Podcast appearances and mentions of Jessica Livingston

If you're in SF: Join us for the Claude Plays Pokemon hackathon this Sunday!If you're not: Fill out the 2025 State of AI Eng survey for $250 in Amazon cards!We are SO excited to share our conversation with Dharmesh Shah, co-founder of HubSpot and creator of Agent.ai.A particularly compelling concept we discussed is the idea of "hybrid teams" - the next evolution in workplace organization where human workers collaborate with AI agents as team members. Just as we previously saw hybrid teams emerge in terms of full-time vs. contract workers, or in-office vs. remote workers, Dharmesh predicts that the next frontier will be teams composed of both human and AI members. This raises interesting questions about team dynamics, trust, and how to effectively delegate tasks between human and AI team members.The discussion of business models in AI reveals an important distinction between Work as a Service (WaaS) and Results as a Service (RaaS), something Dharmesh has written extensively about. While RaaS has gained popularity, particularly in customer support applications where outcomes are easily measurable, Dharmesh argues that this model may be over-indexed. Not all AI applications have clearly definable outcomes or consistent economic value per transaction, making WaaS more appropriate in many cases. This insight is particularly relevant for businesses considering how to monetize AI capabilities.The technical challenges of implementing effective agent systems are also explored, particularly around memory and authentication. Shah emphasizes the importance of cross-agent memory sharing and the need for more granular control over data access. He envisions a future where users can selectively share parts of their data with different agents, similar to how OAuth works but with much finer control. This points to significant opportunities in developing infrastructure for secure and efficient agent-to-agent communication and data sharing.Other highlights from our conversation* The Evolution of AI-Powered Agents – Exploring how AI agents have evolved from simple chatbots to sophisticated multi-agent systems, and the role of MCPs in enabling that.* Hybrid Digital Teams and the Future of Work – How AI agents are becoming teammates rather than just tools, and what this means for business operations and knowledge work.* Memory in AI Agents – The importance of persistent memory in AI systems and how shared memory across agents could enhance collaboration and efficiency.* Business Models for AI Agents – Exploring the shift from software as a service (SaaS) to work as a service (WaaS) and results as a service (RaaS), and what this means for monetization.* The Role of Standards Like MCP – Why MCP has been widely adopted and how it enables agent collaboration, tool use, and discovery.* The Future of AI Code Generation and Software Engineering – How AI-assisted coding is changing the role of software engineers and what skills will matter most in the future.* Domain Investing and Efficient Markets – Dharmesh's approach to domain investing and how inefficiencies in digital asset markets create business opportunities.* The Philosophy of Saying No – Lessons from "Sorry, You Must Pass" and how prioritization leads to greater productivity and focus.Timestamps* 00:00 Introduction and Guest Welcome* 02:29 Dharmesh Shah's Journey into AI* 05:22 Defining AI Agents* 06:45 The Evolution and Future of AI Agents* 13:53 Graph Theory and Knowledge Representation* 20:02 Engineering Practices and Overengineering* 25:57 The Role of Junior Engineers in the AI Era* 28:20 Multi-Agent Systems and MCP Standards* 35:55 LinkedIn's Legal Battles and Data Scraping* 37:32 The Future of AI and Hybrid Teams* 39:19 Building Agent AI: A Professional Network for Agents* 40:43 Challenges and Innovations in Agent AI* 45:02 The Evolution of UI in AI Systems* 01:00:25 Business Models: Work as a Service vs. Results as a Service* 01:09:17 The Future Value of Engineers* 01:09:51 Exploring the Role of Agents* 01:10:28 The Importance of Memory in AI* 01:11:02 Challenges and Opportunities in AI Memory* 01:12:41 Selective Memory and Privacy Concerns* 01:13:27 The Evolution of AI Tools and Platforms* 01:18:23 Domain Names and AI Projects* 01:32:08 Balancing Work and Personal Life* 01:35:52 Final Thoughts and ReflectionsTranscriptAlessio [00:00:04]: Hey everyone, welcome back to the Latent Space podcast. This is Alessio, partner and CTO at Decibel Partners, and I'm joined by my co-host Swyx, founder of Small AI.swyx [00:00:12]: Hello, and today we're super excited to have Dharmesh Shah to join us. I guess your relevant title here is founder of Agent AI.Dharmesh [00:00:20]: Yeah, that's true for this. Yeah, creator of Agent.ai and co-founder of HubSpot.swyx [00:00:25]: Co-founder of HubSpot, which I followed for many years, I think 18 years now, gonna be 19 soon. And you caught, you know, people can catch up on your HubSpot story elsewhere. I should also thank Sean Puri, who I've chatted with back and forth, who's been, I guess, getting me in touch with your people. But also, I think like, just giving us a lot of context, because obviously, My First Million joined you guys, and they've been chatting with you guys a lot. So for the business side, we can talk about that, but I kind of wanted to engage your CTO, agent, engineer side of things. So how did you get agent religion?Dharmesh [00:01:00]: Let's see. So I've been working, I'll take like a half step back, a decade or so ago, even though actually more than that. So even before HubSpot, the company I was contemplating that I had named for was called Ingenisoft. And the idea behind Ingenisoft was a natural language interface to business software. Now realize this is 20 years ago, so that was a hard thing to do. But the actual use case that I had in mind was, you know, we had data sitting in business systems like a CRM or something like that. And my kind of what I thought clever at the time. Oh, what if we used email as the kind of interface to get to business software? And the motivation for using email is that it automatically works when you're offline. So imagine I'm getting on a plane or I'm on a plane. There was no internet on planes back then. It's like, oh, I'm going through business cards from an event I went to. I can just type things into an email just to have them all in the backlog. When it reconnects, it sends those emails to a processor that basically kind of parses effectively the commands and updates the software, sends you the file, whatever it is. And there was a handful of commands. I was a little bit ahead of the times in terms of what was actually possible. And I reattempted this natural language thing with a product called ChatSpot that I did back 20...swyx [00:02:12]: Yeah, this is your first post-ChatGPT project.Dharmesh [00:02:14]: I saw it come out. Yeah. And so I've always been kind of fascinated by this natural language interface to software. Because, you know, as software developers, myself included, we've always said, oh, we build intuitive, easy-to-use applications. And it's not intuitive at all, right? Because what we're doing is... We're taking the mental model that's in our head of what we're trying to accomplish with said piece of software and translating that into a series of touches and swipes and clicks and things like that. And there's nothing natural or intuitive about it. And so natural language interfaces, for the first time, you know, whatever the thought is you have in your head and expressed in whatever language that you normally use to talk to yourself in your head, you can just sort of emit that and have software do something. And I thought that was kind of a breakthrough, which it has been. And it's gone. So that's where I first started getting into the journey. I started because now it actually works, right? So once we got ChatGPT and you can take, even with a few-shot example, convert something into structured, even back in the ChatGP 3.5 days, it did a decent job in a few-shot example, convert something to structured text if you knew what kinds of intents you were going to have. And so that happened. And that ultimately became a HubSpot project. But then agents intrigued me because I'm like, okay, well, that's the next step here. So chat's great. Love Chat UX. But if we want to do something even more meaningful, it felt like the next kind of advancement is not this kind of, I'm chatting with some software in a kind of a synchronous back and forth model, is that software is going to do things for me in kind of a multi-step way to try and accomplish some goals. So, yeah, that's when I first got started. It's like, okay, what would that look like? Yeah. And I've been obsessed ever since, by the way.Alessio [00:03:55]: Which goes back to your first experience with it, which is like you're offline. Yeah. And you want to do a task. You don't need to do it right now. You just want to queue it up for somebody to do it for you. Yes. As you think about agents, like, let's start at the easy question, which is like, how do you define an agent? Maybe. You mean the hardest question in the universe? Is that what you mean?Dharmesh [00:04:12]: You said you have an irritating take. I do have an irritating take. I think, well, some number of people have been irritated, including within my own team. So I have a very broad definition for agents, which is it's AI-powered software that accomplishes a goal. Period. That's it. And what irritates people about it is like, well, that's so broad as to be completely non-useful. And I understand that. I understand the criticism. But in my mind, if you kind of fast forward months, I guess, in AI years, the implementation of it, and we're already starting to see this, and we'll talk about this, different kinds of agents, right? So I think in addition to having a usable definition, and I like yours, by the way, and we should talk more about that, that you just came out with, the classification of agents actually is also useful, which is, is it autonomous or non-autonomous? Does it have a deterministic workflow? Does it have a non-deterministic workflow? Is it working synchronously? Is it working asynchronously? Then you have the different kind of interaction modes. Is it a chat agent, kind of like a customer support agent would be? You're having this kind of back and forth. Is it a workflow agent that just does a discrete number of steps? So there's all these different flavors of agents. So if I were to draw it in a Venn diagram, I would draw a big circle that says, this is agents, and then I have a bunch of circles, some overlapping, because they're not mutually exclusive. And so I think that's what's interesting, and we're seeing development along a bunch of different paths, right? So if you look at the first implementation of agent frameworks, you look at Baby AGI and AutoGBT, I think it was, not Autogen, that's the Microsoft one. They were way ahead of their time because they assumed this level of reasoning and execution and planning capability that just did not exist, right? So it was an interesting thought experiment, which is what it was. Even the guy that, I'm an investor in Yohei's fund that did Baby AGI. It wasn't ready, but it was a sign of what was to come. And so the question then is, when is it ready? And so lots of people talk about the state of the art when it comes to agents. I'm a pragmatist, so I think of the state of the practical. It's like, okay, well, what can I actually build that has commercial value or solves actually some discrete problem with some baseline of repeatability or verifiability?swyx [00:06:22]: There was a lot, and very, very interesting. I'm not irritated by it at all. Okay. As you know, I take a... There's a lot of anthropological view or linguistics view. And in linguistics, you don't want to be prescriptive. You want to be descriptive. Yeah. So you're a goals guy. That's the key word in your thing. And other people have other definitions that might involve like delegated trust or non-deterministic work, LLM in the loop, all that stuff. The other thing I was thinking about, just the comment on Baby AGI, LGBT. Yeah. In that piece that you just read, I was able to go through our backlog and just kind of track the winter of agents and then the summer now. Yeah. And it's... We can tell the whole story as an oral history, just following that thread. And it's really just like, I think, I tried to explain the why now, right? Like I had, there's better models, of course. There's better tool use with like, they're just more reliable. Yep. Better tools with MCP and all that stuff. And I'm sure you have opinions on that too. Business model shift, which you like a lot. I just heard you talk about RAS with MFM guys. Yep. Cost is dropping a lot. Yep. Inference is getting faster. There's more model diversity. Yep. Yep. I think it's a subtle point. It means that like, you have different models with different perspectives. You don't get stuck in the basin of performance of a single model. Sure. You can just get out of it by just switching models. Yep. Multi-agent research and RL fine tuning. So I just wanted to let you respond to like any of that.Dharmesh [00:07:44]: Yeah. A couple of things. Connecting the dots on the kind of the definition side of it. So we'll get the irritation out of the way completely. I have one more, even more irritating leap on the agent definition thing. So here's the way I think about it. By the way, the kind of word agent, I looked it up, like the English dictionary definition. The old school agent, yeah. Is when you have someone or something that does something on your behalf, like a travel agent or a real estate agent acts on your behalf. It's like proxy, which is a nice kind of general definition. So the other direction I'm sort of headed, and it's going to tie back to tool calling and MCP and things like that, is if you, and I'm not a biologist by any stretch of the imagination, but we have these single-celled organisms, right? Like the simplest possible form of what one would call life. But it's still life. It just happens to be single-celled. And then you can combine cells and then cells become specialized over time. And you have much more sophisticated organisms, you know, kind of further down the spectrum. In my mind, at the most fundamental level, you can almost think of having atomic agents. What is the simplest possible thing that's an agent that can still be called an agent? What is the equivalent of a kind of single-celled organism? And the reason I think that's useful is right now we're headed down the road, which I think is very exciting around tool use, right? That says, okay, the LLMs now can be provided a set of tools that it calls to accomplish whatever it needs to accomplish in the kind of furtherance of whatever goal it's trying to get done. And I'm not overly bothered by it, but if you think about it, if you just squint a little bit and say, well, what if everything was an agent? And what if tools were actually just atomic agents? Because then it's turtles all the way down, right? Then it's like, oh, well, all that's really happening with tool use is that we have a network of agents that know about each other through something like an MMCP and can kind of decompose a particular problem and say, oh, I'm going to delegate this to this set of agents. And why do we need to draw this distinction between tools, which are functions most of the time? And an actual agent. And so I'm going to write this irritating LinkedIn post, you know, proposing this. It's like, okay. And I'm not suggesting we should call even functions, you know, call them agents. But there is a certain amount of elegance that happens when you say, oh, we can just reduce it down to one primitive, which is an agent that you can combine in complicated ways to kind of raise the level of abstraction and accomplish higher order goals. Anyway, that's my answer. I'd say that's a success. Thank you for coming to my TED Talk on agent definitions.Alessio [00:09:54]: How do you define the minimum viable agent? Do you already have a definition for, like, where you draw the line between a cell and an atom? Yeah.Dharmesh [00:10:02]: So in my mind, it has to, at some level, use AI in order for it to—otherwise, it's just software. It's like, you know, we don't need another word for that. And so that's probably where I draw the line. So then the question, you know, the counterargument would be, well, if that's true, then lots of tools themselves are actually not agents because they're just doing a database call or a REST API call or whatever it is they're doing. And that does not necessarily qualify them, which is a fair counterargument. And I accept that. It's like a good argument. I still like to think about—because we'll talk about multi-agent systems, because I think—so we've accepted, which I think is true, lots of people have said it, and you've hopefully combined some of those clips of really smart people saying this is the year of agents, and I completely agree, it is the year of agents. But then shortly after that, it's going to be the year of multi-agent systems or multi-agent networks. I think that's where it's going to be headed next year. Yeah.swyx [00:10:54]: Opening eyes already on that. Yeah. My quick philosophical engagement with you on this. I often think about kind of the other spectrum, the other end of the cell spectrum. So single cell is life, multi-cell is life, and you clump a bunch of cells together in a more complex organism, they become organs, like an eye and a liver or whatever. And then obviously we consider ourselves one life form. There's not like a lot of lives within me. I'm just one life. And now, obviously, I don't think people don't really like to anthropomorphize agents and AI. Yeah. But we are extending our consciousness and our brain and our functionality out into machines. I just saw you were a Bee. Yeah. Which is, you know, it's nice. I have a limitless pendant in my pocket.Dharmesh [00:11:37]: I got one of these boys. Yeah.swyx [00:11:39]: I'm testing it all out. You know, got to be early adopters. But like, we want to extend our personal memory into these things so that we can be good at the things that we're good at. And, you know, machines are good at it. Machines are there. So like, my definition of life is kind of like going outside of my own body now. I don't know if you've ever had like reflections on that. Like how yours. How our self is like actually being distributed outside of you. Yeah.Dharmesh [00:12:01]: I don't fancy myself a philosopher. But you went there. So yeah, I did go there. I'm fascinated by kind of graphs and graph theory and networks and have been for a long, long time. And to me, we're sort of all nodes in this kind of larger thing. It just so happens that we're looking at individual kind of life forms as they exist right now. But so the idea is when you put a podcast out there, there's these little kind of nodes you're putting out there of like, you know, conceptual ideas. Once again, you have varying kind of forms of those little nodes that are up there and are connected in varying and sundry ways. And so I just think of myself as being a node in a massive, massive network. And I'm producing more nodes as I put content or ideas. And, you know, you spend some portion of your life collecting dots, experiences, people, and some portion of your life then connecting dots from the ones that you've collected over time. And I found that really interesting things happen and you really can't know in advance how those dots are necessarily going to connect in the future. And that's, yeah. So that's my philosophical take. That's the, yes, exactly. Coming back.Alessio [00:13:04]: Yep. Do you like graph as an agent? Abstraction? That's been one of the hot topics with LandGraph and Pydantic and all that.Dharmesh [00:13:11]: I do. The thing I'm more interested in terms of use of graphs, and there's lots of work happening on that now, is graph data stores as an alternative in terms of knowledge stores and knowledge graphs. Yeah. Because, you know, so I've been in software now 30 plus years, right? So it's not 10,000 hours. It's like 100,000 hours that I've spent doing this stuff. And so I've grew up with, so back in the day, you know, I started on mainframes. There was a product called IMS from IBM, which is basically an index database, what we'd call like a key value store today. Then we've had relational databases, right? We have tables and columns and foreign key relationships. We all know that. We have document databases like MongoDB, which is sort of a nested structure keyed by a specific index. We have vector stores, vector embedding database. And graphs are interesting for a couple of reasons. One is, so it's not classically structured in a relational way. When you say structured database, to most people, they're thinking tables and columns and in relational database and set theory and all that. Graphs still have structure, but it's not the tables and columns structure. And you could wonder, and people have made this case, that they are a better representation of knowledge for LLMs and for AI generally than other things. So that's kind of thing number one conceptually, and that might be true, I think is possibly true. And the other thing that I really like about that in the context of, you know, I've been in the context of data stores for RAG is, you know, RAG, you say, oh, I have a million documents, I'm going to build the vector embeddings, I'm going to come back with the top X based on the semantic match, and that's fine. All that's very, very useful. But the reality is something gets lost in the chunking process and the, okay, well, those tend, you know, like, you don't really get the whole picture, so to speak, and maybe not even the right set of dimensions on the kind of broader picture. And it makes intuitive sense to me that if we did capture it properly in a graph form, that maybe that feeding into a RAG pipeline will actually yield better results for some use cases, I don't know, but yeah.Alessio [00:15:03]: And do you feel like at the core of it, there's this difference between imperative and declarative programs? Because if you think about HubSpot, it's like, you know, people and graph kind of goes hand in hand, you know, but I think maybe the software before was more like primary foreign key based relationship, versus now the models can traverse through the graph more easily.Dharmesh [00:15:22]: Yes. So I like that representation. There's something. It's just conceptually elegant about graphs and just from the representation of it, they're much more discoverable, you can kind of see it, there's observability to it, versus kind of embeddings, which you can't really do much with as a human. You know, once they're in there, you can't pull stuff back out. But yeah, I like that kind of idea of it. And the other thing that's kind of, because I love graphs, I've been long obsessed with PageRank from back in the early days. And, you know, one of the kind of simplest algorithms in terms of coming up, you know, with a phone, everyone's been exposed to PageRank. And the idea is that, and so I had this other idea for a project, not a company, and I have hundreds of these, called NodeRank, is to be able to take the idea of PageRank and apply it to an arbitrary graph that says, okay, I'm going to define what authority looks like and say, okay, well, that's interesting to me, because then if you say, I'm going to take my knowledge store, and maybe this person that contributed some number of chunks to the graph data store has more authority on this particular use case or prompt that's being submitted than this other one that may, or maybe this one was more. popular, or maybe this one has, whatever it is, there should be a way for us to kind of rank nodes in a graph and sort them in some, some useful way. Yeah.swyx [00:16:34]: So I think that's generally useful for, for anything. I think the, the problem, like, so even though at my conferences, GraphRag is super popular and people are getting knowledge, graph religion, and I will say like, it's getting space, getting traction in two areas, conversation memory, and then also just rag in general, like the, the, the document data. Yeah. It's like a source. Most ML practitioners would say that knowledge graph is kind of like a dirty word. The graph database, people get graph religion, everything's a graph, and then they, they go really hard into it and then they get a, they get a graph that is too complex to navigate. Yes. And so like the, the, the simple way to put it is like you at running HubSpot, you know, the power of graphs, the way that Google has pitched them for many years, but I don't suspect that HubSpot itself uses a knowledge graph. No. Yeah.Dharmesh [00:17:26]: So when is it over engineering? Basically? It's a great question. I don't know. So the question now, like in AI land, right, is the, do we necessarily need to understand? So right now, LLMs for, for the most part are somewhat black boxes, right? We sort of understand how the, you know, the algorithm itself works, but we really don't know what's going on in there and, and how things come out. So if a graph data store is able to produce the outcomes we want, it's like, here's a set of queries I want to be able to submit and then it comes out with useful content. Maybe the underlying data store is as opaque as a vector embeddings or something like that, but maybe it's fine. Maybe we don't necessarily need to understand it to get utility out of it. And so maybe if it's messy, that's okay. Um, that's, it's just another form of lossy compression. Uh, it's just lossy in a way that we just don't completely understand in terms of, because it's going to grow organically. Uh, and it's not structured. It's like, ah, we're just gonna throw a bunch of stuff in there. Let the, the equivalent of the embedding algorithm, whatever they called in graph land. Um, so the one with the best results wins. I think so. Yeah.swyx [00:18:26]: Or is this the practical side of me is like, yeah, it's, if it's useful, we don't necessarilyDharmesh [00:18:30]: need to understand it.swyx [00:18:30]: I have, I mean, I'm happy to push back as long as you want. Uh, it's not practical to evaluate like the 10 different options out there because it takes time. It takes people, it takes, you know, resources, right? Set. That's the first thing. Second thing is your evals are typically on small things and some things only work at scale. Yup. Like graphs. Yup.Dharmesh [00:18:46]: Yup. That's, yeah, no, that's fair. And I think this is one of the challenges in terms of implementation of graph databases is that the most common approach that I've seen developers do, I've done it myself, is that, oh, I've got a Postgres database or a MySQL or whatever. I can represent a graph with a very set of tables with a parent child thing or whatever. And that sort of gives me the ability, uh, why would I need anything more than that? And the answer is, well, if you don't need anything more than that, you don't need anything more than that. But there's a high chance that you're sort of missing out on the actual value that, uh, the graph representation gives you. Which is the ability to traverse the graph, uh, efficiently in ways that kind of going through the, uh, traversal in a relational database form, even though structurally you have the data, practically you're not gonna be able to pull it out in, in useful ways. Uh, so you wouldn't like represent a social graph, uh, in, in using that kind of relational table model. It just wouldn't scale. It wouldn't work.swyx [00:19:36]: Uh, yeah. Uh, I think we want to move on to MCP. Yeah. But I just want to, like, just engineering advice. Yeah. Uh, obviously you've, you've, you've run, uh, you've, you've had to do a lot of projects and run a lot of teams. Do you have a general rule for over-engineering or, you know, engineering ahead of time? You know, like, because people, we know premature engineering is the root of all evil. Yep. But also sometimes you just have to. Yep. When do you do it? Yes.Dharmesh [00:19:59]: It's a great question. This is, uh, a question as old as time almost, which is what's the right and wrong levels of abstraction. That's effectively what, uh, we're answering when we're trying to do engineering. I tend to be a pragmatist, right? So here's the thing. Um, lots of times doing something the right way. Yeah. It's like a marginal increased cost in those cases. Just do it the right way. And this is what makes a, uh, a great engineer or a good engineer better than, uh, a not so great one. It's like, okay, all things being equal. If it's going to take you, you know, roughly close to constant time anyway, might as well do it the right way. Like, so do things well, then the question is, okay, well, am I building a framework as the reusable library? To what degree, uh, what am I anticipating in terms of what's going to need to change in this thing? Uh, you know, along what dimension? And then I think like a business person in some ways, like what's the return on calories, right? So, uh, and you look at, um, energy, the expected value of it's like, okay, here are the five possible things that could happen, uh, try to assign probabilities like, okay, well, if there's a 50% chance that we're going to go down this particular path at some day, like, or one of these five things is going to happen and it costs you 10% more to engineer for that. It's basically, it's something that yields a kind of interest compounding value. Um, as you get closer to the time of, of needing that versus having to take on debt, which is when you under engineer it, you're taking on debt. You're going to have to pay off when you do get to that eventuality where something happens. One thing as a pragmatist, uh, so I would rather under engineer something than over engineer it. If I were going to err on the side of something, and here's the reason is that when you under engineer it, uh, yes, you take on tech debt, uh, but the interest rate is relatively known and payoff is very, very possible, right? Which is, oh, I took a shortcut here as a result of which now this thing that should have taken me a week is now going to take me four weeks. Fine. But if that particular thing that you thought might happen, never actually, you never have that use case transpire or just doesn't, it's like, well, you just save yourself time, right? And that has value because you were able to do other things instead of, uh, kind of slightly over-engineering it away, over-engineering it. But there's no perfect answers in art form in terms of, uh, and yeah, we'll, we'll bring kind of this layers of abstraction back on the code generation conversation, which we'll, uh, I think I have later on, butAlessio [00:22:05]: I was going to ask, we can just jump ahead quickly. Yeah. Like, as you think about vibe coding and all that, how does the. Yeah. Percentage of potential usefulness change when I feel like we over-engineering a lot of times it's like the investment in syntax, it's less about the investment in like arc exacting. Yep. Yeah. How does that change your calculus?Dharmesh [00:22:22]: A couple of things, right? One is, um, so, you know, going back to that kind of ROI or a return on calories, kind of calculus or heuristic you think through, it's like, okay, well, what is it going to cost me to put this layer of abstraction above the code that I'm writing now, uh, in anticipating kind of future needs. If the cost of fixing, uh, or doing under engineering right now. Uh, we'll trend towards zero that says, okay, well, I don't have to get it right right now because even if I get it wrong, I'll run the thing for six hours instead of 60 minutes or whatever. It doesn't really matter, right? Like, because that's going to trend towards zero to be able, the ability to refactor a code. Um, and because we're going to not that long from now, we're going to have, you know, large code bases be able to exist, uh, you know, as, as context, uh, for a code generation or a code refactoring, uh, model. So I think it's going to make it, uh, make the case for under engineering, uh, even stronger. Which is why I take on that cost. You just pay the interest when you get there, it's not, um, just go on with your life vibe coded and, uh, come back when you need to. Yeah.Alessio [00:23:18]: Sometimes I feel like there's no decision-making in some things like, uh, today I built a autosave for like our internal notes platform and I literally just ask them cursor. Can you add autosave? Yeah. I don't know if it's over under engineer. Yep. I just vibe coded it. Yep. And I feel like at some point we're going to get to the point where the models kindDharmesh [00:23:36]: of decide where the right line is, but this is where the, like the, in my mind, the danger is, right? So there's two sides to this. One is the cost of kind of development and coding and things like that stuff that, you know, we talk about. But then like in your example, you know, one of the risks that we have is that because adding a feature, uh, like a save or whatever the feature might be to a product as that price tends towards zero, are we going to be less discriminant about what features we add as a result of making more product products more complicated, which has a negative impact on the user and navigate negative impact on the business. Um, and so that's the thing I worry about if it starts to become too easy, are we going to be. Too promiscuous in our, uh, kind of extension, adding product extensions and things like that. It's like, ah, why not add X, Y, Z or whatever back then it was like, oh, we only have so many engineering hours or story points or however you measure things. Uh, that least kept us in check a little bit. Yeah.Alessio [00:24:22]: And then over engineering, you're like, yeah, it's kind of like you're putting that on yourself. Yeah. Like now it's like the models don't understand that if they add too much complexity, it's going to come back to bite them later. Yep. So they just do whatever they want to do. Yeah. And I'm curious where in the workflow that's going to be, where it's like, Hey, this is like the amount of complexity and over-engineering you can do before you got to ask me if we should actually do it versus like do something else.Dharmesh [00:24:45]: So you know, we've already, let's like, we're leaving this, uh, in the code generation world, this kind of compressed, um, cycle time. Right. It's like, okay, we went from auto-complete, uh, in the GitHub co-pilot to like, oh, finish this particular thing and hit tab to a, oh, I sort of know your file or whatever. I can write out a full function to you to now I can like hold a bunch of the context in my head. Uh, so we can do app generation, which we have now with lovable and bolt and repletage. Yeah. Association and other things. So then the question is, okay, well, where does it naturally go from here? So we're going to generate products. Make sense. We might be able to generate platforms as though I want a platform for ERP that does this, whatever. And that includes the API's includes the product and the UI, and all the things that make for a platform. There's no nothing that says we would stop like, okay, can you generate an entire software company someday? Right. Uh, with the platform and the monetization and the go-to-market and the whatever. And you know, that that's interesting to me in terms of, uh, you know, what, when you take it to almost ludicrous levels. of abstract.swyx [00:25:39]: It's like, okay, turn it to 11. You mentioned vibe coding, so I have to, this is a blog post I haven't written, but I'm kind of exploring it. Is the junior engineer dead?Dharmesh [00:25:49]: I don't think so. I think what will happen is that the junior engineer will be able to, if all they're bringing to the table is the fact that they are a junior engineer, then yes, they're likely dead. But hopefully if they can communicate with carbon-based life forms, they can interact with product, if they're willing to talk to customers, they can take their kind of basic understanding of engineering and how kind of software works. I think that has value. So I have a 14-year-old right now who's taking Python programming class, and some people ask me, it's like, why is he learning coding? And my answer is, is because it's not about the syntax, it's not about the coding. What he's learning is like the fundamental thing of like how things work. And there's value in that. I think there's going to be timeless value in systems thinking and abstractions and what that means. And whether functions manifested as math, which he's going to get exposed to regardless, or there are some core primitives to the universe, I think, that the more you understand them, those are what I would kind of think of as like really large dots in your life that will have a higher gravitational pull and value to them that you'll then be able to. So I want him to collect those dots, and he's not resisting. So it's like, okay, while he's still listening to me, I'm going to have him do things that I think will be useful.swyx [00:26:59]: You know, part of one of the pitches that I evaluated for AI engineer is a term. And the term is that maybe the traditional interview path or career path of software engineer goes away, which is because what's the point of lead code? Yeah. And, you know, it actually matters more that you know how to work with AI and to implement the things that you want. Yep.Dharmesh [00:27:16]: That's one of the like interesting things that's happened with generative AI. You know, you go from machine learning and the models and just that underlying form, which is like true engineering, right? Like the actual, what I call real engineering. I don't think of myself as a real engineer, actually. I'm a developer. But now with generative AI. We call it AI and it's obviously got its roots in machine learning, but it just feels like fundamentally different to me. Like you have the vibe. It's like, okay, well, this is just a whole different approach to software development to so many different things. And so I'm wondering now, it's like an AI engineer is like, if you were like to draw the Venn diagram, it's interesting because the cross between like AI things, generative AI and what the tools are capable of, what the models do, and this whole new kind of body of knowledge that we're still building out, it's still very young, intersected with kind of classic engineering, software engineering. Yeah.swyx [00:28:04]: I just described the overlap as it separates out eventually until it's its own thing, but it's starting out as a software. Yeah.Alessio [00:28:11]: That makes sense. So to close the vibe coding loop, the other big hype now is MCPs. Obviously, I would say Cloud Desktop and Cursor are like the two main drivers of MCP usage. I would say my favorite is the Sentry MCP. I can pull in errors and then you can just put the context in Cursor. How do you think about that abstraction layer? Does it feel... Does it feel almost too magical in a way? Do you think it's like you get enough? Because you don't really see how the server itself is then kind of like repackaging theDharmesh [00:28:41]: information for you? I think MCP as a standard is one of the better things that's happened in the world of AI because a standard needed to exist and absent a standard, there was a set of things that just weren't possible. Now, we can argue whether it's the best possible manifestation of a standard or not. Does it do too much? Does it do too little? I get that, but it's just simple enough to both be useful and unobtrusive. It's understandable and adoptable by mere mortals, right? It's not overly complicated. You know, a reasonable engineer can put a stand up an MCP server relatively easily. The thing that has me excited about it is like, so I'm a big believer in multi-agent systems. And so that's going back to our kind of this idea of an atomic agent. So imagine the MCP server, like obviously it calls tools, but the way I think about it, so I'm working on my current passion project is agent.ai. And we'll talk more about that in a little bit. More about the, I think we should, because I think it's interesting not to promote the project at all, but there's some interesting ideas in there. One of which is around, we're going to need a mechanism for, if agents are going to collaborate and be able to delegate, there's going to need to be some form of discovery and we're going to need some standard way. It's like, okay, well, I just need to know what this thing over here is capable of. We're going to need a registry, which Anthropic's working on. I'm sure others will and have been doing directories of, and there's going to be a standard around that too. How do you build out a directory of MCP servers? I think that's going to unlock so many things just because, and we're already starting to see it. So I think MCP or something like it is going to be the next major unlock because it allows systems that don't know about each other, don't need to, it's that kind of decoupling of like Sentry and whatever tools someone else was building. And it's not just about, you know, Cloud Desktop or things like, even on the client side, I think we're going to see very interesting consumers of MCP, MCP clients versus just the chat body kind of things. Like, you know, Cloud Desktop and Cursor and things like that. But yeah, I'm very excited about MCP in that general direction.swyx [00:30:39]: I think the typical cynical developer take, it's like, we have OpenAPI. Yeah. What's the new thing? I don't know if you have a, do you have a quick MCP versus everything else? Yeah.Dharmesh [00:30:49]: So it's, so I like OpenAPI, right? So just a descriptive thing. It's OpenAPI. OpenAPI. Yes, that's what I meant. So it's basically a self-documenting thing. We can do machine-generated, lots of things from that output. It's a structured definition of an API. I get that, love it. But MCPs sort of are kind of use case specific. They're perfect for exactly what we're trying to use them for around LLMs in terms of discovery. It's like, okay, I don't necessarily need to know kind of all this detail. And so right now we have, we'll talk more about like MCP server implementations, but We will? I think, I don't know. Maybe we won't. At least it's in my head. It's like a back processor. But I do think MCP adds value above OpenAPI. It's, yeah, just because it solves this particular thing. And if we had come to the world, which we have, like, it's like, hey, we already have OpenAPI. It's like, if that were good enough for the universe, the universe would have adopted it already. There's a reason why MCP is taking office because marginally adds something that was missing before and doesn't go too far. And so that's why the kind of rate of adoption, you folks have written about this and talked about it. Yeah, why MCP won. Yeah. And it won because the universe decided that this was useful and maybe it gets supplanted by something else. Yeah. And maybe we discover, oh, maybe OpenAPI was good enough the whole time. I doubt that.swyx [00:32:09]: The meta lesson, this is, I mean, he's an investor in DevTools companies. I work in developer experience at DevRel in DevTools companies. Yep. Everyone wants to own the standard. Yeah. I'm sure you guys have tried to launch your own standards. Actually, it's Houseplant known for a standard, you know, obviously inbound marketing. But is there a standard or protocol that you ever tried to push? No.Dharmesh [00:32:30]: And there's a reason for this. Yeah. Is that? And I don't mean, need to mean, speak for the people of HubSpot, but I personally. You kind of do. I'm not smart enough. That's not the, like, I think I have a. You're smart. Not enough for that. I'm much better off understanding the standards that are out there. And I'm more on the composability side. Let's, like, take the pieces of technology that exist out there, combine them in creative, unique ways. And I like to consume standards. I don't like to, and that's not that I don't like to create them. I just don't think I have the, both the raw wattage or the credibility. It's like, okay, well, who the heck is Dharmesh, and why should we adopt a standard he created?swyx [00:33:07]: Yeah, I mean, there are people who don't monetize standards, like OpenTelemetry is a big standard, and LightStep never capitalized on that.Dharmesh [00:33:15]: So, okay, so if I were to do a standard, there's two things that have been in my head in the past. I was one around, a very, very basic one around, I don't even have the domain, I have a domain for everything, for open marketing. Because the issue we had in HubSpot grew up in the marketing space. There we go. There was no standard around data formats and things like that. It doesn't go anywhere. But the other one, and I did not mean to go here, but I'm going to go here. It's called OpenGraph. I know the term was already taken, but it hasn't been used for like 15 years now for its original purpose. But what I think should exist in the world is right now, our information, all of us, nodes are in the social graph at Meta or the professional graph at LinkedIn. Both of which are actually relatively closed in actually very annoying ways. Like very, very closed, right? Especially LinkedIn. Especially LinkedIn. I personally believe that if it's my data, and if I would get utility out of it being open, I should be able to make my data open or publish it in whatever forms that I choose, as long as I have control over it as opt-in. So the idea is around OpenGraph that says, here's a standard, here's a way to publish it. I should be able to go to OpenGraph.org slash Dharmesh dot JSON and get it back. And it's like, here's your stuff, right? And I can choose along the way and people can write to it and I can prove. And there can be an entire system. And if I were to do that, I would do it as a... Like a public benefit, non-profit-y kind of thing, as this is a contribution to society. I wouldn't try to commercialize that. Have you looked at AdProto? What's that? AdProto.swyx [00:34:43]: It's the protocol behind Blue Sky. Okay. My good friend, Dan Abramov, who was the face of React for many, many years, now works there. And he actually did a talk that I can send you, which basically kind of tries to articulate what you just said. But he does, he loves doing these like really great analogies, which I think you'll like. Like, you know, a lot of our data is behind a handle, behind a domain. Yep. So he's like, all right, what if we flip that? What if it was like our handle and then the domain? Yep. So, and that's really like your data should belong to you. Yep. And I should not have to wait 30 days for my Twitter data to export. Yep.Dharmesh [00:35:19]: you should be able to at least be able to automate it or do like, yes, I should be able to plug it into an agentic thing. Yeah. Yes. I think we're... Because so much of our data is... Locked up. I think the trick here isn't that standard. It is getting the normies to care.swyx [00:35:37]: Yeah. Because normies don't care.Dharmesh [00:35:38]: That's true. But building on that, normies don't care. So, you know, privacy is a really hot topic and an easy word to use, but it's not a binary thing. Like there are use cases where, and we make these choices all the time, that I will trade, not all privacy, but I will trade some privacy for some productivity gain or some benefit to me that says, oh, I don't care about that particular data being online if it gives me this in return, or I don't mind sharing this information with this company.Alessio [00:36:02]: If I'm getting, you know, this in return, but that sort of should be my option. I think now with computer use, you can actually automate some of the exports. Yes. Like something we've been doing internally is like everybody exports their LinkedIn connections. Yep. And then internally, we kind of merge them together to see how we can connect our companies to customers or things like that.Dharmesh [00:36:21]: And not to pick on LinkedIn, but since we're talking about it, but they feel strongly enough on the, you know, do not take LinkedIn data that they will block even browser use kind of things or whatever. They go to great, great lengths, even to see patterns of usage. And it says, oh, there's no way you could have, you know, gotten that particular thing or whatever without, and it's, so it's, there's...swyx [00:36:42]: Wasn't there a Supreme Court case that they lost? Yeah.Dharmesh [00:36:45]: So the one they lost was around someone that was scraping public data that was on the public internet. And that particular company had not signed any terms of service or whatever. It's like, oh, I'm just taking data that's on, there was no, and so that's why they won. But now, you know, the question is around, can LinkedIn... I think they can. Like, when you use, as a user, you use LinkedIn, you are signing up for their terms of service. And if they say, well, this kind of use of your LinkedIn account that violates our terms of service, they can shut your account down, right? They can. And they, yeah, so, you know, we don't need to make this a discussion. By the way, I love the company, don't get me wrong. I'm an avid user of the product. You know, I've got... Yeah, I mean, you've got over a million followers on LinkedIn, I think. Yeah, I do. And I've known people there for a long, long time, right? And I have lots of respect. And I understand even where the mindset originally came from of this kind of members-first approach to, you know, a privacy-first. I sort of get that. But sometimes you sort of have to wonder, it's like, okay, well, that was 15, 20 years ago. There's likely some controlled ways to expose some data on some member's behalf and not just completely be a binary. It's like, no, thou shalt not have the data.swyx [00:37:54]: Well, just pay for sales navigator.Alessio [00:37:57]: Before we move to the next layer of instruction, anything else on MCP you mentioned? Let's move back and then I'll tie it back to MCPs.Dharmesh [00:38:05]: So I think the... Open this with agent. Okay, so I'll start with... Here's my kind of running thesis, is that as AI and agents evolve, which they're doing very, very quickly, we're going to look at them more and more. I don't like to anthropomorphize. We'll talk about why this is not that. Less as just like raw tools and more like teammates. They'll still be software. They should self-disclose as being software. I'm totally cool with that. But I think what's going to happen is that in the same way you might collaborate with a team member on Slack or Teams or whatever you use, you can imagine a series of agents that do specific things just like a team member might do, that you can delegate things to. You can collaborate. You can say, hey, can you take a look at this? Can you proofread that? Can you try this? You can... Whatever it happens to be. So I think it is... I will go so far as to say it's inevitable that we're going to have hybrid teams someday. And what I mean by hybrid teams... So back in the day, hybrid teams were, oh, well, you have some full-time employees and some contractors. Then it was like hybrid teams are some people that are in the office and some that are remote. That's the kind of form of hybrid. The next form of hybrid is like the carbon-based life forms and agents and AI and some form of software. So let's say we temporarily stipulate that I'm right about that over some time horizon that eventually we're going to have these kind of digitally hybrid teams. So if that's true, then the question you sort of ask yourself is that then what needs to exist in order for us to get the full value of that new model? It's like, okay, well... You sort of need to... It's like, okay, well, how do I... If I'm building a digital team, like, how do I... Just in the same way, if I'm interviewing for an engineer or a designer or a PM, whatever, it's like, well, that's why we have professional networks, right? It's like, oh, they have a presence on likely LinkedIn. I can go through that semi-structured, structured form, and I can see the experience of whatever, you know, self-disclosed. But, okay, well, agents are going to need that someday. And so I'm like, okay, well, this seems like a thread that's worth pulling on. That says, okay. So I... So agent.ai is out there. And it's LinkedIn for agents. It's LinkedIn for agents. It's a professional network for agents. And the more I pull on that thread, it's like, okay, well, if that's true, like, what happens, right? It's like, oh, well, they have a profile just like anyone else, just like a human would. It's going to be a graph underneath, just like a professional network would be. It's just that... And you can have its, you know, connections and follows, and agents should be able to post. That's maybe how they do release notes. Like, oh, I have this new version. Whatever they decide to post, it should just be able to... Behave as a node on the network of a professional network. As it turns out, the more I think about that and pull on that thread, the more and more things, like, start to make sense to me. So it may be more than just a pure professional network. So my original thought was, okay, well, it's a professional network and agents as they exist out there, which I think there's going to be more and more of, will kind of exist on this network and have the profile. But then, and this is always dangerous, I'm like, okay, I want to see a world where thousands of agents are out there in order for the... Because those digital employees, the digital workers don't exist yet in any meaningful way. And so then I'm like, oh, can I make that easier for, like... And so I have, as one does, it's like, oh, I'll build a low-code platform for building agents. How hard could that be, right? Like, very hard, as it turns out. But it's been fun. So now, agent.ai has 1.3 million users. 3,000 people have actually, you know, built some variation of an agent, sometimes just for their own personal productivity. About 1,000 of which have been published. And the reason this comes back to MCP for me, so imagine that and other networks, since I know agent.ai. So right now, we have an MCP server for agent.ai that exposes all the internally built agents that we have that do, like, super useful things. Like, you know, I have access to a Twitter API that I can subsidize the cost. And I can say, you know, if you're looking to build something for social media, these kinds of things, with a single API key, and it's all completely free right now, I'm funding it. That's a useful way for it to work. And then we have a developer to say, oh, I have this idea. I don't have to worry about open AI. I don't have to worry about, now, you know, this particular model is better. It has access to all the models with one key. And we proxy it kind of behind the scenes. And then expose it. So then we get this kind of community effect, right? That says, oh, well, someone else may have built an agent to do X. Like, I have an agent right now that I built for myself to do domain valuation for website domains because I'm obsessed with domains, right? And, like, there's no efficient market for domains. There's no Zillow for domains right now that tells you, oh, here are what houses in your neighborhood sold for. It's like, well, why doesn't that exist? We should be able to solve that problem. And, yes, you're still guessing. Fine. There should be some simple heuristic. So I built that. It's like, okay, well, let me go look for past transactions. You say, okay, I'm going to type in agent.ai, agent.com, whatever domain. What's it actually worth? I'm looking at buying it. It can go and say, oh, which is what it does. It's like, I'm going to go look at are there any published domain transactions recently that are similar, either use the same word, same top-level domain, whatever it is. And it comes back with an approximate value, and it comes back with its kind of rationale for why it picked the value and comparable transactions. Oh, by the way, this domain sold for published. Okay. So that agent now, let's say, existed on the web, on agent.ai. Then imagine someone else says, oh, you know, I want to build a brand-building agent for startups and entrepreneurs to come up with names for their startup. Like a common problem, every startup is like, ah, I don't know what to call it. And so they type in five random words that kind of define whatever their startup is. And you can do all manner of things, one of which is like, oh, well, I need to find the domain for it. What are possible choices? Now it's like, okay, well, it would be nice to know if there's an aftermarket price for it, if it's listed for sale. Awesome. Then imagine calling this valuation agent. It's like, okay, well, I want to find where the arbitrage is, where the agent valuation tool says this thing is worth $25,000. It's listed on GoDaddy for $5,000. It's close enough. Let's go do that. Right? And that's a kind of composition use case that in my future state. Thousands of agents on the network, all discoverable through something like MCP. And then you as a developer of agents have access to all these kind of Lego building blocks based on what you're trying to solve. Then you blend in orchestration, which is getting better and better with the reasoning models now. Just describe the problem that you have. Now, the next layer that we're all contending with is that how many tools can you actually give an LLM before the LLM breaks? That number used to be like 15 or 20 before you kind of started to vary dramatically. And so that's the thing I'm thinking about now. It's like, okay, if I want to... If I want to expose 1,000 of these agents to a given LLM, obviously I can't give it all 1,000. Is there some intermediate layer that says, based on your prompt, I'm going to make a best guess at which agents might be able to be helpful for this particular thing? Yeah.Alessio [00:44:37]: Yeah, like RAG for tools. Yep. I did build the Latent Space Researcher on agent.ai. Okay. Nice. Yeah, that seems like, you know, then there's going to be a Latent Space Scheduler. And then once I schedule a research, you know, and you build all of these things. By the way, my apologies for the user experience. You realize I'm an engineer. It's pretty good.swyx [00:44:56]: I think it's a normie-friendly thing. Yeah. That's your magic. HubSpot does the same thing.Alessio [00:45:01]: Yeah, just to like quickly run through it. You can basically create all these different steps. And these steps are like, you know, static versus like variable-driven things. How did you decide between this kind of like low-code-ish versus doing, you know, low-code with code backend versus like not exposing that at all? Any fun design decisions? Yeah. And this is, I think...Dharmesh [00:45:22]: I think lots of people are likely sitting in exactly my position right now, coming through the choosing between deterministic. Like if you're like in a business or building, you know, some sort of agentic thing, do you decide to do a deterministic thing? Or do you go non-deterministic and just let the alum handle it, right, with the reasoning models? The original idea and the reason I took the low-code stepwise, a very deterministic approach. A, the reasoning models did not exist at that time. That's thing number one. Thing number two is if you can get... If you know in your head... If you know in your head what the actual steps are to accomplish whatever goal, why would you leave that to chance? There's no upside. There's literally no upside. Just tell me, like, what steps do you need executed? So right now what I'm playing with... So one thing we haven't talked about yet, and people don't talk about UI and agents. Right now, the primary interaction model... Or they don't talk enough about it. I know some people have. But it's like, okay, so we're used to the chatbot back and forth. Fine. I get that. But I think we're going to move to a blend of... Some of those things are going to be synchronous as they are now. But some are going to be... Some are going to be async. It's just going to put it in a queue, just like... And this goes back to my... Man, I talk fast. But I have this... I only have one other speed. It's even faster. So imagine it's like if you're working... So back to my, oh, we're going to have these hybrid digital teams. Like, you would not go to a co-worker and say, I'm going to ask you to do this thing, and then sit there and wait for them to go do it. Like, that's not how the world works. So it's nice to be able to just, like, hand something off to someone. It's like, okay, well, maybe I expect a response in an hour or a day or something like that.Dharmesh [00:46:52]: In terms of when things need to happen. So the UI around agents. So if you look at the output of agent.ai agents right now, they are the simplest possible manifestation of a UI, right? That says, oh, we have inputs of, like, four different types. Like, we've got a dropdown, we've got multi-select, all the things. It's like back in HTML, the original HTML 1.0 days, right? Like, you're the smallest possible set of primitives for a UI. And it just says, okay, because we need to collect some information from the user, and then we go do steps and do things. And generate some output in HTML or markup are the two primary examples. So the thing I've been asking myself, if I keep going down that path. So people ask me, I get requests all the time. It's like, oh, can you make the UI sort of boring? I need to be able to do this, right? And if I keep pulling on that, it's like, okay, well, now I've built an entire UI builder thing. Where does this end? And so I think the right answer, and this is what I'm going to be backcoding once I get done here, is around injecting a code generation UI generation into, the agent.ai flow, right? As a builder, you're like, okay, I'm going to describe the thing that I want, much like you would do in a vibe coding world. But instead of generating the entire app, it's going to generate the UI that exists at some point in either that deterministic flow or something like that. It says, oh, here's the thing I'm trying to do. Go generate the UI for me. And I can go through some iterations. And what I think of it as a, so it's like, I'm going to generate the code, generate the code, tweak it, go through this kind of prompt style, like we do with vibe coding now. And at some point, I'm going to be happy with it. And I'm going to hit save. And that's going to become the action in that particular step. It's like a caching of the generated code that I can then, like incur any inference time costs. It's just the actual code at that point.Alessio [00:48:29]: Yeah, I invested in a company called E2B, which does code sandbox. And they powered the LM arena web arena. So it's basically the, just like you do LMS, like text to text, they do the same for like UI generation. So if you're asking a model, how do you do it? But yeah, I think that's kind of where.Dharmesh [00:48:45]: That's the thing I'm really fascinated by. So the early LLM, you know, we're understandably, but laughably bad at simple arithmetic, right? That's the thing like my wife, Normies would ask us, like, you call this AI, like it can't, my son would be like, it's just stupid. It can't even do like simple arithmetic. And then like we've discovered over time that, and there's a reason for this, right? It's like, it's a large, there's, you know, the word language is in there for a reason in terms of what it's been trained on. It's not meant to do math, but now it's like, okay, well, the fact that it has access to a Python interpreter that I can actually call at runtime, that solves an entire body of problems that it wasn't trained to do. And it's basically a form of delegation. And so the thought that's kind of rattling around in my head is that that's great. So it's, it's like took the arithmetic problem and took it first. Now, like anything that's solvable through a relatively concrete Python program, it's able to do a bunch of things that I couldn't do before. Can we get to the same place with UI? I don't know what the future of UI looks like in a agentic AI world, but maybe let the LLM handle it, but not in the classic sense. Maybe it generates it on the fly, or maybe we go through some iterations and hit cache or something like that. So it's a little bit more predictable. Uh, I don't know, but yeah.Alessio [00:49:48]: And especially when is the human supposed to intervene? So, especially if you're composing them, most of them should not have a UI because then they're just web hooking to somewhere else. I just want to touch back. I don't know if you have more comments on this.swyx [00:50:01]: I was just going to ask when you, you said you got, you're going to go back to code. What

In this episode of the ModelFA podcast, host David DeCelle sits down with Ritik Malhotra, the founder and CEO of Savvy Wealth. Ritik shares his impressive entrepreneurial journey, from building internet businesses as a teenager to founding successful startups in cloud storage and fintech. The conversation digs into Ritik's inspiration behind starting Savvy Wealth - his experience working with financial advisors and identifying the industry's need for modernization and technology-driven solutions. Ritik explains how Savvy Wealth's purpose-built AI-powered platform is empowering advisors to increase efficiency, scale their businesses, and spend more time focused on client relationships. Key insights from the discussion include:   How AI is currently impacting the financial advisory industry, from automating compliance and marketing tasks to freeing up advisor time   Examples of advisors seeing 50% time savings on quarterly reviews and 80% reductions in content creation time   Ritik's emphasis on using AI to augment advisors, not replace them, and the importance of the human advisor-client relationship   Savvy Wealth's approach to constantly gathering feedback from advisors to improve their platform and address pain points   Ritik also shares the influential book "Founders at Work" by Jessica Livingston, which inspired him in his entrepreneurial journey. This episode provides valuable insights for financial advisors looking to leverage technology and AI to enhance their practices and better serve their clients. Be sure to visit Savvy Wealth's website and follow them on LinkedIn to learn more about their innovative solutions. Connect with Ritik: Website: https://www.savvywealth.com Email: ritik@savvywealth.com LinkedIn: https://www.linkedin.com/in/ritikmalhotra/   About the Model FA Podcast   The Model FA podcast is a show for fiduciary financial advisors. In each episode, our host David DeCelle sits down with industry experts, strategic thinkers, and advisors to explore what it takes  to build a successful practice — and have an abundant life in the process. We believe in continuous learning, tactical advice, and strategies that work — no “gotchas” or BS. Join us to hear stories from successful financial advisors, get actionable ideas from experts, and re-discover your drive to build the practice of your dreams.    Did you like this conversation? Then leave us a rating and a review in whatever podcast player you use. We would love your feedback, and your ratings help us reach more advisors with ideas for growing their practices, attracting great clients, and achieving a better quality of life. While you are there, feel free to share your ideas about future podcast guests or topics you'd love to see covered.    Our Team: President of Model FA, David DeCelle   If you like this podcast, you will love our community! Join the Model FA Community on Facebook to connect with like-minded advisors and share the day-to-day challenges and wins of running a growing financial services firm.  

What do lizards have to do with product growth? In this episode, Gojko Adzic reveals how unusual user behaviors can unlock massive opportunities for product innovation. Discover the four steps to mastering "Lizard Optimization" and learn how you can turn strange user actions into game-changing insights. Overview In this episode of the Agile Mentors Podcast, host Brian Milner chats with Gojko Adzic about his new book, Lizard Optimization. Gojko explains the concept of finding product growth signals in strange user behaviors, sharing examples where unexpected user actions led to product breakthroughs. He outlines a four-step process for optimizing products by learning, zeroing in, removing obstacles, and double-checking. Gojko also discusses helpful tools like session recorders and observability tools that can enhance product development by uncovering and addressing unique user behaviors. References and resources mentioned in the show: Gojko Adzic 50% OFF Lizard Optimization by Gojko Adzic Mismatch: How Inclusion Shapes Design by Kat Holmes Trustworthy Online Experiments by Ron Kohavi Advanced Certified Scrum Product Owner® Subscribe to the Agile Mentors Podcast Join the Agile Mentors Community Want to get involved? This show is designed for you, and we’d love your input. Enjoyed what you heard today? Please leave a rating and a review. It really helps, and we read every single one. Got an Agile subject you’d like us to discuss or a question that needs an answer? Share your thoughts with us at podcast@mountaingoatsoftware.com This episode’s presenters are: Brian Milner is SVP of coaching and training at Mountain Goat Software. He's passionate about making a difference in people's day-to-day work, influenced by his own experience of transitioning to Scrum and seeing improvements in work/life balance, honesty, respect, and the quality of work. Gojko Adzic is an award-winning software consultant and author, specializing in agile and lean quality improvement, with expertise in impact mapping, agile testing, and behavior-driven development. A frequent speaker at global software conferences, Gojko is also a co-creator of MindMup and Narakeet, and has helped companies worldwide enhance their software delivery, from large financial institutions to innovative startups. Auto-generated Transcript: Brian (00:00) Welcome in Agile Mentors. We're back for another episode of the Agile Mentors podcast. I'm with you as always, Brian Milner. And today, very special guest we have with us. have Mr. Goiko Atshich with us. I hope I said that correctly. Did I say it correctly? Close enough. Okay. Well, welcome in, Goiko. Glad to have you here. Gojko (00:15) Close enough, close enough. Brian (00:21) Very, very, very happy to have Goiko with us. If you're not familiar with Goiko's name, you probably are familiar with some of his work. One of the things I was telling him that we teach in our advanced product owner class every time is impact mapping, which is a tool that Goiko has written about and kind of come up with on his own as well. Gojko (00:21) Thank you very much for inviting me. Brian (00:47) But today we're having him on because he has a new book coming out called Lizard Optimization, Unlock Product Growth by Engaging Long Tail Users. And I really wanted to talk to him about that and help him explain, have him explain to us a little bit about this idea, this new concept that his new book is about. So, Goiko, let's talk about it. Lizard Optimization, in a nutshell, what do you mean by that? What is it? Gojko (01:14) We're going to jump into that, but I just need to correct one of the things you said. I think it's very, very important. You said I came up with impact mapping and I didn't. I just wrote a popular book about that. And it's very important to credit people who actually came up with that. It's kind of the in -use design agency in Sweden. And I think, you know, they should get the credit for it. I literally just wrote a popular book. Brian (01:19) Okay. Gotcha. Gotcha, gotcha. Apologies for that incorrect. Thank you for making that correction. So lizard optimization. Gojko (01:44) So, lizard optimization. Good. So, lizard optimization is an idea to find signals for product ideas and product development ideas in strange user behaviors. When you meet somebody who does something you completely do not understand, why on earth somebody would do something like that? Brian (02:03) Okay. Gojko (02:11) and it looks like it's not done by humans, it looks like it's done by somebody who follows their own lizard logic, using stuff like that as signals to improve our products. Not just for lizards, but for everybody. So the idea came from a very explosive growth phase for one of the products I'm working on, where it... had lots of people doing crazy things I could never figure out why they were doing it. For example, one of the things the tool does is it helps people create videos from PowerPoints. You put some kind of your voiceover in the speaker notes, the tool creates a video by using text to speech engines to create voiceover from the speaker notes, aligns everything and it's all kind of for you. People kept creating blank videos and paying me for this. I was thinking about why on earth would somebody be creating blank videos and it must be a bug and if it's a bug then they want their money back and they'll complain. So I chased up a few of these people and I tried to kind of understand what's going on because I originally thought we have a bug in the development pipeline for the videos. So... I started asking like, you know, I'm using some, I don't know, Google slides or, you know, keynote or whatever to produce PowerPoints. Maybe there's a bug how we read that. And the person, no, no, we, know, official Microsoft PowerPoint. They said, well, can you please open the PowerPoint you uploaded? Do you see anything on the slides when you open it? And the person, no, it's blank. Right? Okay, so it's blank for you as well. I said, yeah. So. Brian (03:48) Yeah. Gojko (03:54) What's going on? so what I've done is through UX interviews and iterating with users and research, we've made it very, very easy to do advanced configuration on text -to -speech. And it was so much easier than the alternative things that people were creating blank PowerPoints just to use the text -to -speech engines so they can then extract the audio track from it. Brian (03:54) Yeah, why? Gojko (04:23) and then use that and it was this whole mess of obstacles I was putting in front of people to get the good audio. It wasn't the original intention of the tool. It wasn't the original value, but people were getting unintended value from it. And then I ended up building just a very simple screen for people to upload the Word document instead of PowerPoints. And it was much faster for users to do that. A month later, there was many audio files being built as videos. Two months later, audio... production overtook video production. then at the moment, people are building many, many more audio files than video files on the platform. So it was an incredible growth because of this kind of crazy insight of what people were doing. kind of usually, at least kind of in the products I worked on before, when you have somebody abusing the product, product management fight against it. There's a wonderful story about this in... Founders at work a book by Jessica Livingston and she talks about this kind of group of super smart people in late 90s who Came up with a very very efficient Cryptography algorithm and a way to compute the cryptography so they can run it on low -power devices like Paul pilots Paul pilots were you know like mobile phones, but in late 90s and Then they had to figure out, how do we monetize this? Why would anybody want to do this? So they came up with the idea to do money transfer pumping, Palm pilots, you know, why not? And kind of the built a website. This was the late nineties as a way of just demoing this software to people who didn't have a Palm pilot device next to them. The idea was that you'd kind of see it on the website, learn about it, then maybe download the Palm pilot app and use it in anger. People kept just using the website, they're not downloading the Palm Pilot app. So the product management really wasn't happy. And they were trying to push people from the website to the Palm Pilot app. were trying to, they were fighting against people using this for money transfer on the web and even prohibiting them from using the logo and advertising it. They had this whole thing where nobody could explain why users were using the website because it was a demo thing. It was not finished. It was not sexy. It was just silly. And Jessica kind of talks to one of these people who insists that it was totally inexplicable. Nobody could understand it. But then a bit later, they realized that the website had one and half million users and that the Pongpilot app had 12 ,000 users. So they kind of decided, well, that's where the product is really. And that's like today, people know them as PayPal. They're one of the biggest payment processes in the world because kind of, you know, they realized this is where the product is going. And I think in many, many companies, people Brian (07:03) Ha ha. Gojko (07:18) stumble upon these things as happy accidents. And I think there's a lot more to it. We can deliberately optimize products by looking for unintended usage and not fighting it, just not fighting it. just understand this is what people are getting as value. And I think for me as a solo product founder and developer and product manager on it, One of the really interesting things is when you have somebody engaging with your product in an unexpected way, most of the difficult work for that user is already done. That person knows about you, they're on your website or they're using your product, the marketing and acquisition work is done. But something's preventing them from achieving their goals or they're achieving some value that you did not really know that they're going to achieve. you know, that's something the product can do to help them and remove these obstacles to success. So that's kind of what lizard optimization is making this process more systematic rather than relying on happy accidents. And by making it more systematic, then we can help product management not fight it and skip this whole phase of trying to fight against our users and claim that users are stupid or non -technical or... They don't understand the product, but they're trying to figure out, well, that's what the real goals are. And then following that. Brian (08:47) That's awesome. So the pivot, right? The pivot from here's what we thought our problem was we were solving to now here's what we're actually solving and we should organize around this actual problem, right? Gojko (09:02) or here's what we're going to solve additionally. This is the problem we've solved, but hey, there's this problem as well. And then the product can grow by solving multiple problems for people and solving related problems and solving it for different groups of people, for example. And that's the really interesting thing because I think if you have a product that's already doing something well for your users and a subset of them are misusing it in some way, then kind of... Brian (09:04) Yeah. Gojko (09:30) The product might already be optimized for the majority of users, but there might be a new market somewhere else. So there might be a different market where we can help kind of a different group of users and then the product can grow. Brian (09:43) Yeah, I like to focus on the user. There's an exercise that we'll do in one of our product owner classes where we have a fake product that is a smart refrigerator. And one of the exercises we try to get them to brainstorm the different kinds of users that they might have for it. And one of the things that always comes out in that class is as they're going through and trying to describe the types of users, they inevitably hit to this crossroads where they start to decide Well, yes, we're thinking of this as a home product, something for people to use in their homes. But then the idea crosses their mind, well, what about commercial kitchens? What about people who might use this in another setting? And it's always an interesting conversation to say, well, now you've got a strategic choice to make, because you can target both. You can target one. You can say, we're ignoring the other and we're only going in this direction. So to me, I think that's kind of one of the interesting crossroad points is to say, how do I know when it's time to not just say, great, we have this other customer segment that we didn't know about, but actually we should start to pivot towards that customer segment and start to really target them. Gojko (11:03) Yeah, think that's a fundamental question of product development, isn't it? Do you keep true to your vision even if it's not coming out or if something else is there that's kind more important than I think? For me, there's a couple of aspects to that. One is, laser focus is really important to launch a product. You can't launch a product by targeting... the whole market and targeting a niche type, figuring out, you know, user personas, figuring out like really, really, this is the product who we think the product, this is the group who we think the product is for and giving them a hundred percent of what they need is much better than giving 2 % to everybody because then the product is irrelevant. But then to grow the product, we need to kind of grow the user base as well. And I think one of the things that... is interesting to look at and this comes from a book called Lean Analytics. It's one of my kind of favorite product management books is to look at the frequency and urgency of usage. If you have a group that's kind of using your product, a subgroup that's using your product very frequently compared to everybody else, that might be kind of the place where you want to go. The more frequently, the more urgently people reach for your product when they have this problem. the more likely they are going to be a good market for it. with kind of another product that I've launched in 2013, we originally thought it's going to be a product for professional users. And we aimed at the professional users. And then we found that a subcategory that we didn't really expect, were kind of teachers and children in schools. we're using it a lot more frequently than professional users. And then we started simplifying the user interface significantly so that it can be used by children. And it's a very, very popular tool in schools now. We are not fighting against other professional tools. We were kind of really one of the first in the education market there. And it's still a very popular tool in the education market because we figured a subgroup that's using it very frequently. Brian (13:14) Hmm. Yeah, that's awesome. How do you know when, you know, what kind of threshold do you look for to determine that, this is, because, you know, in your book, you're talking about, you know, behaviors that are not normal, right? People using your product in a way that you didn't anticipate. And what kind of threshold do you look for to that says, hey, it's worth investigating this? You know, I've got this percentage or this number of people who are using it in this strange way. At what point do you chase that down? Gojko (13:49) I think it's wrong to look at the percentages there. I think it's wrong to look at the percentages because then you get into the game of trying to justify economically helping 0 .1 % of the users. And that's never going to happen because what I like about this is an idea from Microsoft's Inclusive Design and the work of Kat Holmes who wrote a book called Mismatch on Brian (13:52) Okay. Gojko (14:17) assistive technologies and inclusive design for disabled people. And she talks about how it's never ever ever going to be economically justified to optimize a product to help certain disabilities because there's just not enough of them. And there's a lovely example from Microsoft where, Microsoft Inclusive Design Handbook where they talk about three types of, Brian (14:34) Yeah. Gojko (14:44) disabilities, one are permanent. So you have like people without an arm or something like that. And I'm going to kind of throw some numbers out now, order of magnitude stuff. I have these details in the book and there's kind of the micro -inclusive design handbook. Let's say at the moment, the 16 ,000 people in the U .S. without one arm or with a disabled arm. And then you have these kind of situational disabilities where because of an occupation like you have a bartender who needs to carry something all the time or a worker who does it, one arm is not available and they only have one arm to work on and this temporary like a mother carrying a child or something like that. So the other two groups are order of magnitude 20 -30 million. We're not, by making the software work well with one hand, we're not helping 16 ,000 people, we are helping 50 million people. But you don't know that you're helping 50 million people if you're just thinking about like 16 ,000. I think they have this kind of, one of the key ideas of inclusive design is solve for one, kind of help, design for one, but solve for many. So we are actually helping many, many people there. So think when you figure out that somebody is doing something really strange with your product, you're not helping just that one person. Brian (15:45) Right, right. Hmm. Gojko (16:13) you're helping a whole class of your users by making the software better, removing the obstacles to success. this is where I, you know, going back to the PowerPoint thing I mentioned, once we started removing obstacles for people to build the audios quickly, lots of other people started using the product and people started using the product in a different way. And I think this is a lovely example of what Bruce Torazzini talks about is the complexity paradox because He's a famous UX designer and he talks about how once you give people a product, their behavior changes as a result of having the product. So the UX research we've done before there is a product or there is a feature is not completely relevant, but it's a changed context because he talks about people have a certain amount of time to do a task. And then when they have a tool to complete the task faster, they can take on a more complicated task or they can take on an additional task or do something else. I think removing obstacles to use a success is really important. Not because we're helping 0 .1 % of people who we don't understand, but because we can then improve the product for everybody. And I think that's kind of the magic of lizard optimization in a sense, where if we find these things where somebody's really getting stuck. but if we help them not get stuck, then other people will use the product in a much better way. And I think this is, know, the name lizard optimization comes from this article by Scott Alexander, who talks about the lizard man's constant in research. And the article talks about his experiences with a survey that combined some demographic and psychological data. So they were looking at where you live and what your nationality is and what gender you are and then how you respond to certain psychological questions. he said, like there's about 4 % of the answers they could not account for. And one person wrote American is gender. Several people listed Martian as nationality and things like that. some of these, he says some of these things will be people who didn't really understand the question. they were distracted, they were doing something else, or they understood the question but they filled in the wrong box because, know, the thick thumbs and small screens, or they were kind of malicious and just, you know, wanted to see what happens. when you kind of add these people together, they're not an insignificant group. kind of, he says 4%. And if... we can help these people, at least some of these people, and say reduce churn by 1%. That can compound growth. Reducing churn, keeping people around for longer is an incredible way to kind of unlock growth. going back to what we were talking about, some people might be getting stuck because they don't understand the instructions. Some people might be getting stuck because they're using the product in a way you didn't expect. And some people might just like not have the mental capacity to use it the way you expected them to be used. But if we can help these people along, then normal users can use it much, easier. And you mentioned a smart fridge. I still remember there was this one wonderful bug report we had for my other product, which is a collaboration tool. we had a bug report a while ago. that the software doesn't work when it's loaded on a fridge. And it's like, well, it was never intended to be loaded on a fridge. I have no idea how you loaded it on a fridge. It's a mind mapping diagramming tool. It's intended to be used on large screens. Where does a fridge come in? And then we started talking to this person. This was before the whole kind of COVID and work from home disaster. The user was a busy mother and she was kind of trying to collaborate with her colleagues while making breakfast. breakfast for kids and kind of running around the kitchen she wasn't able to kind of pay attention to the laptop or a phone but her fridge had a screen so she loaded the software on the fridge and was able to kind of pay attention to collaboration there and you know we of course didn't optimize the software to run on fridges that's ridiculous but we realized that some people will be using it without a keyboard and without a mouse and then we kind of restructured the toolbar, we made it so that you can use it on devices that don't have a keyboard and then the whole tablet thing exploded and now you get completely different users that don't have keyboards and things like that. I think that's where I think is looking at percentages is a losing game because then you start saying, but 0 .1 % of people use this. But yeah, I think lizard optimization is about using these signals to improve the products for everybody. Brian (21:30) That's a great example. I love that example because you're absolutely right. You're not trying to necessarily solve that one problem because you don't anticipate there's going to be a lot of people who are going to want to run that software on a fridge. However, the takeaway you had from that of, we can do this for people who don't have a keyboard or a mouse. There's another way that they might operate this that could apply to lots of different devices and lots of different scenarios. Now we're talking about a much bigger audience. Now we're talking about opening this up to larger segments of the population. I love that. I think that's a great example. I know you talk about that there's kind of a process for this. Help us understand. You don't have to give away the whole candy story here from the book, but help us kind of understand in broad, terms what kind of process people follow to try to chase these things down. Gojko (22:26) So there's like a four step process that's crystallized for me. And the book is kind of more as a, like a proposal or a process. It's something that works for me and I'm hoping that other people will try it out like that. So it might not necessarily stay like that in a few years if we talk again. And I've narrowed it down to four steps and kind of the four steps start with letters L, Z, R and D. Lizard. And it's kind of so learn how people are misusing your products, zero in on one area, on one behavior change you want to improve, then remove obstacles to use a success and then double check that what you've done actually created the impact you expected to make. I think kind of when we look at people who follow their own logic or people who follow some lizard logic you don't really understand, by definition they're doing something strange. your idea of helping them might not necessarily be effective or it might not go all the way or it might. So double checking at the end that people are actually now doing what you expect them to do or doing something better is really, really, really important. And then using signals from that to improve the kind of feedback loop is critical. I had this one case where people were getting stuck on a payment format entering tax details and The form was reasonably well explained. There was an example in the forum how to enter your tax ID and people were constantly getting stuck. A small percentage of people was getting stuck on it. However, I don't want to lose a small percentage of people that want to pay me on the payment form. So I thought, well, how about if I remove that field from there? I speed it up for everybody and then I can guide them later into entering the tax details to generate an invoice. I thought that was a brilliant idea. tested it with a few users. Everybody loved it, so I released it. And then a week later, I realized that, yes, I've sold it for the people that were getting confused, but I've ended up confusing a totally different group of people that expects the tax fields there. So the net effect was negative. then I went back to the original form. so there's lots of these things where people don't necessarily behave the way you think they will. Brian (24:38) Hahaha. Gojko (24:48) Ron Kohavi has a wonderful book about that called Trustworthy Online Experiments. And he has data from Slack, from Microsoft, from Booking .com and... The numbers are depressive. on one hand, the numbers range from 10 to 30, 40 % success rate for people's ideas. And if leading companies like that do things that don't pan out two thirds of the time, then we have to be honest building our products and say, well, maybe this idea is going to work out, maybe not. Brian (25:03) Hahaha. Wow. Gojko (25:30) the more experimental the population is, the more risky that is. think monitoring and capturing weird user behaviors, capturing errors helps you understand that people are getting stuck. as you said, you don't want to follow everybody. There's going to be a lot of noise there. We need to extract signals from the noise. That's what the second step is about, focusing on one specific thing we want to improve. Then, try to remove obstacles and then double -checking that we've actually removed them. That's the four steps. And there's like a shorter version of all the four steps. It's easier to remember. It's listen alert, zooming, rescue them, and then double check at the end. that's again, LZRD. Brian (26:13) That's awesome. Yeah, I love the process and I love the kind of steps there. Are there tools that you recommend for this that are easier to try to determine these things or chase them down or are there tools that you find are more helpful? Gojko (26:32) So there's lots of tools today for things like A -B testing and looking at experiments and things that are very helpful to do this scale. And it's kind of especially useful for the last step. In terms of kind of focusing and things like that, the five stages of growth from the linear analytics are a good tool. Impact mapping is a good tool. Kind of any focusing product management technique that says, well, these are the business goals we're working on now, or these are the kind of user goals we're working on now. out of, know, 50 lizards we found last week, these three lizards seem to be kind of in that area. And for the first step, spotting when people are getting stuck, there's a bunch of tools that are interesting, like session recorders for web products. There's one from Microsoft called Clarity that's free. There's another called Full Story that's quite expensive. There's a couple of open source one, one is packaged within Matomo analytics application. There's a bunch of these other things. Any kind of observability or monitoring tool is also very useful for this because we can spot when people are getting stuck. One of the things I found particularly helpful is logging all user errors. When a user does something to cause an error condition in a product, the product of course tells them like, know, an error happened. But then... logging it and analyzing that information in the back is really critical. for something like that, people sometimes use web analytics tools or any kind of product analytics. I think what's going to be interesting in the next couple of years, and I think if people start doing this more, is we'll see. more like these technical exception analytics tracking tools mixed with this because most of the product analytics are showing people what they expect to see, not what they don't expect to see. And I'll just give you an example of this way. was really helpful. So I've mentioned the screen where people can upload the Word documents. Occasionally people would select weird file types. So they'll select images, they'll select, I don't know, what else. Brian (28:31) Yeah. Gojko (28:49) Sometimes I guess that's a result of, know, a fat finger press or somebody not selecting the right thing. I have a not insignificant percentage of users every day that try to upload Android package files into a text -to -speech reader. Android package files and application files, I don't know what the right way is to read out an Android application. My best guess is people are doing that. as a, you know, these things where you drop a USB in front of an office and somebody kind of mistakenly plugs it in. So maybe they're hoping that I'll know the Android application on my phone just because they've uploaded it. I don't know, but a small percentage of users was trying to upload files that had SRT and VTT extensions, which are subtitle files. And they were not supported, but Brian (29:31) Yeah. Gojko (29:45) I kept getting information that people are uploading those types of files. And then I said, well, this is interesting because it's a text to speech system. People are uploading subtitle files, there's text in, so why don't I just ignore the timestamps and read the text? I can do that. And I started supporting that. And then some people started complaining that, well, the voice is reading it slower than the subtitles. I said, well, yes, because... Brian (30:11) Ha Gojko (30:12) You know, you're uploading subtitles that were read by an actor in a movie. This is a voice that's reading it at their speed. And then we started talking and it turns out that these people were doing it for corporate educational videos where they have a video in English, they need it in French, German, Spanish and all the else, but they don't want to kind of re -edit the video. They just want an alternate audio track. Okay, I mean, I have the timestamps, we can speed up or slow down the audio, it's not a big deal. And we've done that and this was one of the most profitable features ever. Like a very small percentage of the users need it, but those that need it produce hundreds of thousands of audio files because they translate the corporate training videos. And now, you know, we're getting into that numbers game. If I said, you know, there's like 0 .1 % of people are uploading subtitle files. Brian (30:58) Yeah. Gojko (31:07) then it doesn't matter. if we start thinking about, this is potentially interesting use case, it creates growth on its own because then people find you. And I think my product was the first that was actually doing synchronous subtitles. Competitors are doing it now as well. But it opened the massive, massive market for us. And people, you know, I got there by monitoring user errors, by, you know, the fact that somebody uploaded a file that had an unsupported extension. That was our insight. Brian (31:38) Wow, that's really cool. That's a great story. This is fascinating stuff. And it makes me want to dive deeper into the book and read through it again. But I really appreciate you coming on and sharing this with us, Goiko. This is good stuff. Again, the book is called, Lizard Optimization, Unlock Product Growth by Engaging Long Tail Users. And if I'm right, we talked about this a little bit before. We're going to offer a discount to to the listeners, Gojko (32:07) Yes, we will give you a listen as a 50 % discount on the ebook. the ebook is available from Lean Pub. If you get it from the discount URL that I'll give you, then you'll get a 50 % discount immediately. Brian (32:24) Awesome. So we'll put that in our show notes. If you're interested in that, you can find the show notes. That's a great deal, 50 % off the book and it's good stuff. well, I just, I can't thank you enough. Thanks for making time and coming on and talking this through your book. Gojko (32:40) Thank you, it was lovely to chat to you.

Lenny's Podcast: Product | Growth | Career ✓ Claim Key Takeaways Check out the episode pageRead the full notes @ podcastnotes.orgMike Maples, Jr. is a legendary early-stage startup investor and a co-founder and partner at Floodgate. He's made early bets on transformative companies like Twitter, Lyft, Twitch, Okta, Rappi, and Applied Intuition and is one of the pioneers of seed-stage investing as a category. He's been on the Forbes Midas List eight times and enjoys sharing the lessons he's learned from his years studying iconic companies. In his new book, Pattern Breakers: Why Some Start-Ups Change the Future, co-authored with Peter Ziebelman, he discusses what he's found separates startups and founders that break through and change the world from those that don't. After spending years reviewing the notes and decks from the thousands of startups he's known over the past two decades, he's uncovered three ways that breakthrough founders think and act differently. In our conversation, Mike talks about:• The three elements of breakthrough startup ideas• Why you need to both think and act differently• How to avoid the “comparison trap” and “conformity trap”• The importance of movements, storytelling, and healthy disagreeableness in startup success• How to apply pattern-breaking principles within large companies• Mike's one piece of advice for founders• Much morePre-order Mike's book here and get a second signed copy for free. Limited copies are available, so order ASAP: patternbreakers.com/lenny.—Brought to you by:• Enterpret—Transform customer feedback into product growth• Anvil—The fastest way to build software for documents• Webflow—The web experience platform—Find the transcript at: https://www.lennysnewsletter.com/p/how-to-find-a-great-startup-idea-mike-maples-jr—Where to find Mike Maples, Jr.:• X: https://x.com/m2jr• LinkedIn: https://www.linkedin.com/in/maples/• Substack: https://greatness.substack.com/• Website: https://www.floodgate.com/—Where to find Lenny:• Newsletter: https://www.lennysnewsletter.com• X: https://twitter.com/lennysan• LinkedIn: https://www.linkedin.com/in/lennyrachitsky/—In this episode, we cover:(00:00) Mike's background(03:10) The inspiration behind Pattern Breakers(08:09) Uncovering startup insights(11:37) A quick summary of Pattern Breakers(13:52) Coming up with an idea(15:30) Inflections(17:09) Examples of inflections(28:10) Insights(36:58) The power of surprises(47:36) Founder-future fit(55:33) Advice for aspiring founders(56:41) Living in the future: valid opinions(55:34) Case study: Maddie Hall and Living Carbon(58:40) Identifying lighthouse customers(01:00:53) The importance of desperation in customer needs(01:03:57) Creating movements and storytelling(01:24:22) The role of disagreeableness in startups(01:34:42) Applying these principles within a company(01:40:43) Lightning round—Referenced:• Pattern Breakers: Why Some Start-Ups Change the Future: https://www.amazon.com/Pattern-Breakers-Start-Ups-Change-Future/dp/1541704355• Justin.tv: https://en.wikipedia.org/wiki/Justin.tv• Airbnb's CEO says a $40 cereal box changed the course of the multibillion-dollar company: https://fortune.com/2023/04/19/airbnb-ceo-cereal-box-investors-changed-everything-billion-dollar-company/• Brian Chesky's new playbook: https://www.lennysnewsletter.com/p/brian-cheskys-contrarian-approach• The Unconventional Exit: How Justin Kan Sold His First Startup on eBay: https://medium.datadriveninvestor.com/the-unconventional-exit-how-justin-kan-sold-his-first-startup-on-ebay-4d705afe1354• Kyle Vogt on LinkedIn: https://www.linkedin.com/in/kylevogt/• The State of Telehealth Before and After the COVID-19 Pandemic: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9035352/• The Craigslist Killers: https://www.gq.com/story/craigslist-killers• The social radar: Y Combinator's secret weapon | Jessica Livingston (co-founder of Y Combinator, author, podcast host): https://www.lennysnewsletter.com/p/the-social-radar-jessica-livingston• Michael Seibel on LinkedIn: https://www.linkedin.com/in/mwseibel/• The Airbnb Story: How Three Ordinary Guys Disrupted an Industry, Made Billions ... and Created Plenty of Controversy: https://www.amazon.com/Airbnb-Story-Ordinary-Disrupted-Controversy/dp/0544952669• Scott Cook: https://www.forbes.com/profile/scott-cook/• Chegg: https://www.chegg.com/• Aayush Phumbhra on LinkedIn: https://www.linkedin.com/in/aayush/• Osman Rashid on LinkedIn: https://www.linkedin.com/in/osmanrashid/• Okta: https://www.okta.com/• The Man Who Makes the Future: Wired Icon Marc Andreessen: https://www.wired.com/2012/04/ff-andreessen/• Peter Ludwig on LinkedIn: https://www.linkedin.com/in/peterwludwig/• Qasar Younis on LinkedIn: https://www.linkedin.com/in/qasar/• Paul Allen's website: https://paulallen.com/• Louis Pasteur quote: https://www.forbes.com/quotes/6145/• What was Atrium and why did it fail? https://www.failory.com/cemetery/atrium• Patrick Collison on LinkedIn: https://www.linkedin.com/in/patrickcollison/• Drew Houston on LinkedIn: https://www.linkedin.com/in/drewhouston/• William Gibson's quote: https://www.goodreads.com/quotes/681-the-future-is-already-here-it-s-just-not-evenly• Maddie Hall on LinkedIn: https://www.linkedin.com/in/maddie-hall-76293135/• Living Carbon: https://www.livingcarbon.com• Zenefits (now Trinet): https://connect.trinet.com/• Sam Altman on X: https://x.com/sama• Steve Wozniak on LinkedIn: https://www.linkedin.com/in/wozniaksteve/• Horsley Bridge Partners: https://www.horsleybridge.com/• David Swensen: https://en.wikipedia.org/wiki/David_F._Swensen• Judith Elsea on LinkedIn: https://www.linkedin.com/in/judithelsea/• 7 Powers: The Foundations of Business Strategy: https://www.amazon.com/7-Powers-Foundations-Business-Strategy/dp/0998116319• Business strategy with Hamilton Helmer (author of 7 Powers): https://www.lennysnewsletter.com/p/business-strategy-with-hamilton-helmer• Lyft's Focus on Community and the Story Behind the Pink Mustache: https://techcrunch.com/2012/09/17/lyfts-focus-on-community-and-the-story-behind-the-pink-mustache/• Logan Green on LinkedIn: https://www.linkedin.com/in/logangreen/• John Zimmer on LinkedIn: https://www.linkedin.com/in/johnzimmer11/• Storytelling with Nancy Duarte: How to craft compelling presentations and tell a story that sticks: https://www.lennysnewsletter.com/p/storytelling-with-nancy-duarte-how• Steve Jobs Introducing the iPhone at MacWorld 2007: https://www.youtube.com/watch?v=x7qPAY9JqE4• Jonathan Livingston Seagull: https://www.amazon.com/Jonathan-Livingston-Seagull-Richard-Bach/dp/0743278909• The paths to power: How to grow your influence and advance your career | Jeffrey Pfeffer (author of 7 Rules of Power, professor at Stanford GSB): https://www.lennysnewsletter.com/p/the-paths-to-power-jeffrey-pfeffer• Robin Roberts on LinkedIn: https://www.linkedin.com/in/robin-roberts-393a934b/• Skunkworks: https://www.lockheedmartin.com/en-us/who-we-are/business-areas/aeronautics/skunkworks.html• Vision, conviction, and hype: How to build 0 to 1 inside a company | Mihika Kapoor (Product at Figma): https://www.lennysnewsletter.com/p/vision-conviction-hype-mihika-kapoor• Hard-won lessons building 0 to 1 inside Atlassian | Tanguy Crusson (Head of Jira Product Discovery): https://www.lennysnewsletter.com/p/building-0-to-1-inside-atlassian-tanguy-crusson• Figma: https://www.figma.com/• Atlassian: https://www.atlassian.com/• Vinod Khosla: https://www.khoslaventures.com/team/vinod-khosla/• Top Five Regrets of the Dying: A Life Transformed by the Dearly Departing: https://www.amazon.com/Top-Five-Regrets-Dying-Transformed-ebook/dp/B07KNRLY1L• Chase, Chance, and Creativity: The Lucky Art of Novelty: https://www.amazon.com/Chase-Chance-Creativity-Lucky-Novelty/dp/0262511355• Clay Christensen's books: https://www.amazon.com/stores/Clayton-M.-Christensen/author/B000APPD3Y• Resonate: Present Visual Stories That Transform: https://www.amazon.com/Resonate-Present-Stories-Transform-Audiences/dp/0470632011• Ferrari on Prime: https://www.amazon.com/Ferrari-Adam-Driver/dp/B0CNDBN672• Montblanc fountain pens: https://www.montblanc.com/en-us—Production and marketing by https://penname.co/. For inquiries about sponsoring the podcast, email podcast@lennyrachitsky.com.—Lenny may be an investor in the companies discussed. Get full access to Lenny's Newsletter at www.lennysnewsletter.com/subscribe

Lenny's Podcast: Product | Growth | Career ✓ Claim Key Takeaways Check out the episode pageRead the full notes @ podcastnotes.orgMike Maples, Jr. is a legendary early-stage startup investor and a co-founder and partner at Floodgate. He's made early bets on transformative companies like Twitter, Lyft, Twitch, Okta, Rappi, and Applied Intuition and is one of the pioneers of seed-stage investing as a category. He's been on the Forbes Midas List eight times and enjoys sharing the lessons he's learned from his years studying iconic companies. In his new book, Pattern Breakers: Why Some Start-Ups Change the Future, co-authored with Peter Ziebelman, he discusses what he's found separates startups and founders that break through and change the world from those that don't. After spending years reviewing the notes and decks from the thousands of startups he's known over the past two decades, he's uncovered three ways that breakthrough founders think and act differently. In our conversation, Mike talks about:• The three elements of breakthrough startup ideas• Why you need to both think and act differently• How to avoid the “comparison trap” and “conformity trap”• The importance of movements, storytelling, and healthy disagreeableness in startup success• How to apply pattern-breaking principles within large companies• Mike's one piece of advice for founders• Much morePre-order Mike's book here and get a second signed copy for free. Limited copies are available, so order ASAP: patternbreakers.com/lenny.—Brought to you by:• Enterpret—Transform customer feedback into product growth• Anvil—The fastest way to build software for documents• Webflow—The web experience platform—Find the transcript at: https://www.lennysnewsletter.com/p/how-to-find-a-great-startup-idea-mike-maples-jr—Where to find Mike Maples, Jr.:• X: https://x.com/m2jr• LinkedIn: https://www.linkedin.com/in/maples/• Substack: https://greatness.substack.com/• Website: https://www.floodgate.com/—Where to find Lenny:• Newsletter: https://www.lennysnewsletter.com• X: https://twitter.com/lennysan• LinkedIn: https://www.linkedin.com/in/lennyrachitsky/—In this episode, we cover:(00:00) Mike's background(03:10) The inspiration behind Pattern Breakers(08:09) Uncovering startup insights(11:37) A quick summary of Pattern Breakers(13:52) Coming up with an idea(15:30) Inflections(17:09) Examples of inflections(28:10) Insights(36:58) The power of surprises(47:36) Founder-future fit(55:33) Advice for aspiring founders(56:41) Living in the future: valid opinions(55:34) Case study: Maddie Hall and Living Carbon(58:40) Identifying lighthouse customers(01:00:53) The importance of desperation in customer needs(01:03:57) Creating movements and storytelling(01:24:22) The role of disagreeableness in startups(01:34:42) Applying these principles within a company(01:40:43) Lightning round—Referenced:• Pattern Breakers: Why Some Start-Ups Change the Future: https://www.amazon.com/Pattern-Breakers-Start-Ups-Change-Future/dp/1541704355• Justin.tv: https://en.wikipedia.org/wiki/Justin.tv• Airbnb's CEO says a $40 cereal box changed the course of the multibillion-dollar company: https://fortune.com/2023/04/19/airbnb-ceo-cereal-box-investors-changed-everything-billion-dollar-company/• Brian Chesky's new playbook: https://www.lennysnewsletter.com/p/brian-cheskys-contrarian-approach• The Unconventional Exit: How Justin Kan Sold His First Startup on eBay: https://medium.datadriveninvestor.com/the-unconventional-exit-how-justin-kan-sold-his-first-startup-on-ebay-4d705afe1354• Kyle Vogt on LinkedIn: https://www.linkedin.com/in/kylevogt/• The State of Telehealth Before and After the COVID-19 Pandemic: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9035352/• The Craigslist Killers: https://www.gq.com/story/craigslist-killers• The social radar: Y Combinator's secret weapon | Jessica Livingston (co-founder of Y Combinator, author, podcast host): https://www.lennysnewsletter.com/p/the-social-radar-jessica-livingston• Michael Seibel on LinkedIn: https://www.linkedin.com/in/mwseibel/• The Airbnb Story: How Three Ordinary Guys Disrupted an Industry, Made Billions ... and Created Plenty of Controversy: https://www.amazon.com/Airbnb-Story-Ordinary-Disrupted-Controversy/dp/0544952669• Scott Cook: https://www.forbes.com/profile/scott-cook/• Chegg: https://www.chegg.com/• Aayush Phumbhra on LinkedIn: https://www.linkedin.com/in/aayush/• Osman Rashid on LinkedIn: https://www.linkedin.com/in/osmanrashid/• Okta: https://www.okta.com/• The Man Who Makes the Future: Wired Icon Marc Andreessen: https://www.wired.com/2012/04/ff-andreessen/• Peter Ludwig on LinkedIn: https://www.linkedin.com/in/peterwludwig/• Qasar Younis on LinkedIn: https://www.linkedin.com/in/qasar/• Paul Allen's website: https://paulallen.com/• Louis Pasteur quote: https://www.forbes.com/quotes/6145/• What was Atrium and why did it fail? https://www.failory.com/cemetery/atrium• Patrick Collison on LinkedIn: https://www.linkedin.com/in/patrickcollison/• Drew Houston on LinkedIn: https://www.linkedin.com/in/drewhouston/• William Gibson's quote: https://www.goodreads.com/quotes/681-the-future-is-already-here-it-s-just-not-evenly• Maddie Hall on LinkedIn: https://www.linkedin.com/in/maddie-hall-76293135/• Living Carbon: https://www.livingcarbon.com• Zenefits (now Trinet): https://connect.trinet.com/• Sam Altman on X: https://x.com/sama• Steve Wozniak on LinkedIn: https://www.linkedin.com/in/wozniaksteve/• Horsley Bridge Partners: https://www.horsleybridge.com/• David Swensen: https://en.wikipedia.org/wiki/David_F._Swensen• Judith Elsea on LinkedIn: https://www.linkedin.com/in/judithelsea/• 7 Powers: The Foundations of Business Strategy: https://www.amazon.com/7-Powers-Foundations-Business-Strategy/dp/0998116319• Business strategy with Hamilton Helmer (author of 7 Powers): https://www.lennysnewsletter.com/p/business-strategy-with-hamilton-helmer• Lyft's Focus on Community and the Story Behind the Pink Mustache: https://techcrunch.com/2012/09/17/lyfts-focus-on-community-and-the-story-behind-the-pink-mustache/• Logan Green on LinkedIn: https://www.linkedin.com/in/logangreen/• John Zimmer on LinkedIn: https://www.linkedin.com/in/johnzimmer11/• Storytelling with Nancy Duarte: How to craft compelling presentations and tell a story that sticks: https://www.lennysnewsletter.com/p/storytelling-with-nancy-duarte-how• Steve Jobs Introducing the iPhone at MacWorld 2007: https://www.youtube.com/watch?v=x7qPAY9JqE4• Jonathan Livingston Seagull: https://www.amazon.com/Jonathan-Livingston-Seagull-Richard-Bach/dp/0743278909• The paths to power: How to grow your influence and advance your career | Jeffrey Pfeffer (author of 7 Rules of Power, professor at Stanford GSB): https://www.lennysnewsletter.com/p/the-paths-to-power-jeffrey-pfeffer• Robin Roberts on LinkedIn: https://www.linkedin.com/in/robin-roberts-393a934b/• Skunkworks: https://www.lockheedmartin.com/en-us/who-we-are/business-areas/aeronautics/skunkworks.html• Vision, conviction, and hype: How to build 0 to 1 inside a company | Mihika Kapoor (Product at Figma): https://www.lennysnewsletter.com/p/vision-conviction-hype-mihika-kapoor• Hard-won lessons building 0 to 1 inside Atlassian | Tanguy Crusson (Head of Jira Product Discovery): https://www.lennysnewsletter.com/p/building-0-to-1-inside-atlassian-tanguy-crusson• Figma: https://www.figma.com/• Atlassian: https://www.atlassian.com/• Vinod Khosla: https://www.khoslaventures.com/team/vinod-khosla/• Top Five Regrets of the Dying: A Life Transformed by the Dearly Departing: https://www.amazon.com/Top-Five-Regrets-Dying-Transformed-ebook/dp/B07KNRLY1L• Chase, Chance, and Creativity: The Lucky Art of Novelty: https://www.amazon.com/Chase-Chance-Creativity-Lucky-Novelty/dp/0262511355• Clay Christensen's books: https://www.amazon.com/stores/Clayton-M.-Christensen/author/B000APPD3Y• Resonate: Present Visual Stories That Transform: https://www.amazon.com/Resonate-Present-Stories-Transform-Audiences/dp/0470632011• Ferrari on Prime: https://www.amazon.com/Ferrari-Adam-Driver/dp/B0CNDBN672• Montblanc fountain pens: https://www.montblanc.com/en-us—Production and marketing by https://penname.co/. For inquiries about sponsoring the podcast, email podcast@lennyrachitsky.com.—Lenny may be an investor in the companies discussed. Get full access to Lenny's Newsletter at www.lennysnewsletter.com/subscribe

Lenny's Podcast: Product | Growth | Career ✓ Claim Key Takeaways Check out the episode pageRead the full notes @ podcastnotes.orgMike Maples, Jr. is a legendary early-stage startup investor and a co-founder and partner at Floodgate. He's made early bets on transformative companies like Twitter, Lyft, Twitch, Okta, Rappi, and Applied Intuition and is one of the pioneers of seed-stage investing as a category. He's been on the Forbes Midas List eight times and enjoys sharing the lessons he's learned from his years studying iconic companies. In his new book, Pattern Breakers: Why Some Start-Ups Change the Future, co-authored with Peter Ziebelman, he discusses what he's found separates startups and founders that break through and change the world from those that don't. After spending years reviewing the notes and decks from the thousands of startups he's known over the past two decades, he's uncovered three ways that breakthrough founders think and act differently. In our conversation, Mike talks about:• The three elements of breakthrough startup ideas• Why you need to both think and act differently• How to avoid the “comparison trap” and “conformity trap”• The importance of movements, storytelling, and healthy disagreeableness in startup success• How to apply pattern-breaking principles within large companies• Mike's one piece of advice for founders• Much morePre-order Mike's book here and get a second signed copy for free. Limited copies are available, so order ASAP: patternbreakers.com/lenny.—Brought to you by:• Enterpret—Transform customer feedback into product growth• Anvil—The fastest way to build software for documents• Webflow—The web experience platform—Find the transcript at: https://www.lennysnewsletter.com/p/how-to-find-a-great-startup-idea-mike-maples-jr—Where to find Mike Maples, Jr.:• X: https://x.com/m2jr• LinkedIn: https://www.linkedin.com/in/maples/• Substack: https://greatness.substack.com/• Website: https://www.floodgate.com/—Where to find Lenny:• Newsletter: https://www.lennysnewsletter.com• X: https://twitter.com/lennysan• LinkedIn: https://www.linkedin.com/in/lennyrachitsky/—In this episode, we cover:(00:00) Mike's background(03:10) The inspiration behind Pattern Breakers(08:09) Uncovering startup insights(11:37) A quick summary of Pattern Breakers(13:52) Coming up with an idea(15:30) Inflections(17:09) Examples of inflections(28:10) Insights(36:58) The power of surprises(47:36) Founder-future fit(55:33) Advice for aspiring founders(56:41) Living in the future: valid opinions(55:34) Case study: Maddie Hall and Living Carbon(58:40) Identifying lighthouse customers(01:00:53) The importance of desperation in customer needs(01:03:57) Creating movements and storytelling(01:24:22) The role of disagreeableness in startups(01:34:42) Applying these principles within a company(01:40:43) Lightning round—Referenced:• Pattern Breakers: Why Some Start-Ups Change the Future: https://www.amazon.com/Pattern-Breakers-Start-Ups-Change-Future/dp/1541704355• Justin.tv: https://en.wikipedia.org/wiki/Justin.tv• Airbnb's CEO says a $40 cereal box changed the course of the multibillion-dollar company: https://fortune.com/2023/04/19/airbnb-ceo-cereal-box-investors-changed-everything-billion-dollar-company/• Brian Chesky's new playbook: https://www.lennysnewsletter.com/p/brian-cheskys-contrarian-approach• The Unconventional Exit: How Justin Kan Sold His First Startup on eBay: https://medium.datadriveninvestor.com/the-unconventional-exit-how-justin-kan-sold-his-first-startup-on-ebay-4d705afe1354• Kyle Vogt on LinkedIn: https://www.linkedin.com/in/kylevogt/• The State of Telehealth Before and After the COVID-19 Pandemic: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9035352/• The Craigslist Killers: https://www.gq.com/story/craigslist-killers• The social radar: Y Combinator's secret weapon | Jessica Livingston (co-founder of Y Combinator, author, podcast host): https://www.lennysnewsletter.com/p/the-social-radar-jessica-livingston• Michael Seibel on LinkedIn: https://www.linkedin.com/in/mwseibel/• The Airbnb Story: How Three Ordinary Guys Disrupted an Industry, Made Billions ... and Created Plenty of Controversy: https://www.amazon.com/Airbnb-Story-Ordinary-Disrupted-Controversy/dp/0544952669• Scott Cook: https://www.forbes.com/profile/scott-cook/• Chegg: https://www.chegg.com/• Aayush Phumbhra on LinkedIn: https://www.linkedin.com/in/aayush/• Osman Rashid on LinkedIn: https://www.linkedin.com/in/osmanrashid/• Okta: https://www.okta.com/• The Man Who Makes the Future: Wired Icon Marc Andreessen: https://www.wired.com/2012/04/ff-andreessen/• Peter Ludwig on LinkedIn: https://www.linkedin.com/in/peterwludwig/• Qasar Younis on LinkedIn: https://www.linkedin.com/in/qasar/• Paul Allen's website: https://paulallen.com/• Louis Pasteur quote: https://www.forbes.com/quotes/6145/• What was Atrium and why did it fail? https://www.failory.com/cemetery/atrium• Patrick Collison on LinkedIn: https://www.linkedin.com/in/patrickcollison/• Drew Houston on LinkedIn: https://www.linkedin.com/in/drewhouston/• William Gibson's quote: https://www.goodreads.com/quotes/681-the-future-is-already-here-it-s-just-not-evenly• Maddie Hall on LinkedIn: https://www.linkedin.com/in/maddie-hall-76293135/• Living Carbon: https://www.livingcarbon.com• Zenefits (now Trinet): https://connect.trinet.com/• Sam Altman on X: https://x.com/sama• Steve Wozniak on LinkedIn: https://www.linkedin.com/in/wozniaksteve/• Horsley Bridge Partners: https://www.horsleybridge.com/• David Swensen: https://en.wikipedia.org/wiki/David_F._Swensen• Judith Elsea on LinkedIn: https://www.linkedin.com/in/judithelsea/• 7 Powers: The Foundations of Business Strategy: https://www.amazon.com/7-Powers-Foundations-Business-Strategy/dp/0998116319• Business strategy with Hamilton Helmer (author of 7 Powers): https://www.lennysnewsletter.com/p/business-strategy-with-hamilton-helmer• Lyft's Focus on Community and the Story Behind the Pink Mustache: https://techcrunch.com/2012/09/17/lyfts-focus-on-community-and-the-story-behind-the-pink-mustache/• Logan Green on LinkedIn: https://www.linkedin.com/in/logangreen/• John Zimmer on LinkedIn: https://www.linkedin.com/in/johnzimmer11/• Storytelling with Nancy Duarte: How to craft compelling presentations and tell a story that sticks: https://www.lennysnewsletter.com/p/storytelling-with-nancy-duarte-how• Steve Jobs Introducing the iPhone at MacWorld 2007: https://www.youtube.com/watch?v=x7qPAY9JqE4• Jonathan Livingston Seagull: https://www.amazon.com/Jonathan-Livingston-Seagull-Richard-Bach/dp/0743278909• The paths to power: How to grow your influence and advance your career | Jeffrey Pfeffer (author of 7 Rules of Power, professor at Stanford GSB): https://www.lennysnewsletter.com/p/the-paths-to-power-jeffrey-pfeffer• Robin Roberts on LinkedIn: https://www.linkedin.com/in/robin-roberts-393a934b/• Skunkworks: https://www.lockheedmartin.com/en-us/who-we-are/business-areas/aeronautics/skunkworks.html• Vision, conviction, and hype: How to build 0 to 1 inside a company | Mihika Kapoor (Product at Figma): https://www.lennysnewsletter.com/p/vision-conviction-hype-mihika-kapoor• Hard-won lessons building 0 to 1 inside Atlassian | Tanguy Crusson (Head of Jira Product Discovery): https://www.lennysnewsletter.com/p/building-0-to-1-inside-atlassian-tanguy-crusson• Figma: https://www.figma.com/• Atlassian: https://www.atlassian.com/• Vinod Khosla: https://www.khoslaventures.com/team/vinod-khosla/• Top Five Regrets of the Dying: A Life Transformed by the Dearly Departing: https://www.amazon.com/Top-Five-Regrets-Dying-Transformed-ebook/dp/B07KNRLY1L• Chase, Chance, and Creativity: The Lucky Art of Novelty: https://www.amazon.com/Chase-Chance-Creativity-Lucky-Novelty/dp/0262511355• Clay Christensen's books: https://www.amazon.com/stores/Clayton-M.-Christensen/author/B000APPD3Y• Resonate: Present Visual Stories That Transform: https://www.amazon.com/Resonate-Present-Stories-Transform-Audiences/dp/0470632011• Ferrari on Prime: https://www.amazon.com/Ferrari-Adam-Driver/dp/B0CNDBN672• Montblanc fountain pens: https://www.montblanc.com/en-us—Production and marketing by https://penname.co/. For inquiries about sponsoring the podcast, email podcast@lennyrachitsky.com.—Lenny may be an investor in the companies discussed. Get full access to Lenny's Newsletter at www.lennysnewsletter.com/subscribe

Mike Maples, Jr. is a legendary early-stage startup investor and a co-founder and partner at Floodgate. He's made early bets on transformative companies like Twitter, Lyft, Twitch, Okta, Rappi, and Applied Intuition and is one of the pioneers of seed-stage investing as a category. He's been on the Forbes Midas List eight times and enjoys sharing the lessons he's learned from his years studying iconic companies. In his new book, Pattern Breakers: Why Some Start-Ups Change the Future, co-authored with Peter Ziebelman, he discusses what he's found separates startups and founders that break through and change the world from those that don't. After spending years reviewing the notes and decks from the thousands of startups he's known over the past two decades, he's uncovered three ways that breakthrough founders think and act differently. In our conversation, Mike talks about:• The three elements of breakthrough startup ideas• Why you need to both think and act differently• How to avoid the “comparison trap” and “conformity trap”• The importance of movements, storytelling, and healthy disagreeableness in startup success• How to apply pattern-breaking principles within large companies• Mike's one piece of advice for founders• Much morePre-order Mike's book here and get a second signed copy for free. Limited copies are available, so order ASAP: patternbreakers.com/lenny.—Brought to you by:• Enterpret—Transform customer feedback into product growth• Anvil—The fastest way to build software for documents• Webflow—The web experience platform—Find the transcript at: https://www.lennysnewsletter.com/p/how-to-find-a-great-startup-idea-mike-maples-jr—Where to find Mike Maples, Jr.:• X: https://x.com/m2jr• LinkedIn: https://www.linkedin.com/in/maples/• Substack: https://greatness.substack.com/• Website: https://www.floodgate.com/—Where to find Lenny:• Newsletter: https://www.lennysnewsletter.com• X: https://twitter.com/lennysan• LinkedIn: https://www.linkedin.com/in/lennyrachitsky/—In this episode, we cover:(00:00) Mike's background(03:10) The inspiration behind Pattern Breakers(08:09) Uncovering startup insights(11:37) A quick summary of Pattern Breakers(13:52) Coming up with an idea(15:30) Inflections(17:09) Examples of inflections(28:10) Insights(36:58) The power of surprises(47:36) Founder-future fit(55:33) Advice for aspiring founders(56:41) Living in the future: valid opinions(55:34) Case study: Maddie Hall and Living Carbon(58:40) Identifying lighthouse customers(01:00:53) The importance of desperation in customer needs(01:03:57) Creating movements and storytelling(01:24:22) The role of disagreeableness in startups(01:34:42) Applying these principles within a company(01:40:43) Lightning round—Referenced:• Pattern Breakers: Why Some Start-Ups Change the Future: https://www.amazon.com/Pattern-Breakers-Start-Ups-Change-Future/dp/1541704355• Justin.tv: https://en.wikipedia.org/wiki/Justin.tv• Airbnb's CEO says a $40 cereal box changed the course of the multibillion-dollar company: https://fortune.com/2023/04/19/airbnb-ceo-cereal-box-investors-changed-everything-billion-dollar-company/• Brian Chesky's new playbook: https://www.lennysnewsletter.com/p/brian-cheskys-contrarian-approach• The Unconventional Exit: How Justin Kan Sold His First Startup on eBay: https://medium.datadriveninvestor.com/the-unconventional-exit-how-justin-kan-sold-his-first-startup-on-ebay-4d705afe1354• Kyle Vogt on LinkedIn: https://www.linkedin.com/in/kylevogt/• The State of Telehealth Before and After the COVID-19 Pandemic: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9035352/• The Craigslist Killers: https://www.gq.com/story/craigslist-killers• The social radar: Y Combinator's secret weapon | Jessica Livingston (co-founder of Y Combinator, author, podcast host): https://www.lennysnewsletter.com/p/the-social-radar-jessica-livingston• Michael Seibel on LinkedIn: https://www.linkedin.com/in/mwseibel/• The Airbnb Story: How Three Ordinary Guys Disrupted an Industry, Made Billions ... and Created Plenty of Controversy: https://www.amazon.com/Airbnb-Story-Ordinary-Disrupted-Controversy/dp/0544952669• Scott Cook: https://www.forbes.com/profile/scott-cook/• Chegg: https://www.chegg.com/• Aayush Phumbhra on LinkedIn: https://www.linkedin.com/in/aayush/• Osman Rashid on LinkedIn: https://www.linkedin.com/in/osmanrashid/• Okta: https://www.okta.com/• The Man Who Makes the Future: Wired Icon Marc Andreessen: https://www.wired.com/2012/04/ff-andreessen/• Peter Ludwig on LinkedIn: https://www.linkedin.com/in/peterwludwig/• Qasar Younis on LinkedIn: https://www.linkedin.com/in/qasar/• Paul Allen's website: https://paulallen.com/• Louis Pasteur quote: https://www.forbes.com/quotes/6145/• What was Atrium and why did it fail? https://www.failory.com/cemetery/atrium• Patrick Collison on LinkedIn: https://www.linkedin.com/in/patrickcollison/• Drew Houston on LinkedIn: https://www.linkedin.com/in/drewhouston/• William Gibson's quote: https://www.goodreads.com/quotes/681-the-future-is-already-here-it-s-just-not-evenly• Maddie Hall on LinkedIn: https://www.linkedin.com/in/maddie-hall-76293135/• Living Carbon: https://www.livingcarbon.com• Zenefits (now Trinet): https://connect.trinet.com/• Sam Altman on X: https://x.com/sama• Steve Wozniak on LinkedIn: https://www.linkedin.com/in/wozniaksteve/• Horsley Bridge Partners: https://www.horsleybridge.com/• David Swensen: https://en.wikipedia.org/wiki/David_F._Swensen• Judith Elsea on LinkedIn: https://www.linkedin.com/in/judithelsea/• 7 Powers: The Foundations of Business Strategy: https://www.amazon.com/7-Powers-Foundations-Business-Strategy/dp/0998116319• Business strategy with Hamilton Helmer (author of 7 Powers): https://www.lennysnewsletter.com/p/business-strategy-with-hamilton-helmer• Lyft's Focus on Community and the Story Behind the Pink Mustache: https://techcrunch.com/2012/09/17/lyfts-focus-on-community-and-the-story-behind-the-pink-mustache/• Logan Green on LinkedIn: https://www.linkedin.com/in/logangreen/• John Zimmer on LinkedIn: https://www.linkedin.com/in/johnzimmer11/• Storytelling with Nancy Duarte: How to craft compelling presentations and tell a story that sticks: https://www.lennysnewsletter.com/p/storytelling-with-nancy-duarte-how• Steve Jobs Introducing the iPhone at MacWorld 2007: https://www.youtube.com/watch?v=x7qPAY9JqE4• Jonathan Livingston Seagull: https://www.amazon.com/Jonathan-Livingston-Seagull-Richard-Bach/dp/0743278909• The paths to power: How to grow your influence and advance your career | Jeffrey Pfeffer (author of 7 Rules of Power, professor at Stanford GSB): https://www.lennysnewsletter.com/p/the-paths-to-power-jeffrey-pfeffer• Robin Roberts on LinkedIn: https://www.linkedin.com/in/robin-roberts-393a934b/• Skunkworks: https://www.lockheedmartin.com/en-us/who-we-are/business-areas/aeronautics/skunkworks.html• Vision, conviction, and hype: How to build 0 to 1 inside a company | Mihika Kapoor (Product at Figma): https://www.lennysnewsletter.com/p/vision-conviction-hype-mihika-kapoor• Hard-won lessons building 0 to 1 inside Atlassian | Tanguy Crusson (Head of Jira Product Discovery): https://www.lennysnewsletter.com/p/building-0-to-1-inside-atlassian-tanguy-crusson• Figma: https://www.figma.com/• Atlassian: https://www.atlassian.com/• Vinod Khosla: https://www.khoslaventures.com/team/vinod-khosla/• Top Five Regrets of the Dying: A Life Transformed by the Dearly Departing: https://www.amazon.com/Top-Five-Regrets-Dying-Transformed-ebook/dp/B07KNRLY1L• Chase, Chance, and Creativity: The Lucky Art of Novelty: https://www.amazon.com/Chase-Chance-Creativity-Lucky-Novelty/dp/0262511355• Clay Christensen's books: https://www.amazon.com/stores/Clayton-M.-Christensen/author/B000APPD3Y• Resonate: Present Visual Stories That Transform: https://www.amazon.com/Resonate-Present-Stories-Transform-Audiences/dp/0470632011• Ferrari on Prime: https://www.amazon.com/Ferrari-Adam-Driver/dp/B0CNDBN672• Montblanc fountain pens: https://www.montblanc.com/en-us—Production and marketing by https://penname.co/. For inquiries about sponsoring the podcast, email podcast@lennyrachitsky.com.—Lenny may be an investor in the companies discussed. Get full access to Lenny's Newsletter at www.lennysnewsletter.com/subscribe

Jessica Livingston is a co-founder of Y Combinator, the first and most successful startup accelerator. Y Combinator has funded over 5,000 companies, 200 of which are now unicorns, including Airbnb, Dropbox, DoorDash, Stripe, Coinbase, and Reddit. Jessica played a crucial role in YC's early success, when she was nicknamed the “social radar” because of her uncanny ability to quickly evaluate people—an essential skill when investing in early-stage startups. She's also the host of the popular podcast The Social Radars, where she interviews billion-dollar-startup founders, and the author of the acclaimed book Founders at Work, which captures the origin stories of some of today's most interesting companies. In our conversation, we discuss:• How Jessica gained the affectionate title of the “social radar”• Why defensive founders are a red flag• How to develop your social radar• What she looks for in founders during YC interviews• How YC's early inexperience in angel investing led to the batch model• Her favorite stories from interviews with Airbnb, Rippling, and more• Lessons learned from hosting her own podcast• Much more—Brought to you by:• Enterpret—Transform customer feedback into product growth• Anvil—The fastest way to build software for documents• Vanta—Automate compliance. Simplify security—Find the transcript at: https://www.lennysnewsletter.com/p/the-social-radar-jessica-livingston—Where to find Jessica Livingston:• X: https://x.com/jesslivingston• LinkedIn: https://www.linkedin.com/in/jessicalivingston1/• Podcast: https://www.thesocialradars.com/—Where to find Lenny:• Newsletter: https://www.lennysnewsletter.com• X: https://twitter.com/lennysan• LinkedIn: https://www.linkedin.com/in/lennyrachitsky/—In this episode, we cover:(00:00) Jessica's background(02:42) Thoughts on being under-recognized(07:52) Jessica's superpower: the social radar(15:11) Evaluating founders: key traits and red flags(21:00) The Airbnb story: a lesson in hustle and determination(25:57) A YC success story(28:26) The importance of earnestness(32:45) Confidence vs. defensiveness(34:43) Commitment and co-founder disputes(37:46) Relentless resourcefulness(40:00) Jessica's social radar: origins and insights(43:24) Honing her social radar skills(45:44) Conviction and scams: a Y Combinator story(46:50) The interview process: challenges and insights(48:20) Operationalizing founder evaluation(49:38) Advice for building social radar skills(52:08) The “Reading the Mind in the Eyes” quiz(55:19) Jessica's podcast: The Social Radars(01:00:34) Lessons from podcasting and interviewing(01:09:58) Lightning round—Referenced:• Paul Graham's post about Jessica: https://paulgraham.com/jessica.html• Paul Graham on X: https://x.com/paulg• Robert Tappan Morris: https://en.wikipedia.org/wiki/Robert_Tappan_Morris• Trevor Blackwell on X: https://x.com/tlbtlbtlb• Y Combinator: https://www.ycombinator.com/• “The Founders” examines the rise and legend of PayPal: https://www.economist.com/culture/2022/02/19/the-founders-examines-the-rise-and-legend-of-paypal• Patrick Collison on X: https://x.com/patrickc• John Collison on X: https://x.com/collision• Brian Chesky on LinkedIn: https://www.linkedin.com/in/brianchesky/• Nate Blecharczyk on LinkedIn: https://www.linkedin.com/in/blecharczyk/• Joe Gebbia on LinkedIn: https://www.linkedin.com/in/jgebbia/• Airbnb's CEO says a $40 cereal box changed the course of the multibillion-dollar company: https://fortune.com/2023/04/19/airbnb-ceo-cereal-box-investors-changed-everything-billion-dollar-company/• Parker Conrad on LinkedIn: https://www.linkedin.com/in/parkerconrad/• Zenefits: https://connect.trinet.com/hr-platform• Goat: https://www.goat.com/• Eddy Lu on LinkedIn: https://www.linkedin.com/in/eddylu/• Drew Houston on LinkedIn: https://www.linkedin.com/in/drewhouston/• Arash Ferdowsi on LinkedIn: https://www.linkedin.com/in/arashferdowsi/• Lessons from 1,000+ YC startups: Resilience, tar pit ideas, pivoting, more | Dalton Caldwell (Y Combinator, Managing Director): https://www.lennysnewsletter.com/p/lessons-from-1000-yc-startups•Bitcoin launderer pleads guilty, admits to massive Bitfinex hack: https://www.cnbc.com/2023/08/03/new-york-man-admits-being-original-bitfinex-hacker-during-guilty-plea-in-dc-to-bitcoin-money-laundering.html• Paul Graham's tweet with the facial recognition test: https://x.com/paulg/status/1782875262855663691• SmartLess podcast: https://www.smartless.com• Jason Bateman on X: https://x.com/batemanjason• Will Arnett on X: https://x.com/arnettwill• Sean Hayes on X: https://x.com/seanhayes• The Social Radars with Tony Xu, Co-Founder & CEO of DoorDash: https://www.ycombinator.com/library/Ja-tony-xu-co-founder-ceo-of-doordash• The Social Radars with Brian Chesky: https://www.ycombinator.com/library/JW-brian-chesky-co-founder-ceo-of-airbnb• The Social Radars with Patrick and John Collison: https://www.ycombinator.com/library/Kx-patrick-john-collison-co-founders-of-stripe• The Social Radars with Brian Armstrong: https://www.ycombinator.com/library/K3-brian-armstrong-co-founder-and-ceo-of-coinbase• The Social Radars with Emmett Shear: https://www.ycombinator.com/library/KM-emmett-shear-co-founder-of-twitch• The Social Radars with Paul Graham: https://www.ycombinator.com/library/JV-paul-graham-co-founder-of-y-combinator-and-viaweb• The Social Radars with Adora Cheung: https://www.ycombinator.com/library/L0-adora-cheung-co-founder-of-homejoy-instalab• Founders at Work: Stories of Startups' Early Days: https://www.amazon.com/Founders-Work-Stories-Startups-Early/dp/1430210788• Startup School: https://www.startupschool.org/• The Social Radars with Parker Conrad: https://www.ycombinator.com/library/Ky-parker-conrad-founder-of-zenefits-rippling• Rippling: https://www.rippling.com/• Carry on, Jeeves: https://www.amazon.com/Carry-Jeeves-Dover-Thrift-Editions/dp/0486848957• Very Good, Jeeves: https://www.amazon.com/Very-Good-Jeeves-Wooster-Book-ebook/dp/B0051GST06• Right Ho, Jeeves: https://www.amazon.com/Right-Ho-Jeeves-P-Wodehouse-ebook/dp/B083FFDNHN/• Life: https://www.amazon.com/Life-Keith-Richards-ebook/dp/B003UBTX72/• My Name Is Barbra: https://www.amazon.com/My-Name-Barbra-Streisand/dp/0525429522• Clarkson's Farm on Prime: https://www.amazon.com/Clarksons-Farm-Season-1/dp/B095RHJ52R• Schitt's Creek on Hulu: https://www.hulu.com/series/schitts-creek-a2e7a946-9652-48a8-884b-3ea7ea4de273• Yellowstone on Peacock: https://www.peacocktv.com/stream-tv/yellowstone• Sam Altman on X: https://x.com/sama• Justin Kan on LinkedIn: https://www.linkedin.com/in/justinkan/• Alexis Ohanian on X: https://x.com/alexisohanian• Steve Huffman on LinkedIn: https://www.linkedin.com/in/shuffman56/• Breaking News: Condé Nast/Wired Acquires Reddit: https://techcrunch.com/2006/10/31/breaking-news-conde-nastwired-acquires-reddit/• Charles River Venture: https://www.crv.com/—Production and marketing by https://penname.co/. For inquiries about sponsoring the podcast, email podcast@lennyrachitsky.com.—Lenny may be an investor in the companies discussed. Get full access to Lenny's Newsletter at www.lennysnewsletter.com/subscribe

Did you know the company that owns ChatGPT started as a nonprofit?!?!?!In 2015, a group of investors including Peter Thei, Elon Musk, Jessica Livingston, and more banded together to start OpenAI with the "goal of building safe and beneficial artificial general intelligence for the benefit of humanity."But 3 years later they turned themselves into a "capped" for-profit company. So, what happened?In today's episode, we explore the company behind ChatGPT, detailing why it started as a 501(c)3 what prompted it to change its structure, and why it still has a connection to the not-for-profit space.Be sure to subscribe on Spotify or Apple podcasts.If you're anything like me you like a nice cup of tea to start and finish your day in the nonprofit world. That's why host Swim Karim goes to ArtofTea.com for all his tea needs. Visit Art of Tea for 10% off your next purchase of tea and tea accessories today, right here: https://artoftea.go2cloud.org/SHCSources: OpenAI's statement on their nonprofit structure change: https://openai.com/our-structure/

Welcome to The Nonlinear Library, where we use Text-to-Speech software to convert the best writing from the Rationalist and EA communities into audio. This is: Is there software to practice reading expressions?, published by lsusr on April 24, 2024 on LessWrong. I took the Reading the Mind in the Eyes Test test today. I got 27/36. Jessica Livingston got 36/36. Reading expressions is almost mind reading. Practicing reading expressions should be easy with the right software. All you need is software that shows a random photo from a large database, asks the user to guess what it is, and then informs the user what the correct answer is. I felt myself getting noticeably better just from the 36 images on the test. Short standardized tests exist to test this skill, but is there good software for training it? It needs to have lots of examples, so the user learns to recognize expressions instead of overfitting on specific pictures. Paul Ekman has a product, but I don't know how good it is. Thanks for listening. To help us out with The Nonlinear Library or to learn more, please visit nonlinear.org

Link to original articleWelcome to The Nonlinear Library, where we use Text-to-Speech software to convert the best writing from the Rationalist and EA communities into audio. This is: Is there software to practice reading expressions?, published by lsusr on April 24, 2024 on LessWrong. I took the Reading the Mind in the Eyes Test test today. I got 27/36. Jessica Livingston got 36/36. Reading expressions is almost mind reading. Practicing reading expressions should be easy with the right software. All you need is software that shows a random photo from a large database, asks the user to guess what it is, and then informs the user what the correct answer is. I felt myself getting noticeably better just from the 36 images on the test. Short standardized tests exist to test this skill, but is there good software for training it? It needs to have lots of examples, so the user learns to recognize expressions instead of overfitting on specific pictures. Paul Ekman has a product, but I don't know how good it is. Thanks for listening. To help us out with The Nonlinear Library or to learn more, please visit nonlinear.org

(0:00) Intro.(1:10) About the podcast sponsor: The American College of Governance Counsel.(1:57) Start of interview.(2:40) Leah's "origin story." (3:41) Her time at IBM.(4:48) Her founding story of TaskRabbit (Boston, 2008).(12:43) The evolution of her board at TaskRabbit, and how to think about (startup) board composition and scaling. (20:31) First CEO succession (after $12m Series B in 2012).(25:10) Her return as CEO, raising a Series C, and adding 3 strategic independent directors.(26:13) On hiring Stacy Brown-Philpot as COO, and successor to CEO role.(30:45) Distinguishing between startup directors (management, investor, and independent directors).(36:01) Transitioning to investing as a general partner at Fuel Capital. Motto: "We're on your corner, not in your kitchen"(40:55) On the role of CEO coaches (vs board directors or advisors).(42:44) About YPO. "It has been a hugely influential organization for me."(45:21) Her thoughts on boardroom diversity. Reference to the LCDA.(48:42) Innovation in the boardroom, risks and opportunities of AI.(51:29)  Books that have greatly influenced her life: Founders at Work by Jessica Livingston (2007)Books by Adam Grant.(51:51) Her mentors.(52:25) Quotes that she thinks of often or lives her life by.(52:50) An unusual habit or absurd thing that she loves.(54:15) The living person she most admires.Leah Solivan is a General Partner at Fuel Capital, a Silicon Valley-based seed stage venture capital firm. Prior to that, she was the founder, CEO and Executive Chair at TaskRabbit.You can follow her on social media at:Twitter: @labunleashed You can follow Evan on social media at:Twitter: @evanepsteinLinkedIn: https://www.linkedin.com/in/epsteinevan/ Substack: https://evanepstein.substack.com/__You can join as a Patron of the Boardroom Governance Podcast at:Patreon: patreon.com/BoardroomGovernancePod__Music/Soundtrack (found via Free Music Archive): Seeing The Future by Dexter Britain is licensed under a Attribution-Noncommercial-Share Alike 3.0 United States License

History of OpenAI an American artificial intelligence (AI) that researches artificial intelligence with the declared intention of developing "safe and beneficial" artificial general intelligence, which it defines as "highly autonomous systems that outperform humans at most economically valuable work".OpenAI was founded in 2015 by Ilya Sutskever, Greg Brockman, Trevor Blackwell, Vicki Cheung, Andrej Karpathy, Durk Kingma, Jessica Livingston, John Schulman, Pamela Vagata, and Wojciech Zaremba, with Sam Altman and Elon Musk serving as the initial board members. Microsoft provided OpenAI Global LLC with a $1 billion investment in 2019 and a $10 billion investment in 2023.

Jessica Livingston, Festival Director, and Ghana Sharma, Co-Chair + Culinary Director, are in the Concord TV podcast studio to talk about this year's Concord Multicultural Festival on September 24 and Welcoming Week, September 8-17. More information about this year's event is available at: https://concordnhmulticulturalfestival.org/home.

All Paul Graham essay's, brought to life in audio format. This is a third party project, independent from Paul Graham, and produced by Wondercraft AI.

Tyler and Y Combinator co-founder Paul Graham sat down at his home in the English countryside to discuss what areas of talent judgment his co-founder and wife Jessica Livingston is better at, whether young founders have gotten rarer, whether he still takes a dim view of solo founders, how to 2x ambition in the developed world, on the minute past which a Y Combinator interviewer is unlikely to change their mind, what YC learned after rejecting companies, how he got over his fear of flying, Florentine history, why almost all good artists are underrated, what's gone wrong in art, why new homes and neighborhoods are ugly, why he wants to visit the Dark Ages, why he's optimistic about Britain and San Fransisco, the challenges of regulating AI, whether we're underinvesting in high-cost interruption activities, walking, soundproofing, fame, and more. Read a full transcript enhanced with helpful links. Recorded July 15th, 2023. Other ways to connect Follow us on Twitter and Instagram Follow Tyler on Twitter Follow Paul on Twitter Join our Discord Email us: cowenconvos@mercatus.gmu.edu Learn more about Conversations with Tyler and other Mercatus Center podcasts here. Photo credit: Dave Thomas

This is a recap of the top 10 posts on Hacker News on June 23rd, 2023.This podcast was generated by Wondercraft: https://www.wondercraft.ai/?utm_source=hackernews_recap Please ping at team AT wondercraft.ai with feedback.(00:39): Windows NT on 600MHz machine opens apps instantly. What happened?Original post: https://news.ycombinator.com/item?id=36446933&utm_source=wondercraft_ai(02:02): About GitHub's use of your dataOriginal post: https://news.ycombinator.com/item?id=36444839&utm_source=wondercraft_ai(03:26): California will begin backing intentional burns to control wildfireOriginal post: https://news.ycombinator.com/item?id=36447077&utm_source=wondercraft_ai(05:11): EU Advocate General: Technical Standards must be freely available without chargeOriginal post: https://news.ycombinator.com/item?id=36448789&utm_source=wondercraft_ai(06:52): The Pentagon's $52k trash canOriginal post: https://news.ycombinator.com/item?id=36445693&utm_source=wondercraft_ai(08:22): Arwes: Futuristic Sci-Fi UI Web FrameworkOriginal post: https://news.ycombinator.com/item?id=36446637&utm_source=wondercraft_ai(09:48): Jessica Livingston (2015)Original post: https://news.ycombinator.com/item?id=36449894&utm_source=wondercraft_ai(11:20): Two US lawyers fined for submitting fake court citations from ChatGPTOriginal post: https://news.ycombinator.com/item?id=36447433&utm_source=wondercraft_ai(12:59): What Is a Transformer Model?Original post: https://news.ycombinator.com/item?id=36449788&utm_source=wondercraft_ai(14:27): New study suggests that lab-grown meat produces up to 25 times more CO2Original post: https://news.ycombinator.com/item?id=36446846&utm_source=wondercraft_aiThis is a third-party project, independent from HN and YC. Text and audio generated using AI, by wondercraft.ai. Create your own studio quality podcast with text as the only input in seconds at app.wondercraft.ai. Issues or feedback? We'd love to hear from you: team@wondercraft.ai

In this episode of Ventures, I (https://linkedin.com/in/wclittle) compare ChatGPT, Bard, HuggingChat, and BingChat regarding their recommendations for startup books and front-end code for a standard startup landing page. I compare the user experience of each LLM, the limitations of each for these use cases, and show how ChatGPT and Bard seem to be current leaders in these examples.Visit https://satchel.works/@wclittle/ventures-episode-149 for more information. You can watch this episode via video here. 0:03 - Tee-up for the episode, talking about the 4 LLMs I'll be comparing, and where to find the audio/video of this episode. 1:14 - Talking about startup books, starting with ChatGPT's list: "The Lean Startup" by Eric Ries"Zero to One" by Peter Thiel and Blake Masters"The Startup Owner's Manual" by Steve Blank and Bob Dorf"Founders at Work" by Jessica Livingston"The Hard Thing About Hard Things" by Ben Horowitz"Venture Deals" by Brad Feld and Jason Mendelson"Disciplined Entrepreneurship" by Bill Aule"Crossing the Chasm" by Geoffrey A. Moore2:47 - Talking about Google Bard's list: The Lean Startup by Eric RiesZero to One by Peter ThielThe Hard Thing About Hard Things by Ben HorowitzThe Mom Test by Rob FitzpatrickThe Startup Owner's Manual by Steve BlankThe $100 Startup by Chris GuillebeauFounders at Work by Jessica LivingstonLost and Founder by Rand FishkinThe Innovator's Dilemma by Clayton ChristensenThe Art of Startup Fundraising by Steve Blank and Bob Dorf3:25 - HuggingChat's “list” - which was just The Lean Startup4:12 - Bing Chat's list: Lost and Founder by Rand Fishkin, The Startup Checklist by David S. Rose, and The Lean Startup by Eric Ries1. Other books recommended for startup founders include The Founder's Dilemmas by Noam Wasserman, Secrets of Sand Hill Road by Scott Kupor, and Zero to One by Peter Thiel and Blake Masters1. According to Benzinga, the best startup books for beginners are “Zero to One,” “Creativity Inc.” and “The Lean Startup”1. “The Startup Owner's Manual” by Steve Blank is also recommended as one of the best startup books2. Other books that are recommended for business startup include “The War of Art” by Steven Pressfield, “The 4-Hour Workweek” by Tim Ferris, and “Purple Cow” by Seth Godin1.6:06 - Diving into front-end code with the 4 LLMs, including asking it for the CSS for the recommended HTML code that Bard and ChatGPT put out (HuggingChat and Bing weren't able to generate code for me). 

0:00 Intro to Paul Buccheit8:14 History of different emails13:03 Building in public, 100 happy users19:43 Gmail, over the years27:35 Platforms built on open APIs33:00 Iterate internally, User Experience36:00 Advice for founders and startups, YC culture50:00 Founders: Keep trying! Learn from failures53:00 The state of Artificial Intelligence1:00:00 I am nothing - identity, imposter syndrome, ego Paul Buchheit is a computer engineer and entrepreneur who is best known as the creator of Gmail. He worked at a number of Silicon Valley companies, including Intel and Compaq, before joining Google in 1999. At Google, he played a key role in the development of a number of products, including AdSense and Google's search infrastructure.See omnystudio.com/listener for privacy information.

Carlota Perez is a researcher who has studied hype cycles for much of her career. She's affiliated with the University College London, the University of Sussex, The Tallinn University of Technology in Astonia and has worked with some influential organizations around technology and innovation. As a neo-Schumpeterian, she sees technology as a cornerstone of innovation. Her book Technological Revolutions and Financial Capital is a must-read for anyone who works in an industry that includes any of those four words, including revolutionaries.  Connecticut-based Gartner Research was founded by GideonGartner in 1979. He emigrated to the United States from Tel Aviv at three years old in 1938 and graduated in the 1956 class from MIT, where he got his Master's at the Sloan School of Management. He went on to work at the software company System Development Corporation (SDC), the US military defense industry, and IBM over the next 13 years before starting his first company. After that failed, he moved into analysis work and quickly became known as a top mind in the technology industry analysts. He often bucked the trends to pick winners and made banks, funds, and investors lots of money. He was able to parlay that into founding the Gartner Group in 1979.  Gartner hired senior people in different industry segments to aid in competitive intelligence, industry research, and of course, to help Wall Street. They wrote reports on industries, dove deeply into new technologies, and got to understand what we now call hype cycles in the ensuing decades. They now boast a few billion dollars in revenue per year and serve well over 10,000 customers in more than 100 countries.  Gartner has developed a number of tools to make it easier to take in the types of analysis they create. One is a Magic Quadrant, reports that identify leaders in categories of companies by a vision (or a completeness of vision to be more specific) and the ability to execute, which includes things like go-to-market activities, support, etc. They lump companies into a standard four-box as Leaders, Challengers, Visionaries, and Niche Players. There's certainly an observer effect and those they put in the top right of their four box often enjoy added growth as companies want to be with the most visionary and best when picking a tool. Another of Gartner's graphical design patterns to display technology advances is what they call the “hype cycle”. The hype cycle simplifies research from career academics like Perez into five phases.  * The first is the technology trigger, which is when a breakthrough is found and PoCs, or proof-of-concepts begin to emerge in the world that get press interested in the new technology. Sometimes the new technology isn't even usable, but shows promise.  * The second is the Peak of Inflated Expectations, when the press picks up the story and companies are born, capital invested, and a large number of projects around the new techology fail. * The third is the Trough of Disillusionment, where interest falls off after those failures. Some companies suceeded and can show real productivity, and they continue to get investment. * The fourth is the Slope of Enlightenment, where the go-to-market activities of the surviving companies (or even a new generation) begin to have real productivity gains. Every company or IT department now runs a pilot and expectations are lower, but now achievable. * The fifth is the Plateau of Productivity, when those pilots become deployments and purchase orders. The mainstream industries embrace the new technology and case studies prove the promised productivity increases. Provided there's enough market, companies now find success. There are issues with the hype cycle. Not all technologies will follow the cycle. The Gartner approach focuses on financials and productivity rather than true adoption. It involves a lot of guesswork around subjective, synthetical, and often unsystematic research. There's also the ever-resent observer effect. However, more often than not, the hype is seperated from the tech that can give organizations (and sometimes all of humanity) real productivity gains. Further, the term cycle denotes a series of events when it should in fact be cyclical as out of the end of the fifth phase a new cycle is born, or even a set of cycles if industries grow enough to diverge. ChatGPT is all over the news feeds these days, igniting yet another cycle in the cycles of AI hype that have been prevalent since the 1950s. The concept of computer intelligence dates back to the 1942 with Alan Turing and Isaac Asimov with “Runaround” where the three laws of robotics initially emerged from. By 1952 computers could play themselves in checkers and by 1955, Arthur Samuel had written a heuristic learning algorthm he called “temporal-difference learning” to play Chess. Academics around the world worked on similar projects and by 1956 John McCarthy introduced the term “artificial intelligence” when he gathered some of the top minds in the field together for the McCarthy workshop. They tinkered and a generation of researchers began to join them. By 1964, Joseph Weizenbaum's "ELIZA" debuted. ELIZA was a computer program that used early forms of natural language processing to run what they called a “DOCTOR” script that acted as a psychotherapist.  ELIZA was one of a few technologies that triggered the media to pick up AI in the second stage of the hype cycle. Others came into the industry and expectations soared, now predictably followed by dilsillusionment. Weizenbaum wrote a book called Computer Power and Human Reason: From Judgment to Calculation in 1976, in response to the critiques and some of the early successes were able to then go to wider markets as the fourth phase of the hype cycle began. ELIZA was seen by people who worked on similar software, including some games, for Apple, Atari, and Commodore.  Still, in the aftermath of ELIZA, the machine translation movement in AI had failed in the eyes of those who funded the attempts because going further required more than some fancy case statements. Another similar movement called connectionism, or mostly node-based artificial neural networks is widely seen as the impetus to deep learning. David Hunter Hubel and Torsten Nils Wiesel focused on the idea of convultional neural networks in human vision, which culminated in a 1968 paper called  "Receptive fields and functional architecture of monkey striate cortex.” That built on the original deep learning paper from Frank Rosenblatt of Cornell University called "Principles of Neurodynamics: Perceptrons and the Theory of Brain Mechanisms" in 1962 and work done behind the iron curtain by Alexey Ivakhnenko on learning algorithms in 1967. After early successes, though, connectionism - which when paired with machine learning would be called deep learning when Rina Dechter coined the term in 1986, went through a similar trough of disillusionment that kicked off in 1970. Funding for these projects shot up after the early successes and petered out ofter there wasn't much to show for them. Some had so much promise that former presidents can be seen in old photographs going through the models with the statiticians who were moving into computing. But organizations like DARPA would pull back funding, as seen with their speech recognition projects with Cargegie Mellon University in the early 1970s.  These hype cycles weren't just seen in the United States. The British applied mathemetician James Lighthill wrote a report for the British Science Research Council, which was published in 1973. The paper was called “Artificial Intelligence: A General Survey” and analyzed the progress made based on the amount of money spent on artificial intelligence programs. He found none of the research had resulted in any “major impact” in fields that the academics had undertaken. Much of the work had been done at the University of Edinbourgh and funding was drastically cut, based on his findings, for AI research around the UK. Turing, Von Neumann, McCarthy, and others had either intentially or not, set an expectation that became a check the academic research community just couldn't cash. For example, the New York Times claimed Rosenblatt's perceptron would let the US Navy build computers that could “walk, talk, see, write, reproduce itself, and be conscious of its existence” in the 1950s - a goal not likely to be achieved in the near future even seventy years later. Funding was cut in the US, the UK, and even in the USSR, or Union of the Soviet Socialist Republic. Yet many persisted. Languages like Lisp had become common in the late 1970s, after engineers like Richard Greenblatt helped to make McCarthy's ideas for computer languages a reality. The MIT AI Lab developed a Lisp Machine Project and as AI work was picked up at other schools like Stanford began to look for ways to buy commercially built computers ideal to be Lisp Machines. After the post-war spending, the idea that AI could become a more commercial endeavor was attractive to many. But after plenty of hype, the Lisp machine market never materialized. The next hype cycle had begun in 1983 when the US Department of Defense pumped a billion dollars into AI, but that spending was cancelled in 1987, just after the collapse of the Lisp machine market. Another AI winter was about to begin. Another trend that began in the 1950s but picked up steam in the 1980s was expert systems. These attempt to emulate the ways that humans make decisions. Some of this work came out of the Stanford Heuristic Programming Project, pioneered by Edward Feigenbaum. Some commercial companies took the mantle and after running into barriers with CPUs, by the 1980s those got fast enough. There were inflated expectations after great papers like Richard Karp's “Reducibility among Combinatorial Problems” out of UC Berkeley in 1972. Countries like Japan dumped hundreds of millions of dollars (or yen) into projects like “Fifth Generation Computer Systems” in 1982, a 10 year project to build up massively parallel computing systems. IBM spent around the same amount on their own projects. However, while these types of projects helped to improve computing, they didn't live up to the expectations and by the early 1990s funding was cut following commercial failures. By the mid-2000s, some of the researchers in AI began to use new terms, after generations of artificial intelligence projects led to subsequent AI winters. Yet research continued on, with varying degrees of funding. Organizations like DARPA began to use challenges rather than funding large projects in some cases. Over time, successes were found yet again. Google Translate, Google Image Search, IBM's Watson, AWS options for AI/ML, home voice assistants, and various machine learning projects in the open source world led to the start of yet another AI spring in the early 2010s. New chips have built-in machine learning cores and programming languages have frameworks and new technologies like Jupyter notebooks to help organize and train data sets. By 2006, academic works and open source projects had hit a turning point, this time quietly. The Association of Computer Linguistics was founded in 1962, initially as the Association for Machine Translation and Computational Linguistics (AMTCL). As with the ACM, they have a number of special interest groups that include natural language learning, machine translation, typology, natural language generation, and the list goes on. The 2006 proceedings on the Workshop of Statistical Machine Translation began a series of dozens of workshops attended by hundreds of papers and presenters. The academic work was then able to be consumed by all, inlcuding contributions to achieve English-to-German and Frnech tasks from 2014. Deep learning models spread and become more accessible - democratic if you will. RNNs, CNNs, DNNs, GANs.  Training data sets was still one of the most human intensive and slow aspects of machine learning. GANs, or Generative Adversarial Networks were one of those machine learning frameworks, initially designed by Ian Goodfellow and others in 2014. GANs use zero-sum game techniques from game theory to generate new data sets - a genrative model. This allowed for more unsupervised training of data. Now it was possible to get further, faster with AI.  This brings us into the current hype cycle. ChatGPT was launched in November of 2022 by OpenAI. OpenAI was founded as a non-profit in 2015 by Sam Altman (former cofounder of location-based social network app Loopt and former president of Y Combinator) and a cast of veritable all-stars in the startup world that included:  * Reid Hoffman, former Paypal COO, LinkedIn founder and venture capitalist. * Peter Thiel, former cofounder of Paypal and Palantir, as well as one of the top investors in Silicon Valley. * Jessica Livingston, founding partner at Y Combinator. * Greg Brockman, an AI researcher who had worked on projects at MIT and Harvard OpenAI spent the next few years as a non-profit and worked on GPT, or Generative Pre-trained Transformer autoregression models. GPT uses deep learning models to process human text and produce text that's more human than previous models. Not only is it capable of natural language processing but the generative pre-training of models has allowed it to take a lot of unlabeled text so people don't have to hand label weights, thus automated fine tuning of results. OpenAI dumped millions into public betas by 2016 and were ready to build products to take to market by 2019. That's when they switched from a non-profit to a for-profit. Microsoft pumped $1 billion into the company and they released DALL-E to produce generative images, which helped lead to a new generation of applications that could produce artwork on the fly. Then they released ChatGPT towards the end of 2022, which led to more media coverage and prognostication of world-changing technological breakthrough than most other hype cycles for any industry in recent memory. This, with GPT-4 to be released later in 2023. ChatGPT is most interesting through the lens of the hype cycle. There have been plenty of peaks and plateaus and valleys in artificial intelligence over the last 7+ decades. Most have been hyped up in the hallowed halls of academia and defense research. ChatGPT has hit mainstream media. The AI winter following each seems to be based on the reach of audience and depth of expectations. Science fiction continues to conflate expectations. Early prototypes that make it seem as though science fiction will be in our hands in a matter of weeks lead media to conjecture. The reckoning could be substantial. Meanwhile, projects like TinyML - with smaller potential impacts for each use but wider use cases, could become the real benefit to humanity beyond research, when it comes to everyday productivity gains. The moral of this story is as old as time. Control expectations. Undersell and overdeliver. That doesn't lead to massive valuations pumped up by hype cycles. Many CEOs and CFOs know that a jump in profits doesn't always mean the increase will continue. Some intentially slow expectations in their quarterly reports and calls with analysts. Those are the smart ones.

John Coogan is the co-founder of Lucy and co-founder and CTO of Soylent. He is also the creator of a YouTube channel ( @JohnCooganPlus ) with over 257,000 subscribers. In this conversation, we spoke about why Paul Graham played a pivotal role in John's life, early stories from his days at YCombinator, what his YouTube channel has given him, why he's optimistic about technology, and much more. Sponsored by... Aarthi & Sriram's Good Time Show (@AarthiAndSriram). Listen to conversations with founders, athletes, and technologists who have made it from being outsiders to insiders. Past guests include Elon Musk, Mark Zuckerberg, Naomi Osaka, Gary Vaynerchuk, and many more – https://www.youtube.com/@AarthiAndSriram Resources • Founders At Work by Jessica Livingston – https://www.amazon.com/Founders-Work-Stories-Startups-Early/dp/1430210788 • Paul Graham's Essays – http://www.paulgraham.com John's Links • YouTube: @JohnCooganPlus • Twitter: https://twitter.com/johncoogan My Links ✉️ Newsletter: https://dannymiranda.substack.com

ExtraaEdge is an early start-up founded in 2016, currently powering 350+ Educational Institutes with the cloud computing solution. Founded by Abhishek Ballabh (ex. HSBC Data Scientist) & Sushil Mundada (ex. HSBC - Lead BA & CRM Architect) ExtraaEdge delivers all the required tools for End-to-End automation of admissions processes. With their combined passion for education and knowledge expertise in Data and CRM, they contribute by developing ExtraaEdge.In this episode, Varun and Arvind explore Abhishek Ballabh's journey of founding ExtraaEdge and his experience of SaaSBOOMi's SGx program. Abhishek shares various marketing insights and anecdotes about his journey so far and how his new found “yoda-mindset” has taken him and his company from a primarily hustle-driven outbound sales company to now being predominantly inbound marketing-driven organization.Listen on as this trio share some great insights and banter.Key Take aways0:41 Welcome and Introduction to Abhishek Ballabh & ExtraaEdge2:59 CAGR Banter4:03 Marketing Org Chart7:17 Desi Perception of Marketing & Sales8:41 Coupling the effects of SGx and Covid10:18 Lessons learnt in SGx put to practice11:43 SGx - Growth Vipassana for entrepreneurs 12:44 Who reports to Marketing?19:58 Founder Branding21:20 Channeling one's Yoda-mindset22:49 Being the face of the brand26:55 ExtraaEdge's transformation Pre and Post SGx30:41 Apple's DRI concept 31:56 Transformation from Outbound Hustle to Inbound Marathon36:28 First International client through ABM41:57 Maximizing MarTech through ExtraaEdge Certification Academy48:46 Being synonymous with your industry49:47 Stacking Marketing Dominoes across the prospects journey52:10 The push and pull of Marketing & Sales53:39 Founder being a student of Marketing & ConclusionKey Mentions1:48  HSBC  2:09 Mindtickle  4:41 Jessica Livingston, Paul Graham, Y Combinator  6:30 Sushil Mundada 6:43 SaaSBOOMi Growth X program  8:25 Sachin Bhatia  20:20 Niti Ratnaparkhi 20:58 Yoda (Star Wars)  30:16 Apple30:18 Steve Jobs  36:52 Ankit Oberoi & Ad Pushup  37:20 Bits Pilani Dubai39:35 Nikhil Sutar  43:46 Career Guide 48:01 Robert Cialdini  48:47 Almabase  Happy watching! 

In today's episode, Anissa (@anissadphotography) and Jessica Livingston (@jesslivingphoto) talk about how to balance entrepreneurship and motherhood as full-time wedding photographers. Tune in as we chat about topics including mom guilt, burnout, setting boundaries, outsourcing, and more!Follow along with Jessica here: www.instagram.com/jesslivingphoto----------------Socials:Instagram: www.instagram.com/anissadphotographyTik Tok: www.tiktok.com/anissadphotographyPinterest: www.pinterest.com/anissadphotographyFacebook: www.facebook.com/anissadphotographyWebsite:https://anissadphotography.com/links

Nathan Chan's entrepreneurial skills have seen him in chairs opposite the likes of Richard Branson, Arianna Huffington, Tony Robbins, Jessica Livingston, Seth Godin, Mark Cuban, and Tim Ferriss. When he's not interviewing other rockstar business leaders, he heads up Foundr the global media and education company for entrepreneurs. WATCH ON SPOTIFY: ⁠⁠⁠⁠⁠⁠⁠Click here⁠⁠⁠⁠⁠⁠ WATCH ON YOUTUBE: ⁠⁠⁠⁠⁠⁠Click here⁠⁠⁠⁠ .

Welcome to Episode 440 of the Yeukai Business Show. In this episode, Adit Jain discusses How to Think like your Customers to Scale your Success. So, if you want to know more about How to Scale your Succes, tune in now! In this episode, you'll discover: How to create a continuously evolving culture to serve Enterprise clients betterHow to win Enterprise customersOvercoming recruitment challenges when scaling your business About Adit Jain An IIT-Delhi grad and Y-Combinator alumni, Adit prides himself on the astute understanding of what employee-facing teams need in order to deliver a stellar employee experience. Post a summer training at EY and a marketing internship at IIM Lucknow, Adit began his entrepreneurial journey with Chatteron in 2015. A study of why Chatteron wasn't making enough money, brought him and the other co-founders to the concept of Leena AI, and there has been no looking back since then. The three books Adit draws inspiration from are: Zero to One by Peter Thiel, Founders at Work: Stories of Startups by Jessica Livingston, and Good to Great: Why some companies make the transition and others don't by James C. Collins. His advice to young aspiring entrepreneurs? “Listen to Nike and ‘Just do it.' If you are looking to start, if you have that burning sensation of wanting to build something, I think the best thing is to begin. I have noticed that people who just start and persevere are way more likely to succeed in entrepreneurship than people who wait for the opportune moment. You might fail in your first venture and will pivot several times, but in the end, all of it will be worth it.” More Information Learn more about How to Scale your Success at https://leena.ai/  LinkedIn Facebook Thanks for Tuning In! Thanks so much for being with us this week. Have some feedback you'd like to share? Please leave a note in the comments section below! If you enjoyed this episode on How to Expand your Business, please share it with your friends by using the social media buttons you see at the bottom of the post. Don't forget to subscribe to the show on iTunes to get automatic episode updates for our "Yeukai Business Show !" And, finally, please take a minute to leave us an honest review and rating on iTunes. They really help us out when it comes to the ranking of the show and I make it a point to read every single one of the reviews we get. Please leave a review right now Thanks for listening!

We had to audible this week because technical difficulties killed the interview we were supposed to have for you.  But this is an episode I've wanted to do for some time.  We dove in on the writing called "What You Can't Say" by Paul Graham.  It was written back in 2004 by Paul but it feels like an important piece to come back to with everything that's gone down the last couple of years.  Fabi and I don't agree on everything about this piece but we had fun debating the finer points. Paul Graham is a programmer, writer, and investor. In 1995, he and Robert Morris started Viaweb, the first software as a service company. Viaweb was acquired by Yahoo in 1998, where it became Yahoo Store. In 2001 he started publishing essays on paulgraham.com, which now gets around 25 million page views per year. In 2005 he and Jessica Livingston, Robert Morris, and Trevor Blackwell started Y Combinator, the first of a new type of startup incubator. Since 2005 Y Combinator has funded over 3000 startups, including Airbnb, Dropbox, Stripe, and Reddit. In 2019 he published a new Lisp dialect written in itself called Bel. Paul is the author of On Lisp (Prentice Hall, 1993), ANSI Common Lisp (Prentice Hall, 1995), and Hackers & Painters (O'Reilly, 2004). He has an AB from Cornell and a PhD in Computer Science from Harvard, and studied painting at RISD and the Accademia di Belle Arti in Florence.     Join us on Telegram at https://t.me/tcrpodcast   Resources from this episode:   Paul Graham - What You Can't Say: http://www.paulgraham.com/say.html   Christopher Ryan Podcast (Tangentially Speaking) about this same writing: https://chrisryanphd.com/418-wmtbg-discussion-of-what-you-cant-say-by-paul-graham/    

Jessica Livingston is a co-founder and partner at Y Combinator. Y Combinator (YC) is a startup fund and program. Since 2005, YC has invested in over 3,000 companies including Airbnb, DoorDash, Stripe, Instacart, Dropbox, and Coinbase. The combined valuation of YC companies is approaching $1T. Y Combinator was the first startup accelerator, and continues to lead the space with programs and resources that support founders throughout the life of their company.

Jessica Livingston is a co-founder and partner at Y Combinator. Y Combinator (YC) is a startup fund and program. Since 2005, YC has invested in over 3,000 companies including Airbnb, DoorDash, Stripe, Instacart, Dropbox, and Coinbase. The combined valuation of YC companies is approaching $1T. Y Combinator was the first startup accelerator, and continues to lead the space with programs and resources that support founders throughout the life of their company.

In this talk Jessica Livingston - author of Founders at work - talks about the many lessons she learned from her interviews with the likes of Paul Graham, Steve Wozniak, Mitch Kapor and Joel Spolsky to name but a few. --- Send in a voice message: https://anchor.fm/business-of-software/message

Le podcast "Il était une fois l'entrepreneur" est l'ex podcast "l'apprenti", le podcast des histoires d'entrepreneurs. Y Combinator est un mythe dans la Silicon Valley. Plus qu'un mythe Y Combinator est avant tout une école et un accélérateur pour les meilleures startups du monde. Et c'est Paul Graham accompagné de Trevor Blackwell, Robert Morris et Jessica Livingston qui ont lancé ce projet en 2005. Depuis, Y Combinator a lancé et investi dans des startups mondialement connues: Dropbox, Airbnb, Reddit, Justin.tv, Xobni et Stripe. Découvrez le parcours d'une startup, CampusCred et de ses 3 fondateurs, Sagar Shah, Brian Campbell et Ben Pellow. De la rencontre avec Paul Graham, l'iconique fondateur avec l'accompagnement de Jessica Livingston jusqu'au Demo Day. Vous saurez tout. Inspire Média, le média des histoires d'entreprises et d'entrepreneurs.

Investors have pumped capital into emerging markets since the beginning of civilization. Egyptians explored basic mathematics and used their findings to build larger structures and even granaries to allow merchants to store food and serve larger and larger cities. Greek philosophers expanded on those learnings and applied math to learn the orbits of planets, the size of the moon, and the size of the earth. Their merchants used the astrolabe to expand trade routes. They studied engineering and so learned how to leverage the six simple machines to automate human effort, developing mills and cranes to construct even larger buildings. The Romans developed modern plumbing and aqueducts and gave us concrete and arches and radiant heating and bound books and the postal system.  Some of these discoveries were state sponsored; others from wealthy financiers. Many an early investment was into trade routes, which fueled humanities ability to understand the world beyond their little piece of it and improve the flow of knowledge and mix found knowledge from culture to culture.  As we covered in the episode on clockworks and the series on science through the ages, many a scientific breakthrough was funded by religion as a means of wowing the people. And then autocrats and families who'd made their wealth from those trade routes. Over the centuries of civilizations we got institutions who could help finance industry.  Banks loan money using an interest rate that matches the risk of their investment. It's illegal, going back to the Bible to overcharge on interest. That's called usury, something the Romans realized during their own cycles of too many goods driving down costs and too few fueling inflation. And yet, innovation is an engine of economic growth - and so needs to be nurtured.  The rise of capitalism meant more and more research was done privately and so needed to be funded. And the rise of intellectual property as a good. Yet banks have never embraced startups.  The early days of the British Royal Academy were filled with researchers from the elite. They could self-fund their research and the more doing research, the more discoveries we made as a society. Early American inventors tinkered in their spare time as well. But the pace of innovation has advanced because of financiers as much as the hard work and long hours. Companies like DuPont helped fuel the rise of plastics with dedicated research teams. Railroads were built by raising funds. Trade grew. Markets grew. And people like JP Morgan knew those markets when they invested in new fields and were able to grow wealth and inspire new generations of investors. And emerging industries ended up dominating the places that merchants once held in the public financial markets.  Going back to the Venetians, public markets have required regulation. As banking became more a necessity for scalable societies it too required regulation - especially after the Great Depression. And yet we needed new companies willing to take risks to keep innovation moving ahead., as we do today And so the emergence of the modern venture capital market came in those years with a few people willing to take on the risk of investing in the future. John Hay “Jock” Whitney was an old money type who also started a firm. We might think of it more as a family office these days but he had acquired 15% in Technicolor and then went on to get more professional and invest. Jock's partner in the adventure was fellow Delta Kappa Epsilon from out at the University of Texas chapter, Benno Schmidt. Schmidt coined the term venture capital and they helped pivot Spencer Chemicals from a musicians plant to fertilizer - they're both nitrates, right? They helped bring us Minute Maid. and more recently have been in and out of Herbalife, Joe's Crab Shack, Igloo coolers, and many others. But again it was mostly Whitney money and while we tend to think of venture capital funds as having more than one investor funding new and enterprising companies.  And one of those venture capitalists stands out above the rest. Georges Doriot moved to the United States from France to get his MBA from Harvard. He became a professor at Harvard and a shrewd business mind led to him being tapped as the Director of the Military Planning Division for the Quartermaster General. He would be promoted to brigadier general following a number of massive successes in the research and development as part of the pre-World War II military industrial academic buildup.  After the war Doriot created the American Research and Development Corporation or ARDC with the former president of MIT, Karl Compton, and engineer-turned Senator Ralph Flanders - all of them wrote books about finance, banking, and innovation. They proved that the R&D for innovation could be capitalized to great return. The best example of their success was Digital Equipment Corporation, who they invested $70,000 in in 1957 and turned that into over $350 million in 1968 when DEC went public, netting over 100% a year of return. Unlike Whitney, ARDC took outside money and so Doriot became known as the first true venture capitalist. Those post-war years led to a level of patriotism we arguably haven't seen since. John D. Rockefeller had inherited a fortune from his father, who built Standard Oil. To oversimplify, that company was broken up into a variety of companies including what we now think of as Exxon, Mobil, Amoco, and Chevron. But the family was one of the wealthiest in the world and the five brothers who survived John Jr built an investment firm they called the Rockefeller Brothers Fund. We might think of the fund as a social good investment fund these days. Following the war in 1951, John D Rockefeller Jr endowed the fund with $58 million and in 1956, deep in the Cold War, the fund president Nelson Rockefeller financed a study and hired Henry Kissinger to dig into the challenges of the United States. And then came Sputnik in 1957 and a failed run for the presidency of the United States by Nelson in 1960.  Meanwhile, the fund was helping do a lot of good but also helping to research companies Venrock would capitalize. The family had been investing since the 30s but Laurance Rockefeller had setup Venrock, a mashup of venture and Rockefeller. In Venrock, the five brothers, their sister, MIT's Ted Walkowicz, and Harper Woodward banded together to sprinkle funding into now over 400 companies that include Apple, Intel, PGP, CheckPoint, 3Com, DoubleClick and the list goes on. Over 125 public companies have come out of the fund today with an unimaginable amount of progress pushing the world forward. The government was still doing a lot of basic research in those post-war years that led to standards and patents and pushing innovation forward in private industry. ARDC caught the attention of a number of other people who had money they needed to put to work. Some were family offices increasingly willing to make aggressive investments. Some were started by ARDC alumni such as Charlie Waite and Bill Elfers who with Dan Gregory founded Greylock Partners. Greylock has invested in everyone from Red Hat to Staples to LinkedIn to Workday to Palo Alto Networks to Drobo to Facebook to Zipcar to Nextdoor to OpenDNS to Redfin to ServiceNow to Airbnb to Groupon to Tumblr to Zenprise to Dropbox to IFTTT to Instagram to Firebase to Wandera to Sumo Logic to Okta to Arista to Wealthfront to Domo to Lookout to SmartThings to Docker to Medium to GoFundMe to Discord to Houseparty to Roblox to Figma. Going on 800 investments just since the 90s they are arguably one of the greatest venture capital firms of all time.  Other firms came out of pure security analyst work. Hayden, Stone, & Co was co-founded by another MIT grad, Charles Hayden, who made his name mining copper to help wire up the world in what he expected to be an increasingly electrified world. Stone was a Wall Street tycoon and the two of them founded a firm that employed Joe Kennedy, the family patriarch, Frank Zarb, a Chairman of the NASDAQ and they gave us one of the great venture capitalists to fund technology companies, Arthur Rock.  Rock has often been portrayed as the bad guy in Steve Jobs movies but was the one who helped the “Traitorous 8” leave Shockley Semiconductor and after their dad (who had an account at Hayden Stone) mentioned they needed funding, got serial entrepreneur Sherman Fairchild to fund Fairchild Semiconductor. He developed tech for the Apollo missions, flashes, spy satellite photography - but that semiconductor business grew to 12,000 people and was a bedrock of forming what we now call Silicon Valley. Rock ended up moving to the area and investing. Parlaying success in an investment in Fairchild to invest in Intel when Moore and Noyce left Fairchild to co-found it.  Venture Capital firms raise money from institutional investors that we call limited partners and invest that money. After moving to San Francisco, Rock setup Davis and Rock, got some limited partners, including friends from his time at Harvard and invested in 15 companies, including Teledyne and Scientific Data Systems, which got acquired by Xerox, taking their $257,000 investment to a $4.6 million dollar valuation in 1970 and got him on the board of Xerox. He dialed for dollars for Intel and raised another $2.5 million in a couple of hours, and became the first chair of their board. He made all of his LPs a lot of money. One of those Intel employees who became a millionaire retired young. Mike Markulla invested some of his money and Rock put in $57,000 - growing it to $14 million and went on to launch or invest in companies and make billions of dollars in the process.  Another firm that came out of the Fairchild Semiconductor days was Kleiner Perkins. They started in 1972, by founding partners Eugene Kleiner, Tom Perkins, Frank Caufield, and Brook Byers. Kleiner was the leader of those Traitorous 8 who left William Shockley and founded Fairchild Semiconductor. He later hooked up with former HP head of Research and Development and yet another MIT and Harvard grad, Bill Perkins. Perkins would help Corning, Philips, Compaq, and Genentech - serving on boards and helping them grow.  Caufield came out of West Point and got his MBA from Harvard as well. He'd go on to work with Quantum, AOL, Wyse, Verifone, Time Warner, and others.  Byers came to the firm shortly after getting his MBA from Stanford and started four biotech companies that were incubated at Kleiner Perkins - netting the firm over $8 Billion dollars. And they taught future generations of venture capitalists. People like John Doerr - who was a great seller at Intel but by 1980 graduated into venture capital bringing in deals with Sun, Netscape, Amazon, Intuit, Macromedia, and one of the best gambles of all time - Google. And his reward is a net worth of over $11 billion dollars. But more importantly to help drive innovation and shape the world we live in today.  Kleiner Perkins was the first to move into Sand Hill Road. From there, they've invested in nearly a thousand companies that include pretty much every household name in technology. From there, we got the rise of the dot coms and sky-high rent, on par with Manhattan. Why? Because dozens of venture capital firms opened offices on that road, including Lightspeed, Highland, Blackstone, Accel-KKR, Silver Lake, Redpoint, Sequoia, and Andreesen Horowitz. Sequoia also started in the 70s, by Don Valentine and then acquired by Doug Leone and Michael Moritz in the 90s. Valentine did sales for Raytheon before joining National Semiconductor, which had been founded by a few Sperry Rand traitors and brought in some execs from Fairchild. They were venture backed and his background in sales helped propel some of their earlier investments in Apple, Atari, Electronic Arts, LSI, Cisco, and Oracle to success. And that allowed them to invest in a thousand other companies including Yahoo!, PayPal, GitHub, Nvidia, Instagram, Google, YouTube, Zoom, and many others.  So far, most of the firms have been in the US. But venture capital is a global trend.  Masayoshi Son founded Softbank in 1981 to sell software and then published some magazines and grew the circulation to the point that they were Japan's largest technology publisher by the end of the 80s and then went public in 1994. They bought Ziff Davis publishing, COMDEX, and seeing so much technology and the money in technology, Son inked a deal with Yahoo! to create Yahoo! Japan. They pumped $20 million into Alibaba in 2000 and by 2014 that investment was worth $60 billion. In that time they became more aggressive with where they put their money to work. They bought Vodafone Japan, took over competitors, and then the big one - they bought Sprint, which they merged with T-Mobile and now own a quarter of the combined companies. An important aspect of venture capital and private equity is multiple expansion. The market capitalization of Sprint more than doubled with shares shooting up over 10%. They bought Arm Limited, the semiconductor company that designs the chips in so many a modern phone, IoT device, tablet and even computer now. As with other financial firms, not all investments can go great. SoftBank pumped nearly $5 billion into WeWork. Wag failed. 2020 saw many in staff reductions. They had to sell tens of billions in assets  to weather the pandemic. And yet with some high profile losses, they sold ARM for a huge profit, Coupang went public and investors in their Vision Funds are seeing phenomenal returns across over 200 companies in the portfolios. Most of the venture capitalists we mentioned so far invested as early as possible and stuck with the company until an exit - be it an IPO, acquisition, or even a move into private equity. Most got a seat on the board in exchange for not only their seed capital, or the money to take products to market, but also their advice. In many a company the advice was worth more than the funding. For example, Randy Komisar, now at Kleiner Perkins, famously recommended TiVo sell monthly subscriptions, the growth hack they needed to get profitable. As the venture capital industry grew and more and more money was being pumped into fueling innovation, different accredited and institutional investors emerged to have different tolerances for risk and different skills to bring to the table. Someone who built an enterprise SaaS company and sold within three years might be better served to invest in and advise another company doing the same thing. Just as someone who had spent 20 years running companies that were at later stages and taking them to IPO was better at advising later stage startups who maybe weren't startups any more. Here's a fairly common startup story. After finishing a book on Lisp, Paul Graham decides to found a company with Robert Morris. That was Viaweb in 1995 and one of the earliest SaaS startups that hosted online stores - similar to a Shopify today. Viaweb had an investor named Julian Weber, who invested $10,000 in exchange for 10% of the company. Weber gave them invaluable advice and they were acquired by Yahoo! for about $50 million in stock in 1998, becoming the Yahoo Store.  Here's where the story gets different. 2005 and Graham decides to start doing seed funding for startups, following the model that Weber had established with Viaweb. He and Viaweb co-founders Robert Morris (the guy that wrote the Morris worm) and Trevor Blackwell start Y Combinator, along with Jessica Livingston. They put in $200,000 to invest in companies and with successful investments grew to a few dozen companies a year. They're different because they pick a lot of technical founders (like themselves) and help the founders find product market fit, finish their solutions, and launch. And doing so helped them bring us Airbnb, Doordash, Reddit, Stripe, Dropbox and countless others. Notice that many of these firms have funded the same companies. This is because multiple funds investing in the same company helps distribute risk. But also because in an era where we've put everything from cars to education to healthcare to innovation on an assembly line, we have an assembly line in companies. We have thousands of angel investors, or humans who put capital to work by investing in companies they find through friends, family, and now portals that connect angels with companies.  We also have incubators, a trend that began in the late 50s in New York when Jo Mancuso opened a warehouse up for small tenants after buying a warehouse to help the town of Batavia. The Batavia Industrial Center provided office supplies, equipment, secretaries, a line of credit, and most importantly advice on building a business. They had made plenty of money on chicken coops and though that maybe helping companies start was a lot like incubating chickens and so incubators were born.  Others started incubating. The concept expanded from local entrepreneurs helping other entrepreneurs and now cities, think tanks, companies, and even universities, offer incubation in their walls. Keep in mind many a University owns a lot of patents developed there and plenty of companies have sprung up to commercialize the intellectual property incubated there. Seeing that and how technology companies needed to move faster we got  accelerators like Techstars, founded by David Cohen, Brad Feld, David Brown, and Jared Polis in 2006 out of Boulder, Colorado. They have worked with over 2,500 companies and run a couple of dozen programs. Some of the companies fail by the end of their cohort and yet many like Outreach and Sendgrid grow and become great organizations or get acquired. The line between incubator and accelerator can be pretty slim today. Many of the earlier companies mentioned are now the more mature venture capital firms. Many have moved to a focus on later stage companies with YC and Techstars investing earlier. They attend the demos of companies being accelerated and invest. And the fact that founding companies and innovating is now on an assembly line, the companies that invest in an A round of funding, which might come after an accelerator, will look to exit in a B round, C round, etc. Or may elect to continue their risk all the way to an acquisition or IPO.  And we have a bevy of investing companies focusing on the much later stages. We have private equity firms and family offices that look to outright own, expand, and either harvest dividends from or sell an asset, or company. We have traditional institutional lenders who provide capital but also invest in companies. We have hedge funds who hedge puts and calls or other derivatives on a variety of asset classes. Each has their sweet spot even if most will opportunistically invest in diverse assets. Think of the investments made as horizons. The Angel investor might have their shares acquired in order to clean up the cap table, or who owns which parts of a company, in later rounds. This simplifies the shareholder structure as the company is taking on larger institutional investors to sprint towards and IPO or an acquisition. People like Arthur Rock, Tommy Davis, Tom Perkins, Eugene Kleiner, Doerr, Masayoshi Son, and so many other has proven that they could pick winners. Or did they prove they could help build winners? Let's remember that investing knowledge and operating experience were as valuable as their capital. Especially when the investments were adjacent to other successes they'd found. Venture capitalists invested more than $10 billion in 1997. $600 million of that found its way to early-stage startups. But most went to preparing a startup with a product to take it to mass market. Today we pump more money than ever into R&D - and our tax systems support doing so more than ever. And so more than ever, venture money plays a critical role in the life cycle of innovation. Or does venture money play a critical role in the commercialization of innovation? Seed accelerators, startup studios, venture builders, public incubators, venture capital firms, hedge funds, banks - they'd all have a different answer. And they should. Few would stick with an investment like Digital Equipment for as long as ARDC did. And yet few provide over 100% annualized returns like they did.  As we said in the beginning of this episode, wealthy patrons from Pharaohs to governments to industrialists to now venture capitalists have long helped to propel innovation, technology, trade, and intellectual property. We often focus on the technology itself in computing - but without the money the innovation either wouldn't have been developed or if developed wouldn't have made it to the mass market and so wouldn't have had an impact into our productivity or quality of life.  The knowledge that comes with those who provide the money can be seen with irreverence. Taking an innovation to market means market-ing. And sales. Most generations see the previous generations as almost comedic, as we can see in the HBO show Silicon Valley when the cookie cutter industrialized approach goes too far. We can also end up with founders who learn to sell to investors rather than raising capital in the best way possible, selling to paying customers. But there's wisdom from previous generations when offered and taken appropriately. A coachable founder with a vision that matches the coaching and a great product that can scale is the best investment that can be made. Because that's where innovation can change the world.

We take spreadsheets for granted, but they were actually an incredible innovation that transformed small business. In this episode we talk about the history of spreadsheets and why they are so important. We cover the first popular spreadsheet program, VisiCalc, which was the "killer app" for the Apple II. Then we talk about Lotus 1-2-3 and why it displaced VisiCalc. We finish with Microsoft Excel and areas where spreadsheets are being stretched too thin. Show Notes Episode 16: The Personal Computer Revolution VisiCalc via Wikipedia Episode 22: Why was the IBM PC a Big Deal? Lotus 1-2-3 via Wikipedia Founders at Work by Jessica Livingston via Amazon Episode 21: How have UIs Evolved? Microsoft Excel via Wikipedia Excel: Why using Microsoft's tool caused Covid-19 results to be lost via BBC Follow us on Twitter @KopecExplains. Theme “Place on Fire” Copyright 2019 Creo, CC BY 4.0 Find out more at http://kopec.live

Founders at Work (2007) is a behind-the-scenes exploration at what went on in the early days of the United States’ 30 most successful startups. Telling their stories in the founders’ own words, Jessica Livingston explores the triumphs and tribulations which characterized the early days of companies like Hotmail and Blogger.com to chart their journey from fledgling startup to global corporations. *** Do you want more free audiobook summaries like this? Download our app for free at QuickRead.com/App and get access to hundreds of free book and audiobook summaries.

Justin's at grandma's house this weekend, enjoying a spacious backyard with a barbecue grill, but despite his hopes, it's not nearly big enough to grow enough maple trees for a steady syrup supply. Nonetheless summer is here and the yard is a nice change from LA lock-down. Mark's been doing a little bit of algorithm study for interview prep. Justin's not a fan of that kind of interview since it's so far away from what kinds of work people do on the job. Mark agrees, but seen it useful at one old job which was flooded with applicants who couldn't fizzbuzz. Not by modulo, not by nested loops and not by a big while loop with multiple counters! Please go to Reactor.am and leave comments on the episodes if you feel so inclined! How much has YC changed? One former founder on twitter feels they've lost their soul. He loved the tight network and personal attention from people like PG and Jessica Livingston. Now, the batches are huge and attracting a different sort of person (see link below). We know nothing about YC internally, but it reminds Mark of what he saw happen at his old coding school. When he was a student, the founders were putting everything they had into making sure he and his classmates succeeded. Later batches were larger, students were more risk adverse and more credentialed. Justin's come up with a name for his Roblox game and is now putting together sound effects and music for it. He's got an outline for the game map done and is now starting to work on getting the polish on the center part of it up to a release-quality level. Mark's been thinking about different kinds of content he can make for Alchemist Camp to help the top of the funnel. The full-on screencasts are what people pay for. However he could write four technical tutorials in the time it takes to make a single screencast. Non-technical writing is even faster and more easily shared, but also less valued. What's the idea balance? Mentioned Fizzbuzz The Only Reason My App Worked Was Due to a Slow Database We can't send email more than 500 miles Y Combinator has lost its soul: A YC founder's perspective Questing Log Nugget Academy Alchemist Camp Justin's goals for next time Focus on the Roblox game Work on Nugget login pathways Mark's goals for next time 2 screencasts Algo prep Five UI components for Phoenix Igniter Video version at https://youtu.be/3Y6YfZFV_Qw Comment at https://reactor.am/podcasts/23 On Apple Podcasts at https://podcasts.apple.com/gb/podcast/reactor/id1500109358 Recorded on 2020-07-22

Welcome to the History of Computing Podcast, where we explore the history of information technology. Because understanding the past prepares us to innovate (and sometimes cope with) the future! Today we're going to look at Y Combinator.  Here's a fairly common startup story. After finishing his second book on Lisp, Paul Graham decides to found a company. He and Robert Morris start Viaweb in 1995, along with Trevor Blackwell. Some of the code came from Lisp - you know, like the books Graham had worked on. It was one of the earliest SaaS startups, which let users host online stores - similar to Shopify today. Viaweb had an investor named Julian Weber, who invested $10,000 in exchange for 10% of the company. Weber gave them invaluable advice. By 1998 they were acquired by Yahoo! for about $50 million in stock, which was a little shy of half a million shares. Viaweb would became the Yahoo Store. Both Graham and Morris have PhDs from Harvard. Here's where the story gets different. Graham would write a number of essays, establishing himself as an influencer of sorts. 2005 rolls around and Graham decides to start doing seed funding for startups, following the model that Weber had established with Viaweb. He gets the gang back together, hooking up with his Viaweb co-founders Robert Morris (the guy that wrote the Morris worm) and Trevor Blackwell, and adding girlfriend and future wife Jessica Livingston - and they create Y Combinator.  Graham would pony up $100,000, Morris and Blackwell would each chip in $50,000 and they would start with $200,000 to invest in companies. Being Harvard alumni, it was called Cambridge Seed. And as is the case with many of the companies they invest in, the name would change quickly, to Y Combinator.  They would hold their first session in Boston and called it the Summer Founders Program. And they got a great batch of startups! So they decided to do it again, this time in Mountain View, using space provided by Blackwell. This time, a lot more startups applied and they decided to run two a year, one in each location. And they had plenty of startups looking to attend. But why?  There have always been venture capital firms. Well, not always, but ish. They invest in startups. And incubators had become more common in business since the 1950s. The incubators mostly focused on planning, launching, and growing a company. But accelerators we just starting to become a thing, with the first one maybe being Colorado Venture Centers in 2001. The concept of accelerators really took off because of Y Combinator though. There have been incubators and accelerators for a long, long time. Y Combinator didn't really create those categories. But they did change the investment philosophy of many. You see, Y Combinator is an investor and a school. But. They don't provide office space to companies. They have an open application process. They invest in the ideas of founders they like. They don't invest much. But they get equity in the company in return. They like hackers. People that know how to build software. People who have built companies and sold companies. People who can help budding entrepreneurs.  Graham would launch Hacker News in 2007. Originally called Startup News, it's a service like Reddit that was developed in a language Graham co-wrote called Arc. I guess Arc would be more a stripped down dialect of Lisp, built in Racket. He'd release Arc in 2008. I wonder why he prefers technical founders… They look for technical founders. They look for doers. They look for great ideas, but they focus on the people behind the ideas. They coach on presentation skills, pitch decks, making products. They have a simple motto: “Make Something People Want”. And it works. By 2008 they were investing in 40 companies a year and running a program in Boston and another in Silicon Valley. It was getting to be a bit much so they dropped the Boston program and required founders who wanted to attend the program to move to the Bay Area for a couple of months. They added office hours to help their founders and by 2009 the word was out, Y Combinator was the thing every startup founder wanted to do. Sequoia Capital ponied up $2,000,000 and Y Combinator was able to grow to 60 investments a year. And it was working out really well. So Sequoia put in another $8,250,000 round.  The program is a crash course in building a startup. They look to grow fast. They host weekly dinners that Graham used to cook. Often with guest speakers from the VC community or other entrepreneurs. They build towards Demo Day, where founders present to crowds of investors.  It kept growing. It was an awesome idea but it took a lot of work. The more the word spread, the more investments like Yuri Milner wanted to help fun every company that graduated from Y Combinator. They added non profits in 2013 and continued to grow. By 2014, Graham stepped down as President and handed the reigns to Sam Altman. The amount they invested went up to $120,000. More investments required more leaders and others would come in to run various programs. Altman would step down in 2019. They would experiment with some other ideas but in the end, the original concept was perfect. Several alumni would come back and contribute to the success of future startups. People from companies like Justin.tv and twitch. In fact, their cofounder Michel Seibel would recommend Y Combinator to the founders of Airbnb. He ran Y Combinator Core for a while. Many of the founders who had good exits have gone from starting companies to investing in companies.   Y Combinator changed the way seed investments happen. By 2015, a third of startups got their Series A funding from accelerators. The combined valuation of the Y Combinator companies who could be surveyed is well over $150 billion dollars in market capitalization. Graduates include Airbnb, Stripe, Dropbox, Coinbase, DoorDash, Instacart, Reddit. Massive success has led to over 15,000 applicants for just a few spots. To better serve so many companies, they created a website called Startup School in 2017 and over 1,500 startups went through it in the first year alone.  Y Combinator has been quite impactful in a lot of companies. More important than the valuations and name brands, graduates are building software people want. They're iterating societal change, spurring innovation at a faster pace. They're zeroing in on helping founders build what people want rather than just spinning their wheels and banging their heads against the wall trying to figure out why people aren't buying what they're selling.  My favorite part of Y Combinator has been the types of founders they look for. They give $150,000 to mostly technical founders. And they get 7% of the company in exchange for that investment. And their message of finding the right product market fit has provided them with massive returns on their investments. At. This point they've helped over 2,000 companies by investing and countless others with the startup School and by promoting them on Hacker News.  Not a lot of people can say they changed the world. But this crew did. And there's a chance Airbnb, Doordash, Reddit, Stripe, Dropbox and countless others would have launched and succeeded, but we're all better off for the thousands of companies who have gone through YC having done so. So thank you for helping us get there.  And thank you, listeners, for tuning in to this episode of the History of Computing Podcast. We are so, so lucky to have you. Have a great day.   

8. Founders at Work Founders at Work is a 2009 compilation of interviews with startup founders by Y Combinator founding partner Jessica Livingston. Interview subjects include the founders of famous brands like Apple, PayPal, and Adobe, as well as lesser known founders with equally remarkable stories. In this episode we discuss recurring themes of the interviews, founders that offered particular insight, and broad takeaways from the entire volume. Show Notes Founders at Work on Amazon Jessica Livington on Twitter David Kopec's review of Losing the Signal Venture Capital - Silicon Valley Ponzi Scheme - Chamath Palihapitiya via YouTube Find out more at http://businessbooksandco.com

Do you want more free audiobook summaries like this? Download our app for free at QuickRead.com/App and get access to hundreds of free book and audiobook summaries. Founders at Work (2007) is a behind-the-scenes exploration at what went on in the early days of the United States’ 30 most successful startups. Telling their stories in the founders’ own words, Jessica Livingston explores the triumphs and tribulations which characterized the early days of companies like Hotmail and Blogger.com to chart their journey from fledgling startup to global corporations.

Discussing how we make stuff now? with @Julespieri Work 2.0 Podcast #FutureofWork #Work2dot0 #Podcast In this podcast Jules Pieri talks about her book on "How we make stuff now". She shares some of the best practices as shared by many creators and their manufacturing journey. This conversation is great for anyone looking to see how a creative designers and creators could leverage this new reality of manufacturing revolution. Jules's Recommended Read: The Opposable Mind: How Successful Leaders Win Through Integrative Thinking by Roger L. Martin https://amzn.to/2WdSZ41 Founders at Work: Stories of Startups' Early Days 1st Corrected ed., Corr. by Jessica Livingston https://amzn.to/2Q7l2wm Radical Candor: Be a Kick-Ass Boss Without Losing Your Humanity by Kim Scott https://amzn.to/2w2DcWS Jules Book: How We Make Stuff Now: Turn Ideas into Products That Build Successful Businesses by Jules Pieri https://amzn.to/2Ecu0Ui Podcast Link: iTunes: http://math.im/jofitunes Youtube: http://math.im/jofyoutube Jules's BIO: Jules Pieri is Co-founder & CEO of The Grommet, a site that has launched more than 3,000 innovative consumer products since 2008. The company's Citizen Commerce™ movement is reshaping how products are discovered, shared, and bought. Jules started her career as an industrial designer for technology companies and was an executive at Keds, Stride Rite, and Playskool. The Grommet is her third startup, following roles as VP at Continuum and President of Ziggs. In 2017, Ace Hardware acquired a majority stake in The Grommet. She was named one of Fortune's Most Powerful Women Entrepreneurs in 2013 and one of Goldman Sachs' 100 Most Interesting Entrepreneurs in 2014. She is an Entrepreneur in Residence Emeritus at Harvard Business School and an investing partner at XFactor Ventures. About #Podcast: Work 2.0 Podcast is created to spark the conversation around the future of work, worker and workplace. This podcast invite movers and shakers in the industry who are shaping or helping us understand the transformation in work. Wanna Join? If you or any you know wants to join in, Register your interest by emailing: info@analyticsweek.com Want to sponsor? Email us @ info@analyticsweek.com Keywords: Work 2.0 Podcast, #FutureOfWork, #FutureOfWorker, #FutureOfWorkplace, #Work, #Worker, #Workplace,

3 Good books in 8 minutes. Need I say more. The books are: 1. Good to Great: Why Some Companies Make the Leap and Others Don't by Jim Collins — https://amzn.to/2LqVKZh 2. Founders at Work: Stories of Startups' Early Days by Jessica Livingston — https://amzn.to/2LtuQzS 3. Essentialism: The Disciplined Pursuit of Less by Greg McKeown — https://amzn.to/2NakjMa Full blog post here: https://www.consulting.com/channel/3-good-books-in-8-mins

Margaret (Margo) Wu is a Vice-President on the Georgian Partners investment team involved in deal sourcing, due diligence and supporting portfolio companies. Prior to joining Georgian Partners, Margaret was a Senior Product Manager at Amazon, where she developed mobile metrics and content strategies for marketers and site merchandisers across Europe.   Previous to this, she co-founded a biotech company called Uma Bioseed and served as the Chief Operating Officer at OneSpout.com, where she held a variety of responsibilities ranging from business development and product management to finance and accounting. While there, she helped the company grow to a peak of 350,000 users. Margaret began her career as a consultant in Accenture’s technology practice.     Episode Overview:   This episode is all about VC's (Venture Capital) but how to make a lasting impression and get that investment. This is relevant episode to everyone that wants to make an impression in their corporate world (with their bosses), networking .. wherever you are pitching something. In this episode, Margaret Wu and I talk about: How Georgian Partners differentiate from other & areas of focus How to get noticed by VCs - warm handshakes What to do and what NOT to do! Best practices based on the numerous mistakes that entrepreneurs make How Toronto job market is bigger than Silicon Valley and others - tonnes of opportunities Books, Books Books!!! I’m seriously thinking of starting a business book club     About Gregorian Partners:   Georgian Partners is a thesis-driven growth equity funds currently investing in 4 areas where we believe that first movers will outperform their market competitors: Applied AI, Conversational AI, Security First, and Trust.     Lessons Learned:   Fail fast - figure out which one works for you If you are meeting a VC, it is like going on a first date. You have to be bring a little bit of compassion, be an interesting person. You have to be able to articulate your vision in a very convincing way. Don't approach a VC if there are errors in your numbers     Book Recommended:   Art of the Start 2.0 by Guy Kawasaki Venture Deals: Be Smarter Than Your Lawyer and Venture Capitalist by Brad Feld and Jason Mendelson Founders at Work: Stories of Startups' Early Days by Jessica Livingston     Connect with Margaret:   Website: Georgian Partners LinkedIn: Margaret Wu and Georgian Partners Twitter: @GeorgianPrtnrs Facebook: Georgian Partners

Recorded live at our Female Founders Conference in New York, an AMA with Kat Manalac, Kirsty Nathoo, Adora Cheung, Holly Liu, Jessica Livingston, and Carolynn Levy.This panel was hosted by Sharon Pope, Head of Marketing Programs at YC.We’re also posting the other talks from the Female Founders Conference today. You can see all of them and read the transcripts at blog.ycombinator.comIf you’d like to learn more about the Female Founders Conference, head over to femalefoundersconference.org

Tim Cook addresses Facebook privacy, U.S./China relations at Beijing event. http://bit.ly/qlearly140 Tesla to Slow Deliveries in Norway on Report of Dangerous Trucks. http://bit.ly/qlearly141 NBA testing 99-cent stream that lets you watch the final quarter of a live game. http://bit.ly/qlearly142 Telegram chalks up 200M MAUs for its messaging app. http://bit.ly/qlearly143 HBO’s Silicon Valley gets the VR treatment for Season 5. http://bit.ly/qlearly144 American Express quietly acquired UK fintech startup Cake for $13.3M. http://bit.ly/qlearly145 The US Postal Service Is Looking at Backing Up Data With Blockchain. http://bit.ly/qlearly146 Y Combinator’s Jessica Livingston on Dropbox IPO: ‘It was just a dream of ours’. http://bit.ly/qlearly147 Facebook was warned about app permissions in 2011 http://bit.ly/qlearly148 This week in tech, 20 years ago (Yesterday, March 24) http://bit.ly/qlearly149 Thank you for tuning in, we will be back tomorrow ✌️

Hosain Rahman is the CEO and Founder of Jawbone. Jessica Livingston is a cofounder of YC.Jessica interviewed Hosain during our 2014 Startup School and this is the recording of their conversation. If you'd like to check out our 2016 Startup School videos they're in a playlist here.

Diane Greene is SVP of Google Cloud and she was also the CEO and cofounder of VMware.Jessica Livingston is cofounder of YC.

This Q&A was recorded at our fourth annual Female Founders Conference.Partners: Kat Manalac, Jessica Livingston, Adora Cheung, Anu Hariharan, Carolynn Levy, and Kirsty Nathoo.

Sam Altman interviews Jessica Livingston for a series called How To Build The Future, which you can watch on YC’s YouTube channel: https://youtube.com/ycombinator. Read the transcript here: https://blog.ycombinator.com/jessica-livingston-on-htfbt/

Technology companies have become a powerful way to build the future. Our goal with this series is to share advice about how you can do it, too.

Dubbed "the world's most powerful startup incubator" by Fast Company, Y Combinator (YC) has been plucking startups from garages, dorm rooms, coffee shops, and assorted founder hangouts for over a decade. With a combined valuation of more than $65 billion among its alumni, a list that reads like a who’s who of startup fame—think AirBNB, Reddit, Dropbox, Instacart, Scribd, Weebly—YC has become a Silicon Valley institution. It is described as an elite founders boot camp, a place where ideas are incubated, annihilated, refined, and polished for a period of three months, ready to be served up to a bevy of hungry investors. As co-founder of this entrepreneurial playground, Jessica Livingston has seen it all: the tears, the tantrums, and the triumphs, while getting a bird’s eye view of some of the startup world’s biggest success stories. In this interview you will learn: How to close the gap between a failed startup and a wildly successful one What Y Combinator is looking for when they take on new startups The exact process that Y Combinator puts startups through in order to ensure success The key traits and qualities shared by every successful founder What signs to look out for that your startup may be doomed & much more!

Mit Motivation Monday präsentiere ich dir jeden Montag ein Zitat von einer berühmten Persönlichkeit. Für einen motivierten Start in die Woche. MM #45: Tue Dinge, die nicht skalieren - Paul Graham Paul Graham ist ein englischer Unternehmer, Programmierer und Buchautor. 1995 gründete er zusammen mit Robert Tappan Morris das Unternehmen Viaweb, welches seinen Kunden unter anderem Online Shops verkaufte. Sie verkauften das Unternehmen 1995 an Yahoo, die es zum Yahoo Store machten. Seit seinem Exit ist Paul Graham hoch angesehener Unternehmer und mittlerweile auch Investor. 2005 gründete er zusammen mit Robert Morris, Trevor Blackwell und Jessica Livingston den Seed Accelerator Y Combinator. Hier geht es zu dem vieldiskutierten Beitrag von Paul Graham zum Thema: "Tue Dinge, die nicht skalieren": http://paulgraham.com/ds.html Tue Dinge, die nicht skalieren Besonders zu Beginn deiner Selbstständigkeit ist es wichtig, Dinge zu tun, die nicht skalieren - denn so hebst du dich vom Markt und deiner Konkurrenz ab. Zeige dem Kunden, wie wichtig er ist. Erledige die Akquise von Kunden persönlich, nicht automatisiert. Antworte den Kunden nach einem Kauf persönlich, nicht automatisert - so baust du eine Marke auf, die langfristig auch skalierbar wird! Mehr dazu in der heutigen Motivation Monday Folge! Meine Gedanken zum Zitat von Norman Mailer erfährst du in der heutigen Podcast-Folge. Die Shownotes mit allen Links und weitere Infos findest du unter: http://sidepreneur.de/mm45 Ich wünsche dir eine produktive Woche und freue mich über dein Feedback, Michael ------------------------------------- Shownotes zur heutigen Folge: http://sidepreneur.de/mm45 Bewerte doch bitte den Sidepreneur Podcast und hilf uns, mehr Sidepreneure zu erreichen. Du weißt nicht wie? Hier geht’s zu einer Anleitung. ++++++++++++++++++ Schon mal über eine Mastermind-Gruppe nachgedacht, um dich persönlich weiterzuentwickeln und dein Business auf das nächste Level zu heben? WIR STARTEN AM 05. MÄRZ! Du weißt nicht woher du andere Masterminds nehmen sollst? Dann schau mal bei MastermindGroups.de vorbei, meinem Projekt, bei dem wir dich in Mastermind-Gruppen vermitteln! ++++++++++++++++++

Mit Motivation Monday präsentiere ich dir jeden Montag ein Zitat von einer berühmten Persönlichkeit. Für einen motivierten Start in die Woche. MM #45: Tue Dinge, die nicht skalieren - Paul Graham Paul Graham ist ein englischer Unternehmer, Programmierer und Buchautor. 1995 gründete er zusammen mit Robert Tappan Morris das Unternehmen Viaweb, welches seinen Kunden unter anderem Online Shops verkaufte. Sie verkauften das Unternehmen 1995 an Yahoo, die es zum Yahoo Store machten. Seit seinem Exit ist Paul Graham hoch angesehener Unternehmer und mittlerweile auch Investor. 2005 gründete er zusammen mit Robert Morris, Trevor Blackwell und Jessica Livingston den Seed Accelerator Y Combinator. Hier geht es zu dem vieldiskutierten Beitrag von Paul Graham zum Thema: "Tue Dinge, die nicht skalieren": http://paulgraham.com/ds.html Tue Dinge, die nicht skalieren Besonders zu Beginn deiner Selbstständigkeit ist es wichtig, Dinge zu tun, die nicht skalieren - denn so hebst du dich vom Markt und deiner Konkurrenz ab. Zeige dem Kunden, wie wichtig er ist. Erledige die Akquise von Kunden persönlich, nicht automatisiert. Antworte den Kunden nach einem Kauf persönlich, nicht automatisert - so baust du eine Marke auf, die langfristig auch skalierbar wird! Mehr dazu in der heutigen Motivation Monday Folge! Meine Gedanken zum Zitat von Norman Mailer erfährst du in der heutigen Podcast-Folge. Die Shownotes mit allen Links und weitere Infos findest du unter: http://sidepreneur.de/mm45 Ich wünsche dir eine produktive Woche und freue mich über dein Feedback, Michael ------------------------------------- Shownotes zur heutigen Folge: http://sidepreneur.de/mm45 Bewerte doch bitte den Sidepreneur Podcast und hilf uns, mehr Sidepreneure zu erreichen. Du weißt nicht wie? Hier geht’s zu einer Anleitung. ++++++++++++++++++ Schon mal über eine Mastermind-Gruppe nachgedacht, um dich persönlich weiterzuentwickeln und dein Business auf das nächste Level zu heben? WIR STARTEN AM 05. MÄRZ! Du weißt nicht woher du andere Masterminds nehmen sollst? Dann schau mal bei MastermindGroups.de vorbei, meinem Projekt, bei dem wir dich in Mastermind-Gruppen vermitteln! ++++++++++++++++++

Episode 23 of Startup School Radio: Host Aaron Harris interviews Jessica Livingston, cofounder of and Partner at Y Combinator. Also on the show: Nick Damiano and Shreya Mehta from Zenflow.

Jessica Livingston, partner at Y Combinator. Source: http://podfm.ru/goto/376c01b

FounderLine is a live weekly webcast devoted to helping startup founders succeed, hosted by seven-time startup entrepreneur and investor Joe Beninato. Each week, Joe welcomes an experienced entrepreneur or investor to discuss startup-related topics and field questions from entrepreneurs around the world. FounderLine is broadcast live, and viewers are welcome to send questions via email or twitter. For more information, go to founderline.com. In this episode, host Joe Beninato and guest Jessica Livingston of Y Combinator answer viewer questions including: - What are some definitive steps that founders can take to define a lasting culture for their startups? - If you were to name a few of the common attributes of the unicorns that have come through YC, what would they be? - How do you keep an investor from poaching your startup's product/idea - developing it with another team? - We are considering applying for YC. What are some of the things that stand out in the applications and make it more likely to get accepted? - As a mom and startup founder myself, I'm wondering how to fit it all in...being a good employee, wife, mom, etc. Any tips?  

This week, host Ken Ray talks with Jessica Livingston - author of Founders at Work: Stories of Start-Ups Early Days.