The Growth Podcast

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.
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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
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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.
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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.
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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
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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

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.


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
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.
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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
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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.
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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
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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.
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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.
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Check out the conversation on Apple, Spotify and YouTube.
Brought to you by Linear: Plan and build products like the best.
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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.
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Where to Find Mike
* LinkedIn
* Youtube
* Website
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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.
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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
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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.
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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.
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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
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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