The Growth Podcast

Today’s Episode
Discovery might be the most important core PM skill for building great products.
But most PMs are unprepared to do discovery in AI. PMs run surveys incorrectly, conduct interviews poorly, and end up with poor insights.
Today will give you the roadmap to avoid all those mistakes.
Caitlin Sullivan is a user research expert who runs courses teaching PMs how to do AI-powered discovery. And in today’s episode, she shows you exactly how she does it.
We’re talking live demos. Step-by-step workflows. Real survey data. Real interview transcripts.
This is a masterclass in discovery. The kind that moves the needle.
----
Brought to you by:
Maven: Get 15% off Caitlin’s courses with code AAKASHxMAVEN
Pendo: The #1 software experience management platform
Jira Product Discovery: Plan with purpose, ship with confidence
Kameleoon: AI experimentation platform
Amplitude: The market-leader in product analytics
----
Key Takeaways:
1. Replicate the human process - Good AI analysis mirrors how experienced researchers work: comb through data first, then synthesize. Never jump straight to "give me themes."2. Use multi-step prompting - Load context in one prompt, run per-participant analysis in the next, then verify. Cramming everything into one prompt degrades quality.3. Code before you count - For surveys, apply inductive coding labels to every response before asking for patterns. Skipping this step leads to miscategorized, unreliable results.4. Always audit AI's work - Force the model to re-check its own analysis. It catches contradictions, overexaggerated intensity ratings, and miscoded responses regularly.5. Claude wins on nuance, Gemini wins on frequency - Claude gives more thorough, complete analysis by default. Gemini surfaces top-frequency themes faster but misses smaller patterns.6. Define everything explicitly - Quotes, ratings, emotional intensity levels, contradiction types. If you assume the model shares your definitions, you'll get inconsistent results.7. Markdown files beat raw transcripts - Converting transcripts to structured markdown improves accuracy and helps you work around token limits on non-Max plans.8. Parallelize with Claude Code agents - Set up agent markdown files for interview and survey analysis, then run both simultaneously. Cuts total analysis time in half again.
----
Related Content
Newsletters:
How to Do Product Discovery Right
Advanced Techniques: Continuous Discovery
Customer Interviews: Advanced Techniques
Podcasts:
Teresa Torres’ Guide to AI Discovery
Complete Course: AI Product Discovery
Ultimate Guide to Knowing Your Users as a PM
----
PS. Please subscribe on YouTube and follow on Apple & Spotify. It helps!
If you want to advertise, email productgrowthppp at gmail.


This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes, visit www.news.aakashg.com/subscribe

Today’s Episode
Most PMs are drowning in tools.
You log into JIRA. Then Figma. Then Confluence. Then Notion. Then Google Analytics. Then Slack.
Twenty different tabs. Twenty different logins. Zero flow state.
Mike Bal runs product at David’s Bridal, a company undergoing massive digital transformation.
And he operates from a single interface.
Cursor and Claude Desktop sit at the center. Everything else connects through MCP and custom integrations.
Research? Manus feeds into Claude. Analytics? Clarity exports into Cursor. Design? Figma pulls directly into his projects.
This isn’t a tool stack. It’s an operating system.
Today, Mike shows you exactly how to build it.
----
If you want access to my AI tool stack - Dovetail, Arize, Linear, Descript, Reforge Build, DeepSky, Relay.app, Magic Patterns, Speechify, and Mobbin - grab Aakash’s bundle.
Are you searching for a PM job? Join me + 29 others for an intensive 12-week experience to master getting a PM job. Only 9 seats left.
----
Check out the conversation on Apple, Spotify and YouTube.
Brought to you by Linear: Plan and build products like the best.
----
Key Takeaways:
1. Operating systems beat tool stacks - Stop logging into 20 different UIs. Build one central interface through Cursor and Claude Desktop that connects to everything. The composable mindset adapts to your needs.
2. MCP changes PM workflows forever - Model Context Protocol lets you connect JIRA, Figma, GitHub, Notion, Confluence through natural language. Check ticket status without opening JIRA. Compare designs without manual cross-reference.
3. Design validation takes 30 seconds now - "Find my Confluence doc about Feature X, load this Figma design, compare them and tell me what I missed." Used to take 1-2 hours of manual comparison work.
4. Manus dominates heavy research - Gives you multiple file outputs: sample CSVs, combined datasets, data sources report, quick start guide, markdown summary. All traceable back to sources. ChatGPT just gives responses.
5. Research must stay external until vetted - The "conspiracy theorist LLM" problem is real. If you automatically feed everything into your system, AI anchors to wrong information. Vet research separately, then bring validated context in.
6. PMs can build what required engineers - Mike built a colorization app for e-commerce in one morning. Migrated content to Sanity CMS in a few hours. All from natural language prompts in Cursor.
7. Context switching kills productivity - Every time you open a new tab, you lose flow state. The operating system keeps you in one interface. The AI handles the context switching for you.
8. Corporate IT restrictions become irrelevant - You already have Cursor or Claude Desktop. You already use JIRA, Figma, GitHub. Connect them through a better interface. No new tool approvals needed.
9. Analytics workflows save massive time - Export Clarity data, upload to Cursor, prompt "analyze drop-offs and create visualizations." Takes 10 minutes vs hours of manual Excel work.
10. AI native PMs think in prompts - "What do I need to do? What are the steps? What tools will help?" Treat AI as an extension of yourself, not a separate tool to learn.
----
Where to Find Mike
* LinkedIn
* Youtube
* Website
----
Related Content
Newsletters:
* AI Product Strategy
* How to Build AI Products
* AI Agents for PMs
* Product Requirements Documents
Podcasts:
* AI Prototyping for PMs
* How to Become an AI PM
* Everything You Need to Know About AI
PS. Please subscribe on YouTube and follow on Apple & Spotify. It helps!


This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes, visit www.news.aakashg.com/subscribe

Today’s Episode
ChatGPT just made huge waves with its Atlas browser. Perplexity made waves before that with its Comet browser. And Atlassian just spent a billion dollars to buy Dia.
Big companies are making big moves in the AI browser space.
But should you use an AI browser? Is it safe? Will it make you more effective as a PM?
I asked this question at Berkeley last month during my keynote. Out of 500 PMs in the room, literally two hands went up.
That needs to change.
Naman Pandey has tested these browsers more extensively than anyone else. He runs the Ready Set Do podcast and has spent hundreds of hours finding the real use cases that actually work.
Today, we’re putting all three browsers head-to-head. Same prompts. Same tasks. Live demos.
You’ll see which browser wins for each use case, where they fall over, and the exact workflows to use them as a PM.
----
Check out the conversation on Apple, Spotify and YouTube.
Brought to you by:
* Jira Product Discovery: Plan with purpose, ship with confidence
* Mobbin: Discover real-world design inspiration
* Pendo: The #1 Software Experience Management Platform
* Product Faculty: Get $550 off the AI PM Certification with code AAKASH550C7
* Land PM job: 12-week experience to master getting a PM job
----
If you want access to my AI tool stack - Dovetail, Arize, Linear, Descript, Reforge Build, DeepSky, Relay.app, Magic Patterns, and Mobbin - for free, grab Aakash’s bundle.
Are you searching for a PM job? Join me + 29 others for an intensive 12-week experience to master getting a PM job. Only 14 seats left.
----
Key Takeaways:
1. AI agent browsers are underhyped for PMs - Only 2 out of 500 PMs at Berkeley were using them. If you're doing web research, competitor analysis, or data scraping, you're leaving hours on the table every week.
2. The three browsers serve different purposes - ChatGPT Atlas for deep research across multiple pages. Perplexity Comet for real-time quick lookups. Arc Dia for workflow automation. They're not competing head-to-head.
3. Atlas dominates data extraction - Scrape YC companies, find recruiters on LinkedIn, build competitor comparison tables. What took 2-3 hours now takes 10 minutes with one prompt.
4. Comet wins on speed for real-time info - Stock prices, sports scores, breaking news. It's the fastest by far. Perfect for quick research sprints across Reddit, Twitter, and news sites.
5. Dia automates repeated workflows - Monitor competitor pricing weekly. Document onboarding flows. Generate recurring reports. Set it once, let it run on schedule.
6. Tab context is the hidden superpower - Open 5 competitor sites. Ask "What's the common pricing strategy?" The AI reads all tabs and synthesizes insights. Eliminates copy-paste friction.
7. The job seeker use case is mind-blowing - "Find 20 PMs at Google, get their LinkedIn profiles, draft personalized DMs." Atlas does this in 15 minutes. Used to take 2-3 hours manually.
8. Onboarding analysis becomes trivial - "Go through Notion's signup flow, capture screenshots, document each step." Dia does this in 5-10 minutes. Perfect for competitive analysis.
9. Don't log into sensitive accounts - Banking, email, social media with private data - keep these in your regular browser. Use AI browsers only for public research and data extraction.
10. The slowness matters less than you think - Yes, they're slow compared to Google. But if the alternative is 2 hours of manual work, waiting 10 minutes is a massive win. Batch requests and walk away.
----
Related Content
Newsletters:
* AI Product Strategy
* How to Build AI Products
* AI Prototyping Tutorial
* How to Become an AI PM
* Ultimate Guide to Onboarding
Podcasts:
* How to Build ChatGPT Apps
* AI Prototyping for PMs
* Everything You Need to Know About AI
* AI Product Management Course
----
PS. Please subscribe on YouTube and follow on Apple & Spotify. It helps!
If you want to advertise, email productgrowthppp at gmail.


This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes, visit www.news.aakashg.com/subscribe

This is a free preview of a paid episode. To hear more, visit www.news.aakashg.com

Today’s Episode
When you first start using AI prototyping tools, you get wowed.
You type “create me a CRM application” and boom a fully functioning app appears in 60 seconds.
But here’s the problem.
It looks generic. The styling is basic. The features are vanilla. You’d never ship this to customers.
This is AI slop.
Sachin Rekhi was the former Head of Product at LinkedIn Sales Navigator. He’s now teaching thousands of PMs at Reforge how to master AI prototyping.
----
Brought to you by - Reforge:
Get 1 month free of Reforge Build (the AI prototyping tool built for PMs) with code BUILD
----
Key Takeaways:
1. Product shaping changes everything - Anthropic builds multiple prototypes for every problem, launches internally, sees what people use, then productionizes winners. This used to only be possible at Apple with massive labs.
2. AI slop is real - Type "create a CRM" and you get generic styling, vanilla features, basic scenarios. Looks magical but you'd never ship it. The challenge is going from slop to production-grade prototypes.
3. The 15-skill mastery ladder - Apprentice level: prompting, editing, design consistency. Journeyman: versioning, debugging, diverging. Master: functional prototyping, product shaping, analytics integration.
4. Design consistency starts with baselining - Take screenshot of your product. Recreate it. Iterate until perfect. Save as template. Now every prototype inherits your design system automatically.
5. Diverging is the secret weapon - Generate 4 design variants instead of 1. Magic Patterns has this built in. Or use multiple tools to get 8 options. Evaluate alternatives like designers do.
6. Functional prototypes unlock real validation - Integrate OpenAI API for actual responses. Add PostHog for session recordings and heatmaps. Build surveys. Track clicks. Test with real data, not mockups.
7. The tools face-off: which to actually use - Bolt for speed. V0 for beautiful UIs. Replit for full-stack. Magic Patterns for product teams with diverging. Reforge Build for context integration. Cursor for technical PMs.
8. The $5/month unlimited execution hack - Host n8n on Hostinger instead of paying per execution. Get unlimited runs. Build workflow that backs up to Google Drive for version history.
9. PMs can build what used to require engineering - Calendar integration. Email agents. Analytics dashboards. Multi-model comparison. Survey collection. All from prompts. No code required.
10. Traditional workflows beat agents for production - Workflows save tokens, run faster, and are more reliable. Use agents only when tasks need real decision-making. For known processes, use workflows.
----
Where to Find Sachin
* LinkedIn
* Newseltter
* X
* Youtube
* Reforge cources
----
Related Content
Newsletters:
* AI Product Strategy
* How to Build AI Products
* AI Prototype to Production
* AI Prototyping Tutorial
* Product Requirements Documents
Podcasts:
* AI Prototyping for PMs
* How to Become an AI PM
* Everything You Need to Know About AI
----
PS. Please subscribe on YouTube and follow on Apple & Spotify. It helps!
If you want to advertise, email productgrowthppp at gmail.

This is a free preview of a paid episode. To hear more, visit www.news.aakashg.com

Today’s Episode
ChatGPT Apps might be the next billion-dollar opportunity.
Or they might be another ChatGPT feature that gets abandoned in 6 months.
I genuinely don’t know yet.
But when people say “this could be the new App Store,” my ears perk up. I spent four years building an iOS app in the early days of the App Store. The distribution was incredible. We grew fast purely because of where we were.
So when OpenAI announced the ChatGPT App Store, I needed to understand it.
I brought in Colin Matthews to break it down. Colin is one of my go-to sources for technical product topics. Our AI prototyping collaborations have been some of your favorite episodes.
Today, we’re exploring ChatGPT Apps and what they mean for you as a product builder.
----
Check out the conversation on Apple, Spotify and YouTube.
Brought to you by:
* Maven: Get $500 off with my code on Coil Build ChatGPT Apps course
* Vanta: Automate compliance, Get $1,000 with my link
* Land PM job: 12-week experience to master getting a PM job
* Mobbin: Discover real-world design inspiration
* NayaOne: Airgapped cloud-agnostic sandbox
----
If you want access to my AI tool stack - Dovetail, Arize, Linear, Descript, Reforge Build, DeepSky, Relay.app, Magic Patterns, and Mobbin - for free, grab Aakash’s bundle.
Are you searching for a PM job? Join me + 29 others for an intensive 12-week experience to master getting a PM job. Only 23 seats left.
----
Key Takeaways:
1. ChatGPT apps = MCP + widgets - The Model Context Protocol (invented by Anthropic) lets AI agents call external tools. OpenAI added UI widgets on top to create embedded app experiences directly in chat.
2. 900M weekly active users = massive distribution opportunity - This is the new SEO. Early data shows 26% higher conversion from AI traffic vs traditional search. Every enterprise will eventually build here.
3. You're building for multiple platforms - MCP works across ChatGPT, Claude (coming soon), Cursor, and other AI tools. Build once, distribute everywhere. Gemini doesn't support it yet.
4. Apps get called based on tool descriptions - Your metadata matters. Like SEO but for LLMs. Run evals to test if correct prompts trigger your tools. Iterate on descriptions to improve discovery.
5. Three eval categories: direct, indirect, negative - Direct: user names your app. Indirect: user describes outcome. Negative: irrelevant request shouldn't trigger your tool. Test all three systematically.
6. PMs should prototype but engineers ship production - Use tools like Chippy to prototype quickly and test concepts. Show stakeholders real interactions. Engineering team builds the production version.
7. Enterprise-first, solo builders second - Large companies (Target, Uber, Canva) are early adopters chasing distribution. But huge opportunity for indie builders once public marketplace launches.
8. Best opportunities: embedded collaboration tools - Spreadsheets, task lists, whiteboards where ChatGPT can partner with you. Not just search results—actual interactive experiences.
9. Error analysis on observability logs is critical - Track what prompts triggered which tools with what parameters. Look for mismatches between expected and actual behavior. Iterate tool descriptions.
10. Marketplace launching by end of 2024/early 2025 - Currently only launch partners can publish. Public marketplace coming soon means anyone can ship apps and reach ChatGPT's massive user base.
----
Where to Find Colin
* LinkedIn
* Newsletter
Related Content
Newsletters:
* AI Prototyping Tutorial
* How to Build AI Products
* AI Product Strategy
* Complete Course: AI Product Management
Podcasts:
* AI Prototyping for PMs
* How to Become an AI PM
* Everything You Need to Know About AI
----
PS. Please subscribe on YouTube and follow on Apple & Spotify. It helps!
If you want to advertise, email productgrowthppp at gmail.