Lenny RAG MCP Server

{ "episode": { "guest": "Logan Kilpatrick", "expertise_tags": [ "Developer Relations", "AI/ML", "OpenAI", "Product Strategy", "API Design", "Prompt Engineering" ], "summary": "Logan Kilpatrick, Head of Developer Relations at OpenAI, discusses the transformative impact of ChatGPT and GPT-4 on product development and business efficiency. The conversation covers OpenAI's operational culture emphasizing high agency and urgency, practical applications of AI tools across industries, and strategic guidance for builders leveraging OpenAI APIs. Logan shares insights on prompt engineering techniques, the GPT store launch, internal efficiency gains through custom GPTs, and upcoming model improvements. He emphasizes that competitive advantage comes from vertical specialization rather than general-purpose AI, and discusses how new interfaces and agent capabilities will expand AI adoption.", "key_frameworks": [ "High Agency + Urgency as hiring criteria", "Context is all you need (prompt engineering)", "Vertical specialization vs horizontal competition", "Build for GPT-5 capabilities, not GPT-4 limitations", "Measure in hundreds (compounding attempts)", "AI as human augmentation, not replacement" ] }, "topics": [ { "id": "topic_1", "title": "OpenAI's Internal Culture During Board Crisis", "summary": "Logan discusses the dramatic Thanksgiving weekend when Sam Altman was removed from the CEO role. He describes the shock of the situation, the company's transparency, and most notably how quickly the team refocused on work afterwards. He reflects on being grateful the crisis happened when stakes were lower, and how it united the rapidly growing team around shared experiences like ChatGPT and GPT-4 launches.", "timestamp_start": "00:04:20", "timestamp_end": "00:08:20", "line_start": 31, "line_end": 48 }, { "id": "topic_2", "title": "New AI Interfaces and Multimodal Interactions", "summary": "Logan highlights emerging user interfaces for AI beyond chat, including Rabbit R-1 hardware and TL Draw's infinite canvas approach. He explains why multimodal and non-chat interfaces represent the future, allowing users to interact with AI in more natural, visual ways. He identifies 2024 as the year of multimodal AI and new UX paradigms.", "timestamp_start": "00:08:31", "timestamp_end": "00:09:53", "line_start": 52, "line_end": 63 }, { "id": "topic_3", "title": "Strategic Positioning for Builders: Where Not to Compete with OpenAI", "summary": "Logan clarifies OpenAI's focus on general-purpose use cases and explains where builders should specialize to avoid direct competition. He uses Harvey (legal AI) as an example of vertical specialization, advising founders that general assistants competing with ChatGPT require radical differentiation. The key insight is that OpenAI won't build vertical solutions like AI sales agents, leaving room for domain-specific companies.", "timestamp_start": "00:10:36", "timestamp_end": "00:12:59", "line_start": 70, "line_end": 81 }, { "id": "topic_4", "title": "Internal Efficiency Gains Through AI Tools", "summary": "Lenny and Logan discuss how companies are using ChatGPT and custom GPTs to improve internal efficiency without hiring more engineers. They cover engineering productivity (50%+ improvement), marketing use cases with ad generation GPTs, and data analysis tools. Logan mentions the Harvard Business School study on AI efficiency gains and emphasizes how GPTs enable domain-specific automation without technical expertise.", "timestamp_start": "00:13:42", "timestamp_end": "00:18:25", "line_start": 85, "line_end": 111 }, { "id": "topic_5", "title": "Prompt Engineering: Theory and Practical Techniques", "summary": "Logan explains why prompt engineering exists—models are eager to answer but lack context about the user's goals. He uses the human analogy to frame context provision as natural communication. Key techniques include adding context, using examples, and even small details like smiley faces. He references OpenAI's prompt engineering guide and explains how fidelity improvements will eventually reduce the need for careful prompting.", "timestamp_start": "00:19:13", "timestamp_end": "00:26:03", "line_start": 121, "line_end": 165 }, { "id": "topic_6", "title": "GPTs Store Launch and Capabilities", "summary": "Logan describes the GPT store as enabling non-developers to create custom ChatGPT versions with context, files, instructions, and integrated tools. He highlights capabilities like code interpreter, browsing, image generation, and external API connections. The most exciting aspect is empowering non-technical users to solve complex problems. Future monetization and easier API connections are coming soon.", "timestamp_start": "00:26:29", "timestamp_end": "00:30:25", "line_start": 169, "line_end": 198 }, { "id": "topic_7", "title": "OpenAI's Operating Model: High Agency and Urgency", "summary": "Logan identifies high agency and urgency as the top two attributes OpenAI looks for in hiring. He explains how high-agency people don't wait for consensus but see problems and solve them. The Assistants API example shows teams self-organizing around customer feedback. This contrasts with traditional companies that require multiple department approvals. The culture enables speed through trust and empowerment rather than process.", "timestamp_start": "00:32:48", "timestamp_end": "00:35:55", "line_start": 217, "line_end": 231 }, { "id": "topic_8", "title": "OpenAI's Planning, Roadmapping, and Prioritization", "summary": "Logan explains OpenAI's planning process balances mission focus (achieving AGI) with reliability and customer needs. While using H1 and quarterly planning, OpenAI is not OKR-driven. The roadmap is flexible to accommodate rapid change in the AI space. Core tenets include reliability over new features and ensuring robust API experiences. Success metrics include adoption, revenue (as proxy for compute), and developer growth.", "timestamp_start": "00:36:14", "timestamp_end": "00:40:12", "line_start": 235, "line_end": 255 }, { "id": "topic_9", "title": "Speed and Innovation: Slack Culture and Coordination", "summary": "Logan credits OpenAI's heavy reliance on Slack as crucial to moving fast. The real-time communication enables quick team coordination across offices and time zones. Sam Altman uses Slack as his number one app. It creates company culture and allows cross-functional collaboration faster than in-person movement. This is highlighted as a key operational advantage.", "timestamp_start": "00:40:45", "timestamp_end": "00:42:33", "line_start": 259, "line_end": 272 }, { "id": "topic_10", "title": "Research Team and GPU Constraints", "summary": "Logan explains how OpenAI keeps the research team intentionally small to maximize productivity. In a GPU-constrained world, adding researchers is a net productivity loss unless they significantly level up the group. Engineering teams benefit from adding people (more code written), but researchers sharing limited GPU capacity actually slow down. This shapes OpenAI's organizational structure and growth strategy.", "timestamp_start": "00:43:19", "timestamp_end": "00:44:46", "line_start": 280, "line_end": 285 }, { "id": "topic_11", "title": "OpenAI's Product Roadmap: New Modalities and Agent Future", "summary": "Logan outlines the near-term future: expanding ChatGPT interfaces (voice, image generation, image input), moving GPTs toward agents that work asynchronously over time, and using GPTs to onboard new users through vertical-specific templates. The agent paradigm shift from synchronous chat to asynchronous task delegation is emphasized. New modalities and improved accessibility are priorities.", "timestamp_start": "00:44:58", "timestamp_end": "00:47:07", "line_start": 289, "line_end": 305 }, { "id": "topic_12", "title": "Building for GPT-5: Practical Expectations and Strategy", "summary": "Logan advises builders to plan for GPT-5's capabilities rather than current limitations, but tempers expectations. GPT-5 will be faster and more capable, but won't be magical—it will be a better tool for the same problems. Companies solving vertical use cases will benefit most. The key insight: avoid assuming revolutionary change; instead, plan incremental improvements in existing use cases. Normalcy will set in quickly.", "timestamp_start": "00:48:22", "timestamp_end": "00:50:39", "line_start": 310, "line_end": 324 }, { "id": "topic_13", "title": "OpenAI's B2B Strategy: Teams, Enterprise, and Domain-Specific Applications", "summary": "OpenAI offers Teams (multiple ChatGPT subscriptions), ChatGPT Enterprise (with SSO), and API access for developers. The core value for B2B is sharing domain-specific GPTs and prompt templates internally and across teams. Enterprise features include security controls, higher limits, and ability to restrict GPT store access. The biggest unlock is collaboration on custom AI applications.", "timestamp_start": "00:51:01", "timestamp_end": "00:52:29", "line_start": 328, "line_end": 339 }, { "id": "topic_14", "title": "New Model Releases: GPT-4 Turbo Updates and Embeddings v3", "summary": "Logan announces updated GPT-4 Turbo fixing the 'laziness' phenomenon and third-generation embeddings models. Embeddings v3 offers state-of-the-art performance, especially for non-English languages (historically weak), and costs 5x less. The use case is grounding AI responses in knowledge bases—like Lenny's podcast search. Embeddings enable 62,000 pages embedded for $1, unlocking question-answering over custom content.", "timestamp_start": "00:52:45", "timestamp_end": "00:55:07", "line_start": 343, "line_end": 354 }, { "id": "topic_15", "title": "Product Opportunities: Beyond Chat Interfaces", "summary": "Logan identifies the biggest opportunity for product builders: non-chat AI experiences. While chat is valuable, differentiated interfaces (like infinite canvas, specialized dashboards) create competitive advantage. He gives an example of a product that lets users query online sentiment about topics conversationally (e.g., 'What are people saying about GPT-4?') instead of using traditional dashboards with filters. Building core experiences from scratch with AI—not bolting on chat—is the key.", "timestamp_start": "00:55:34", "timestamp_end": "00:57:21", "line_start": 358, "line_end": 367 }, { "id": "topic_16", "title": "Call to Action: Now is the Time to Build with AI", "summary": "Logan encourages builders to create AI applications now. He offers to help developers get started and emphasizes this is the optimal moment. The world needs more solutions. Builders should reach out via Twitter or LinkedIn if they need support. He reinforces that this is the time to innovate, and OpenAI is invested in seeing cool applications built.", "timestamp_start": "00:58:31", "timestamp_end": "00:59:28", "line_start": 382, "line_end": 404 }, { "id": "topic_17", "title": "Lightning Round: Books, Movies, Interview Techniques, and Philosophy", "summary": "Logan shares book recommendations (One Room Schoolhouse by Sal Khan on how AI enables personalized education; Why We Sleep), favorite film (Gran Turismo), interview questions (asking about deeply held beliefs people disagree with), favorite product (Manta Sleep weighted mask), and personal motto ('Measure in Hundreds'—success comes from multiple attempts and compounding). These reveal his values around learning, perseverance, and health.", "timestamp_start": "00:59:34", "timestamp_end": "01:03:38", "line_start": 406, "line_end": 462 }, { "id": "topic_18", "title": "Bug Reporting and Community Feedback: How to Help OpenAI", "summary": "Logan asks users who encounter bugs to provide reproducible examples and shared chats. The model laziness issue was hard to debug without specific prompts and examples. OpenAI needs tangible, shareable context to fix problems. Community feedback with evidence is critical. This is how OpenAI improves—through detailed, actionable bug reports from users.", "timestamp_start": "01:04:55", "timestamp_end": "01:05:49", "line_start": 478, "line_end": 480 }, { "id": "topic_19", "title": "Call to Action: Start Using ChatGPT Now", "summary": "Lenny and Logan encourage listeners to stop theorizing and actually use ChatGPT. Hands-on experimentation reveals insights theory doesn't. Logan emphasizes that humans using AI tools will outcompete those without them—it's not AI replacing humans but augmented humans replacing non-augmented ones. ChatGPT costs $10-20/month (expensable at many companies), and now is the best time to learn and build.", "timestamp_start": "01:06:34", "timestamp_end": "01:07:39", "line_start": 484, "line_end": 505 } ], "insights": [ { "id": "i1", "text": "Finding people who are high agency and work with urgency is one of the most important hiring criteria. High agency people don't wait for 50 people's consensus—they see a problem and solve it immediately.", "context": "Logan explaining OpenAI's two core hiring attributes", "topic_id": "topic_7", "line_start": 1, "line_end": 2 }, { "id": "i2", "text": "It's a benefit that the OpenAI board crisis happened when the stakes were lower. If something similar happened 5-10 years in the future after transformative AGI progress, the consequences would be far worse.", "context": "Logan reflecting on the November board drama from a strategic perspective", "topic_id": "topic_1", "line_start": 43, "line_end": 44 }, { "id": "i3", "text": "The most surprising part of the board crisis was how quickly everyone got back to work. The entire company laser-focused immediately on the mission, which speaks to the caliber of the team.", "context": "Logan's key observation about OpenAI's resilience", "topic_id": "topic_1", "line_start": 38, "line_end": 39 }, { "id": "i4", "text": "2024 is the year of multimodal AI, but more importantly it's the year that people are really pushing the boundaries of new UX paradigms around AI—not just chat-based interfaces.", "context": "Logan discussing emerging interface trends beyond ChatGPT", "topic_id": "topic_2", "line_start": 62, "line_end": 63 }, { "id": "i5", "text": "OpenAI is deeply focused on general use cases. Builders should not try to compete with OpenAI on general assistants—instead, specialize in vertical applications where domain knowledge and custom UIs provide defensibility.", "context": "Strategic advice for founders building on OpenAI APIs", "topic_id": "topic_3", "line_start": 70, "line_end": 75 }, { "id": "i6", "text": "If you're going to try to build the next general assistant to compete with ChatGPT, it has to solve so many problems that ChatGPT doesn't that people actively choose it. Otherwise, you're competing on execution against our R&D effort, which is just hard.", "context": "Logan's realistic assessment of competing with OpenAI on general AI", "topic_id": "topic_3", "line_start": 79, "line_end": 81 }, { "id": "i7", "text": "Engineering is one of the highest leverage use cases for AI, with improvements on the order of at least 50% efficiency gain, especially for lower-hanging-fruit software engineering tasks.", "context": "Logan citing research on AI's impact on engineering productivity", "topic_id": "topic_4", "line_start": 89, "line_end": 90 }, { "id": "i8", "text": "With GPTs, companies can now build domain-specific applications that incorporate their company's nuance and voice, making AI solutions much more particular to their business needs than generic ChatGPT.", "context": "Logan explaining GPTs' value for enterprise personalization", "topic_id": "topic_4", "line_start": 92, "line_end": 93 }, { "id": "i9", "text": "Prompt engineering exists because models are trained to give you an answer to whatever you ask—'garbage in, garbage out.' The model doesn't tell you it lacks context; it just gives its best answer without enough information.", "context": "Logan explaining the fundamental reason prompt engineering is necessary", "topic_id": "topic_5", "line_start": 121, "line_end": 128 }, { "id": "i10", "text": "Imagine a model as a human with human-level intelligence but absolutely zero context. It doesn't know who you are, what you do, or what your goals are. This is why you get generic responses—people forget to provide context.", "context": "Logan's core framing of how to think about prompting models", "topic_id": "topic_5", "line_start": 128, "line_end": 129 }, { "id": "i11", "text": "Context is all you need. It's the only thing that matters when prompting language models. More context directly enables better outputs.", "context": "Logan's fundamental principle for prompt engineering", "topic_id": "topic_5", "line_start": 152, "line_end": 153 }, { "id": "i12", "text": "When prompting for information about someone, the model needs actual context about that person—links to their blog, tweets, background. For famous people with lots of online presence, the prompt works better. For less-known individuals, you must provide extra context.", "context": "Logan's advice on prompting for interview questions about specific people", "topic_id": "topic_5", "line_start": 143, "line_end": 146 }, { "id": "i13", "text": "Small details like adding a smiley face increase model performance by ~1-2%, which might seem trivial but compounds across large text generation tasks. The model is trained on human-to-human communication, so emotional signals matter.", "context": "Logan explaining why even small prompt adjustments help", "topic_id": "topic_5", "line_start": 158, "line_end": 164 }, { "id": "i14", "text": "Models will eventually learn to request clarification and higher-fidelity descriptions themselves, similar to how DALL-E auto-enhances vague prompts. This will reduce the need for careful prompt engineering over time.", "context": "Logan on the future of prompt engineering becoming unnecessary", "topic_id": "topic_5", "line_start": 131, "line_end": 137 }, { "id": "i15", "text": "The real value of GPTs is not just the custom prompts, but the ability to upload files, give custom instructions, integrate tools (code interpreter, browsing, image generation), and connect to external APIs without coding.", "context": "Logan describing GPT capabilities beyond simple prompt customization", "topic_id": "topic_6", "line_start": 179, "line_end": 180 }, { "id": "i16", "text": "The most exciting thing about GPTs is empowering non-developers to solve complex problems by giving models enough context. Non-technical users are now unlocked to build sophisticated applications.", "context": "Logan's main insight about GPTs' democratization potential", "topic_id": "topic_6", "line_start": 182, "line_end": 183 }, { "id": "i17", "text": "When monetization comes to the GPT store, it will be a huge unlock for creators. Getting paid based on GPT usage will open people's eyes to the business opportunity.", "context": "Logan anticipating the impact of GPT store monetization", "topic_id": "topic_6", "line_start": 185, "line_end": 186 }, { "id": "i18", "text": "People see problems and solve them without waiting for consensus or approvals. They hear customer challenges and immediately work on solutions. This speed comes from trust in high-agency people, not from formal processes.", "context": "Logan illustrating how high-agency culture enables speed", "topic_id": "topic_7", "line_start": 224, "line_end": 225 }, { "id": "i19", "text": "OpenAI's Assistants API came from teams recognizing developer feedback about needing higher-level abstractions, self-organizing to plan and build the solution quickly, without top-down direction.", "context": "Logan's example of high-agency teams self-organizing around customer needs", "topic_id": "topic_7", "line_start": 230, "line_end": 231 }, { "id": "i20", "text": "The core guiding principle for OpenAI's roadmap is: will this help us get to AGI? Everything is filtered through the mission lens, even if it conflicts with optimizing for user engagement.", "context": "Logan explaining OpenAI's prioritization framework", "topic_id": "topic_8", "line_start": 236, "line_end": 237 }, { "id": "i21", "text": "Reliability is a core tenet of roadmapping. OpenAI will deprioritize new features and shinier opportunities to focus on robust, reliable API experiences when reliability is the problem.", "context": "Logan on OpenAI's operational values", "topic_id": "topic_8", "line_start": 239, "line_end": 240 }, { "id": "i22", "text": "OpenAI is not an OKR-driven company. Planning is at a higher level—H1 and quarterly goals—with flexibility to adapt as ground truths change in the fast-moving AI space.", "context": "Logan on OpenAI's non-traditional planning approach", "topic_id": "topic_8", "line_start": 248, "line_end": 249 }, { "id": "i23", "text": "Revenue is not the goal—it's a proxy for obtaining compute. Compute enables better model training, which gets OpenAI closer to AGI. This is the multi-step logic behind seemingly business-focused metrics.", "context": "Logan explaining OpenAI's strategic metrics framework", "topic_id": "topic_8", "line_start": 254, "line_end": 255 }, { "id": "i24", "text": "OpenAI has a Slack-heavy culture that enables real-time coordination across distributed teams faster than even walking over to someone's desk. This is crucial for moving fast.", "context": "Logan identifying Slack as a key operational tool", "topic_id": "topic_9", "line_start": 260, "line_end": 261 }, { "id": "i25", "text": "In a GPU-constrained world, adding new researchers is often a net productivity loss unless they significantly level up the entire team. Adding engineers is different—more engineers means more code and better throughput.", "context": "Logan explaining OpenAI's intentionally small research team", "topic_id": "topic_10", "line_start": 281, "line_end": 285 }, { "id": "i26", "text": "The next evolution of GPTs is moving from synchronous interaction (chat) to asynchronous agents that work over time. Users will say 'go do this thing and let me know when you're done,' not 'give me the answer now.'", "context": "Logan on the agent future of AI products", "topic_id": "topic_11", "line_start": 293, "line_end": 296 }, { "id": "i27", "text": "GPTs will onboard the next hundreds of millions of people to AI by providing specific, vertical solutions instead of a blank slate. People don't know what to do with 'unlimited possibilities' but understand 'solves this specific problem.'", "context": "Logan on GPTs as the on-ramp for mainstream AI adoption", "topic_id": "topic_11", "line_start": 299, "line_end": 303 }, { "id": "i28", "text": "Don't plan for GPT-5 to be revolutionary—plan for it to be a better tool for solving existing problems. Build assuming incremental improvements, not magical breakthroughs. Avoid unrealistic expectations.", "context": "Logan's pragmatic advice for building with future models in mind", "topic_id": "topic_12", "line_start": 317, "line_end": 324 }, { "id": "i29", "text": "Normalize AI tools quickly in your mental model. Products that assume widespread adoption and normalization of AI will have an edge over those betting on constant amazement and revolutionary change.", "context": "Logan on strategic positioning for the AI future", "topic_id": "topic_12", "line_start": 323, "line_end": 324 }, { "id": "i30", "text": "The core value of OpenAI's B2B offering is sharing domain-specific GPTs and prompt templates internally, enabling teams to collaborate on custom AI applications that solve their unique problems.", "context": "Logan on OpenAI's B2B differentiation", "topic_id": "topic_13", "line_start": 332, "line_end": 333 }, { "id": "i31", "text": "Embeddings v3 enables grounding AI in facts. By embedding knowledge bases and finding semantic similarity between queries and stored information, AI can answer with evidence instead of hallucinating.", "context": "Logan explaining embeddings use case for reliable AI", "topic_id": "topic_14", "line_start": 350, "line_end": 351 }, { "id": "i32", "text": "The biggest opportunity for product builders is moving beyond chat interfaces. Differentiated UX built from the ground up with AI at the core—not chat bolted on—creates defensible products.", "context": "Logan identifying the biggest product opportunity", "topic_id": "topic_15", "line_start": 359, "line_end": 360 }, { "id": "i33", "text": "Building products that let users ask natural language questions to get data-grounded answers (instead of dashboard filtering) is the magical unlock. It's about reducing friction and making AI the core experience.", "context": "Logan giving an example of non-chat product innovation", "topic_id": "topic_15", "line_start": 362, "line_end": 366 }, { "id": "i34", "text": "It's not AI that will replace humans—it's humans augmented with AI tools that will replace non-augmented humans in the job market. The competitive advantage goes to people and teams using these tools.", "context": "Logan's perspective on AI and employment", "topic_id": "topic_19", "line_start": 485, "line_end": 486 }, { "id": "i35", "text": "Now is the best time to learn AI tools. Every day you delay learning and using ChatGPT is a day you're falling behind in competitive ability for your job and projects.", "context": "Logan's call to action on learning AI", "topic_id": "topic_19", "line_start": 485, "line_end": 486 }, { "id": "i36", "text": "'Measure in hundreds' is a principle for success. If you've failed five times, you've failed and tried zero times. Success is built on compounding multiple attempts. You need to try enough to win.", "context": "Logan's personal motto on persistence and iteration", "topic_id": "topic_17", "line_start": 461, "line_end": 462 } ], "examples": [ { "id": "ex1", "explicit_text": "At my previous company... we had this feedback from developers that people wanted these higher levels of abstraction on top of our existing APIs, and a bunch of folks on the team just came together and were like, 'Hey, let's put together what the plan would look like to build something like this,' And then very quickly came together and actually built the actual API that now powers so many people's assistant applications", "inferred_identity": "OpenAI - Assistants API", "confidence": "high", "tags": [ "OpenAI", "API design", "Developer feedback", "High agency", "Self-organization", "Assistants API", "Iteration speed", "Internal collaboration" ], "lesson": "Teams with high agency don't wait for top-down directives; they identify customer problems and self-organize to build solutions quickly, creating valuable developer infrastructure.", "topic_id": "topic_7", "line_start": 230, "line_end": 231 }, { "id": "ex2", "explicit_text": "There's a really great Harvard Business School study about... the order of magnitude of efficiency gain for those folks who are using AI tools, I think it was chat GPT specifically in those use cases that they were using, comparatively against folks who aren't using AI.", "inferred_identity": "Harvard Business School / Boston Consulting Group study on AI productivity", "confidence": "medium", "tags": [ "Research", "AI productivity", "ChatGPT", "Efficiency gains", "Empirical data", "Benchmarking", "Business impact" ], "lesson": "Academic research validates that companies using AI tools (specifically ChatGPT) see measurable efficiency improvements compared to competitors, quantifying the competitive advantage.", "topic_id": "topic_4", "line_start": 86, "line_end": 87 }, { "id": "ex3", "explicit_text": "There's a really great... Zapier... All of the stuff that Zapier has done with GPTs is the most useful stuff that you could imagine. You can go so far with what... as a third party developer, integrate Zapier without knowing how to code into your GPT. So they're pushing a lot of this stuff, and then basically all 5,000 connections that are possible with Zapier today, you can bring into your GPT and essentially enable it to do anything.", "inferred_identity": "Zapier", "confidence": "high", "tags": [ "Zapier", "GPTs", "No-code integration", "API connections", "Workflow automation", "Developer experience", "Extensibility", "Third-party integrations" ], "lesson": "Zapier's GPT integration allows non-technical users to automate 5,000+ integrations without coding, demonstrating how third-party platforms can extend AI capabilities and create compelling experiences.", "topic_id": "topic_6", "line_start": 191, "line_end": 192 }, { "id": "ex4", "explicit_text": "Harvey... it's this legal AI use case where they're building custom models and tools to help lawyers and people at legal firms and stuff like that. And that's a great example of our models are probably never going to be as capable as some of the things that Harvey's doing because our goal and our mission is really to solve this very general use case and then people can do things like fine-tuning and build all their own custom UI and product features on top of that.", "inferred_identity": "Harvey (Legal AI startup)", "confidence": "high", "tags": [ "Harvey", "Legal tech", "Vertical specialization", "Fine-tuning", "Custom models", "Domain expertise", "B2B SaaS", "Competitive positioning" ], "lesson": "Vertical-specific companies like Harvey can build better domain-specific legal AI than OpenAI by combining fine-tuning with legal expertise and custom UX, demonstrating the strategy for competing on OpenAI's platform.", "topic_id": "topic_3", "line_start": 71, "line_end": 72 }, { "id": "ex5", "explicit_text": "A good friend, his name's Dennis Yang, he works at Chime, and he told me about two things that they're doing at Chime that seem to be providing value. One is he built a GPT that helps write ads for Facebook and Google just gives you ideas for ads to run, and so that takes a little load off the marketing team or the growth team. And then he built another GPT that delivers experiment results, kind of like a data scientist, with here's the result of this experiment. And then you could talk to it and ask for like, 'Hey, how much longer do you think we should run this for,' or, 'What might this imply about our product'", "inferred_identity": "Chime (fintech company)", "confidence": "high", "tags": [ "Chime", "Fintech", "Internal tools", "Marketing automation", "Ad generation", "Data analysis", "Experiment analysis", "Internal GPTs", "Growth team", "Productivity" ], "lesson": "Companies are building internal GPTs for marketing (ad copywriting) and data analysis (experiment interpretation), reducing specialist dependencies and accelerating decision-making without hiring more people.", "topic_id": "topic_4", "line_start": 95, "line_end": 96 }, { "id": "ex6", "explicit_text": "I've seen some interesting GPTs around the planning use cases, like you want to do OKR planning for your team or something like that. I just actually saw somebody tweet it literally yesterday. I've seen some cool venture capital ones of doing diligence on a deal flow, which is kind of interesting, and getting some different perspectives. I think all of those horizontal use cases where you can bring in a different personality and get perspective on different things I think is really cool. I've personally used a GPT, the private GPT that I use myself that helps with some of the planning stuff for different quarters, and just making sure that I'm being consistent in how I'm framing things like driving back to individual metrics", "inferred_identity": "Logan Kilpatrick's personal planning GPT + VC diligence use cases", "confidence": "high", "tags": [ "Planning", "OKR", "Venture capital", "Deal diligence", "Strategic planning", "Consistency checking", "Metrics alignment", "Internal use case", "Personal productivity" ], "lesson": "GPTs are being used for non-technical planning tasks (OKRs, VC diligence, quarterly planning) where they serve as a consistency checker and secondary perspective provider, forcing better thinking through structured prompts.", "topic_id": "topic_4", "line_start": 104, "line_end": 105 }, { "id": "ex7", "explicit_text": "TL Draw... they're sort of building this infinite canvas experience and you can imagine how, as you're interacting with an AI on a daily basis, you might want to jump over to your infinite canvas, which the AI has filled in all the details and you might see a reference to a file and to a video and all of these different things... it's such a cool way. It actually makes a lot more sense for us as humans to see stuff in that type of format than, I think, just listing out a bunch of stuff in chat.", "inferred_identity": "TL Draw", "confidence": "high", "tags": [ "TL Draw", "Infinite canvas", "Visual interface", "AI-powered design", "Product design", "Non-chat UX", "Multimodal interaction", "New interfaces" ], "lesson": "Building new AI interfaces beyond chat (like infinite canvas) makes information more human-comprehensible and allows users to visualize and build on AI outputs, creating better user experiences.", "topic_id": "topic_2", "line_start": 59, "line_end": 60 }, { "id": "ex8", "explicit_text": "Rabbit R-1... consumer hardware device", "inferred_identity": "Rabbit R-1 (consumer AI device)", "confidence": "high", "tags": [ "Rabbit R-1", "Hardware", "Consumer AI", "New interfaces", "AI devices", "Consumer product" ], "lesson": "Hardware devices specifically designed for AI interactions represent a new interface paradigm for how consumers will interact with AI in the post-smartphone era.", "topic_id": "topic_2", "line_start": 53, "line_end": 53 }, { "id": "ex9", "explicit_text": "If you were interviewing Tom Cruise or something like that, somebody who has a lot of information about them on the internet. It probably works a little bit better [for prompt engineering].", "inferred_identity": "Tom Cruise (celebrity example)", "confidence": "high", "tags": [ "Celebrity", "Public figures", "Prompt engineering", "Training data availability", "Model knowledge", "Information availability" ], "lesson": "Models have more training data on famous public figures, so prompts about them work better. Prompts about less-known individuals need additional context provision to overcome the model's knowledge limitations.", "topic_id": "topic_5", "line_start": 146, "line_end": 147 }, { "id": "ex10", "explicit_text": "The Canva... GPT... is currently the top GPT", "inferred_identity": "Canva GPT", "confidence": "high", "tags": [ "Canva", "Design", "GPT store", "Top GPT", "Design tool integration", "Popular application" ], "lesson": "Popular design tools are leveraging GPTs to add AI capabilities, demonstrating how existing platforms can quickly enhance their offerings with custom AI applications.", "topic_id": "topic_6", "line_start": 189, "line_end": 189 }, { "id": "ex11", "explicit_text": "Universal Primer which helps you learn. It's described as, 'Learn everything about anything,' and basically, I think, it's kind of this Socratic method of helping you learn stuff... it's the number two education GPT.", "inferred_identity": "Universal Primer by Runway (created by CEO)", "confidence": "high", "tags": [ "Universal Primer", "Runway", "Education", "Learning", "Socratic method", "Knowledge acquisition", "GPT store", "Top education GPT" ], "lesson": "Educational GPTs using Socratic dialogue enable interactive learning, demonstrating how AI can personalize education through conversation rather than content delivery.", "topic_id": "topic_6", "line_start": 200, "line_end": 201 }, { "id": "ex12", "explicit_text": "I've used this product that allows you to essentially manage or view the conversations that are happening online around certain topics and stuff like that. So I can go and look online. What are people saying about GPT-4? And what I just said out loud, 'What are people saying about GPT-4,' is the actual question that I have. And in a normal product experience today, I have to go into a bunch of dashboards and change a bunch of filters and stuff like that.", "inferred_identity": "Sentiment/conversation tracking product (unnamed)", "confidence": "medium", "tags": [ "Analytics", "Sentiment analysis", "Social monitoring", "Conversation tracking", "Data query", "Natural language interface", "AI-powered dashboard" ], "lesson": "Products that convert natural language questions into data queries (replacing traditional dashboards) provide superior user experience and demonstrate the power of AI in making data accessible to non-technical users.", "topic_id": "topic_15", "line_start": 362, "line_end": 364 }, { "id": "ex13", "explicit_text": "I'll share an example. So I have this good friend, his name's Visua... Visua... Wait, there's another product. Can you go and look online. What are people saying about GPT-4? Visualelectric.com, which I think is doing exactly this. It's basically a tool specifically built for creatives, I think specifically graphic design, to help them create imagery.", "inferred_identity": "Visualelectric.com", "confidence": "high", "tags": [ "Visualelectric", "Graphic design", "Creative tools", "AI-powered design", "Image generation", "Infinite canvas", "Creative professionals", "DALL-E integration" ], "lesson": "Specialized AI tools for creatives (graphic designers) that go beyond simple image generation to provide an integrated design canvas show how vertical products can create compelling experiences.", "topic_id": "topic_15", "line_start": 368, "line_end": 369 }, { "id": "ex14", "explicit_text": "I had a bunch of really great studies they publish around copilots and you could use those as an analogy for what people are getting from ChatGPT as well.", "inferred_identity": "GitHub Copilot (productivity studies)", "confidence": "high", "tags": [ "GitHub Copilot", "Productivity research", "Code generation", "Developer tools", "Efficiency metrics", "Empirical studies" ], "lesson": "GitHub Copilot studies provide empirical evidence of AI's impact on engineering productivity and can be used as analogs for ChatGPT's broader productivity benefits.", "topic_id": "topic_4", "line_start": 89, "line_end": 90 }, { "id": "ex15", "explicit_text": "I wrote a long time ago and came back to recently, is the One Room Schoolhouse by Sal Khan. Incredible... AI is what is going to enable Sal Khan's vision of a teacher per student to actually happen.", "inferred_identity": "Sal Khan / Khan Academy", "confidence": "high", "tags": [ "Khan Academy", "Education", "Personalized learning", "Teacher per student", "Education AI", "Learning at scale" ], "lesson": "AI is the technology that can finally realize the vision of personalized one-on-one tutoring at scale, validating education as a high-impact domain for AI applications.", "topic_id": "topic_17", "line_start": 413, "line_end": 414 }, { "id": "ex16", "explicit_text": "I'm a sucker for a good inspirational human story. So I watched, with my family recently over the holidays, this Gran Turismo movie, and it's a story about someone who, a kid from London, who grew up doing SIM racing, which is a virtual race car, and did this competition, ended up becoming a real professional race car driver through some competition.", "inferred_identity": "Gran Turismo film", "confidence": "high", "tags": [ "Gran Turismo", "Film", "SIM racing", "Real-world competition", "Skill transfer", "Video games", "Inspiration" ], "lesson": "Inspirational stories about people overcoming odds through dedication apply to the AI era—people using AI tools to amplify their abilities will outcompete those without them.", "topic_id": "topic_17", "line_start": 419, "line_end": 420 }, { "id": "ex17", "explicit_text": "I have this really nice sleep mask from this company called... Not being paid. I just say this, but it's called Manta Sleep or something like that. It's a weighted sleep mask and it feels incredible when I... I don't know. Maybe I just have a heavy head or something like that, but it feels good to wear a weighted sleep mask at night.", "inferred_identity": "Manta Sleep", "confidence": "high", "tags": [ "Manta Sleep", "Sleep tech", "Weighted mask", "Product", "Sleep quality", "Wellness" ], "lesson": "Specialized sleep products (weighted masks) are valuable tools for optimizing rest, which Logan emphasizes is foundational to productivity and success.", "topic_id": "topic_17", "line_start": 437, "line_end": 438 }, { "id": "ex18", "explicit_text": "Lennybot.com. And my assumption was that lennybot.com is actually powered by embedding. So you take all of the corpus of knowledge. You take all the recordings, your blog post. You embed them, and then when people ask questions, you can actually go in and see the similarity between the question and the corpus of knowledge and then provide an answer to somebody's question and reference an empirical fact, something that's true from your knowledge base.", "inferred_identity": "Lennybot.com (Lenny's podcast search tool)", "confidence": "high", "tags": [ "Lennybot", "Embeddings", "Semantic search", "Knowledge base", "Podcast search", "Grounded QA", "Lenny's podcast" ], "lesson": "Embeddings enable grounding AI responses in actual knowledge bases, allowing products like podcast search to provide citations and factual answers instead of hallucinations.", "topic_id": "topic_14", "line_start": 350, "line_end": 351 }, { "id": "ex19", "explicit_text": "I think this is one of the more challenging pieces at OpenAI. There's so many. Everyone wants everything from us, and today, especially, in the world of ChatGPT and how large and well-used our API is, people will just come to us and say, 'Hey, we want all of these things.'", "inferred_identity": "OpenAI's customer demand management challenge", "confidence": "high", "tags": [ "OpenAI", "Product prioritization", "Customer demands", "Scope management", "High demand", "Resource constraints" ], "lesson": "Popular platforms face the challenge of managing unlimited demand from customers while maintaining focus on mission-critical work—requires strong prioritization principles and saying no.", "topic_id": "topic_8", "line_start": 236, "line_end": 237 }, { "id": "ex20", "explicit_text": "Sam was talking about how Slack is his number one most used app on his phone and, 'I don't even look at the time thing on my phone anymore because I don't want to know how long I'm using Slack'", "inferred_identity": "Sam Altman (OpenAI CEO)", "confidence": "high", "tags": [ "Sam Altman", "Slack", "Communication", "Collaboration tool", "Tool adoption", "Internal culture" ], "lesson": "Even the CEO of one of the fastest-growing companies spends most of their mobile time in Slack, demonstrating its centrality to organizational communication and coordination.", "topic_id": "topic_9", "line_start": 263, "line_end": 264 } ] }