How I AI

Claire Vo
  • 4.8（143則評分）
  • 科技
  • 每週更新

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.

  1. “Nobody wanted to do this work”: How Emmy Award–winning filmmakers use AI to automate the tedious parts of documentaries

    5 小時前

    “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transforms chaotic archival research into structured, searchable dataHow AI-powered OCR is making previously illegible historical documents accessibleWhy Tim uses different AI models for different tasks (Claude for coding, OpenAI for images, Whisper for audio)How vector embeddings enable semantic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 jordan@penname.co.

    48 分鐘
  2. How this CEO turned 25,000 hours of sales calls into a self-learning go-to-market engine | Matt Britton (Suzy)

    11月10日

    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 founders Zapier—The most connected AI orchestration platform — Where to find Matt Britton: LinkedIn: linkedin.com/in/mattbbritton Instagram: 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 jordan@penname.co.

    43 分鐘
  3. The complete beginner’s guide to coding with AI: from PRD to generating your very first lines of code

    11月5日

    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 capabilities My 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 functionality The basics of using GitHub to track changes and manage your code repository as a non-technical person Why starting with a personal project hub is the best way to ease into AI-assisted coding My 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 jordan@penname.co.

    45 分鐘
  4. “Vibe analysis”: How Faire’s data team uses AI to investigate conversion drops, analyze experiment results, and convert raw data into executive-ready insights

    11月3日

    “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 codebases 2. Techniques for creating a semantic layer that helps AI understand your business data 3. How to build end-to-end analytics workflows using Cursor and Model Context Protocols (MCPs) 4. Ways to automate experiment analysis and create standardized reports 5. How AI can help design and analyze customer surveys 6. Strategies for creating executive-ready documents from raw data analysis 7. Why every team member should have access to code repositories—not just engineers — Brought to you by: Zapier—The most connected AI orchestration platform Brex—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 jordan@penname.co.

    1 小時 3 分鐘
  5. Vibe-coding a kid-friendly AI fortune teller for your Halloween festivities | Marco Casalaina (Microsoft VP)

    10月31日

    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 jordan@penname.co.

    12 分鐘
  6. “Cursor is a much better product manager than I ever was”: How this PM uses AI for PRDs, Jira tickets, and replying to coworkers | Dennis Yang (Chime)

    10月27日

    “Cursor is a much better product manager than I ever was”: How this PM uses AI for PRDs, Jira tickets, and replying to coworkers | Dennis Yang (Chime)

    Dennis Yang is the Principal Product Manager for Generative AI at Chime, where he’s pioneered AI workflows that meaningfully increase productivity. While most people use Cursor as a coding tool, Dennis has turned it into a comprehensive product-management system that automates PRD creation, documentation management, ticket creation, status reporting, and even comment responses—without writing code. In this episode, he shares his end-to-end workflow and how non-technical professionals can leverage AI-powered IDEs. What you’ll learn: Why Cursor is the perfect hub for product management (even if you don’t code)How to use MCPs (Model Context Protocols) to push content between Cursor, Confluence, and NotionThe workflow for creating PRDs in Cursor and automatically responding to commentsHow to automate Jira ticket creation directly from your PRDsA system for generating comprehensive status reports without manual workHow to prototype AI products in minutes using Cursor as a “super MVP” environmentWhy source-controlled markdown files might replace traditional SaaS tools— Brought to you by: Zapier—The most connected AI orchestration platform Brex—The intelligent finance platform built for founders — Where to find Dennis Yang: Twitter/X: https://twitter.com/sinned LinkedIn: https://www.linkedin.com/in/dennisyang/ Chime: https://www.chime.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 Dennis Yang (03:00) Why Cursor is ideal for product management workflows (04:53) Setting up Cursor for non-coding use cases with markdown preview (09:35) Creating PRDs in Cursor and using source control for documentation (10:33) Using MCPs to publish content to Confluence and Notion (11:38) Bridging the gap between engineering and product (17:00) Reading and responding to document comments with AI assistance (21:37) Creating comprehensive Jira tickets directly from PRDs (25:51) Generating automated status reports from Jira data (30:23) Building a morning briefing system with ChatGPT (35:03) Generating personal morning briefings using ChatGPT (40:04) The “super MVP” approach to AI product development (46:37) Lightning round and final thoughts — Tools referenced: • Cursor: https://cursor.com/ • Confluence: https://www.atlassian.com/software/confluence • Notion: https://www.notion.so/ • Jira: https://www.atlassian.com/software/jira • ChatGPT: https://chat.openai.com/ • Claude: https://claude.ai/ • Git: https://git-scm.com/ — Other references: • News API: https://newsapi.org/ • Semrush: https://www.semrush.com/ — Production and marketing by https://penname.co/. For inquiries about sponsoring the podcast, email jordan@penname.co.

    50 分鐘
  7. Claude Skills explained: How to create reusable AI workflows

    10月22日

    Claude Skills explained: How to create reusable AI workflows

    Today I dive into Anthropic’s latest feature that lets anyone create reusable workflows for Claude—no coding required. I break down exactly what Claude Skills are, how to build them from scratch, and how to use them inside Claude Code and Cursor to automate recurring AI tasks like generating PRDs, writing changelog summaries, and turning demo notes into follow-up emails. What you’ll learn: What Claude Skills are and how they differ from Claude Projects and custom GPTsHow to structure a Skill (metadata, instructions, and linked files)Why defining workflows in natural language beats rigid automation toolsHow to create Claude Skills using Claude Code and CursorHow to validate your skills with Python scripts and folder referencesHow to upload and use Claude Skills inside Claude’s web or desktop appPractical examples: turning changelogs into newsletters, demo notes into emails, and more— Brought to you by: ChatPRD—An AI copilot for PMs and their teams — 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 (01:39) What are Claude Skills and how do they work? (08:30) The structure of Claude Skills files (11:00) Demo: Creating Skills using Claude’s built-in skill creator (16:08) A more efficient workflow: Creating Skills with Cursor (17:42) Using Python validation scripts (18:37) Testing Skills with Claude Code (20:52) Creating a changelog-to-newsletter Skill (22:16) Creating a demo-to-follow-up-email Skill (23:45) Uploading Skills to the Claude web interface (26:04) Conclusion and summary — Tools referenced: • Claude: https://claude.ai/ • Claude Code: https://claude.ai/code • Cursor: https://cursor.sh/ — Other references: • Equipping agents for the real world with Agent Skills: https://www.anthropic.com/engineering/equipping-agents-for-the-real-world-with-agent-skills • Anthropic Skills Documentation: https://docs.claude.com/en/docs/claude-code/skills?utm_source=chatgpt.com • Claude Projects:https://claude.ai/projects — Production and marketing by https://penname.co/. For inquiries about sponsoring the podcast, email jordan@penname.co.

    27 分鐘
  8. How this Yelp AI PM works backward from “golden conversations” to create high-quality prototypes using Claude Artifacts and Magic Patterns | Priya Badger

    10月20日

    How this Yelp AI PM works backward from “golden conversations” to create high-quality prototypes using Claude Artifacts and Magic Patterns | Priya Badger

    Priya Badger, a product manager at Yelp, shares her innovative approach to designing AI-powered products by starting with example conversations rather than traditional wireframes or PRDs. In this episode, she demonstrates how she uses Claude and Magic Patterns to prototype Yelp’s AI assistant features—from exploring conversation flows to designing user interfaces. What you’ll learn: 1. How to use example conversations as your first “wireframe” when designing conversational AI products 2. A step-by-step workflow for using Claude to generate and refine sample conversations that guide your AI product development 3. Techniques for creating interactive prototypes with Claude Artifacts that use real LLM responses without complex API integrations 4. How to use Magic Patterns’ Inspiration mode to rapidly explore multiple UI variations for your AI features 5. Why starting with conversations and working backward to system prompts creates more natural AI interactions 6. How to apply these AI prototyping techniques to personal projects to build your AI product management skills — Brought to you by: GoFundMe Giving Funds—One account. Zero hassle. Persona—Trusted identity verification for any use case — Where to find Priya Badger: LinkedIn: https://www.linkedin.com/in/priyamathewprofile/ Substack: https://almostmagic.substack.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 Priya (02:54) The unique challenges of managing AI-powered products (04:33) Using example conversations as a starting point for design (05:53) Demo: Prompting Claude to generate sample conversations (09:10) Prototyping advice (09:53) Testing with multiple example images and scenarios (15:03) Refining conversations based on qualitative assessment (15:59) Demo: Creating interactive prototypes with Claude Artifacts (21:22) Using Magic Patterns to design the user interface (25:30) Exploring multiple design variations with Inspiration mode (31:02) Quick summary (33:35) How to apply these AI prototyping techniques to personal projects (38:57) Final thoughts — Tools referenced: • Claude: https://claude.ai/ • Magic Patterns: https://magicpatterns.com/ • Lovable: https://lovable.ai/ • Figma: https://www.figma.com/ • ChatGPT: https://chat.openai.com/ — Other references: • How to build prototypes that actually look like your product | Colin Matthews (product leader, AI prototyping instructor at Maven): https://www.lennysnewsletter.com/p/how-to-build-prototypes-that-actually — Production and marketing by https://penname.co/. For inquiries about sponsoring the podcast, email jordan@penname.co.

    42 分鐘
顯示全部 (36)

預告片

4.8
（滿分 5 顆星）
143 則評分

簡介

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.

資訊

  • 創作者
    Claire Vo
  • 活躍年代
    2025年
  • 集數
    36
  • 年齡分級
    兒少適宜
  • 版權
    © Claire Vo
  • 節目網站
    How I AI

你可能也會喜歡