How I AI

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:

1. What Claude Skills are and how they differ from Claude Projects and custom GPTs
2. How to structure a Skill (metadata, instructions, and linked files)
3. Why defining workflows in natural language beats rigid automation tools
4. How to create Claude Skills using Claude Code and Cursor
5. How to validate your skills with Python scripts and folder references
6. How to upload and use Claude Skills inside Claude’s web or desktop app
7. Practical 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 [email protected].

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 [email protected].

Hamel Husain, an AI consultant and educator, shares his systematic approach to improving AI product quality through error analysis, evaluation frameworks, and prompt engineering. In this episode, he demonstrates how product teams can move beyond “vibe checking” their AI systems to implement data-driven quality improvement processes that identify and fix the most common errors. Using real examples from client work with Nurture Boss (an AI assistant for property managers), Hamel walks through practical techniques that product managers can implement immediately to dramatically improve their AI products.

What you’ll learn:

1. A step-by-step error analysis framework that helps identify and categorize the most common AI failures in your product

2. How to create custom annotation systems that make reviewing AI conversations faster and more insightful

3. Why binary evaluations (pass/fail) are more useful than arbitrary quality scores for measuring AI performance

4. Techniques for validating your LLM judges to ensure they align with human quality expectations

5. A practical approach to prioritizing fixes based on frequency counting rather than intuition

6. Why looking at real user conversations (not just ideal test cases) is critical for understanding AI product failures

7. How to build a comprehensive quality system that spans from manual review to automated evaluation

—

Brought to you by:

GoFundMe Giving Funds—One account. Zero hassle: https://gofundme.com/howiai

Persona—Trusted identity verification for any use case: https://withpersona.com/lp/howiai

—

Where to find Hamel Husain:

Website: https://hamel.dev/

Twitter: https://twitter.com/HamelHusain

Course: https://maven.com/parlance-labs/evals

GitHub: https://github.com/hamelsmu

—

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 Hamel Husain

(03:05) The fundamentals: why data analysis is critical for AI products

(06:58) Understanding traces and examining real user interactions

(13:35) Error analysis: a systematic approach to finding AI failures

(17:40) Creating custom annotation systems for faster review

(22:23) The impact of this process

(25:15) Different types of evaluations

(29:30) LLM-as-a-Judge

(33:58) Improving prompts and system instructions

(38:15) Analyzing agent workflows

(40:38) Hamel’s personal AI tools and workflows

(48:02) Lighting round and final thoughts

—

Tools referenced:

• Claude: https://claude.ai/

• Braintrust: https://www.braintrust.dev/docs/start

• Phoenix: https://phoenix.arize.com/

• AI Studio: https://aistudio.google.com/

• ChatGPT: https://chat.openai.com/

• Gemini: https://gemini.google.com/

—

Other references:

• Who Validates the Validators? Aligning LLM-Assisted Evaluation of LLM Outputs with Human Preferences: https://dl.acm.org/doi/10.1145/3654777.3676450

• Nurture Boss: https://nurtureboss.io

• Rechat: https://rechat.com/

• Your AI Product Needs Evals: https://hamel.dev/blog/posts/evals/

• A Field Guide to Rapidly Improving AI Products: https://hamel.dev/blog/posts/field-guide/

• Creating a LLM-as-a-Judge That Drives Business Results: https://hamel.dev/blog/posts/llm-judge/

• Lenny’s List on Maven: https://maven.com/lenny

—

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

Amir Klein is a product manager at Monday.com, leading their AI agents initiative. Despite taking two months of paternity leave, he ranked #4 out of 90 PMs in AI tool usage at his company. In this episode, Amir reveals how he’s become “highly dependent and maybe incapable” of doing his job without AI, showing his custom GPT workflows that help him manage context switching, analyze customer feedback, improve his writing, and prepare for product interviews.

What you’ll learn:

1. How to create project-specific “second brains” in Claude and ChatGPT that hold context for you across multiple workstreams
2. A step-by-step process for using Claude to build a Reddit scraper that gathers thousands of customer conversations, without coding expertise
3. How to analyze large datasets of customer feedback using AI to identify patterns, priorities, and key discussion points
4. A workflow for creating custom GPTs that help you improve specific skills based on manager feedback
5. Techniques for using GPT voice mode to conduct realistic mock interviews that provide candid feedback on your responses
6. Why “everything is text” should be your mindset when feeding information into AI tools, from PDFs to slide decks
7. How to use AI to respond quickly to stakeholder requests even when you’re context switching between multiple projects

—

Brought to you by:

GoFundMe Giving Funds—One account. Zero hassle.

Miro—A collaborative visual platform where your best work comes to life

—

Where to find Amir Klein:

LinkedIn: https://www.linkedin.com/in/amir-klein-9b8444189/

—

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 Amir

(03:11) Using custom GPT project folders as “second brains”

(06:24) Building a Reddit scraper with Claude’s help

(11:02) Analyzing 34,000 rows of Reddit conversations

(14:06) How to build effective custom GPT knowledge bases

(18:04) Creating a custom writing coach from Lenny’s Newsletter

(21:53) Using AI for professional development and feedback

(24:08) Preparing for product interviews with GPT voice mode

(31:49) Additional use cases for voice mode

(33:04) Recap of Amir’s AI workflows

(35:43) Lightning round and final thoughts

—

Tools referenced:

• Claude: https://claude.ai/

• ChatGPT: https://chat.openai.com/

• Reddit API: https://www.reddit.com/dev/api/

• Python: https://www.python.org/

• Slack: https://slack.com/

—

Other references:

• Wes Kao: https://weskao.com/

• Become a better communicator: Specific frameworks to improve your clarity, influence, and impact | Wes Kao (coach, entrepreneur, advisor): https://www.lennysnewsletter.com/p/become-a-better-communicator-specific

• On Writing Well by William Zinsser: https://www.amazon.com/Writing-Well-Classic-Guide-Nonfiction/dp/0060891548

• The Elements of Style by Strunk and White: https://www.amazon.com/Elements-Style-Fourth-William-Strunk/dp/020530902X

• Exponent YouTube channel: https://www.youtube.com/c/ExponentTV

• monday.com: https://monday.com/

—

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

Ravi Mehta, now a product advisor, has built and scaled products used by millions. His past roles include Chief Product Officer at Tinder, Entrepreneur in Residence at Reforge, and senior product leadership positions at Facebook, TripAdvisor, and Xbox. In this episode, Ravi demonstrates his data-driven approach to AI prototyping that produces dramatically better results than traditional "vibe prototyping." He also shares his structured framework for generating professional-quality images in Midjourney that look like they were shot by a professional photographer.

What you’ll learn:

1. Why most product managers and designers are “vibe prototyping” with AI and getting mediocre results
2. How to use JSON data models instead of design systems as the foundation for better AI prototypes
3. A simple three-part framework for structuring Midjourney prompts to get professional-quality photos
4. How to use Claude and Unsplash’s MCP server to generate realistic data and images for your prototypes
5. Why real data (not Lorem Ipsum) is critical for getting meaningful feedback from stakeholders
6. The film stock “cheat code” that instantly elevates your AI-generated photos

—

Brought to you by:

Google Gemini—Your everyday AI assistant

Persona—Trusted identity verification for any use case

—

Where to find Ravi Mehta:

Website: https://www.ravi-mehta.com/

Reforge: https://www.reforge.com/profiles/ravi-mehta

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

X: https://x.com/ravi_mehta

—

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 Ravi and data-driven prototyping

(02:31) The problem with “vibe prototyping” in product development

(04:18) Spec-driven prototyping vs. data-driven prototyping

(05:27) Demo: Spec-driven approach to prototyping

(08:26) Limitations of the basic AI prototype approach

(11:24) The data-driven prototyping approach explained

(12:08) Demo: Data-driven prototyping

(17:45) Creating a prototype with the generated JSON data

(23:33) Comparing the quality difference between approaches

(26:44) Modifying the prototype

(28:53) Benefits of this approach

(34:40) Structured Midjourney prompting

(36:20) The subject-setting-style framework for better image prompts

(44:27) Using camera metadata to refine your results

(48:54) Lightning round and final thoughts

—

Tools referenced:

• Claude: https://claude.ai/

• Reforge Build: https://www.reforge.com/build

• Midjourney: https://www.midjourney.com/

• Unsplash MCP: https://github.com/okooo5km/unsplash-mcp-server-go?utm_source=chatgpt.com

—

Other references:

• Reforge AI Strategy Course: https://www.reforge.com/courses/ai-strategy

—

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

Lee Robinson is the head of AI education at Cursor, where he teaches people how to build software with AI. Previously, he helped build Vercel and Next.js as an early employee. In this episode, he demonstrates how Cursor's AI-powered code editor bridges the gap between beginners and experienced developers through automated error fixing, parallel task execution, and writing assistance. Lee walks through practical examples of using Cursor's agent to improve code quality, manage technical debt, and even enhance your writing by eliminating common AI patterns and clichés.

What you'll learn:

1. How to use Cursor's AI agent to automatically detect and fix linting errors without needing to understand complex terminal commands

2. A workflow for running parallel coding tasks by focusing on your main work while the agent handles secondary features in the background

3. Why setting up typed languages, linters, formatters, and tests creates guardrails that help AI tools generate better code

4. How to create custom commands for code reviews that automatically check for security issues, test coverage, and other quality concerns

5. A technique for improving your writing by creating a custom prompt with banned words and phrases that eliminates AI-generated patterns

6. Strategies for managing context in AI conversations to maintain high-quality responses and avoid degradation

7. Why looking at code—even when you don't fully understand it—is one of the best ways to learn programming

—

Brought to you by:

Google Gemini—Your everyday AI assistant

Persona—Trusted identity verification for any use case

—

Where to find Lee Robinson:

Twitter/X: https://twitter.com/leeerob

Website: https://leerob.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 Lee

(02:04) Understanding Cursor's three-panel interface

(06:27) The importance of typed languages, linters, and tests

(11:28) Demo: Using the agent to automatically fix lint errors

(15:17) Running parallel coding tasks with the agent

(18:50) Setting up custom rules

(23:24) Understanding the different AI models

(24:48) Micro-slicing agent chats for better success

(27:22) Tips for effective agent usage

(29:00) Using AI to improve your writing

(35:47) Lightning round and final thoughts

—

Tools referenced:

• Cursor: https://cursor.com/

• ChatGPT: https://chat.openai.com/

• JavaScript: https://developer.mozilla.org/en-US/docs/Web/JavaScript

• Python: https://www.python.org/

• TypeScript: https://www.typescriptlang.org/

• Git: https://git-scm.com/

—

Other references:

• Linting: https://en.wikipedia.org/wiki/Lint_(software)

—

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

Terry Lin is a product manager and developer who built Cooper’s Corner, an AI-powered fitness tracking app that works across iPhone and Apple Watch. Frustrated with traditional fitness apps that require extensive setup and manual logging, Terry created a solution that lets users simply speak their exercises, weights, and reps. The app automatically structures this data and provides analytics on workout consistency and progress. In this episode, Terry shares his vibe-coding process using Cursor and Xcode and explains how he optimizes his codebase for AI collaboration.

What you’ll learn:

1. How Terry built a voice-powered fitness tracker that works across iPhone and Apple Watch

2. His “dual-wielding” workflow, using Cursor for coding and Xcode for building and debugging

3. Terry’s three-step process for working with AI: create, review, and execute

4. Why optimizing your codebase for AI collaboration can dramatically improve productivity

5. How to use index cards and GPT-4 to rapidly prototype mobile interfaces

6. A technique for “vibe refactoring” that keeps code organized and optimized for both human and AI readability

7. His “rubber duck” technique to better understand generated code and improve your learning process

—

Brought to you by:

Paragon—Ship every SaaS integration your customers want

Miro—A collaborative visual platform where your best work comes to life

—

Where to find Terry Lin:

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

GitHub: https://github.com/itsmeterrylin

—

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 Terry and his fitness tracker app

(02:30) Demo of the voice-powered workout tracking across devices

(06:40) Analytics and history views for tracking consistency

(07:20) Dual-wielding Cursor and Xcode for mobile development

(09:05) Building a v1 using AI tools

(11:19) A three-step AI workflow: create, review, execute

(19:38) Token conservation and vibe refactoring explained

(23:25) Optimizing file sizes for better AI performance

(25:28) Using “rubber duck” rules to learn from AI-generated code

(28:13) Prototyping with index cards and GPT-4

(31:20) Human creativity and the last 10%

(32:29) Lightning round and final thoughts

—

Tools referenced:

• Cursor: https://cursor.sh/

• Xcode: https://developer.apple.com/xcode/

• GPT-4: https://openai.com/gpt-4

• UX Pilot: https://uxpilot.ai/

• Figma: https://www.figma.com/

• Linear: https://linear.app/

—

Other references:

• Apple UI Kit: https://developer.apple.com/design/human-interface-guidelines/

—

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

Scott Wu is the co-founder and CEO of Cognition Labs, the creators of Devin, an AI agent designed to function as a junior engineer on software development teams. In this conversation, Scott demonstrates how his team uses their own product to accelerate development workflows, reduce engineering toil, and handle routine tasks asynchronously. Scott walks us through real examples of how Devin integrates into Cognition’s daily operations—from researching and implementing new features to responding to crashes and handling frontend fixes. He explains how Devin differs from traditional AI coding assistants by functioning more like a team member than a tool, allowing engineers to delegate well-scoped tasks while focusing on higher-level problems.

What you’ll learn:

1. How to use DeepWiki to research your codebase and generate better prompts for AI engineering tasks

2. A workflow for treating AI agents as asynchronous junior engineers who can handle multiple tasks while you attend meetings

3. Why public channels create better learning environments for both humans and AI when implementing engineering solutions

4. The top five engineering tasks AI excels at: frontend fixes, version upgrades, documentation, incident response, and testing

5. How to implement a “first line of defense” system where AI agents analyze crashes before humans need to intervene

6. A technique for bringing voice AI into meetings as an additional participant to answer questions without disrupting flow

—

Brought to you by:

Google Gemini—Your everyday AI assistant

Vanta—Automate compliance. Simplify security.

—

Where to find Scott Wu:

X: https://x.com/ScottWu46

LinkedIn: https://www.linkedin.com/in/scott-wu-8b94ab96/

—

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 Scott Wu and Devin

(03:53) Where Devin excels

(06:08) Using DeepWiki to research codebases and create better prompts

(10:27) Prompting tips

(11:24) The asynchronous nature of working with Devin

(13:38) Multithreading tasks

(14:43) Using Devin to implement an MCP server integration

(18:38) Setting up workflows in Slack for first-line responses

(23:22) Encouraging AI adoption in public Slack channels

(25:50) Top five engineering tasks for Devin

(32:17) Using ChatGPT voice as a meeting participant

(35:57) Lightning round

—

Tools referenced:

• Devin: https://devin.ai/

• DeepWiki: https://deepwiki.org/

• ChatGPT: https://chat.openai.com/

• Windsurf: https://windsurf.ai/

• Slack: https://slack.com/

• Linear: https://linear.app/

• GitHub: https://github.com/

—

Other references:

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

• TanStack Router: https://tanstack.com/router/

—

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

Anjan Panneer Selvam is the Chief Product and Technology Officer at Acolyte Health, where he’s pioneering the use of AI across the entire product development lifecycle. In this episode, he demonstrates how AI tools can dramatically accelerate alignment between stakeholders, reduce development time from months to minutes, and enable teams to validate ideas with customers before committing engineering resources.

What you’ll learn:

1. How to transform meeting transcripts into interactive prototypes in under 30 minutes using ChatGPT, Lovable, and other AI tools

2. A step-by-step workflow for creating market analyses and competitive research in minutes instead of days

3. How to build a “living product library” that allows sales and customer success teams to demo prototypes to customers before engineering begins

4. Techniques for using AI to break deadlocks with engineering by demonstrating what’s possible without requiring technical expertise

5. Why AI enables faster stakeholder alignment by converting abstract ideas into tangible, interactive experiences

6. How to use ChatPRD to validate product requirements and ensure you’ve considered all critical aspects before engaging engineering

—

Brought to you by:

Notion—The best AI tools for work: https://www.notion.com/howiai

Lovable—Build apps by simply chatting with AI: https://lovable.dev/

—

Where to find Anjan Panneer Selvam:

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

—

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 Anjan

(02:36) How AI changes the relationship between product and engineering

(04:08) Workflow for converting stakeholder ideas into prototypes

(08:50) Using the Limitless pendant to capture meeting transcripts

(12:45) Creating interactive prototypes with Lovable

(15:57) Benefits of using prototypes instead of documentation

(19:07) Conducting market research with Perplexity

(21:45) Creating presentation decks with Gamma

(23:08) AI doesn’t replace PMs; it elevates them

(25:05) Using ChatPRD to validate product requirements

(29:10) Building a living product library for sales and customer success

(35:50) Breaking deadlocks with engineering using Rork for mobile prototypes

(39:00) Takeaways for building with AI

(42:34) Cultural implications of AI in product development

(45:20) Strategies for when AI doesn’t give you what you want

—

Tools referenced:

• ChatGPT: https://chat.openai.com/

• Lovable: https://lovable.dev/

• Limitless: https://www.limitless.ai/

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

• Gamma: https://gamma.app/

• ChatPRD: https://www.chatprd.ai/

• Rork: https://rork.com/

• v0: https://v0.dev/

• Magic Patterns: https://www.magicpatterns.com/

—

Other references:

• React Flow: https://reactflow.dev/

• Figma: https://www.figma.com/

• Acolyte Health: https://acolytehealth.com/

• Meta Ray-Ban glasses: https://www.ray-ban.com/usa/ray-ban-meta-ai-glasses

—

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

Tomasz Tunguz is the founder of Theory Ventures, which invests in early-stage enterprise AI, data, and blockchain companies. In this episode, Tomasz reveals his custom-built “Parakeet Podcast Processor,” which helps him extract value from 36 podcasts weekly without spending 36 hours listening. He walks through his terminal-based workflow that downloads, transcribes, and summarizes podcast content, extracting key insights, investment theses, and even generating blog post drafts. We explore how AI enables hyper-personalized software experiences that weren’t feasible before recent advances in language models.

What you’ll learn:

1. How to build a terminal-based podcast processing system that downloads, transcribes, and extracts key insights from multiple podcasts daily

2. A workflow for using Nvidia’s Parakeet and other AI tools to clean transcripts and generate structured summaries of podcast content

3. How to extract actionable investment theses and company mentions from podcast transcripts using AI prompting techniques

4. A systematic approach to generating blog post drafts with AI that maintains your personal writing style through iterative feedback

5. Why using an “AP English teacher” grading system can help improve AI-generated content through multiple revision cycles

6. How to leverage Claude Code for maintaining and updating personal productivity tools with minimal friction

—

Brought to you by:

Notion—The best AI tools for work

Miro—A collaborative visual platform where your best work comes to life

—

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 Tomasz Tunguz:

Blog: https://tomtunguz.com/

Theory Ventures: https://theory.ventures/

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

X: https://x.com/ttunguz

—

In this episode, we cover:

(00:00) Introduction to Tomasz Tunguz

(03:32) Overview of the podcast ripper system and its components

(05:06) Demonstration of the transcript cleaning process

(06:59) Extracting quotes, investment theses, and company mentions

(10:20) Why Tomasz prefers terminal-based tools

(12:38) The benefits of personalized software versus off-the-shelf solutions

(15:31) A workflow for generating blog posts from podcast insights

(17:34) Using the “AP English teacher” grading system for blog posts

(18:25) Challenges with matching personal writing style using AI

(22:00) Tomasz’s three-iteration process for improving blog posts

(26:13) The grading prompt and evaluation criteria

(28:16) AI’s role in writing education

(30:28) Final thoughts

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Tools referenced:

• Whisper (OpenAI): https://openai.com/research/whisper

• Parakeet: https://build.nvidia.com/nvidia/parakeet-ctc-0_6b-asr

• Ollama: https://ollama.com/

• Gemma 3: https://deepmind.google/models/gemma/gemma-3/

• Claude: https://claude.ai/

• Claude Code: https://claude.ai/code

• Gemini: https://gemini.google.com/

• FFmpeg: https://ffmpeg.org/

• DuckDB: https://duckdb.org/

• LanceDB: https://lancedb.com/

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Other references:

• 35 years of product design wisdom from Apple, Disney, Pinterest, and beyond | Bob Baxley: https://www.lennysnewsletter.com/p/35-years-of-product-design-wisdom-bob-baxley

• Dan Luu’s blog post on latency: https://danluu.com/input-lag/

• GitHub CEO: The AI Coding Gold Rush, Vibe Coding & Cursor: https://www.readtobuild.com/p/github-ceo-the-ai-coding-gold-rush

• Stanford Named Entity Recognition library: https://nlp.stanford.edu/software/CRF-NER.html

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