Today’s Episode

Designing with AI isn’t about prompting.

Most PMs think they understand AI design because they can write a good prompt. They’re wrong.

Real AI design is about understanding the entire workflow, the system, the constraints, and the behaviors.

Xinran Ma runs Design with AI, one of the top newsletters on AI design. He’s been studying AI design tools for three years. And he hasn’t shared most of this information publicly before.

In today’s episode, we’re going live. We’re building real prototypes. We’re showing you the exact workflows that top 1% designers use.

By the end of this episode, you’ll know the entire workflow from PRD to prototype to product.

----

Check out the conversation on Apple, Spotify and YouTube.

Brought to you by:

* NayaOne: Airgapped cloud-agnostic sandbox

* Pendo: The #1 software experience management platform

* Maven: The cohort-based course platform powering the future of learning

* Bolt: Ship AI-powered products 10x faster

* Gamma: Turn customer feedback into product decisions with AI

----

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.

----

Key takeaways:

Key Takeaways:1. AI design covers five areas not just prompts - Prompting, ideation, design/prototyping, workflows, and staying conscious. Most people think better prompts equal better design. That's just 20% of the skill.2. Use Google AI Studio for quick design variations - Upload 2-3 visual references. Describe what you want. Generate three different design directions in 5 minutes. What used to take 3-4 hours now takes 15 minutes.3. Lovable builds functional prototypes in seconds - Describe the experience you want to build. Lovable generates a working prototype in 60 seconds. Not mockups—actual clickable experiences you can test with users.4. Match tools to specific use cases - Custom GPT for effective prompts. Lovable for high-quality prototypes. Magic Patterns for design variations. Google AI Studio for free exploration. Cursor for full-stack experiences. Claude Code as all-purpose best.5. Good design passes four layers not just visual - Visual representation, problem-solving, design principles, and implementation feasibility. Most people stop at layer one. Great design works at all four layers.6. Context matters more than prompt length - Don't say "design a button." Say "design a primary CTA button for B2B SaaS onboarding where users connect calendar. Professional brand." Specificity drives quality.7. Visual references anchor AI output - Upload 2-4 screenshots showing the aesthetic you want. These show AI what "modern and minimal" means to you. The quality difference is massive versus text-only prompts.8. Iteration speed determines final quality - The magic isn't in the first output. It's in the 10th iteration after you've refined and tweaked. Review, identify issues, tell AI how to fix, repeat.9. Always validate with real users - AI tools make generating designs easy. Only users tell you if those designs actually help. Show prototypes to 3-5 users. Watch them try to use it.10. Workflows changed from linear to parallel - Before AI: sequential steps taking weeks. After AI: describe, generate, iterate freely. This is how top 1% designers work now.

----

Where to Find Xinran

* LinkedIn

* Newsletter

* Maven course

Related Content

Newsletters:

* AI Prototyping Tutorial

* AI Prototype to Production

* How to Build AI Products

* Prompt Engineering

* Product Requirements Documents

Podcasts:

* Advanced Guide to AI Prototyping with Sachin Rekhi

* 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.

Check out the conversation on Apple, Spotify and YouTube.

Brought to you by - Reforge:

Get 1 month free of Reforge Build (the AI prototyping tool built for PMs) with code BUILD

Today’s Episode

Ankit Shukla is BACK after his gangbusters episode, that is my #2 most popular of all time. This time he's diving deep on one of the most important new AI skills for PMs: Evals.

Whether you're working on AI features now or not, this is a skill you want to have an intuitive understanding of. So, I'm building on my library of eval episodes with today's drop.

I've never heard someone explain evals from first principles as intuitively as Ankit has with this one. Hope you enjoy as much as I did!

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.

Where to find Ankit Shukla

* HelloPM

* Twitter (X)

* LinkedIn

* YouTube

Related Content

Newsletters:

* AI Evals

* AI Testing

* LLM Judges

Podcasts:

* How to Do AI Evals Step-by-Step with Real Production Data

* The PM’s role in AI Evals

* AI Evals Live

PS. Please subscribe on YouTube and follow on Apple & Spotify. It helps!

email productgrowthppp at gmail dot com for sponsorships.

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.

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!

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.

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.

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.

Today’s Episode

Everyone’s demoing AI features. Few are shipping them to production reliably.

The gap? Evals.

Not the theoretical kind. The real-world kind that catches bugs before users do.

Hamel Husain and Shreya Shankar train people at OpenAI, Anthropic, Google, and Meta on how to build AI products that actually work. Their Maven course is the top-grossing course on the platform.

Today, they’re walking you through their complete eval process.

----

Brought to you by:

* The AI Evals Course for PMs & Engineers: You get $800 with this link

* Vanta: Automate compliance, Get $1,000 with my link

* Jira Product Discovery: Plan with purpose, ship with confidence

* Land PM job: 12-week experience to master getting a PM job

* Pendo: the #1 Software Experience Management Platform

----

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. AI evals are the #1 most important new skill for PMs in 2025 - Even Claude Code teams do evals upstream. For custom applications, systematic evaluation is non-negotiable. Dog fooding alone isn't enough at scale.

2. Error analysis is the secret weapon most teams skip - Looking at 100 traces teaches you more than any generic metric. Hamel: "If you try to use helpfulness scores, the LLM won't catch the real product issues."

3. Use observability tools but don't depend on them completely - Brain Trust, LangSmith, Arise all work. But Shreya and Hamel teach students to vibe code their own trace viewers. Sometimes CSV files are enough to start.

4. Never use agreement as your eval metric - It's a trap. A judge that always says "pass" can have 90% accuracy if failures are rare. Use TPR (true positive rate) and TNR (true negative rate) instead.

5. Open coding then axial coding reveals patterns - Write notes on 100 traces without root cause analysis. Then categorize into 5-6 actionable themes. Use LLMs to help but refine manually.

6. Product managers must do the error analysis themselves - Don't outsource to developers. Engineers lack domain context. Hamel: "It's almost a tragedy to separate the prompt from the product manager because it's English."

7. Real traces reveal what demos hide - Chat GPT said the assistant was correct but missed: wrong bathroom configuration, markdown in SMS, double-booked tours, ignored handoff requests.

8. Binary scores beat 1-5 scales for LLM judges - Easier to validate alignment. Business decisions are binary anyway. LLMs struggle with nuanced numerical scoring.

9. Code-based evals for formatting, LLM judges for subjective calls - Markdown in text messages? Write a simple assertion. Human handoff quality? Need an LLM judge with proper rubric.

10. Start with traces even before launch - Dog food your own app. Recruit friends as beta testers. Generate synthetic inputs only as last resort. Error analysis works best with real user behavior.-

---

Where To Find Them

* LinkedIn:

* Hamel: Hamel’s LinkedIn

* Shreya: Shreya’s LinkedIn

* AI Evals Course: World’s best AI Evals Course (You get $800 off with this link)

Related Content

Newsletters:

* AI Evals

* AI PM Observability

* AI Testing

* LLM Judge

* AI Prototype to Production

* AI Product Strategy

* How to Build AI Products

* Prompt Engineering

Podcasts:

* AI Evals: Everything You Need to Know to Start

* Everything you need to know about AI (for PMs and builders)

* Carl Vellotti on Claude Code

* Marily Nika on Google AI PM Tool Stack

* Pawel Huryn on n8n for PMs

PS. Please subscribe on YouTube and follow on Apple & Spotify. It helps!

Today's Episode

Carl Vellotti is back.

His first episode hit 30k views. My most popular episode in months.

Today, he’s giving you the advanced playbook for Claude Code.

Claude Code hit $1 billion ARR in December. Fastest product ever to reach that milestone. Under 6 months.

Carl has been building with Claude Code every single day. He’s made all the mistakes. He’s learned all the expert workflows and tips.

In today’s episode, he shares everything.

----

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.

----

Brought to you by:

Amplitude: The market-leader in product analytics

Pendo: the #1 Software Experience Management Platform

Jira Product Discovery: Move discovery and roadmapping out of spreadsheets

Miro: The innovation workspace

----

Key Takeaways

1. Claude Code hit $1B ARR in 6 months - fastest product ever. Anthropic optimized for power users doing deep work. Fewer users, insanely deep usage. Carl: "I spend all day in Claude Code. It gets to the point where you never really have to leave."

2. MCPs connect Claude to everything in 2 minutes - Model Context Protocol invented by Anthropic. One command to install Linear, Google Workspace, Slack. Essential stack: documents first, task management second, communication third, data sources fourth.

3. End-to-end PM workflow takes one morning instead of one week - Survey creation → analysis → PRD → presentation → 19 tickets. All through MCPs. Carl didn't use templates. Opus 4.5 handled everything.

4. Skills are pre-built templates but don't always auto-trigger yet - Solution: specify explicitly. "Create presentation using your presentation skill." Build Skills for anything you do more than twice. That's the compound effect.

5. Real Google Slides, not images - Carl's presentation skill created 19 fully editable slides from PRD. Takes a few minutes because Claude creates slides in parallel. "This is saving you hours. We used to spend entire afternoons turning PRDs into decks."

6. GitHub turns Claude into remote worker - Create issues from your phone. @mention Claude. It works while you're away. Carl's journal is a private GitHub repo. Voice transcriptions → issues → Claude updates markdown.

7. Workflows beat agents for production - Level 1 workflow: 5,000 tokens, 40 seconds. Level 3 agent: 90,000 tokens, 90 seconds. Carl's rule: "If something can be expressed as code, code it. Leave agents for cognitive work."

8. Production best practices separate hobbyists from pros - Set error workflows. Add retry logic (3 attempts, 1-second delays). Pin data during development. Use reasoning effort appropriately. Write detailed tool descriptions.

9. Real challenge is building reusable workflows - Learning basics takes a day. First workflow takes hours. But having 50 workflows built over months means everything is automated. Carl: "Because I have workflows for all these things already built, I can do it all in one shot."

10. Tools improve faster than you can learn them - Carl: "By the time you see this, there might be Claude 5." Your workflows from today will 10x when better models drop. PMs who started 6 months ago aren't just 6 months ahead, they're 100x ahead because systems compound with every improvement.

----

Where to Find Carl Vellotti

* Linkedin

* X (Twitter)

* Instagram

* Newsletter

Related Content

Newsletters:

* How to Use Claude Code Like a Pro

* Steal 6 of My Claude Skills

* Claude Skills Tutorial

* AI Agents: The Ultimate Guide for PMs

* Practical AI Agents

Podcasts:

* The Ultimate Guide to n8n for PMs, with Pawel Huryn

* This is what a Google AI PM’s Tool Stack Looks Like, with Marily Nika

* How to Use Claude Code Like a Pro (Carl’s First Episode)

----

PS. Please subscribe on YouTube and follow on Apple & Spotify. It helps!

If you want to advertise, email productgrowthppp at gmail.

Marily Nika and I filmed my 7th most popular episode a year ago.

She broke down everything on how to become an AIPM because she’s done it. She is an AIPM at Google with 11 years of experience.

Today, she’s back with a completely new episode. She’s dropping every AI tool she uses daily as an AIPM.

Not theory. Not hype. The actual 6 tools she uses multiple times every single day.

Today she’s dropping all the expert tips and tricks. She’s made the mistakes so you don’t have to.----Today’s guide covers:

* The AI PM Tool Stack

* Google AI Studio for Prototyping

* Opal for Mini Apps

* Notebook LM for Domain Expertise

* Perplexity for Reddit Research

* ChatGPT for Your Voice

* Fireflies and Getting Tools at Work

* The 18-Month AIPM Roadmap

* The AIPM Interview Red Flags

----Check out the conversation on Apple, Spotify and YouTube.

Brought to you by:

* Jira Product Discovery: Move discovery and roadmapping out of spreadsheets

* Vanta: Automate compliance across 35+ frameworks

* Maven: Improve your PM skills with awesome courses. Discount with my link

* Mobbin: Discover real-world design inspiration

* Product Faculty: Get $500 off the AI PM Certification with code AAKASH25----

Key Takeaways

1. Marily's stack is just 6 tools - Google AI Studio for prototyping, Opal for mini apps, Notebook LM for learning, Perplexity for user research, ChatGPT for PRDs, and Fireflies for meetings. Each maps to one PM workflow. No tool hopping.

2. Prototype before writing PRDs. Marily builds a working app in AI Studio first, then brings engineers in to debate the actual thing. Saves weeks of back-and-forth on documents. PRDs are now for complex cross-functional work only.

3. Notebook LM learned a 4-hour video in 15 minutes. Marily had an interview the next day. She uploaded the job description and a 4-hour investor relations video. It gave her 15 key points. She memorized them and crushed the interview.

4. Perplexity's Reddit filter is your secret weapon. Turn off web search, turn on discussions and opinions. Ask "would young professionals be interested in a fitness ring?" Get 20+ Reddit sources instantly. Know what users actually want.

5. Marily's PRD generator has 10,000+ users. She trained a ChatGPT custom GPT with her voice and old PRDs. It asks probing questions before generating. Gets PRDs done in days instead of weeks. She's not embarrassed to use AI at work.

6. Tool selection has 4 rules. Does it save 10x time (not 2x)? Does it work across contexts? Does it work with your company's limitations? Does it compound over time? If not all 4, delete it.

7. "Be like a crab" for AIPM roles. Move adjacent to your experience. Hearing aids → AirPods PM. ESPN journalism → Meta sports AI PM. Don't ignore your past experience. It's your competitive advantage.

8. Red flag in AIPM interviews - missing PM craft. Ex-ML scientists dive into algorithms without asking why, who, and how to measure success. AI changes HOW you fulfill use cases, not the use cases themselves.

9. Who replaces PMs? Not AI. PMs who use AI when you're not. Don't be embarrassed about using AI at work. It's not cheating. The gap is widening between PMs who use these tools and those who don't.

10. Every PM will become an AIPM by 2026. Show Marily any product and she'll find the AI use case. Even retail stores use cameras and AI to monitor which areas get traffic. The AIPM title will just become PM.

----

Where to Find Marily

* LinkedIn

* Twitter

* Website

* YouTube

* Newsletter

* Maven - Her course is part of my special curation with Maven

----

Related Content

Newsletters

* AI Foundations for PMs

* How to Become an AIPM

* AI Prototyping for PMs

* AI PRDs

* The Complete AI PM Course

* AI Product Strategy

* ChatGPT for PMs

Podcasts

* Pawel Huryn on n8n

* Alex Danilowicz on AI Prototyping

* Hamza Farooq on Landing AIPM Jobs

* Todd Olson on Upskilling to AIPM

* Rachel Wolan on AI Product Leadership----

If you want access to AI tools for free, get Aakash’s bundle and access to 9 AI products for an entire year.

Are you searching for a PM job? Join me + 29 others for an intensive 12-week experience to master getting a PM job.

----

PS. Please subscribe on YouTube and follow on Apple & Spotify. It helps!If you want to advertise, email productgrowthppp at gmail.