• FDA Biologics License Application: https://www.fda.gov/vaccines-blood-biologics/development-approval-process-cber/biologics-license-applications-bla-process-cber• Protected health information (PHI): https://www.hhs.gov/hipaa/for-professionals/privacy/laws-regulations/index.html—Production and marketing by https://penname.co/. For inquiries about sponsoring the podcast, email [email protected].

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• Mastering ChatGPT: Advanced techniques for workplace communication and productivity | Hiten Shah

  Hiten Shah is a serial founder who has started several analytics and security companies, including Crazy Egg and KISSmetrics. The latest one, Nira, was acquired by Dropbox in 2024. In this episode, he shares how he turns ChatGPT from a simple chatbot into a personal workplace coach, sales strategist, and productivity multiplier.What you’ll learn:How to create AI versions of your boss by loading operating manuals and personality tests into ChatGPT projectsA simple approach for turning sales frameworks into customized discovery call scripts for any productWhy context is everything—and how to load ChatGPT with the right information before asking for outputsThe “show it what great looks like” technique that dramatically improves AI responsesHow to build a personal AI coach using your own personality assessments and communication styleWhy you should use temporary sessions for random queries to keep your main ChatGPT memory clean—Brought to you by:Paragon—Ship every SaaS integration your customers wantNotion—The best AI tools for work—Where to find Hiten Shah:Blog: https://hitenism.com/X: https://twitter.com/hnshahLinkedIn: https://www.linkedin.com/in/hnshah/—Where to find Claire Vo:ChatPRD: https://www.chatprd.ai/Website: https://clairevo.com/LinkedIn: https://www.linkedin.com/in/clairevo/X: https://x.com/clairevo—In this episode, we cover:(00:00) Introduction to Hiten(02:55) Why Hiten primarily uses ChatGPT(04:12) The importance of context and memory management(07:58) Demo: Creating “What Would Morgan Do” project(13:30) Using personality types to improve AI coaching(16:20) Building a personal operating system in ChatGPT(20:55) Mixing structured frameworks and personal context(23:20) Demo: Winning by Design sales framework implementation(30:00) Creating discovery call scripts(31:44) Using ChatGPT’s deep research feature to understand Claire’s leadership style(36:30) Lightning round and final thoughts—Tools referenced:• ChatGPT: https://chat.openai.com/• Claude: https://claude.ai/—Other references:• Hiten's Google Doc: https://docs.google.com/document/d/1j15hoR3qZLQMJuW-mtfYFyhXM0CpYHQkZJuUgqHBsZs/edit?tab=t.0• Winning by Design: https://winningbydesign.com/• Enneagram: https://www.enneagraminstitute.com/• Human Design: https://humandesign.tools/• Myers-Briggs: https://www.myersbriggs.org/• DISC: https://www.discprofile.com/• Lex: https://lex.page/• The Lean Startup: https://theleanstartup.com/• Sean Ellis score: https://pmfsurvey.com/—Production and marketing by https://penname.co/. For inquiries about sponsoring the podcast, email [email protected].

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• How to build prototypes that actually look like your product | Colin Matthews (Product leader, AI prototyping instructor at Maven)

  Colin Matthews is a product manager, founder, and hobbyist engineer. After spending the past eight years in healthtech, he recently left his role as a PM at Datavant to go full-time on building his own products. He is currently a top Maven instructor, helping PMs build their first AI prototype. In this episode, he shares a step-by-step workflow for creating component libraries from screenshots that stay true to your brand and reveals a clever Chrome extension trick for extracting code from any website to build reusable components.What you’ll learn:1. How to create component libraries from screenshots that match your brand’s design system2. A Chrome extension that can extract components directly from any website with a single click3. Why forking prototypes is the key to efficient iteration without breaking your baseline4. The structured prompting technique that makes AI tools actually listen to your instructions5. How to introduce AI prototyping to your team without stepping on designers’ toes6. The debugging approach that solves 90% of AI prototyping errors—Brought to you by:WorkOS—Make your app enterprise-ready todayNotion—The best AI tools for work —Go deeper with Colin’s in-depth post in Lenny’s Newsletter:https://www.lennysnewsletter.com/p/how-to-get-your-entire-team-prototyping—Where to find Colin Matthews:LinkedIn: https://www.linkedin.com/in/colinmatthews-pm/Tech For Product newsletter: https://colinmatthews.substack.com/Tech For Product one-day team workshop: https://teams.techforproduct.com/Maven course: AI Prototyping for PMs: https://bit.ly/3FQgZmw—Where to find Claire Vo:ChatPRD: https://www.chatprd.ai/Website: https://clairevo.com/LinkedIn: https://www.linkedin.com/in/clairevo/X: https://x.com/clairevo—In this episode, we cover:(00:00) Introduction to Colin Matthews(02:46) Creating component libraries from screenshots in v0(05:50) Using prompts to extract components from existing products(06:31) Building an Airbnb prototype from component libraries(11:36) Using the Magic Patterns Chrome extension to extract components directly from websites(18:38) The importance of improving components rather than the composed application(20:15) Using forks and versions for iterative prototyping(25:05) Managing team dynamics when introducing AI prototyping(26:54) Final thoughts—Tools referenced:• v0: https://v0.dev/• Magic Patterns: https://magicpatterns.com/• Magic Patterns Chrome Extension: https://chromewebstore.google.com/detail/html-to-react-figma-by-ma/chgehghmhgihgmpmdjpolhkcnhkokdfp?hl=en• Cursor: https://cursor.sh/• ChatGPT: https://chat.openai.com/• Bolt: https://bolt.new/—Other references:• Colin’s AI prototyping prompt library: https://technical-foundations.notion.site/16c8fafdb669800ea6eeca11f40d046c?v=16c8fafdb6698069a6e4000c84a9ff2c• Airbnb: https://www.airbnb.com/• Notion: https://www.notion.so/• Amplitude: https://amplitude.com/• PostHog: https://posthog.com/• Figma: https://www.figma.com/• GitHub: https://github.com/—Production and marketing by https://penname.co/. For inquiries about sponsoring the podcast, email [email protected].

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