Lenny RAG MCP Server

{ "episode": { "guest": "Marily Nika", "expertise_tags": [ "AI Product Management", "Machine Learning", "Google Glass", "Computer Vision", "Speech Recognition", "Metaverse", "Product Strategy" ], "summary": "Marily Nika, a product leader at Meta and former Google engineer, discusses the intersection of AI and product management. She emphasizes that AI should solve real problems, not be pursued for novelty. The conversation covers practical applications of AI tools like ChatGPT for PMs, the future where all PMs become AI PMs working with research scientists, and how to avoid the \"shiny object trap.\" Marily shares frameworks for identifying when AI makes sense versus when it's premature, discusses her popular Maven course on AI product management, and provides resources for PMs wanting to learn AI fundamentals without deep technical backgrounds.", "key_frameworks": [ "The Shiny Object Trap - validating real problems before pursuing AI solutions", "Desirable-Viable-Feasible triangle with research scientist perspective", "AI PM vs Generalist PM - solving problems vs shipping products", "Product lifecycle differences for AI vs traditional products", "Research-to-Production pipeline and PM role as bridge" ] }, "topics": [ { "id": "topic_1", "title": "Introduction and Staying Updated on AI/ML News", "summary": "Discussion of how to stay current with rapidly evolving AI landscape through newsletters, blogs, and industry resources. Marily recommends MIT Technology Review and Andreessen Horowitz publications as sources that increasingly cover AI integration.", "timestamp_start": "00:00", "timestamp_end": "05:00", "line_start": 1, "line_end": 48 }, { "id": "topic_2", "title": "What's Overhyped vs Undervalued in AI", "summary": "Marily discusses how AI is both overhyped and underhyped simultaneously. While generative AI tools like ChatGPT receive heavy attention, many underappreciated applications exist including lie detection and specialized use cases beyond text generation.", "timestamp_start": "04:40", "timestamp_end": "06:00", "line_start": 40, "line_end": 48 }, { "id": "topic_3", "title": "Using ChatGPT in Product Management Work", "summary": "Practical applications of ChatGPT for PMs including mission statement generation, user persona creation, and ideation. Marily emphasizes using ChatGPT as a refinement tool after establishing initial direction rather than as a complete replacement for thinking.", "timestamp_start": "06:00", "timestamp_end": "08:30", "line_start": 49, "line_end": 75 }, { "id": "topic_4", "title": "The Future: All PMs Will Be AI PMs", "summary": "Marily's vision that future product management will inherently involve AI, requiring all PMs to work with research scientists to build personalized experiences, recommendations, and automation into products. This represents a fundamental shift in PM responsibilities and skillsets.", "timestamp_start": "08:30", "timestamp_end": "11:15", "line_start": 76, "line_end": 102 }, { "id": "topic_5", "title": "Getting Started with AI for PMs Without Technical Background", "summary": "Entry points for non-technical PMs to engage with AI including finding data-driven opportunities in existing products, starting small with interns, and avoiding the temptation to build AI for its own sake. Emphasis on changing mindset about what can be improved with data.", "timestamp_start": "11:15", "timestamp_end": "13:30", "line_start": 103, "line_end": 117 }, { "id": "topic_6", "title": "The Shiny Object Trap and When NOT to Use AI", "summary": "Marily warns against implementing AI without a clear problem statement. She advises against using AI for MVPs, recommends prototyping with mockups instead, and emphasizes finding the intersection of desirable user needs, viable business models, and feasible technical solutions.", "timestamp_start": "13:30", "timestamp_end": "16:00", "line_start": 112, "line_end": 138 }, { "id": "topic_7", "title": "Data Requirements for ML Models", "summary": "Discussion of how much data is needed for different AI applications. Requirements vary dramatically by use case - simple image classification may need 15-20 labeled examples while NLP applications require thousands. Data acquisition and synthesis are significant challenges.", "timestamp_start": "15:46", "timestamp_end": "18:00", "line_start": 124, "line_end": 138 }, { "id": "topic_8", "title": "Building Custom Models vs Using Existing Solutions", "summary": "When should companies build proprietary models versus leveraging existing tools like GPT? For large tech companies offering speech/vision services, building custom models with diverse data creates competitive advantage. For most others, existing solutions are more practical.", "timestamp_start": "16:53", "timestamp_end": "18:30", "line_start": 130, "line_end": 138 }, { "id": "topic_9", "title": "Understanding Models and Training - Simple Explanations", "summary": "Marily uses a child-learning analogy to explain what ML models are. Models learn patterns from repeated examples (like a child learning animal names) and output probability-based predictions. Training is the process of feeding examples to help the model recognize patterns.", "timestamp_start": "18:34", "timestamp_end": "21:00", "line_start": 139, "line_end": 155 }, { "id": "topic_10", "title": "Real-World AI Applications at Google and Meta", "summary": "Marily shares Google Glass experience with real-time translation and transcription enabling cross-language communication. She emphasizes that cutting-edge AI applications are now real products, not science fiction, and represent the intersection of translation, transcription, and display technologies.", "timestamp_start": "21:00", "timestamp_end": "23:00", "line_start": 157, "line_end": 161 }, { "id": "topic_11", "title": "Will AI Replace Product Managers?", "summary": "Marily argues AI will not replace PMs but will enhance their work by automating tedious tasks like documentation and freeing time for strategic thinking. AI might unlock new areas of product management not yet identified, making the role more valuable.", "timestamp_start": "23:00", "timestamp_end": "24:20", "line_start": 163, "line_end": 168 }, { "id": "topic_12", "title": "Skills PMs Should Develop for the AI Era", "summary": "Marily recommends PMs learn coding fundamentals even if no-code tools exist, using a piano lesson analogy. Understanding how AI tools work gives PMs confidence and mental models to make better decisions, similar to how learning music fundamentals helps musicians create original works.", "timestamp_start": "24:00", "timestamp_end": "26:30", "line_start": 169, "line_end": 176 }, { "id": "topic_13", "title": "Resources for Learning to Code and AI", "summary": "Marily recommends online platforms like Coursera, Stanford's Introduction to AI course, Career Foundry, General Assembly, and Coding Dojo for learning programming. She emphasizes finding the right learning format (self-paced vs community-based) based on individual preferences.", "timestamp_start": "26:00", "timestamp_end": "28:30", "line_start": 181, "line_end": 189 }, { "id": "topic_14", "title": "Building an AI PM Course and Curriculum Design", "summary": "Marily describes her three-week Maven course covering AI product development lifecycle, idea generation, productionization, research scientist collaboration, and career positioning. She emphasizes treating course creation like a product with user research, iteration, and community building.", "timestamp_start": "28:00", "timestamp_end": "42:30", "line_start": 191, "line_end": 312 }, { "id": "topic_15", "title": "Course Structure and Workshops", "summary": "The three-week course includes nine workshops covering product lifecycle, idea generation, productionization, and career pathways. Students build end-to-end AI products as final projects. Most exciting component is final presentation where students showcase their work and receive feedback.", "timestamp_start": "35:30", "timestamp_end": "40:00", "line_start": 233, "line_end": 269 }, { "id": "topic_16", "title": "No-Code Tools for Building AI Models", "summary": "Marily recommends Google Cloud AutoML as a tool for training custom ML models without coding. Example of industrial wind turbine maintenance company reducing manual inspection from 3 weeks to hours using drone photos with AutoML.", "timestamp_start": "38:48", "timestamp_end": "40:30", "line_start": 271, "line_end": 284 }, { "id": "topic_17", "title": "Course Development Process and Evolution", "summary": "Marily treats course creation as a product, starting with audience hypothesis and validation interviews. She initially targeted software engineers transitioning to PM but pivoted to include current PMs. Three-week duration proved optimal. She updates course with new sections as AI landscape evolves.", "timestamp_start": "40:30", "timestamp_end": "42:30", "line_start": 287, "line_end": 299 }, { "id": "topic_18", "title": "Challenges of AI Product Management", "summary": "Marily outlines four key challenges: uncertainty in research outcomes, changing priorities mid-project, difficulty obtaining quality data, and different career progression metrics for research-heavy roles. Success requires team leadership, flexibility, and clear communication with hiring managers about success measures.", "timestamp_start": "29:30", "timestamp_end": "31:30", "line_start": 199, "line_end": 210 }, { "id": "topic_19", "title": "Getting Buy-In for ML Investment and Sustaining Teams", "summary": "Using adjacent product examples as precedent helps convince leadership. Proposing rollback plans and maximum negative impact reduces perceived risk. Team success and company culture that welcomes failure are crucial for maintaining investment over long-term model improvement cycles.", "timestamp_start": "31:15", "timestamp_end": "34:30", "line_start": 211, "line_end": 231 }, { "id": "topic_20", "title": "Lightning Round Q&A and Personal Recommendations", "summary": "Marily recommends books (Inspired by Marty Cagan, You Look Like a Thing and I Love You, Adventures of Women in Tech Workbook), Boz's podcast, The White Lotus TV series, and the Lensa AI app. She shares her favorite interview question about explaining complex concepts simply.", "timestamp_start": "44:18", "timestamp_end": "47:40", "line_start": 319, "line_end": 395 } ], "insights": [ { "id": "i1", "text": "Don't do AI for the sake of doing AI. Make sure there is a problem there, make sure there is a pain point that needs to be solved in a smart way.", "context": "Warning against the shiny object trap in AI implementation", "topic_id": "topic_6", "line_start": 1, "line_end": 2 }, { "id": "i2", "text": "In the future everything will be AI by default. Even if you have something that's technology focused, you'll see a lot of AI starting to get sprinkled in there.", "context": "Vision of how AI will integrate across all products", "topic_id": "topic_4", "line_start": 37, "line_end": 38 }, { "id": "i3", "text": "Charging PT and technology is enhancing our work. It's enhancing us. It does not steal from us.", "context": "Responding to fears about AI replacing human creativity", "topic_id": "topic_2", "line_start": 44, "line_end": 44 }, { "id": "i4", "text": "ChatGPT is better at mission statements because they'll be read by leadership, junior people, stakeholders, and competitors. You need words that everyone understands, even a kid could understand and get inspired.", "context": "Why AI-generated content can surpass human-written content in certain contexts", "topic_id": "topic_3", "line_start": 68, "line_end": 68 }, { "id": "i5", "text": "You cannot watch Netflix without needing a recommender system. After you watch White Lotus or Stranger Things, you will want something similar to watch.", "context": "AI recommendations as table stakes for modern products", "topic_id": "topic_4", "line_start": 80, "line_end": 80 }, { "id": "i6", "text": "A lot of PMs don't know how to approach researchers. A lot of people don't like the uncertainty that research has. When you're working with research, it's more like we're going to try this and then in a year if it doesn't work out we're going to shut the written down.", "context": "Cultural barrier between product and research teams", "topic_id": "topic_4", "line_start": 89, "line_end": 89 }, { "id": "i7", "text": "The AI PM helps their team or company solve the right problem, whereas a generalist PM helps their team build and ship the right product.", "context": "Fundamental difference between AI PM and traditional PM roles", "topic_id": "topic_6", "line_start": 116, "line_end": 116 }, { "id": "i8", "text": "Don't do it for your MVP. Do not waste time of data scientists that can train models using powerful machines. Create a Figma prototype and fake what the AI is going to be doing.", "context": "MVP strategy for AI features", "topic_id": "topic_6", "line_start": 122, "line_end": 122 }, { "id": "i9", "text": "You should use AI where you think you already have some data or data from an adjacent product that you feel you can leverage for your own product to create something meaningful.", "context": "Data availability as a key prerequisite for AI investment", "topic_id": "topic_6", "line_start": 122, "line_end": 122 }, { "id": "i10", "text": "AI systems are not easy to develop. There is a life cycle of a machine learning project and after scoping you need to figure out how much data do we need, where do I find this data, and sometimes people synthesize their own fake data.", "context": "Hidden complexity in ML implementation", "topic_id": "topic_7", "line_start": 128, "line_end": 128 }, { "id": "i11", "text": "If everyone takes the exact same dataset, then the quality that every single company is producing is going to be the exact same. So you do want to diversify, you do want to collect your own data.", "context": "Competitive advantage through proprietary data", "topic_id": "topic_8", "line_start": 134, "line_end": 134 }, { "id": "i12", "text": "It's your responsibility as a PM to decide where the bar is, where do we launch. The recognition of whether this folder is a category dog is good enough for the users, it's like 70% accurate, 80% accurate.", "context": "PM accountability for quality thresholds in ML products", "topic_id": "topic_8", "line_start": 137, "line_end": 137 }, { "id": "i13", "text": "A model is like a kid's brain. It has the ability to take an input, an image, and say I recognize what this is, but I'm 70% sure about this. You output the probability as well of the certainty.", "context": "Accessible explanation of ML models", "topic_id": "topic_9", "line_start": 143, "line_end": 143 }, { "id": "i14", "text": "The model learns in a smart brain how to identify specific things that we don't even understand. Patterns are not in the form of if this is gray then this means this.", "context": "Opacity of machine learning decision-making", "topic_id": "topic_9", "line_start": 149, "line_end": 149 }, { "id": "i15", "text": "These things are real, the technology is here, it's just a matter of connecting the pieces to the puzzle in order to see them coming to life. There's no science fiction anymore.", "context": "AI capabilities exist but require product thinking to realize", "topic_id": "topic_10", "line_start": 161, "line_end": 161 }, { "id": "i16", "text": "AI will make everything better and free time for you to do other things that are less tedious. It will unlock new areas of product management that we haven't realized but are there.", "context": "AI as multiplier of PM impact rather than replacement", "topic_id": "topic_11", "line_start": 167, "line_end": 167 }, { "id": "i17", "text": "Even if ChatGPT or this no code applications may be able to do this for us, learning code gives you a different approach, a different mindset, a different confidence to know how things work.", "context": "Value of foundational knowledge despite tool availability", "topic_id": "topic_12", "line_start": 173, "line_end": 173 }, { "id": "i18", "text": "If you learn the fundamentals and how you know where things started at the beginning, it's going to help you along the way to create on your own if you want to.", "context": "Foundational learning enables independent creation", "topic_id": "topic_12", "line_start": 173, "line_end": 173 }, { "id": "i19", "text": "Anything where you can get data behind the behavior with users can be improved with AI. You can make it more secure, personalize it, enhance it with fraud detection, make it more ethical, faster, more accurate.", "context": "Broad applicability of AI across product dimensions", "topic_id": "topic_5", "line_start": 107, "line_end": 107 }, { "id": "i20", "text": "It's all about changing the mindset of PMs. Take a step back and think about what you can do with the data that's just lying and sitting around.", "context": "Data as underutilized PM resource", "topic_id": "topic_5", "line_start": 110, "line_end": 110 }, { "id": "i21", "text": "Figure out what the problem is that you will get a data scientist to create a model for solving, but there needs to be a problem, there needs to be audience, there needs to be a user and a pain going for it.", "context": "Prerequisites for AI implementation", "topic_id": "topic_6", "line_start": 116, "line_end": 116 }, { "id": "i22", "text": "If you're already at a company that is having AI researchers and AI research scientists, shadow them and spend an hour of their week just talking to them. This is going to open your mind.", "context": "Learning strategy for PMs entering AI space", "topic_id": "topic_14", "line_start": 197, "line_end": 197 }, { "id": "i23", "text": "Users don't know what they want until you show it to them. People don't know how to use AI. People will never have imagined ChatGPT can be what it is.", "context": "Rethinking user research and innovation in AI context", "topic_id": "topic_14", "line_start": 236, "line_end": 236 }, { "id": "i24", "text": "Getting good data is hard. You may need to be creative, figure out ways for data collection. You may get on the street and ask for people to actually contribute data.", "context": "Data acquisition challenges", "topic_id": "topic_18", "line_start": 209, "line_end": 209 }, { "id": "i25", "text": "Usually product managers get ahead the more they launch. But if you're in a research role you're not going to launch as often, so clarify with hiring managers early on what does progress mean.", "context": "Career implications of AI PM roles", "topic_id": "topic_18", "line_start": 209, "line_end": 209 }, { "id": "i26", "text": "Use examples of adjacent products and propose a little contingency plan. Hey if that doesn't work out, here's the rollback plan, here's the maximum impact it will have done in a negative way.", "context": "De-risking ML investment pitches", "topic_id": "topic_19", "line_start": 218, "line_end": 218 }, { "id": "i27", "text": "The more you work on this specific company, the more trust you get. If the culture is such that failing is going to be welcome, then you can just go ahead and do this sort of thing.", "context": "Trust and culture as enablers of ML investment", "topic_id": "topic_19", "line_start": 221, "line_end": 221 }, { "id": "i28", "text": "Research scientists and research orgs are not as siloed as they used to be. The more companies invest on staffing this layer between productionizing and research, the more you're going to see this bridge creating goods products.", "context": "Evolving relationship between research and product", "topic_id": "topic_19", "line_start": 227, "line_end": 227 }, { "id": "i29", "text": "You need a PM to take research and actually figure out ways to monetize it. PMs bridging the gap is crucial for companies to come up with meaningful use cases for users.", "context": "PM role in commercializing research", "topic_id": "topic_19", "line_start": 230, "line_end": 230 }, { "id": "i30", "text": "Teaching and crystallizing thoughts is one of the best ways to learn it yourself. Learning from students, learning from explaining is just so viable.", "context": "Teaching as a learning mechanism", "topic_id": "topic_14", "line_start": 311, "line_end": 311 } ], "examples": [ { "id": "e1", "explicit_text": "At my previous company Google, which was then the RVR team working on Google Glass, they had a video on last year's Google IO where someone wore Google Glass that translated real-time between languages.", "inferred_identity": "Google Glass / Google RVR team", "confidence": "high", "tags": [ "Google", "Google Glass", "translation", "real-time", "computer vision", "wearable", "speech recognition", "AR" ], "lesson": "Demonstrates how combining transcription, translation, and display creates transformative products that unlock borders of communication through technology.", "topic_id": "topic_10", "line_start": 160, "line_end": 161 }, { "id": "e2", "explicit_text": "I was watching Netflix and recommender systems are now essential. After you watch White Lotus or Stranger Things, you want something similar recommended, not a romantic comedy.", "inferred_identity": "Netflix", "confidence": "high", "tags": [ "Netflix", "recommender system", "personalization", "streaming", "user experience", "content discovery" ], "lesson": "Shows how AI-powered recommendations are now table stakes for product experience, not a differentiator.", "topic_id": "topic_4", "line_start": 80, "line_end": 80 }, { "id": "e3", "explicit_text": "Early stage entrepreneurs reach out to me saying they want to train a model to prove there's a market. I tell them no, create a Figma prototype and fake what the AI is going to be doing.", "inferred_identity": "Generic early-stage startups", "confidence": "medium", "tags": [ "startup", "MVP", "prototype", "validation", "lean methodology", "model training" ], "lesson": "Demonstrates the waste of building models for MVPs when prototypes can validate market demand faster and cheaper.", "topic_id": "topic_6", "line_start": 122, "line_end": 122 }, { "id": "e4", "explicit_text": "There was a company that had a lot of winter banks and they would have people manually go take a look with huge ladders. Eventually they got drones to fly and take photos, uploaded them to AutoML, and reduced maintenance time from three weeks of work to a few hours.", "inferred_identity": "Wind turbine maintenance company (inferred from context about winter banks and maintenance)", "confidence": "high", "tags": [ "wind turbines", "maintenance", "drone", "computer vision", "AutoML", "efficiency", "industrial", "predictive maintenance" ], "lesson": "Shows how applying no-code AI tools to existing data sources (drone photos) can create massive operational efficiency gains.", "topic_id": "topic_16", "line_start": 275, "line_end": 278 }, { "id": "e5", "explicit_text": "In my course, a student was able to create a little model that took x-rays found online and could tell what was wrong if something was wrong with that patient within three weeks.", "inferred_identity": "Course student project", "confidence": "high", "tags": [ "healthcare", "x-ray analysis", "medical imaging", "machine learning", "no-code tools", "education" ], "lesson": "Demonstrates that non-technical PMs can build meaningful AI applications in three weeks using no-code tools.", "topic_id": "topic_15", "line_start": 262, "line_end": 263 }, { "id": "e6", "explicit_text": "Two students paired up and actually raised funding, which is mind blowing to me.", "inferred_identity": "Maven course cohort students", "confidence": "medium", "tags": [ "course project", "startup", "fundraising", "AI product", "validation" ], "lesson": "Shows that learning structured AI PM frameworks can lead to fundable product ideas.", "topic_id": "topic_15", "line_start": 251, "line_end": 251 }, { "id": "e7", "explicit_text": "Someone wanted to create a longer recommender system and say, 'we think this is what's wrong with you, here are the steps you should follow' for medical diagnoses.", "inferred_identity": "Course student project", "confidence": "medium", "tags": [ "healthcare", "recommender system", "diagnosis", "treatment", "machine learning" ], "lesson": "Illustrates how AI PM frameworks enable building sophisticated products combining diagnosis and recommendations.", "topic_id": "topic_15", "line_start": 263, "line_end": 263 }, { "id": "e8", "explicit_text": "I subscribe to your newsletter Lenny, and I'm a big fan of the download by MIT Ecology Review or Atelier.", "inferred_identity": "MIT Technology Review, Andreessen Horowitz", "confidence": "high", "tags": [ "MIT Technology Review", "Andreessen Horowitz", "newsletter", "AI news", "technology research" ], "lesson": "Shows where PMs should source AI knowledge - from research-focused publications that cover AI integration broadly.", "topic_id": "topic_1", "line_start": 37, "line_end": 38 }, { "id": "e9", "explicit_text": "I read an article this morning where writers are complaining they're fearful that writing online is going to die and everything they've been studying for will be replaced.", "inferred_identity": "General online writing community", "confidence": "low", "tags": [ "content creation", "AI anxiety", "job displacement", "creative writing", "generative AI" ], "lesson": "Demonstrates the pervasive anxiety around AI replacing knowledge workers, which Marily refutes.", "topic_id": "topic_2", "line_start": 44, "line_end": 44 }, { "id": "e10", "explicit_text": "I read research that said AI can now detect place, lie detection, whether it's for security reasons or at work or anything like that.", "inferred_identity": "Academic research on lie detection", "confidence": "low", "tags": [ "lie detection", "security", "AI capabilities", "research", "emerging applications" ], "lesson": "Shows the breadth of AI applications beyond text generation that PMs should be aware of.", "topic_id": "topic_2", "line_start": 47, "line_end": 47 }, { "id": "e11", "explicit_text": "When I'm at work trying to come up with a nice mission statement for my PM role, ChatGPT produces something fantastic on the first try.", "inferred_identity": "Meta (Marily's employer)", "confidence": "high", "tags": [ "Meta", "mission statement", "ChatGPT", "PM workflow", "content generation" ], "lesson": "Shows how ChatGPT can enhance strategic PM deliverables beyond their initial drafts.", "topic_id": "topic_3", "line_start": 53, "line_end": 53 }, { "id": "e12", "explicit_text": "For a fitness band without a screen product area, ChatGPT will provide user segments like young professionals interested but don't have time, people that don't want to charge wearables every day.", "inferred_identity": "Hypothetical fitness band example", "confidence": "medium", "tags": [ "fitness wearables", "personas", "user segmentation", "ChatGPT", "product strategy" ], "lesson": "Demonstrates how ChatGPT generates user insights PMs wouldn't intuitively arrive at.", "topic_id": "topic_3", "line_start": 74, "line_end": 74 }, { "id": "e13", "explicit_text": "Airbnb when you mentioned experimentation platform slicing results by device, country and user stage.", "inferred_identity": "Airbnb", "confidence": "high", "tags": [ "Airbnb", "experimentation", "A/B testing", "analytics", "segmentation" ], "lesson": "References how sophisticated experimentation infrastructure enables better product decisions.", "topic_id": "topic_5", "line_start": 14, "line_end": 14 }, { "id": "e14", "explicit_text": "I have this workbook I originally launched with Alana Karen for women in tech trying to navigate working in tech called Adventures of Women in Tech Workbook.", "inferred_identity": "Marily Nika + Alana Karen collaboration", "confidence": "high", "tags": [ "women in tech", "career development", "workbook", "mentorship", "professional growth" ], "lesson": "Shows value of creating educational products to help underrepresented groups in tech.", "topic_id": "topic_20", "line_start": 335, "line_end": 335 }, { "id": "e15", "explicit_text": "Boz is the CEO of Facebook and has a great podcast.", "inferred_identity": "Boz / Meta CEO", "confidence": "high", "tags": [ "Meta", "Facebook", "executive", "podcast", "thought leadership" ], "lesson": "Shows how executives use podcasts to share product and business thinking.", "topic_id": "topic_20", "line_start": 347, "line_end": 347 }, { "id": "e16", "explicit_text": "The White Lotus - me and my husband just binge watched the whole thing. It's just so different, so mind blowing.", "inferred_identity": "HBO / Max's The White Lotus", "confidence": "high", "tags": [ "television", "HBO", "entertainment", "storytelling", "series" ], "lesson": "References cultural products that demonstrate excellent execution in entertainment.", "topic_id": "topic_20", "line_start": 353, "line_end": 353 }, { "id": "e17", "explicit_text": "I ask people in interviews, how would you explain a database to a three year old?", "inferred_identity": "Marily Nika interview practice", "confidence": "high", "tags": [ "interview question", "communication", "technical explanation", "simplification" ], "lesson": "Shows the importance of communication ability in technical roles.", "topic_id": "topic_20", "line_start": 365, "line_end": 365 }, { "id": "e18", "explicit_text": "The Lensa app where you plug in your photos and see what they would look like as fantastic AI heroes. I tried the male version because it was so much cooler.", "inferred_identity": "Lensa app / Prisma Labs", "confidence": "high", "tags": [ "Lensa", "generative AI", "image generation", "consumer app", "AI tools" ], "lesson": "Demonstrates consumer enthusiasm for AI image generation tools.", "topic_id": "topic_20", "line_start": 371, "line_end": 371 }, { "id": "e19", "explicit_text": "Marty Kagan wrote Inspired, which is all about how to create tech products people love.", "inferred_identity": "Marty Kagan / Inspired book", "confidence": "high", "tags": [ "product management", "book", "product strategy", "best practices" ], "lesson": "Recommends foundational product management literature.", "topic_id": "topic_20", "line_start": 323, "line_end": 323 }, { "id": "e20", "explicit_text": "You Look Like a Thing and I Love You - it's about how AI works and why it's making the world a weirder place.", "inferred_identity": "Book by Janelle Shane", "confidence": "high", "tags": [ "AI education", "book", "machine learning", "accessible explanation" ], "lesson": "Recommends entertaining yet educational book on AI fundamentals.", "topic_id": "topic_20", "line_start": 335, "line_end": 335 } ] }