Anish Acharya is an entrepreneur and general partner at Andreessen Horowitz, focusing on consumer investing and AI-native products. In this episode, he demonstrates how AI can be used for creative and personal projects beyond typical work applications. He walks through creating an AI-generated Tiny Desk Concert for Notorious B.I.G. and Kurt Cobain, building a book cataloging app using video analysis, and using browser automation for personal finance insights. Anish shares how these technologies allow anyone to bring creative ideas to life with minimal technical expertise, transforming what would have been impossible projects just a few years ago into accessible weekend activities.

What you’ll learn:

1. A step-by-step workflow for creating AI-generated music videos featuring artists like Kurt Cobain and Notorious B.I.G.

2. How to extract vocals from existing tracks to create unique audio combinations for your AI-generated videos

3. A simple method for cataloging your book or record collection using video analysis and Gemini Flash

4. How to use Comet to analyze personal finances and get investment recommendations without manual data analysis

5. Ways AI is transforming childhood learning and play by enabling interactive storytelling and creative exploration

—

Brought to you by:

Notion—The best AI tools for work

Lenny’s List on Maven—Hands-on AI education curated by Lenny and Claire

—

Where to find Anish Acharya:

• Andreessen Horowitz: https://a16z.com/author/anish-acharya/

• LinkedIn: https://www.linkedin.com/in/anishacharya/

• X: https://x.com/illscience

—

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 Anish Acharya

(⁠03:05⁠) How AI transforms creative constraints in music and video

(⁠06:00⁠) Creating an AI-generated Notorious B.I.G. Tiny Desk Concert

(⁠07:36⁠) Using GPT-4o to generate still images

(⁠09:27⁠) Using Hedra to animate still frame images

(⁠10:40⁠) Adding custom audio to video

(⁠11:30⁠) Using Adobe Audition to clip and sync audio

(⁠15:42⁠) How to use Demucs to extract vocals from any song

(⁠16:36⁠) Using Hedra to generate a Tiny Desk Concert featuring Kurt Cobain

(⁠19:40⁠) Creating a ’90s-style Nirvana music video with Veo 3

(⁠27:40⁠) Building a book collection cataloging tool with Gemini Flash

(⁠35:35⁠) Using the Comet browser for personal finance analysis

(⁠37:20⁠) How AI is transforming childhood learning and play

(⁠41:23⁠) Tips for getting better results from AI tools

—

Tools referenced:

• GPT-4o: https://openai.com/index/hello-gpt-4o/

• Hedra: https://www.hedra.com/

• Adobe Audition: https://www.adobe.com/products/audition.html

• Demucs: https://github.com/facebookresearch/demucs

• Perplexity: https://www.perplexity.ai/

• Veo 3: https://deepmind.google/models/veo/

• Kapwing: https://www.kapwing.com/

• Cursor: https://cursor.com/

• Google AI Studio: https://makersuite.google.com/

• Gemini Flash: https://ai.google.dev/gemini-api

• Comet: https://www.perplexity.ai/comet

—

Other references:

• Anish’s Notorious B.I.G. AI-generated Tiny Desk Concert: https://x.com/illscience/status/1935721063876550939

• NPR Tiny Desk Concerts: https://www.npr.org/series/tiny-desk-concerts/

• Notorious B.I.G.: https://en.wikipedia.org/wiki/The_Notorious_B.I.G.

• Kurt Cobain: https://www.kurtcobain.com/

• Robinhood: https://robinhood.com

—

Production and marketing by https://penname.co/. For inquiries about sponsoring the podcast, email [email protected].

Wade Chambers, Chief Engineering Officer at Amplitude, shares how his team built Moda—an internal AI tool that gives employees access to enterprise data across multiple systems, enabling faster product development and decision-making while fostering cross-functional collaboration.

What you’ll learn:

1. How Amplitude built a powerful internal AI tool in just 3 to 4 weeks of engineers’ spare time

2. A social engineering approach that made their AI tool go viral company-wide in just one week

3. How product managers use AI to analyze customer feedback across multiple data sources and identify key themes

4. A streamlined workflow that compresses research, PRD creation, and prototyping into a single meeting

5. Why role-swapping exercises with AI tools build empathy and cross-functional fluency across product, design, and engineering teams

6. How AI tools are helping engineering teams tackle persistent tech debt challenges more effectively

—

Brought to you by:

CodeRabbit—Cut code review time and bugs in half. Instantly.

Vanta—Automate compliance and simplify security

—

25k giveaway:

 To celebrate 25,000 YouTube followers, we’re doing a giveaway. Win a free year of my favorite AI products, including v0, Replit, Lovable, Bolt, Cursor, and, of course, ChatPRD, by leaving a rating and review on your favorite podcast app and subscribing to the podcast on YouTube. To enter: https://www.howiaipod.com/giveaway.

—

Where to find Wade Chambers:

LinkedIn: https://www.linkedin.com/in/wadechambers/

Amplitude: https://amplitude.com/blog/meet-the-team-wade-chambers

—

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 Wade Chambers

(02:53) The build vs. buy decision for internal AI tools

(04:55) What Moda is and how it works

(07:19) The social engineering approach to adoption

(09:17) Demo of Moda in Slack

(10:58) Data sources Moda has access to

(12:43) Analyzing customer feedback themes with Moda

(17:41) Behind the scenes: how Moda works technically

(23:24) Creating a PRD from a single customer insight

(27:30) How teams actually use AI-generated PRDs

(29:09) Impact on product development velocity

(32:37) Engineers, designers, and PMs swapping roles

(34:38) Recap of creating Moda

(36:00) Lightning round and final thoughts

—

Tools referenced:

• Glean: https://www.glean.com/

• ChatGPT: https://chat.openai.com/

• Cursor: https://cursor.com/

• Bolt: https://bolt.new/

• Figma: https://www.figma.com/

• Lovable: https://lovable.dev/

• v0: https://v0.dev/

—

Other references:

• Amplitude: https://amplitude.com/

• Slack: https://slack.com/

• Confluence: https://www.atlassian.com/software/confluence

• Jira: https://www.atlassian.com/software/jira

• Salesforce: https://www.salesforce.com/

• Zendesk: https://www.zendesk.com/

• Google Drive: https://drive.google.com/

• Productboard: https://www.productboard.com/

• Zoom: https://zoom.us/

• Asana: https://asana.com/

• Dropbox: https://www.dropbox.com/

• GitHub: https://github.com/

• HubSpot: https://www.hubspot.com/

• Abnormal Security: https://abnormalsecurity.com/

—

Production and marketing by https://penname.co/. For inquiries about sponsoring the podcast, email [email protected].

In this episode, I share my hands-on experience with OpenAI’s GPT-5, the company’s new frontier model. As one of the first users outside of OpenAI to test the model, I put GPT-5 head-to-head with GPT-4.1 across real-world product use cases—from writing PRDs to generating code to assisting with visual design work. This is my unfiltered look at what GPT-5 can (and can’t) do—and how it changes the game for builders.

What you’ll learn:

1. How GPT-5 differs from previous models with its engineering-focused approach to problem-solving and tendency to prioritize technical details over business context

2. A comparative analysis of how GPT-5 and GPT-4.1 generate different types of product requirement documents and prototypes for the same prompt

3. Why GPT-5 excels at technical writing, functional requirements, and code generation while potentially skipping important business discovery questions

4. The model’s impressive spatial awareness capabilities when generating images for interior design and other visual tasks

5. Practical considerations for choosing the right model based on your specific use case and audience

6. How GPT-5’s extensive tool-calling behavior and bullet-point communication style reflect its engineering-oriented design

—

Brought to you by ChatPRD—an AI copilot for PMs and their teams: https://www.chatprd.ai/howiai

—

25k giveaway:

 To celebrate 25,000 YouTube followers, we’re doing a giveaway. Win a free year of my favorite AI products, including v0, Replit, Lovable, Bolt, Cursor, and, of course, ChatPRD, by leaving a rating and review on your favorite podcast app and subscribing to the podcast on YouTube. To enter: https://www.howiaipod.com/giveaway

—

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 GPT-5

(04:34) Testing GPT-5 in ChatPRD for document generation

(07:10) Comparing GPT-5 and GPT-4.1 on business vs. technical orientation

(11:22) Side-by-side comparison of PRDs generated by both models

(15:23) Where GPT-5 excels: Technical considerations and documentation quality

(17:35) Comparing prototypes generated from different model outputs

(19:57) Testing homepage critique capabilities between models

(23:14) OpenAI’s strengths in API design and developer support

(25:37) GPT-5’s performance as a coding assistant

(27:26) Examining GPT-5 in ChatGPT’s interface

(28:50) Testing GPT-5’s front-end design capabilities

(31:17) Personal use case: bathroom remodel planning

(33:45) Comparing GPT-5 vs. GPT-4 for interior design visualization

(38:10) Summary of key findings and recommendations

—

Tools referenced:

• OpenAI: https://openai.com/

• ChatGPT: https://chat.openai.com/

• Claude: https://claude.ai/

• Gemini: https://gemini.google.com/

• Cursor: https://cursor.sh/

• v0: https://v0.dev/

• Lovable: https://lovable.dev/

• Bolt: https://bolt.com/

• LaunchDarkly AI Configs: https://launchdarkly.com/docs/home/ai-configs

—

Other reference:

• Benjamin Moore paints: https://www.benjaminmoore.com/

—

Production and marketing by https://penname.co/. For inquiries about sponsoring the podcast, email [email protected].

Andrew Mason (founder of Groupon, now CEO of Descript) and Nabeel Hyatt (General Partner at Spark Capital) teamed up to open a physical board-game social club in Berkeley, with AI as their business partner. In this episode, they break down how they used Claude to generate a full business plan, model financials, plan the space layout, navigate Berkeley permitting, categorize hundreds of games using a custom Dewey Decimal–style system, and build an AI concierge that matches players with games via text. They also share how working on this side project helped rewire how they use AI in their day jobs—and why more people should use AI to build real-world things.

What you’ll learn:

1. How to use Claude Projects as your business copilot to create comprehensive business plans, financial projections, and space layouts

2. A workflow for categorizing hundreds of board games using an AI-generated “Dewey Decimal System” that makes game discovery intuitive

3. How they built an AI concierge service that matches players with games and coordinates group play sessions via text message

4. Why AI enables side projects that would otherwise be impossible due to time constraints and specialized knowledge requirements

5. A simple system for creating customer personas that inform your business model and event programming

6. How to use model context protocols (MCPs) to connect AI assistants to business tools like Airtable without complex coding

—

Brought to you by:

Lovable—Build apps by simply chatting with AI

Persona—Trusted identity verification for any use case

—

Where to find Andrew Mason:

LinkedIn: https://www.linkedin.com/in/andrewmason/

X: https://x.com/andrewmason

—

Where to find Nabeel Hyatt:

LinkedIn: https://www.linkedin.com/in/nabeelhyatt/

X: https://x.com/nabeel

—

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 the board-game social club concept

(02:44) How AI made a challenging side project possible

(06:14) Using Claude as a business copilot for planning

(12:53) Developing customer personas with AI

(15:45) Using AI to determine business viability

(21:02) Navigating Berkeley real estate and permitting

(25:18) Building an AI concierge for game matchmaking

(28:10) Database design with Airtable for non-technical founders

(32:04) Creating a custom board-game categorization system

(36:20) Demo of the text-based AI concierge service

(40:38) Enabling experiences that wouldn’t exist without AI

(43:42) Lightning round and final thoughts

—

Tools referenced:

• Claude: https://claude.ai/

• Airtable: https://airtable.com/

• n8n: https://n8n.io/

• Twilio: https://www.twilio.com/

• Cursor: https://cursor.sh/

• Windsurf: https://www.windsurf.io/

• Python: https://www.python.org/

—

Other references:

• Model context protocol (MCP): https://www.anthropic.com/news/model-context-protocol

• Tabletop Library: https://tabletoplibrary.com/

• Descript: https://www.descript.com/

—

Production and marketing by https://penname.co/. For inquiries about sponsoring the podcast, email [email protected].

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:

1. A practical, repeatable workflow for turning any working script or function into a custom MCP—and exposing it to natural-language control
2. How to transform messy CSVs into visualizations, HTML reports, and actionable business insights without needing a data science background
3. Ways to hook Goose into live business systems (e.g. Square inventory, payments) so analysis flows directly into operational action
4. The thinking behind Block’s decision to open-source Goose
5. Lessons from Block’s bottom-up meets top-down adoption model
6. Why organizational transformation, not just picking the right LLM, will separate AI winners from laggards over the next few years
7. How to scale an internal MCP catalog
8. The 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].

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 documentation

2. A systematic approach to using AI agents like Devin and Cursor to analyze and reduce test noise in large codebases

3. How to leverage AI tools to document your codebase more effectively by extracting knowledge from existing sources

4. Why “what’s good for humans is also good for LLMs” should guide your documentation strategy

5. A custom GPT workflow for improving interview feedback quality and coaching interviewers

6. 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 today

Lenny’s List on Maven—Hands-on AI education curated by Lenny and Claire

—

Where to find Zach Davis:

LaunchDarkly: https://www.launchdarkly.com

LinkedIn: 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].

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:

1. 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 minutes
2. A step-by-step system for detecting and redacting PHI (protected health information) in clinical trial data using Claude
3. How to build user-friendly interfaces for non-technical colleagues using Streamlit to democratize AI tools
4. How to use Claude’s prompt generator to create powerful communication frameworks that help PMs navigate complex stakeholder situations
5. Why transparency about AI costs is crucial for gaining organizational buy-in and tracking ROI
6. A 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].

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:

1. How to create AI versions of your boss by loading operating manuals and personality tests into ChatGPT projects
2. A simple approach for turning sales frameworks into customized discovery call scripts for any product
3. Why context is everything—and how to load ChatGPT with the right information before asking for outputs
4. The “show it what great looks like” technique that dramatically improves AI responses
5. How to build a personal AI coach using your own personality assessments and communication style
6. Why 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 want

Notion—The best AI tools for work

—

Where to find Hiten Shah:

Blog: https://hitenism.com/

X: https://twitter.com/hnshah

LinkedIn: 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].

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 system

2. A Chrome extension that can extract components directly from any website with a single click

3. Why forking prototypes is the key to efficient iteration without breaking your baseline

4. The structured prompting technique that makes AI tools actually listen to your instructions

5. How to introduce AI prototyping to your team without stepping on designers’ toes

6. The debugging approach that solves 90% of AI prototyping errors

—

Brought to you by:

WorkOS—Make your app enterprise-ready today

Notion—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

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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

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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

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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/

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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/

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Production and marketing by https://penname.co/. For inquiries about sponsoring the podcast, email [email protected].

John Blackman, a 91-year-old retired electrical engineer, shares how he used Claude and Replit to build a complex application for his church’s community service events—with no prior software development experience and for less than $350. His app allows event organizers to create events, recruit volunteers, and manage sign-ups, with a standout feature for organizing free oil changes for participants.

What you’ll learn:

1. How John used Claude to create detailed product requirements and user stories
2. John’s philosophy on embracing new technology throughout his career
3. The exact process for integrating third-party APIs (like VIN lookup for oil changes) with minimal technical knowledge
4. How he automated report generation for volunteer management and resource planning
5. How the software generates personalized Impact Passports for event participants
6. Why letting AI build without preconceived notions of “correct” implementation can lead to faster, more functional results
7. How to troubleshoot common development-to-production issues when working with AI coding tools

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Brought to you by:

WorkOS—Make your app enterprise-ready today

Orkes—The enterprise platform for reliable applications and agentic workflows

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Where to find John Blackman:

Website: http://johnbeng.com/

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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

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In this episode, we cover:

(00:00) Introduction to John Blackman and his background

(02:55) John’s impressive career

(03:59) How the church project started

(05:06) Using Claude to create a development roadmap and requirements document

(07:29) The concept of the Impact Passport for event participants

(08:57) Generating user stories and requirements with Claude

(10:32) The multi-tenant architecture with system and local church administrators

(12:54) Building the application with Replit

(13:32) Demo of the administrator interface and event management features

(17:56) Specialized reports for different services (food pantry, vision center, oil changes)

(20:30) The participant registration flow with QR code scanning

(21:55) Adding new features like volunteer name tag generation

(24:40) Troubleshooting AI “rabbit trails” during development

(26:09) Challenges moving from development to production

(27:13) John’s lack of coding experience

(29:42) The advantage of having no preconceived notions about implementation

(30:25) Total development costs and timeline

(31:31) Impact and reception from the church community

(32:42) Lightning round and final thoughts

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Tools referenced:

• Claude: https://claude.ai/

• Replit: https://replit.com/

• SendGrid: https://sendgrid.com/

• AutoCAD: https://www.autodesk.com/products/autocad/

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Other references:

• OpenAI API: https://openai.com/api/

• VIN (vehicle identification number): https://en.wikipedia.org/wiki/Vehicle_identification_number

• Multi-tenant architecture: https://en.wikipedia.org/wiki/Multitenancy

• Role-based access control: https://en.wikipedia.org/wiki/Role-based_access_control

• Excel: https://www.microsoft.com/en-us/microsoft-365/excel

• Docusign: https://www.docusign.com/

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Production and marketing by https://penname.co/. For inquiries about sponsoring the podcast, email [email protected].