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How I AI

Claire Vo
How I AI
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  • “Nobody wanted to do this work”: How Emmy Award–winning filmmakers use AI to automate the tedious parts of documentaries
    Tim McAleer is a producer at Ken Burns’s Florentine Films who is responsible for the technology and processes that power their documentary production. Rather than using AI to generate creative content, Tim has built custom AI-powered tools that automate the most tedious parts of documentary filmmaking: organizing and extracting metadata from tens of thousands of archival images, videos, and audio files. In this episode, Tim demonstrates how he’s transformed post-production workflows using AI to make vast archives of historical material actually usable and searchable.What you’ll learn:How Tim built an AI system that automatically extracts and embeds metadata into archival images and footageThe custom iOS app he created that transforms chaotic archival research into structured, searchable dataHow AI-powered OCR is making previously illegible historical documents accessibleWhy Tim uses different AI models for different tasks (Claude for coding, OpenAI for images, Whisper for audio)How vector embeddings enable semantic search across massive documentary archivesA practical approach to building custom AI tools that solve specific workflow problemsWhy AI is most valuable for automating tedious tasks rather than replacing creative work—Brought to you by:Brex—The intelligent finance platform built for founders—Where to find Tim McAleer:Website: https://timmcaleer.com/LinkedIn: https://www.linkedin.com/in/timmcaleer/—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 Tim McAleer(02:23) The scale of media management in documentary filmmaking(04:16) Building a database system for archival assets(06:02) Early experiments with AI image description(08:59) Adding metadata extraction to improve accuracy(12:54) Scaling from single scripts to a complete REST API(15:16) Processing video with frame sampling and audio transcription(19:10) Implementing vector embeddings for semantic search(21:22) How AI frees up researchers to focus on content discovery(24:21) Demo of “Flip Flop” iOS app for field research(29:33) How structured file naming improves workflow efficiency(32:20) “OCR Party” app for processing historical documents(34:56) The versatility of different app form factors for specific workflows(40:34) Learning approach and parallels with creative software(42:00) Perspectives on AI in the film industry(44:05) Prompting techniques and troubleshooting AI workflows—Tools referenced:• Claude: https://claude.ai/• ChatGPT: https://chat.openai.com/• OpenAI Vision API: https://platform.openai.com/docs/guides/vision• Whisper: https://github.com/openai/whisper• Cursor: https://cursor.sh/• Superwhisper: https://superwhisper.com/• CLIP: https://github.com/openai/CLIP• Gemini: https://deepmind.google/technologies/gemini/—Other references:• Florentine Films: https://www.florentinefilms.com/• Ken Burns: https://www.pbs.org/kenburns/• Muhammad Ali documentary: https://www.pbs.org/kenburns/muhammad-ali/• The American Revolution series: https://www.pbs.org/kenburns/the-american-revolution/• Archival Producers Alliance: https://www.archivalproducersalliance.com/genai-guidelines• Exif metadata standard: https://en.wikipedia.org/wiki/Exif• Library of Congress: https://www.loc.gov/—Production and marketing by https://penname.co/. For inquiries about sponsoring the podcast, email [email protected].
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  • How this CEO turned 25,000 hours of sales calls into a self-learning go-to-market engine | Matt Britton (Suzy)
    Matt Britton is the founder and CEO of Suzy, a consumer insights platform that has raised over $100 million in venture capital and works with top brands like Coca-Cola, Google, Procter & Gamble, and Nike. Matt is also the bestselling author of YouthNation, a blueprint for understanding the seismic shifts shaping our future economy, and Generation AI, which explores how Gen Alpha and artificial intelligence will transform business, culture, and society. In this episode, Matt demonstrates how he built a comprehensive AI workflow using Zapier that transforms customer call transcripts into a wealth of actionable intelligence. Despite not being a coder, Matt created a system that automatically generates call summaries, sentiment analysis, coaching feedback, follow-up emails, SEO-optimized blog posts, and more—all from a single customer conversation.What you’ll learn:How to build a trigger-based workflow that automatically scrapes and processes customer call transcripts from platforms like GongA systematic approach to quantifying customer sentiment on a 1-10 scale that has proven highly predictive of churn and upsell opportunitiesHow to create an automated coaching system that provides personalized feedback to sales reps after every customer interactionA workflow for extracting keywords from customer conversations to inform Google ad campaigns without manual interventionTechniques for automatically generating privacy-compliant blog content from customer calls that drives organic traffic and paid search performanceWhy CEOs and executives need to build AI skills firsthand rather than delegating implementation to engineering teamsHow to use Google Sheets as structured databases for AI lookups and enrichment within automated workflows—Brought to you by:Brex—The intelligent finance platform built for foundersZapier—The most connected AI orchestration platform—Where to find Matt Britton:LinkedIn: linkedin.com/in/mattbbrittonInstagram: https://www.instagram.com/mattbrittonnyc/Company: https://www.suzy.com/—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 Matt Britton(02:36) Why Zapier became the backbone of Matt’s AI automations(04:17) Identifying your core business problem(09:02) How Matt built the initial trigger automation with Browse AI(13:42) The value of CEOs getting hands-on with building(14:00) Scraping and processing call transcripts(20:14) Using LLMs to generate call summaries and sentiment scores(23:25) Creating a Slack channel for real-time call insights(26:17) Extracting keywords for Google Ads campaigns(28:35) Building an AI coach for sales and customer success teams(29:48) Creating a follow-up email writer for post-call communication(35:25) Generating redacted blog content from customer conversations(37:51) How this approach changes team building and hiring priorities(40:19) Matt’s prompting techniques and final thoughts—Tools referenced:• Zapier: https://zapier.com/• Gong: https://www.gong.io/• Browse AI: https://www.browse.ai/• ChatGPT: https://chat.openai.com/—Other references:• Qualtrics: https://www.qualtrics.com/• SurveyMonkey: https://www.surveymonkey.com/• Slack: https://slack.com/• Google Sheets: https://www.google.com/sheets/about/—Production and marketing by https://penname.co/. For inquiries about sponsoring the podcast, email [email protected].
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  • The complete beginner’s guide to coding with AI: from PRD to generating your very first lines of code
    This episode is for complete beginners. I walk you through how to build your very first coding project using AI tools—even if you’ve never written a line of code. Together, we’ll create a personal project hub that automatically generates documentation and lets you build interactive prototypes. I’ll show you the process step by step—from setting up a repository, to creating AI agents that help with specific tasks, to deploying a functional web app locally.What you’ll learn:How to set up a simple Next.js application from scratch using Cursor’s AI agent capabilitiesMy workflow for creating AI agents that generate consistent documentation (like PRDs in Markdown format)How to build and display clickable prototypes without worrying about complex backend functionalityThe basics of using GitHub to track changes and manage your code repository as a non-technical personWhy starting with a personal project hub is the best way to ease into AI-assisted codingMy favorite practical tips for iterating on designs and functionality using AI tools—without needing deep technical expertise—Brought to you by:ChatPRD—An AI copilot for PMs and their teams—In this episode, we cover:(00:00) Introduction(05:11) Starting with a requirements document in ChatPRD(08:22) Attempting to use v0 for initial prototyping(15:02) Pivoting to Cursor for initial prototyping(20:20) Running the app locally and reviewing the initial version(24:07) Setting up GitHub for version control(27:09) Creating an AI agent for writing PRDs(31:04) Using the agent to create a sample PRD(35:00) Building a prototype based on the PRD(37:00) Testing and improving the prototype(40:00) Adding documentation and improving the design(43:20) Recap of the complete workflow—Tools referenced:• Cursor: https://cursor.com/• ChatPRD: https://www.chatprd.ai/• v0: https://v0.dev/• GitHub Desktop: https://desktop.github.com/• Next.js: https://nextjs.org/• Tailwind CSS: https://tailwindcss.com/—Other references:• Lovable: https://lovable.ai/• Bolt: https://bolt.new/• Claude Code: https://www.claude.com/product/claude-code• Markdown: https://www.markdownguide.org/• GitHub: https://github.com/—Production and marketing by https://penname.co/. For inquiries about sponsoring the podcast, email [email protected].
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  • “Vibe analysis”: How Faire’s data team uses AI to investigate conversion drops, analyze experiment results, and convert raw data into executive-ready insights
    Tim Trueman and Alexa Cerf from Faire’s data team demonstrate how AI tools are revolutionizing data analysis workflows. They show how data teams, product managers, and engineers can use tools like Cursor, ChatGPT, and custom agents to investigate business metrics, analyze experiment results, and extract insights from user surveys—all while dramatically reducing the time and technical expertise required.What you’ll learn:1. How to use AI to investigate sudden drops in business metrics by searching documentation and codebases2. Techniques for creating a semantic layer that helps AI understand your business data3. How to build end-to-end analytics workflows using Cursor and Model Context Protocols (MCPs)4. Ways to automate experiment analysis and create standardized reports5. How AI can help design and analyze customer surveys6. Strategies for creating executive-ready documents from raw data analysis7. Why every team member should have access to code repositories—not just engineers—Brought to you by:Zapier—The most connected AI orchestration platformBrex—The intelligent finance platform built for founders—Where to find Tim Trueman:LinkedIn: https://www.linkedin.com/in/tim-trueman-99788592/—Where to find Alexa Cerf:LinkedIn: https://www.linkedin.com/in/alexandra-cerf/—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 Tim and Alexa from Faire(02:53) The challenge of analyzing product quality and usage(04:14) Breaking down what analytics actually involves beyond data manipulation(05:46) Demo: Investigating a conversion rate drop using enterprise AI search(09:05) Using ChatGPT Deep Research to analyze code changes(12:40) Leveraging Cursor as the ultimate context engine for code analysis(18:55) Analyzing a new product feature’s performance with Cursor(26:27) How semantic layers make AI tools more effective for data analysis(30:00) Using Model Context Protocols (MCPs) to connect AI with data tools(34:17) Creating visualizations and dashboards with Mode integration(37:04) Generating structured analysis documents with Notion integration(44:39) Building custom agents to automate experiment result documentation(53:10) Designing and analyzing customer surveys(59:40) Lightning round and final thoughts—Tools referenced:• Cursor: https://cursor.com/• ChatGPT: https://chat.openai.com/• Notion: https://www.notion.so/• Snowflake: https://www.snowflake.com/• Mode: https://mode.com• Qualtrics: https://www.qualtrics.com/• GitHub: https://github.com/—Other references:• Model Context Protocol (MCP): https://www.anthropic.com/news/model-context-protocol• Faire Careers: https://www.faire.com/careers—Production and marketing by https://penname.co/. For inquiries about sponsoring the podcast, email [email protected].
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  • Vibe-coding a kid-friendly AI fortune teller for your Halloween festivities | Marco Casalaina (Microsoft VP)
    In this impromptu Halloween special, Marco Casalaina (VP of Products for Core AI at Microsoft) demonstrates how he uses GitHub Spark to quickly build a mobile app that generates kid-friendly fortunes for trick-or-treaters.—Where to find Marco Casalaina:LinkedIn: https://www.linkedin.com/in/marcocasalaina/X: https://x.com/amrcn_werewolf?lang=en—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) Intro(00:40) Marco’s Halloween fortune teller tradition(02:54) Using GitHub Spark to create a fortune teller app(04:32) Using Spec Kit for scoping out complex feature specs(06:53) Making fortunes more concrete and kid-friendly(10:20) Closing thoughts—Tools referenced:• GitHub Spark: https://github.com/features/spark• SpecKit: https://github.com/github/spec-kit• GitHub Copilot: https://github.com/features/copilot• Cursor: https://cursor.com/• Claude Code: https://www.claude.com/product/claude-code—Production and marketing by https://penname.co/. For inquiries about sponsoring the podcast, email [email protected].
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About How I AI

How I AI, hosted by Claire Vo, is for anyone wondering how to actually use these magical new tools to improve the quality and efficiency of their work. In each episode, guests will share a specific, practical, and impactful way they’ve learned to use AI in their work or life. Expect 30-minute episodes, live screen sharing, and tips/tricks/workflows you can copy immediately. If you want to demystify AI and learn the skills you need to thrive in this new world, this podcast is for you.
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