How I AI

I put three cutting-edge AI models to the test in a head-to-head design competition. Using the exact same prompt, I challenged Google’s Gemini 3, Anthropic’s Opus 4.5, and OpenAI’s Codex 5.1 to redesign my blog page, evaluating them on visual design quality, user experience improvements, and SEO optimization capabilities. One model produced a beautiful, polished, production-ready redesign. One was fine. And one completely whiffed. If you’re trying to figure out where each model fits in your workflow—design, planning, back-end, or something else—this episode will save you a lot of trial and error.

What you’ll learn:

1. How each AI model approaches the same design challenge differently
2. Why planning capabilities dramatically impact design quality
3. The specific visual and functional improvements each model made
4. Which model excels at front-end design versus back-end functionality
5. How to strategically choose the right AI model for different parts of your workflow
6. The importance of model-switching based on specific use cases

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Blog design: https://www.chatprd.ai/blog

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Brought to you by:

Lovable—Build apps by simply chatting with AI

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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 the AI design challenge

(01:25) The question: Which model is the better designer?

(03:08) The prompt used for all three models

(04:10) Gemini 3 Pro’s approach and results

(06:00) Opus 4.5’s approach and results

(10:54) Codex 5.1’s approach and disappointing results

(14:51) Comparing the three designs side by side

(16:03) Analyzing the change logs and SEO improvements from each model

(22:43) Final verdict

(23:00) Conclusion and next steps

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Tools referenced:

• Gemini 3 Pro: https://deepmind.google/models/gemini/pro/

• Anthropic Opus 4.5: https://www.anthropic.com/news/claude-opus-4-5

• OpenAI Codex 5.1: https://platform.openai.com/docs/models/gpt-5.1-codex

• Cursor: https://cursor.com/

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

Marily Nika, AI Product Lead at Google and founder of the AI Product Academy, demonstrates how product managers can leverage AI tools to dramatically accelerate their workflow. Using a smart-fridge concept as an example, Marily walks us through the exact workflow she uses to build products faster: doing user research with Reddit debates, generating PRDs with custom GPTs, prototyping with v0, and even creating stakeholder-ready video mockups using VEO and Sora. She shows how “tool hopping” between specialized AI applications creates a powerful workflow that transforms traditional PM processes and enables more compelling product storytelling.

What you’ll learn:

1. How to use Perplexity’s “discussions and opinions” filter to mine Reddit for user insights and create pro/con agent debates that reveal product-market fit requirements
2. A workflow for transforming market research into comprehensive PRDs using custom GPTs that maintain your personal voice and style
3. Techniques for turning PRDs into interactive prototypes using v0.dev that make your product vision tangible for stakeholders
4. How to create persuasive product videos using Flow and Sora that communicate your vision more effectively than traditional presentations
5. Why “tool hopping” between specialized AI applications creates a more powerful workflow than using a single tool
6. How to use NotebookLM as an interactive judge for product demos and pitch competitions

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Brought to you by:

WorkOS—Make your app enterprise-ready today

Miro—The AI Innovation Workspace where teams discover, plan, and ship breakthrough products

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Where to find Marily Nika:

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

Website: https://www.marilynika.me/

Substack: https://marily.substack.com/

AI Product Management Bootcamp & Certification by AI Product Academy: https://bit.ly/4p8tn2r

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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 Marily Nika

(02:54) Smart-fridge use case inspiration

(06:15) Using Perplexity to mine Reddit for user research

(11:19) Creating a comprehensive PRD with ChatGPT

(13:40) Building an interactive prototype with v0

(16:20) Using prototypes as stakeholder influence tools in product reviews

(21:30) Generating product videos with Flow and Sora

(30:17) The complete 20-minute product workflow, from research to video

(32:06) Using NotebookLM as an AI judge for product demo days

(37:38) What to do when AI tools aren’t giving you what you want

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Tools referenced:

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

• ChatGPT: https://chat.openai.com/

• v0.dev: https://v0.dev/

• Flow (Google Labs): https://labs.google/flow/about

• Sora: https://openai.com/sora

• NotebookLM: https://notebooklm.google/

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Other references:

• AI Product Management Bootcamp: https://maven.com/lenny/ai-product-management

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

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

Lucas Werthein, the COO and co-founder of Cactus, shares how he built a personalized AI wellness coach using ChatGPT to optimize his athletic performance while managing past injuries. After multiple surgeries on his knees, shoulder, and foot, Lucas created a system that synthesizes data from medical imaging, blood tests, wearable devices, and nutrition plans to provide personalized recommendations. His AI coach helps him balance competitive tennis, weightlifting, and running a company while maintaining his goal of “feeling 25 in a 40-year-old body.” Lucas demonstrates how this approach transforms siloed health information into actionable insights that protect joints, optimize recovery, and extend peak performance.

What you’ll learn:

1. How to configure a ChatGPT with multiple data types, including MRIs, x-rays, blood tests, and wearable metrics, to create a comprehensive health profile
2. A framework for setting clear performance boundaries that prioritize joint protection, energy optimization, and injury prevention
3. Techniques for using AI to balance nutrition around special events like social dinners while maintaining performance goals
4. How to use images and videos to get AI feedback on physical symptoms and injury recovery timelines
5. A method for validating and contextualizing medical advice by having AI synthesize information from multiple health-care providers
6. Why creating clear rules and anti-prompts helps AI deliver practical, evidence-based recommendations instead of trendy supplements or extreme protocols

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Copy Lucas’s Health Coach Prompt: https://www.lennysnewsletter.com/p/how-to-create-your-own-ai-performance-coach

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Brought to you by:

WorkOS—Make your app enterprise-ready today

Google Gemini—Your everyday AI assistant

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Where to find Lucas Werthein:

Website: https://cactus.is/

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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 Lucas’s athletic background and injury history

(04:55) The challenge of synthesizing siloed health data

(06:11) Building a GPT to optimize performance and recovery

(09:57) Demonstrating the data types integrated into the AI coach

(13:54) Configuring the GPT with clear performance goals and boundaries

(16:31) Setting realistic expectations for the AI coach

(17:50) Creating nutrition, training, and recovery frameworks

(21:47) Establishing hard boundaries and anti-prompts

(24:25) Example: Managing nutrition around special events

(27:30) Accessibility and affordability of on-demand coaching

(28:24) Practical examples and real-life scenarios

(29:31) Using AI for injury management and recovery planning

(34:19) Validating expert opinions and translating medical advice

(37:25) Vision for the future of AI in personal health coaching

(43:27) Other AI workflows: synthetic clients and AI co-founders

(48:48) Final thoughts on AI reliability and evolution

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Tool referenced:

• ChatGPT: https://chat.openai.com/

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Other references:

• InBody scan: https://inbodyusa.com/

• Whoop: https://www.whoop.com/

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

In today’s pre-Thanksgiving episode, I walk you through how I vibe coded my very own “Thanksgiving party hub” using Lovable—and how I transformed it from AI-generated slop into something warm, personal, and genuinely useful. I show you exactly how I upleveled the typography, visuals, and structure using Google Fonts and Midjourney style references, and then I share one of my favorite real-life AI hacks: how to turn any messy online recipe into a clean, step-by-step, kid-friendly version that’s actually usable while you’re cooking. This is a cozy, practical walkthrough of my real design process—the little tricks I use to make AI-built apps feel handcrafted instead of generic.

What you’ll learn:

1. How to build a fully functional Thanksgiving party hub in Lovable—guests, dishes, recipes, and photos
2. How I uplevel AI-generated designs using Google Fonts and Tailwind
3. How to use Midjourney style references to create custom images that match your aesthetic
4. How to add custom features to vibe-coded apps, like dietary preferences and allergen tags
5. How to iterate on layouts inside Lovable using screenshots and small, targeted prompts
6. How I use ChatGPT to restructure recipes so the measurements are embedded directly in each step
7. How to make recipes kid-friendly and easier to follow using a simple formatting prompt

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Brought to you by:

WorkOS—Make your app enterprise-ready today

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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 the Thanksgiving party hub concept

(02:20) Starting a project in Lovable and initial design assessment

(04:59) Upleveling typography with Google Font combinations

(08:36) Creating custom header images with Midjourney

(11:39) Adjusting aspect ratios for Midjourney images

(14:22) Fixing design issues incrementally

(18:52) Adding dietary-restriction functionality

(23:36) AI recipe reformatting for easier cooking

(26:02) Thoughts on ChatGPT 5.1

(30:51) Final implementation and recipe sharing

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Tools referenced:

• Lovable: https://lovable.dev/

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

• Google Fonts: https://fonts.google.com/

• ChatGPT: https://chat.openai.com/

• Canva Font Combinations: https://www.canva.com/font-combinations/

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Other references:

• Polenta and Sausage Stuffing Recipe: https://www.epicurious.com/recipes/food/views/polenta-and-sausage-stuffing-233030

• Runaway Pancakes (kid-friendly recipe site): https://runawaypancakes.com/

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

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:

1. How Tim built an AI system that automatically extracts and embeds metadata into archival images and footage
2. The custom iOS app he created that transforms chaotic archival research into structured, searchable data
3. How AI-powered OCR is making previously illegible historical documents accessible
4. Why Tim uses different AI models for different tasks (Claude for coding, OpenAI for images, Whisper for audio)
5. How vector embeddings enable semantic search across massive documentary archives
6. A practical approach to building custom AI tools that solve specific workflow problems
7. Why AI is most valuable for automating tedious tasks rather than replacing creative work

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Brought to you by:

Brex—The intelligent finance platform built for founders

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Where to find Tim McAleer:

Website: https://timmcaleer.com/

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

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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 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

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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/

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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/

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

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:

1. How to build a trigger-based workflow that automatically scrapes and processes customer call transcripts from platforms like Gong
2. A systematic approach to quantifying customer sentiment on a 1-10 scale that has proven highly predictive of churn and upsell opportunities
3. How to create an automated coaching system that provides personalized feedback to sales reps after every customer interaction
4. A workflow for extracting keywords from customer conversations to inform Google ad campaigns without manual intervention
5. Techniques for automatically generating privacy-compliant blog content from customer calls that drives organic traffic and paid search performance
6. Why CEOs and executives need to build AI skills firsthand rather than delegating implementation to engineering teams
7. How to use Google Sheets as structured databases for AI lookups and enrichment within automated workflows

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Brought to you by:

Brex—The intelligent finance platform built for founders

Zapier—The most connected AI orchestration platform

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Where to find Matt Britton:

LinkedIn: linkedin.com/in/mattbbritton

Instagram: https://www.instagram.com/mattbrittonnyc/

Company: https://www.suzy.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 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

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Tools referenced:

• Zapier: https://zapier.com/

• Gong: https://www.gong.io/

• Browse AI: https://www.browse.ai/

• ChatGPT: https://chat.openai.com/

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Other references:

• Qualtrics: https://www.qualtrics.com/

• SurveyMonkey: https://www.surveymonkey.com/

• Slack: https://slack.com/

• Google Sheets: https://www.google.com/sheets/about/

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

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:

1. How to set up a simple Next.js application from scratch using Cursor’s AI agent capabilities

2. My workflow for creating AI agents that generate consistent documentation (like PRDs in Markdown format)

3. How to build and display clickable prototypes without worrying about complex backend functionality

4. The basics of using GitHub to track changes and manage your code repository as a non-technical person

5. Why starting with a personal project hub is the best way to ease into AI-assisted coding

6. My favorite practical tips for iterating on designs and functionality using AI tools—without needing deep technical expertise

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Brought to you by:

ChatPRD—An AI copilot for PMs and their teams

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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

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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/

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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/

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

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

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Brought to you by:

Zapier—The most connected AI orchestration platform

Brex—The intelligent finance platform built for founders

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Where to find Tim Trueman:

LinkedIn: https://www.linkedin.com/in/tim-trueman-99788592/

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Where to find Alexa Cerf:

LinkedIn: https://www.linkedin.com/in/alexandra-cerf/

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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 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

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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/

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Other references:

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

• Faire Careers: https://www.faire.com/careers

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

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.

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Where to find Marco Casalaina:

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

X: https://x.com/amrcn_werewolf?lang=en

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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) 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

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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

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

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:

1. Why Cursor is the perfect hub for product management (even if you don’t code)
2. How to use MCPs (Model Context Protocols) to push content between Cursor, Confluence, and Notion
3. The workflow for creating PRDs in Cursor and automatically responding to comments
4. How to automate Jira ticket creation directly from your PRDs
5. A system for generating comprehensive status reports without manual work
6. How to prototype AI products in minutes using Cursor as a “super MVP” environment
7. Why source-controlled markdown files might replace traditional SaaS tools

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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/

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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 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

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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/

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Other references:

• News API: https://newsapi.org/

• Semrush: https://www.semrush.com/

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