Product Growth Podcast

Aakash Gupta
  • 4.7（27則評分）
  • 科技
  • 每週更新兩次

The latest insights into how great products grow, how to be a better PM or product leader, and how to get a PM job. www.news.aakashg.com

  1. 3 天前

    He built the top AI agent startup | Flo Crivello, Former PM, now CEO & Founder, Lindy AI

    If you’ve ever said “I just wish I had an assistant who knew exactly how I think”... Lindy is that assistant. These agents aren’t demos. They’re real, customizable workflows anyone can build. No code. Flo Crivello (founder of Lindy and ex-Cruise/YC) joined us to show how his personal AI stack runs his entire workday: From triaging emails, summarizing meetings, blocking spam, managing contacts, and even sourcing candidates. We’re not talking theory here. You’ll see what’s possible today (no prompting skills, no code), just real agents doing real work when you give them instructions in plain English Language. If you’ve ever wondered: “What can AI actually do for me right now?” This episode answers it - line by line, screen by screen. ---- Brought to you by: Mobbin: Discover real-world design inspiration Jira Product Discovery: Build the right thing Product Faculty: #1 AI PM Certification (Class Starts: 15 Sep, get $500 off) ---- Timestamps: AI Agents Can Replace Your Team - 00:00:00 The Top 5 AI Agents Every Entrepreneur Needs - 00:03:13 The Golden Framework: When to Build an Agent - 00:08:50 Keeping AI Agents Safe Without Killing Innovation - 00:09:42 From Doer to Orchestrator: The New Management Mindset - 00:11:08 Lindy vs ChatGPT: Individual Tools vs Work Platforms - 00:13:23 Managing Agent Performance, Permissions & Costs - 00:14:26 AD: Mobbin - 00:15:56 AD: Jira Product Discovery - 00:16:55 Managing Context and Token Costs - 00:17:51 Lindy vs Competitors and Zapier - 00:18:58 Inside Lindy's 5x Growth in 6 Months - 00:20:38 The Salesperson Managing 40 AI Employees - 00:21:45 The Soham Scandal: Hiring the 5-Job Engineer - 00:23:20 From PM to AI Visionary: The Founding Story - 00:28:24 AD: Maven - 00:31:21 AD: AI PM Certification - 00:32:08 The Pivot Philosophy: Action Produces Information - 00:32:54 Should Every PM Become an AI Founder? - 00:37:17 Talk to Customers, Build Product: Skip Everything Else - 00:38:45 The Valley of Death: Surviving the Hardest Pivot - 00:41:02 The AI Agent Agency Gold Rush - 00:44:08 Big Tech is Missing the AI Agent Revolution - 00:47:42 The 10-Year Vision: Fully Autonomous Companies - 00:48:01 ---- Key Takeaways: Takeaways: 01. Most startups are stuck in execution mode, when they should be in vision mode. Instead of spending 80% of time shipping and 20% dreaming, it should be the reverse. The best early-stage companies obsess over the product like artists—not analysts. That’s how you create something magnetic from day one. Not with OKRs. With taste. 2. AI agents aren’t just features, they’re teammates. The real unlock with agents is that they collaborate like coworkers. You don’t “use” them, you talk to them. You delegate. They respond. They improve. And suddenly you’re not just automating—you’re offloading real cognitive work. 3. You don’t build AI, you build trust. That’s the actual job. Trust is earned slowly: confirmation prompts, human-in-the-loop controls, clear audit trails. Give users the steering wheel first… and gradually ease them into self-driving. That’s how agents move from novelty to necessity. 4. The smaller the scope, the sharper the tool. Most teams overbuild. The smarter play is to shrink the surface area until there’s zero ambiguity. That’s when you get magic: crisp execution, no confusion, and fast iteration. Think razor blade, not Swiss Army knife. 5. From systems thinking to obsessive craft. Coming from Uber taught how to reason through complex marketplaces. But what unlocked breakthrough product thinking was obsessing over the UX like an artist—not just designing for logic, but for love. 6. The future of work is voice → delegation → done. You won’t click through dashboards or jump tabs. You’ll just say, “Handle my recruiting outreach,” and it’ll happen. Behind the scenes: agents coordinating tasks, tracking progress, personalizing copy. And all you did was ask. 7. Write Like Your Career Depends on It, Because It Does. Clarity of thought = clarity of writing. He said the best PMs he’s worked with are excellent writers. Not because it looks good, but because it reflects structured thinking. 8. Product Sense Is a Muscle. He builds product by imagining it from the user’s emotional POV. Not “What features should we ship?” but “What would delight the user in this moment?” 9. You Can't Delegate Taste. No matter how senior you are, if you're not involved in the details of product quality, you’ll lose the magic. He reviews designs himself, edits copy, and obsesses over UX, because product taste is not outsourceable. 10. Go Where Product Is Sacred. A PM’s growth is tied to the culture. He picked Uber because product rigor was high. At Lindy, he made product obsession part of the DNA. If your company doesn’t value product deeply, leave. ---- Check out the conversation on Apple, Spotify and YouTube. ---- Related Podcasts: We Built an AI Employee in 62 mins (Cursor, ChatGPT, Gibson, Crew AI) Bolt Tutorial from the CEO This $20M AI Founder Is Challenging Elon and Sam Altman | Roy Lee, Cluely ---- P.S. More than 85% of you aren't subscribed yet. If you can subscribe on YouTube, follow on Apple & Spotify, my commitment to you is that we'll continue making this content better. ---- 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

    53 分鐘

  2. 8月12日

    Teresa Torres' Step-by-Step Guide to AI Product Discovery

    In today’s episode, we have one of the two voices I wanted most when I started this podcast: Teresa Torres. Alongside Marty Cagan, she was in my top guests to have. That’s because she has trained over 17,000 PMs in 100 countries. And in today’s episode, she’s breaking down one of the most important elements of PMing: discovery. She gives a masterclass on how to use the learnings from her smash hit book Continuous Discovery Habits for the AI age, covering both: 1. How to do discovery for non-AI features with AI tools 2. How to discovery for AI features If you’ve ever wondered why your product ideas sometimes flop, even when the interviews and research looked promising… you’re about to find out why! ---- Brought to you by: Miro: The innovation workspace Jira Product Discovery: Build the right thing Parlance Labs: Practical consulting that improves your AI Product Faculty: #1 AI PM Certification (Class Starts: 15 Sep, get $500 off) ---- Timestamps: Teresa's Background - 0:00 Story-Based Interviewing - 3:20 Fake Discovery Signs - 4:08 Assumption Testing - 4:39 Continuous Discovery Framework - 5:35 AI Changes Discovery - 8:01 AI Synthesis Concerns - 9:21 AI Prototyping Era - 12:45 Ads - 15:45 AI Prototyping Workflow - 17:32 Common Interview Mistakes - 22:24 Interview Synthesis - 24:26 OST Updates - 28:53 Discovery Theater - 30:52 Ads - 32:15 Real Product Management - 34:03 AI Product Discovery - 35:29 Context Engineering - 39:16 Orchestration Explained - 42:03 Error Analysis - 46:01 Observability & Traces - 46:05 Claude Code Demo - 49:15 Business Numbers - 52:56 ---- Key Takeaways: Takeaways: 01. Stop Shipping Blind. Your AI product isn't truly valuable until you validate it. Go beyond just building; understand user needs deeply with personas, journey maps, and jobs-to-be-done. 02. MOM Test = Your Secret Weapon. The "MOM Test" is about asking questions that even your most supportive friend can't lie about. Don't ask if users "would" use your AI. Ask about their past behaviors and real problems. This helps you define success metrics and avoid building a fancy toy nobody needs.  03. Evaluate Everything, Relentlessly. AI Evals are not just a technical task for engineers, but the most critical tool for Product Managers to build high-quality, trustworthy AI products. Use them to understand, refine, and continuously improve your AI. 04. Passion Won't Land the Job. Proof Will. "I'm passionate"...great I guess, but recruiters want to see what you've done. Your portfolio is your direct line to showing you can actually do the job. 05. Build Your AI Portfolio. Now. Don't wait for experience. Create product teardowns of AI tools, develop case studies, or launch small side projects. This is your living, breathing proof of thinking and skill. 06. Forget the Resume. Add Value. The ultimate job hack? Identify a problem at a target company and propose a solution, or even build a prototype before you apply. This showcases initiative and concrete skills. 07. You’re At Fault (Brutal, I Know). Nailing Prompt Engineering is a direct path to better AI outputs. If your AI misbehaves, it's often your fault for unclear instructions. Refine your prompts for smarter, more reliable AI. 08. Generic Resumes In The Bin! Forget sending generic resumes into the void. There are three distinct approaches: just a resume, adding a portfolio and cover letter, or the ultimate "Value Add" where you solve a company's problem before applying.  09. AI Will Do Your Dishes (Metaphorically). While AI Agents promise incredible autonomy and action, remember they still need clear goals and defined tasks. So, while your AI PM dream is big, maybe don't expect it to clean your dishes (yet) – stick to email automation for now! 10. Don't Trust LLMs Blindly. LLMs are powerful. But they need continuous human validation and evaluation frameworks. Automate grading where possible, but always, always, have a human in the loop for critical judgment. ---- Check out the conversation on Apple or Spotify and the demo on YouTube. ---- Related Podcasts: AI Product Discovery: Complete Course How to Do Product Discovery Right with Pawel Huryn Marty Cagan on the 4 Key Risks and Importance of Discovery How to Survey and Learn From Your Users with George Harter ---- P.S. More than 85% of you aren't subscribed yet. If you can subscribe on YouTube, follow on Apple & Spotify, my commitment to you is that we'll continue making this content better. ---- 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

    56 分鐘

  3. 8月9日

    Building in Public: The 7 AI Tools I'm Using in My $1M+/Yr Business

    I made $1M in the last 12 months — with zero full-time employees. In this podcast, I’ll share and show you (watch the YT version) the 7 AI workflows that helped me scale faster, save over $400K in costs, and launch multiple income streams, all powered by AI agents. You’ll see exactly how I use tools to: - Automate my inbox - Build and ship SaaS prototypes - Write and produce video ads - Grow a podcast to 50K+ listeners/episode - Repurpose and distribute content across platforms - Book high-profile guests - Run a content engine with zero human ops ---- Brought to you by: Miro: The innovation workspace is your team’s new canvas Jira Product Discovery: Build the right thing Mobbin: Discover real-world design inspiration Parlance Labs: Practical consulting that improves your AI ---- Timestamps: Preview – 00:00:00 AI Workflow 1 (Zapier) – 00:00:48 AI Workflow 2 (v0) – 00:03:13 AI Workflow 3 (Cursor) – 00:08:55 AI Workflow 4 (v0.3) – 00:12:04 AI Workflow 5 (Lindy) – 00:16:32 AI Workflow 6 (Riverside) – 00:19:03 AI Workflow 7 (Claude Copilot) – 00:22:47 Summarizing Everything – 00:27:52 ---- Check out the conversation on Apple, Spotify and YouTube. ---- Related Podcasts: This PM Built a Six-Figure ($100K+) AI Side Hustle He Built a $2M/Yr One-Person Business - Steal His Playbook This PM was Laid Off - Now he has 125K followers Her Layoff Went Viral - Now She has 300K+ Subscribers How I make $18K/mo with a niche podcast (STEAL THIS) This Ex Amazon VP Makes $950K In Retirement ---- P.S. More than 85% of you aren't subscribed yet. If you can subscribe on YouTube, follow on Apple & Spotify, my commitment to you is that we'll continue making this content better. ---- 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

    31 分鐘

  4. 7月27日

    Full Roadmap: Become an AI PM in 2025 | Ankit Shukla, HelloPM Founder

    If this 80 minutes podcast doesn't make you an AI PM, I'll delete my channel! Yes, I know what I just said. But this is too good! You’ll learn: - How to become an AI PM without any tech experience - 2 main categories of AI PMs and how to break into each - How to build great AI products your users love - All concepts like Evals, RAG, MCP, etc - Creating AI PM portfolio And so much more ---- Brought to you by: The AI Evals Course for PMs & Engineers: You get $800 with this link Maven: Get $100 off my curation of their top courses AI PM Certification: Get $500 with code AAKASH25 ---- Timestamps: Two Types of PMs in the World Right Now – 00:00:00 How Much AI PMs Make in the US & India – 00:01:49 Why Jobs Aren’t Marketed as “AI PM” Roles – 00:04:19 Live Slide Share Session Begins (AI PDLC) – 00:05:35 What People Get Wrong About AI PM Jobs – 00:08:51 Why AI Product Management Is Here to Stay – 00:11:00 Product Development Lifecycle Explained – 00:12:35 Ad 1: AI Evaluations – 00:14:47 Ad 2: Maven Courses – 00:15:46 Understanding PM Canals (Idea Sources) – 00:16:34 How AI Will Transform the Traditional Product Cycle – 00:18:55 The Two Branches of AI: Predictive vs Generative – 00:23:10 Diving Deeper into Generative AI – 00:27:17 Ad 3: AI PM Certification – 00:28:47 How to Build Your Own AI Use Case Database – 00:29:34 Why You Shouldn’t Use AI for Everything – 00:34:10 Understanding Problem Space vs Solution Space – 00:36:51 Most Common Mistake People Make Entering AI PM – 00:40:49 The Building Blocks of AI – 00:47:01 Contextualization (Prompts, Fine-Tuning, RAG) – 00:50:24 The Limitations of AI — Why You Still Need Evals – 00:56:18 Case Study: AI-First Job Search Website – 00:58:49 Prompt Engineering Breakdown for the Case Study – 01:02:09 Understanding and Leveraging AI Agents – 01:03:40 Introducing the MCP Framework – 01:08:07 How to Build Your AI PM Portfolio – 01:11:37 Your Next 7 Steps to Becoming an AI PM – 01:16:04 Closing Thoughts and Final Notes – 01:18:41 ---- Key Takeaways: Takeaways: 01. Stop Shipping Blind. Your AI product isn't truly valuable until you validate it. Go beyond just building; understand user needs deeply with personas, journey maps, and jobs-to-be-done. 02. MOM Test = Your Secret Weapon. The "MOM Test" is about asking questions that even your most supportive friend can't lie about. Don't ask if users "would" use your AI. Ask about their past behaviors and real problems. This helps you define success metrics and avoid building a fancy toy nobody needs.  03. Evaluate Everything, Relentlessly. AI Evals are not just a technical task for engineers, but the most critical tool for Product Managers to build high-quality, trustworthy AI products. Use them to understand, refine, and continuously improve your AI. 04. Passion Won't Land the Job. Proof Will. "I'm passionate"...great I guess, but recruiters want to see what you've done. Your portfolio is your direct line to showing you can actually do the job. 05. Build Your AI Portfolio. Now. Don't wait for experience. Create product teardowns of AI tools, develop case studies, or launch small side projects. This is your living, breathing proof of thinking and skill. 06. Forget the Resume. Add Value. The ultimate job hack? Identify a problem at a target company and propose a solution, or even build a prototype before you apply. This showcases initiative and concrete skills. 07. You’re At Fault (Brutal, I Know). Nailing Prompt Engineering is a direct path to better AI outputs. If your AI misbehaves, it's often your fault for unclear instructions. Refine your prompts for smarter, more reliable AI. 08. Generic Resumes In The Bin! Forget sending generic resumes into the void. There are three distinct approaches: just a resume, adding a portfolio and cover letter, or the ultimate "Value Add" where you solve a company's problem before applying.  09. AI Will Do Your Dishes (Metaphorically). While AI Agents promise incredible autonomy and action, remember they still need clear goals and defined tasks. So, while your AI PM dream is big, maybe don't expect it to clean your dishes (yet) – stick to email automation for now! 10. Don't Trust LLMs Blindly. LLMs are powerful. But they need continuous human validation and evaluation frameworks. Automate grading where possible, but always, always, have a human in the loop for critical judgment. ---- Check out the conversation on Apple or Spotify and the demo on YouTube. ---- Related Podcasts: If you only have 2 hrs, this is how to become an AI PM How to Become, and Succeed as, an AI PM | The Marily Nika Episode Complete Course: AI Product Management Tutorial of Top 5 AI Prototyping Tools ---- P.S. More than 85% of you aren't subscribed yet. If you can subscribe on YouTube, follow on Apple & Spotify, my commitment to you is that we'll continue making this content better. ---- 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

    1 小時 21 分鐘

  5. 7月19日

    How Linear Built a $1.25B Unicorn with Just 2 PMs

    Nan Yu shares Linear's proven product-building method that helped them took Linear to a $1.25B product with just 2 PMs. You’ll learn: - How top companies like Linear are actually building AI features - Secrets to building a lean product that users genuinely love - Nan Yu’s advice for landing top PM jobs. - The Linear method in detail ---- 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 AI PM Certification: Get $500 with code AAKASH25 Maven: Get $100 off my curation of their top courses ---- Timestamps: Preview – 00:00:00 Why are all the GIANTS using Linear? – 00:02:09 Linear's Core Functionality – 00:04:07 Ad (AI Evals) – 00:04:44 Ad (Vanta) – 00:05:44 Linear Method: How They Build Product – 00:06:36 Principles of the Linear Method – 00:09:16 Saying No to Busy Work / Busy Work Is a Result of “Lack of Clarity” – 00:12:14 Linear's Roadmap Planning Horizon – 00:14:43 Ad (AI PM) – 00:15:06 Ad (Maven) – 00:15:53 Planning Process – 00:16:41 Linear's Take on OKRs – 00:19:13 Setting The Bar High – 00:26:06 Story: Building The Smallest Version Possible – 00:28:49 Public Roadmaps are DANGEROUS – 00:34:20 Applying Linear Method to AI Features (Agents as Users) – 00:38:18 Mantra: Solve Real Problems for Real People – 00:43:08 Landing a Head of Product Role at Linear – 00:46:08 Story: How the Initial Contact with Linear Happened – 00:48:16 Advice for Aspiring PMs at Linear – 00:49:50 Making it to unicorn status with 2 PMs & 40 engineers – 00:53:18 Everlane's Pricing Strategy (Cost-Plus) – 00:53:44 Shift from B2C Retail to Product-Led B2B SaaS – 00:55:10 Can Today's PMs Shift Industries? – 00:57:22 Closing Notes – 00:59:49 ---- Key Takeaways: Takeaways: 1. Don't Chase AI Hype, Solve Real Pain: Instead of building flashy AI features (that no one needs), Linear observes where users are actually struggling (like juggling many AI tools for async work) and builds direct solutions. It's about fixing real problems, not just jumping on trends. 2. Your Users Aren't a Feature Checklist. Listen intently to user feedback, but don't take every request literally. The goal is to understand their core frustrations and goals, not just to say "yes" to every demand that could lead to bloat. 3. Purpose Over PR Stunts. Forget building the "loudest possible feature." Linear's core mantra is to solve real problems for real people. Focus on making the most useful thing, and the noise will follow. 4. Run Marathons, Not Sprints. Sustainable momentum beats endless "sprints" every DAMN time. Find a comfortable pace you can maintain not just tomorrow, but a year from now. Burnout is a warning sign, not a badge of honor. 5. Scope Small, Ship Often. Unshipped code is debt. By making tasks as tiny as possible, you get the reward of shipping constantly. This creates tangible proof of work, gets immediate user feedback, and keeps the motivation high. 6. Quality Isn't a Side Quest. Bugs aren't "minor." They're either critical or high priority. A "little bug" seen a hundred times tells customers you don't care. Quality work is baked into every feature, because a feature isn't done until it's high quality. 7. Clear Talk, Clear Work. Ban jargon. LITERALLY. Words should have one meaning. If "issue" refers to a task in your product, it only refers to that. This ruthless clarity eliminates misunderstandings and speeds up collaboration. 8. Ditch Old-School User Stories. Traditional "As a user, I want X" stories often hide context and slow things down. Everyone understands software now. Cut the BS. Be direct and state the desired outcome clearly.  9. Build to Learn, Not to Delay. Excessive data collection or endless A/B tests shows lack of decision making. Instead, build a smaller version of your idea quickly, put it out there, and let the market tell you if you're right or wrong. Learn fast, move faster. 10. Forget Personas, Find Mike. Stop building for abstract personas. Identify the real people you're working with – "Mike from Brex," "Sarah from Acme Corp." Knowing them by name deepens empathy and leads to better solutions. ---- Check it out on Apple, Spotify, or YouTube. ---- Related Podcasts: Complete Course: AI Product Discovery "Most Product Managers are Bullsh*t Managers" -2x CPO ---- P.S. More than 85% of you aren't subscribed yet. If you can subscribe on YouTube, follow on Apple & Spotify, my commitment to you is that we'll continue making this content better. ---- 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

    1 小時

  6. 7月11日

    The PM’s Role in AI Evals: Step-by-Step

    Today, we’ve got some of our most requested guests yet: Hamel Husain and Shreya Shankar, creators of the world’s best AI Evals cohort. You’ll learn: - Why AI evaluations are the most critical skill for building successful AI products - What common mistakes people are making and how to avoid them - How to effectively "hill climb" towards better AI performance If you're building AI features, or aiming to master how AI Eval actually works, this episode is your step-by-step blueprint. ---- Brought to you by: The AI Evals Course for PMs & Engineers: You get $800 with this link Jira Product Discovery: Plan with purpose, ship with confidence Vanta: Automate compliance, security, and trust with AI (Get $1,000 with my link) AI PM Certification: Get $500 with code AAKASH25 ---- Timestamps: 00:00:00- Preview 00:02:06 - Three reasons PMs NEED evals. 00:04:40 - Why PMs shouldn't view evals as monotonous 00:06:23 - Are evals the hardest part of AI products solved? 00:07:37 - Why can't you just rely on human "vibe checks"? 00:12:11 - Ad 1 (AI Evals Course) 00:13:10 - Ad 2 (Jira Product Discovery) 00:14:06 - Are LLMs good at 1-5ratings? 00:15:45 - The "Whack-a-mole" analogy without evals 00:16:26 - Hallucination problem in emails (Apollo story) 00:21:22 - How Airbnb used machine learning models? 00:23:56 - Evaluating RAG Systems. 00:29:52 - Ad 3 (Vanta) 00:30:56 - Ad 4 (AIPM Certification on Maven) 00:31:42 - Hill Climbing 00:35:51 - Red flag: Suspiciously high eval metrics 00:39:02 - Design principles for effective evals 00:42:42 - How OpenAI approaches evals 00:44:39 - Foundation models are trained on "average taste" 00:49:36 - Cons of fine-tuning 00:51:27 - Prompt engineering vs. RAG vs. Fine-tuning 00:53:00 - Introduction of "The Three Gulfs" framework 00:56:04 - Roadmap for learning AI evals 01:01:41 - Why error analysis is critical for LLMs 01:08:29 - Using LLM as a judge 01:10:15 - Frameworks for systematic problem-solving in labels 01:17:42 - Importance of niche and qualifying clients. (Pro tips) 01:18:43 - $800K for first course cohort! 01:20:15 - Why end a successful cohort? 01:25:49 - GOLD advice for creating a successful course 01:33:39 - Outro ---- Key Takeaways: 1. Stop Guessing. Eval Your AI. Your AI isn’t an MVP without robust evaluations. Build in judgment — or you’re just shipping hope. Without evaluation, AI performance is a happy accident. 2. Error Analysis = Your Superpower. General metrics won’t save you. You need to understand why your AI messed up. Only then can you fix it — not just wish it worked better. 3. 99% Accuracy is a LIE. Suspiciously high metrics usually mean your evaluation setup is broken. Real-world AI is never perfect. If your evals say otherwise, they’re flawed. 4. Fine-Tuning is a Trap (Mostly). Fine-tuning is expensive, brittle, and often unnecessary. Start with smarter prompts and RAG. Only fine-tune if you must. 5. Your Data’s Wild. Understand It. You can’t eyeball everything. Without structured evaluation, you’ll drown in noise and never find patterns or fixes that matter. 6. Models Fail to Generalize. Always. Your AI will break on new data. Don’t blame it. Adapt it. Use RAG, upgrade inputs, and stop expecting out-of-the-box magic. 7. OpenAI Doesn’t Get Your Vibe. Their models are average-taste. Your product isn’t. If you want your brand’s voice in your AI, you must define it yourself — with evals. 8. Trust LLM Judges... but validate them hard. LLMs can scale your evals — but you still need to verify them against human-labeled data. Don’t blindly trust your judge. 9. Your Prompts Are S**T. If your AI is bad, it’s probably your fault. The cheapest, most powerful fix? Sharpen your prompts. Clearer instructions = smarter AI. 10. Let AI Teach You. Seriously. LLM judges aren’t just scoring you — they can teach you. Reviewing how your AI fails is the best way to learn what great outputs should look like. ---- Check it out on Apple, Spotify, or YouTube. ---- Related Podcasts: Complete Course: AI Product Management Tutorial of Top 5 AI Prototyping Tools If you only have 2 hrs, this is how to become an AI PM College Dropout Raised $20M Building AI Tools | Cluely, Roy Lee Bolt CEO and Founder on How he Hit $30M ARR in a Year LogRocket CEO and Founder on How to Build a $100M+ AI Startup Amplitude CEO and Founder on Building the Product Analytics Leader ---- P.S. More than 85% of you aren't subscribed yet. If you can subscribe on YouTube, follow on Apple & Spotify, my commitment to you is that we'll continue making this content better. ---- 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

    1 小時 35 分鐘

  7. 7月8日

    AI Product Discovery: Complete Course

    We could talk a million things with Tanguy Crusson but I’m keeping it to what I like the most about his work - Product Discovery. He shares a rare, behind-the-scenes look at how his team at Jira Product Discovery uncovers real user problems, validates solutions quickly, and avoids wasting time on ideas that won’t land. ---- Brought to you by: Jira Product Discovery: Plan with purpose, ship with confidence Product Faculty: Get $500 off the AI PM certification with code AAKASH25 Vanta: Automate compliance, security, and trust with AI (Get $1,000 with our link) AI PM Certification: Get $500 off Miqdad Jaffer (of OpenAI)’s certification ---- Timestamps: Preview & Intro — 00:00:00 Has AI Actually Changed Product Discovery? — 00:00:27 Ad: Jira Product Discovery — 00:10:50 Ad: AIPM Certification with OpenAI PM — 00:11:45 Where AI Truly Helps in Discovery (Real Use Cases) — 00:12:32 Watch the Live Demo on YouTube for Full Context — 00:14:34 The Discovery Process (Deep Dive) Phase 1 – Wonder: How Atlassian Uncovers Real Problems — 00:16:51 Why Tanguy Hasn’t Written a PRD in 5 Years (+ Wild Engineer Story) — 00:21:04 How to Lead Great User Interviews (Tips from a UX Researcher) — 00:25:24 Ad: Vanta Compliance & Security — 00:28:30 Ad: AI Evals for PMs & Engineers — 00:29:34 The Right Way to Structure Discovery Documents — 00:30:34 How Atlassian Actually Uses Tools for Product Discovery (Full Stack Demo) — 00:33:23 Why This System Works Better Than Traditional PM Workflows — 00:41:24 Moving Through Discovery Phase 2 – Explore: Rapid Prototypes & Real Feedback — 00:41:58 Phase 3 – Make: When the Team Commits to Building — 00:49:04 “Just Ship It” is Bad Advice — Here’s Why — 00:52:24 Can You Trust Feedback from Free Users? — 00:56:00 Phase 4 – Impact: Measuring Real-World Results — 01:07:33 How to Build Real Trust as a Product Manager — 01:14:41 PMs Want to Keep Engineers Busy: Good Strategy or Trap? — 01:17:46 Aakash: “I Wish I Talked to You 10 Years Ago”, Here’s Why — 01:22:46 Closing Reflections & Takeaways — 01:26:33 ---- Key Takeaways: 1. Discovery isn’t a phase, it’s a system. Atlassian runs product discovery continuously, not just “before development.” It’s embedded across problem finding, prototyping, building, and post-launch. 2. Use video, not documents, to communicate user pain. Instead of writing long research summaries, PMs compile 10-minute reels of real customer interviews. Watching raw emotion builds urgency and alignment. 3. Start with ~10 users — not thousands. Atlassian validates ideas with small, focused user groups. It's faster, cheaper, and more revealing than wide surveys or launches. 4. Prototype with whatever is fastest. From AI tools like V0 to basic Figma slides, the goal is speed. You don’t need polished UIs — you need fast feedback on core concepts. 5. Strong user reactions guide investment. When users say “I need this now,” that’s a green light. Mild interest or polite nods? That’s a warning to dig deeper. 6. Build only once you have real pull. They don’t move into development (“Make” stage) until a prototype has strong qualitative validation. Code follows conviction. 7. PMs rotate weekly to tag and analyze feedback. Every week, one PM owns triaging incoming feedback, tagging it to ideas, and surfacing themes. Discovery is part of the rhythm — not a side project. 8. Real discovery requires exposure, not summaries. Dashboards, sanitized reports, and secondhand quotes are not enough. PMs must stay close to raw user input — live or recorded. 9. Post-launch reflection is mandatory. Every shipped feature goes into the “Impact” phase. They assess: is it working? Should we scale, refine, or kill it? 10. Discovery is a team sport. PMs, designers, engineers, even sales, everyone participates in interviews, watches clips, and shapes the product. It’s not just a PM’s job. ---- Check it out on Apple, Spotify, or YouTube. ---- Related Podcasts: How ZoomInfo IPO’d - With CEO/ Founder Henry Shuck How LogRocket Became a $100M+ AI Company - With CEO/ Founder Matt Arbesfeld How Amplitude Became the #1 Product Analytics Tool - With CEO/ Founder Spenser Skates How Zoom Took Over The World - With Zoom's Meetings Product during Covid ( now leads their Events & Webinars product) ---- P.S. More than 85% of you aren't subscribed yet. If you can subscribe on YouTube, follow on Apple & Spotify, my commitment to you is that we'll continue making this content better. ---- 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

    1 小時 28 分鐘

  8. 7月6日

    This $20M AI Founder Is Challenging Elon and Sam Altman | Roy Lee, Cluely

    Amazon banned him. Ivy Leagues kicked him out. And still, he went mega viral, built a $6M ARR AI startup, and raised $15M from a16z… all in a matter of weeks. This might be the craziest founder story you’ll hear all year. If you’re building in a competitive market, struggling to stand out, or just want to learn how to blend controversy with growth, this one’s for you. ---- Brought to you by: AI PM Certification: Get $500 with code AAKASH25 Jira Product Discovery: Plan with purpose, ship with confidence The AI Evals Course for PMs & Engineers: You get $800 with this link ---- Timestamps: 21yo Worth $100M+ - 0:00 Harvard Kicked Me Out - 3:02 Tech Twitter Main Character - 4:33 Ads - 6:44 Controversial = Views Formula - 8:26 Stripper Commercial Brainstorms - 12:47 Liquid Glass Before Apple - 16:55 User Feedback Drives Product - 19:48 Sales Tech & Enterprise - 23:16 Cheating in Meetings - 27:19 Ad - 28:22 How to Fundraise Like Roy - 29:28 Brain Chips End Game - 31:05 Roy vs Elon vs Sam - 33:12 Frat House Culture - 34:44 The Cluely Internship - 37:37 Are We Getting Dumber? - 38:51 Roy Going to Jail? - 40:44 Thanks for Watching - 42:18 ---- Key Takeaways: 1. Don’t wait for permission. He got kicked out of two top schools and used that energy to build something the world couldn’t ignore. If the system doesn’t reward you, build outside it. 2. Design for real behavior, not rules. Interview Coder wasn’t legal or polite, it was effective. It gave users AI help without getting caught. Start with what people actually want. 3. Rethink how AI should show up. Stop building chatbots. Build experiences where AI quietly blends into the workflow. Think overlays, not windows. Think invisible, not interruptive. 4. Your product doesn’t need to sound safe. “Cheat on everything” wasn’t just a headline, it was a magnet for attention. Don’t fear being bold if it reflects what your product actually does. 5. You don’t need a pitch deck if the story tells itself. Cluely raised $15M without running a process. When the traction is undeniable and your product is everywhere, investors come to you. 6. Build virality into your operating system. Don’t “hope” something goes viral. Study what’s working. Run daily idea sessions. Create with the expectation that every post could hit 100M views. 7. Make your content pass two filters or kill it. Can anyone understand it instantly? Will people feel something strong enough to react? If not, it won’t break through. Keep it simple and emotional. 8. Ignore vanity metrics, chase visibility. Don’t waste time measuring click-through rates on content that won’t work next week. Stay focused on showing up everywhere your user lives. 9. You don’t need a big team to move fast. Cluely runs on 4 engineers. No designers. No PMs. Still they've $6M ARR. If you can ship fast and learn faster, you’re already ahead. 10. Build a product that feels inevitable. This isn’t just for sales teams. Cluely is betting on a world where AI support shows up before you even ask for it. Design for that future now. ---- Check it out on Apple, Spotify, or YouTube. ---- Related Podcasts: Bolt CEO and Founder on How he Hit $30M ARR in a Year LogRocket CEO and Founder on How to Build a $100M+ AI Startup Amplitude CEO and Founder on Building the Product Analytics Leader ---- P.S. More than 85% of you aren't subscribed yet. If you can subscribe on YouTube, follow on Apple & Spotify, my commitment to you is that we'll continue making this content better. ---- 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

    43 分鐘
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簡介

The latest insights into how great products grow, how to be a better PM or product leader, and how to get a PM job. www.news.aakashg.com

資訊

  • 創作者
    Aakash Gupta
  • 活躍年代
    2024年 - 2025年
  • 集數
    91
  • 年齡分級
    兒少不宜
  • 版權
    © Aakash Gupta
  • 節目網站
    Product Growth Podcast

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