Discover how Google's NotebookLM revolutionizes learning by transforming documents, PDFs, and YouTube videos into personalized podcasts. In this comprehensive tutorial, we explore NotebookLM's powerful features including AI-generated audio overviews, mind maps, study guides, and interactive chat functionality.In this episode, product managers Fini and Sid Saladi demonstrate how to use NotebookLM for learning complex concepts like AI agents. Watch as they upload multiple sources and generate a thirty-five minute podcast conversation automatically, perfect for learning while commuting or multitasking.Timeline:0:00 - Introduction to NotebookLM and its impact on content creation1:31 - Welcome to the Everything podcast2:12 - Use case: Learning about AI agents with NotebookLM3:04 - NotebookLM interface overview and basic layout3:29 - What is NotebookLM? Quick introduction4:12 - Types of sources you can add (websites, YouTube, Google Docs, PDFs)4:41 - Understanding RAG (Retrieval Augmented Generation)5:07 - Adding sources to your NotebookLM workspace5:55 - Audio overview feature: Converting content to podcasts6:28 - Exploring mind maps and study guides7:00 - Chatting with NotebookLM about daily automation with AI agents9:00 - How to get better answers by adding more sources9:42 - Viewing citations and source references10:17 - Listening to the generated thirty-five minute podcast11:00 - Interactive mode: Joining the AI conversation11:18 - Using NotebookLM for interview preparation12:01 - Feedback options and sharing features12:57 - Generating study guides, FAQs, and timelines13:03 - Adding new sources to update your knowledge base14:27 - Building an AI agent in N8N for podcast recommendations16:08 - The content creator dilemma: Impact on views and watch time17:00 - The future of AI search and content creation revenue models18:17 - Final thoughts on information synthesis toolsTags:NotebookLM tutorial, Google NotebookLM, AI learning tools, convert documents to podcast, NotebookLM features, AI study guide generator, mind map generator, RAG application, retrieval augmented generation, AI agents tutorial, product management podcast, N8N automation, AI podcast generator, automated learning, student study tools, exam preparation AI, NotebookLM 2026, AI education tools, audio overview NotebookLM, interactive AI learning, AI content summarization, vector search AI, Google AI experiments, NotebookLM guide, how to use NotebookLM, AI powered studying, content synthesis tools, YouTube to podcast converter, PDF to audio, document analysis AI, AI search impact, content creator challenges, Google AI mode, perplexity AI alternative, automated study materials, AI interview preparation, behavioral interview practice, STAR method AI, product manager tools, Best Buy product management, startup product management, AI automation daily tasks, podcast recommendation system, machine learning education, artificial intelligence concepts, technology podcast 2026

🚨 Game-changer spotted.We took Perplexity’s new Comet Browser for a spin—and it feels like Chrome and ChatGPT merged into one.In this demo, you’ll see how an AI-powered browser can apply to jobs, analyze portfolios, and run competitor research—all while you’re logged into your own accounts.🔥 What’s inside the demo:✅ Applied to 8 LinkedIn jobs automatically via Easy Apply✅ Live portfolio analysis with actionable investment insights✅ Competitor research on Lenny’s Newsletter (sorry, Lenny 😅)✅ Real-time web scraping + actions, not just summaries✅ Instant Chrome profile import — switch with zero friction💡 Built for Product Managers who want to:Eliminate repetitive research workGet AI help on authenticated websitesSpeed up competitive analysisSave hours of manual data gathering⚠️ Note: Use responsibly. Make sure your company allows AI tools—especially if you’re in banking or healthcare.⏱️ Key timestamps:0:00 – Intro & product overview2:30 – Chrome migration demo5:06 – LinkedIn job application automation11:21 – Competitor analysis (Lenny’s Podcast)13:16 – Live web content analysis15:13 – Portfolio management demo22:28 – Voice mode24:03 – Final thoughts👉 Subscribe for more AI product walkthroughs💬 Drop your favorite use case in the comments🔁 Share if this would upgrade your workflow🏷️ Tags:#ProductManagement #AITools #CometBrowser #Perplexity #AIAgents #Automation #ProductDemo #AIBrowserSecondary:#WorkflowOptimization #CompetitorAnalysis #LinkedInAutomation #AIProductivity #PMTools2025Use-case focused:#AutomatedJobApplications #AIPortfolioAnalysis #AICompetitorResearch #ProductManagementAutomation📊 Why it matters (ROI):8 job applications in minutes—not hoursInstant competitor insights instead of days of researchReal-time access to authenticated data—no exports needed🚀 Questions for you:Which use case would save you the most time?Which logged-in site should this work on next?Want a deeper dive into enterprise workflows?

Struggling to get interview calls despite your experience? Learn how to transform your generic resume into a highly customized, ATS-optimized resume using AI in just 60 seconds!In this video, product management experts reveal why recruiters spend only 6 seconds on your resume and demonstrate a live tutorial using Google Gemini 2.5 Pro (tested against ChatGPT, Claude, and DeepSeek) to create job-specific resumes that get you noticed.🎯 What You'll Learn:Why your resume is your marketing materialHow to customize resumes for specific job descriptionsThe keyword strategy recruiters actually useLive AI resume optimization demo with Google Gemini 2.5 ProExpert prompting techniques for best resultsTimeline:0:00 - Introduction: The Most Important Thing for PM Interviews2:18 - Resume Fundamentals: What Matters Most3:27 - Your Resume as Marketing Material6:03 - The 6-Second Rule: Why Customization Matters7:00 - How to Customize Your Resume for Each Job8:36 - The Recruiter's ATS Keyword Strategy10:06 - AI Resume Optimization Introduction10:17 - Live Demo: Using Google Gemini 2.5 Pro11:10 - Writing the Perfect Prompt for AI12:00 - Real Example: Sally's Resume for Epic Games14:18 - Results: Before vs After Comparison15:25 - Final Takeaways: Generic to Customized in MinutesPerfect for: Product managers, job seekers, career changers, and anyone applying for competitive roles in tech.💡 Stop sending generic resumes! Start landing more interviews today.resume optimization, AI resume builder, product manager resume, Google Gemini 2.5 Pro, resume tips 2025, ATS optimization, job search tips, how to write a resume, product management career, resume customization, AI tools for job search, resume keywords, recruiter tips, tech resume, career advice, job application tips, resume writing, hiring tips, product manager interview, AI prompting, resume hacks, job hunting, career development, tech jobs, PM resume, applicant tracking system, resume formatting, job description matching, professional resume, career coaching

Discover the future of AI automation! In this comprehensive guide, we break down the difference between AI agents and agentic AI, explore real-world applications in HR and travel planning, and discuss how this technology is set to revolutionize every industry.🎯 What You'll Learn:The key differences between AI agents and agentic AIHow multiple agents work together to solve complex problemsReal-world implementation in HR workflows (compensation structure automation)Practical examples: Budget-based trip planning with AIModel selection strategies (OpenAI, Claude, Llama, Mistral)How agentic AI will transform business UIs and user experiences🔥 Perfect for: Product Managers, Tech Leaders, AI Enthusiasts, Entrepreneurs, and anyone interested in understanding the next wave of AI automation.📌 TIMESTAMPS:0:00 - Introduction: What is Agentic AI?0:40 - AI Agents vs Agentic AI: Understanding the Difference1:09 - How Aura AI Uses AI for HR Solutions2:17 - Deep Dive: Agentic Workflows Explained3:37 - Real Example: Building Compensation Structures with AI4:38 - Multiple Agents Working Behind the Scenes5:30 - Model Selection: OpenAI, Llama, Mistral & More6:38 - Choosing the Best AI Model for Your Use Case7:35 - Narrow vs Broad Use Cases in AI8:45 - Engineering Infrastructure for AI Success9:31 - Practical Example: AI-Powered Trip Planning12:20 - Travel Planning: From Manual Search to AI Automation14:19 - How Agents Communicate and Coordinate Tasks16:34 - The Future: How Agentic AI Will Revolutionize Every Company💡 Key Takeaways:✅ Agentic AI uses multiple specialized agents working together autonomously✅ Each agent focuses on specific tasks (job descriptions, market data, flight search, etc.)✅ Different AI models excel at different use cases - no single "best" model✅ Proper engineering infrastructure is crucial for AI accuracy✅ Future businesses will need to adapt from UI-centric to agent-centric approaches🔗 Related Topics: AI automation, machine learning, product management, HR tech, generative AI, LLMs, AI workflows, business automation📢 Subscribe for more content on product management, AI technologies, and insights from industry leaders shaping the future of tech!#AgenticAI #AIAgents #ArtificialIntelligence #ProductManagement #TechInnovation

Learn the essential product metrics that successful Product Managers use to drive growth and measure success. Join Srinath and Sid as they break down key metrics from Customer Lifetime Value to Growth Funnels, sharing practical insights on how to track and optimize each metric for your product's success.For more updates from Everything ProductCheckout our LinkedIn page - https://www.linkedin.com/company/everything-product-podcast💎 Here are more resources for you🚀 The Ultimate PM Launch Pad - Blast Off Your Career Now! 🚀https://www.youtube.com/playlist?list=PL0KSCr65RCTOupMjArBvrML5VWUGzaKqt🧰 Master the PM Toolkit - Equip Yourself for Success! 🧰https://www.youtube.com/playlist?list=PL0KSCr65RCTM4acJOjQYFvOVMzfiEFlqE⚡️ Stay Ahead of the Tech Curve - Charge Your Knowledge Now! ⚡️https://www.youtube.com/playlist?list=PL0KSCr65RCTMvYe4lAYpfdbDoNdPbA33o🎯 Key Highlights:Understanding Customer Lifetime Value and its relationship with CACWhy Net Promoter Score matters for mature productsHow to measure user engagement through MAU/DAUCreating effective Go-to-Market strategiesUsing growth funnels to optimize different stages of product developmentTimestamps:0:00 - Introduction29:52 - Customer Lifetime Value (CLV) explained31:20 - CLV's relationship with customer acquisition costs31:48 - Net Promoter Score (NPS) breakdown33:08 - Go-to-Market Strategy essentials35:12 - Monthly & Daily Active Users (MAU/DAU)38:32 - Customer Acquisition Cost (CAC) deep dive40:10 - Understanding Churn Rate41:36 - User Engagement metrics42:25 - Growth Funnel analysis44:03 - Conversion Rate importance👉 Check out our other videos on Product Management:OKRs for Product ManagersBuilding Product RoadmapsAchieving Product Market FitTags:product management, product metrics, customer lifetime value, net promoter score, product analytics, growth metrics, user engagement, customer acquisition cost, churn rate, product manager skills, product strategy, startup metrics, saas metrics, business metrics, product management career, product growth, user analytics, product development, product marketing, tech product management, business analytics, product management skills, product management guide

Ever wondered how Amazon builds generative AI for millions of sellers? Or how Instagram's recommendation feed knows exactly what you want to watch next? In this deep-dive conversation, we sit down with AI/ML leaders from Amazon and Meta to uncover the real strategies behind building AI products at scale.

Anita shares her journey launching Amazon's first AI image generation solution for sellers, while our Meta engineer breaks down distributed inference and how Instagram's recommendation models actually work.

Key Insights:

→ Why you should validate AI ideas with free tools (Midjourney, Canva) BEFORE building

→ The real difference between AI metrics vs. business metrics

→ How to define "quality" when there's no industry benchmark

→ Why giving users full control over prompts is a mistake

→ How Instagram updates its models without losing your preferences

Timeline:

0:00 - Introduction: Building AI products at scale

1:19 - Launching Amazon's first AI image generation tool

2:06 - Balancing innovation with customer problems

3:16 - The problem: Small sellers can't afford graphic designers

4:03 - Real-world example: Food tech & restaurant images

5:07 - Validating AI with prototypes before building

5:24 - KEY INSIGHT: Use Midjourney/Canva to validate first

6:12 - Quality dimensions: Aesthetics, relevance, proportions

8:48 - Product manager's dilemma: AI metrics vs. business metrics

9:30 - Creating benchmarks when none exist

10:30 - Responsible AI: Safety, watermarks, artifacts

11:04 - Business metrics: Adoption, engagement, retention

12:05 - Defining accuracy in generative AI

13:56 - Don't make users prompt engineers (abstract the complexity)

15:26 - Fundamentals of inference explained

16:09 - Training vs. Inference: The dog analogy

17:00 - Why training and inference aren't binary

18:43 - How Meta does distributed inference

19:32 - How Instagram recommendations actually work

20:26 - Snapshot updates: Keeping models fresh

21:01 - Replacing models without losing user context

22:38 - What is distributed inference? Tree structure explained

23:31 - How Instagram serves personalized content at scale

Who This Is For:

Product managers building AI/ML products

Engineers working on generative AI

Startup founders exploring AI solutions

Anyone curious about how Big Tech AI actually works

Resources Mentioned:

Stable Diffusion aesthetic models

Midjourney for prototyping

Canva for quick validation

🔔 Subscribe for more deep dives into AI product development!

#GenerativeAI #MachineLearning #ProductManagement #Amazon #Meta #Instagram

Discover the inspiring entrepreneurship journeys of two successful founders who left their corporate careers to build thriving startups. Nathan shares how he transitioned from Meta and Microsoft to launch My Garden Emporium, solving urban sustainability challenges in Bangalore. Chris reveals how he built a multi-million dollar B2B gifting platform despite having no product or engineering background.In this podcast, learn about:✅ Overcoming the "golden handcuffs" of corporate life✅ Finding product-market fit and your first 100 customers✅ Pricing strategies that led to 10x growth✅ The minimum regret framework for decision-making✅ Pivoting quickly and iterating fast✅ Hiring your first sales team and scaling revenueTimeline:0:00 - Introduction: The entrepreneurship journey begins0:33 - Nathan's early startup: Computer assembly business in 20031:15 - Moving from Meta/Microsoft to entrepreneurship2:06 - The 20-year corporate career and "golden handcuffs"3:23 - The minimum regret framework for quitting your job4:52 - Why My Garden Emporium? Solving urban sustainability6:54 - From single product to 5 categories in 4 months8:56 - Chris's journey: Building without technical background9:30 - Overcoming imposter syndrome as a salesperson founder10:31 - Finding your first customers through cold outreach11:16 - Getting 50 customers from 200 outreach attempts (25% conversion!)12:28 - Pricing lessons: From $100 to $2,000/month without losing customers13:25 - Transitioning from founder-led sales to hiring sales teams14:29 - Expanding from gift cards to warehouse model15:02 - Product roadmap strategy and building your vision15:26 - Backwards math: Scaling to $1M, $10M, $100M revenueKey Takeaways:Start with MVP and iterate quickly - both founders validated ideas before going all-inCustomer discovery is crucial - talk to potential buyers before buildingPrice confidently - underpricing can limit growth potentialThe worst case? You can always return to corporate if startup failsBuild what you know - leverage your domain expertise🔔 Subscribe for more founder stories, startup strategies, and entrepreneurship insights!#Entrepreneurship #StartupJourney #ProductMarketFit #FounderStories #CorporateToStartupTagsentrepreneurship journey, startup founder stories, leaving corporate job, product market fit, finding first customers, startup pricing strategy, B2B SaaS startup, minimum regret framework, imposter syndrome entrepreneur, non technical founder, customer acquisition strategy, cold outreach tactics, scaling startup revenue, founder led sales, hiring sales team, MVP development, fast iteration startup, pivot strategy, urban sustainability startup, garden emporium, B2B gifting platform, Meta to startup, Microsoft to entrepreneur, Bangalore startups, Indian entrepreneurship, startup validation, customer discovery process, SaaS pricing model, from zero to million revenue, startup growth strategy, quitting corporate life, golden handcuffs, career transition, building without coding, sales background founder, product management career, tech entrepreneurship India, e-commerce platform launch, landscaping business, startup challenges, entrepreneur mindset, risk taking in business, startup success stories, bootstrapping strategies, go to market fit, ideal customer profile, early stage funding, revenue scaling tactics, subscription business model, warehouse logistics startup, work and upwork hiring, remote team building, startup founder advice, leaving big tech companies

Build production-ready websites without writing code! In this episode, we explore how AI coding tools like Lovable and Replit are transforming how Product Managers work. Watch us build real websites in real-time and learn which tool is best for prototyping, MVP development, and design handoffs.

🎯 What You'll Learn:

Build a fully functional website in under 60 minutes

Compare Lovable vs Replit for product prototyping

Best practices for prompting AI coding agents

How to integrate databases, authentication, and APIs without coding

When to use AI tools vs hiring engineers

💡 Key Takeaways for PMs:

Chat with AI agents BEFORE building (treat them like junior developers)

Create PRDs and get AI to review the plan first

Front-end prototyping is exceptional, backend integration needs patience

Always enable confirmation mode to prevent unexpected changes

🛠️ Tools Discussed:

Lovable (lovable.dev) - User-friendly, fast prototyping

Replit (replit.com) - Developer-friendly with built-in deployment

Anthropic's Claude for PRD generation

Supabase for authentication & database

⏱️ Timestamps:

0:00 - Introduction: Building Without Engineers

1:04 - Real Experience with Lovable & AI Coding

2:52 - Life Before vs After AI Coding Tools

4:26 - Best Practice: Treat AI Like Your Developer

5:18 - Live Demo: Building with Replit

8:54 - Database, Authentication & Deployment

12:00 - Building a Podcast Website Live

16:00 - Anthropic Computer Use Integration

16:55 - Lovable Demo: Better UI, Faster Results

19:43 - YouTube API Integration Discussion

📌 Featured Projects Built:

✅ Prompt Template Marketplace for PMs (60 min build)

✅ Podcast Website with Episode Management (13 min build)

✅ User Authentication & Database Integration

✅ Search, Filter & Rating Functionality

🔗 Resources Mentioned:

Replit Templates & Documentation

Lovable Platform

YouTube API Integration

Supabase Backend Services

💬 Your Turn:

Which AI coding tool should we explore next? Comment below!

🎙️ About Everything Product:

Your go-to podcast for Product Management insights, AI tools, and career growth. Hosted by three experienced PMs sharing real experiences and practical advice.

👍 Like this video? Subscribe for weekly PM content!

🔔 Hit the bell icon to never miss an episode!

#ProductManagement #AITools #NoCode #Lovable #Replit #ProductManager #TechForPMs #AIForProductManagers #WebDevelopment #Prototyping