Lenny's Podcast: Product | Career | Growth
00:15:07 · Lazar Jovanovic
The first one is there's something that is known as the context memory window. And for non-technical people, I like to use the Aladdin and the Genie analogy when I explain. It's very simple. Everybody knows the storyline. You rub the lamp, a genie comes out and tells you, "Okay, I'll grant you three wishes. Not 3,000 wishes, not three million, just three at a time." To me, when I translate it into working with AI, that simply means, "Hey, I can only make so many requests within a request at a time for AI to be able to listen, understand what it needs to do, scope it, do the research, read, take all the actions, all the inputs and ingredients that it needs to produce a high quality output." So that's the first part, understanding that there's a limit and it's denominated in tokens. Maybe that's going to be different a year from now, but today there's a token limitation. I'll take an arbitrary number of 100,000 tokens, for example. So when you make a request, a part of those tokens AI spends to read stuff, another to browse the web, another to think, and then another to execute the code. Then there comes the second limitation, which is you, me and you, humans, which is, let's go back to the analogy of the Genie and the Aladdin. I asked the Genie for the first wish, and the first wish is I want to be taller. And guess what happens? Genie makes me 13 feet tall. All of a sudden, I can't sit in the car, I can't get into my house. I'm a dysfunctional human being because I was not specific. So the part that we need to optimize for today, it's going to get better, but today it's still not there yet, is that AI just don't understand what do you mean when you say, "You know what I mean?" You do when I tell you that. We as humans, I'm 36, so I have 36 years of experience of living as a human to know what you mean, but AI doesn't have that. So you need to be specific, you need to provide references, you need to provide the right context. So what I've learned is how to combat that part. And I think because I can't control the first part, which is the token memory window, the quality of the LLM models, you are 100% control of the latter. And that's what I want to dive into today as well and just try to teach people, "Okay, if I'm the malleable part, how do I fix that part?" I think that's the key lesson here.
00:17:41 · Lenny Rachitsky
[00:17:41] S1: This is so helpful. And I love this metaphor of the Genie. This piece about clarity is such a thread I've been noticing across people that have been successful using AI tools. And it feels like an emerging core skill is learning clarity in the ask of the AI. Do you have any advice or anything you do there to help be better at being clear with what you want?
00:18:08 · Lazar Jovanovic
[00:18:08] S0: Yeah. So first of all, as you said yourself right now, you need to be good at understanding what clarity means and how to translate it. In my terms, clarity means understanding what tasteful looks like, what's good enough versus what's world-class, what's magical. And I developed that through something that I heard from you, you mentioned before, which is exposure time, making sure that I'm exposing myself to content and to people and to relationships or whatever that are going to help me to level up in that domain. Again, it goes back to self-awareness. I knew even before I joined Lovable, I was like, "Okay." Even before I started using Lovable or any AI tools, first thing that I knew was like, "I don't know how to code." So my first thing was like, "Oh, I can build. Wow, amazing." But a week later it was like, "Oh, I can build, but I'm not fast enough." So I optimized for speed. So I was like, "Oh, I can build and I can build so fast." And then two weeks later, my development cycle that I'm in began, and it's still ongoing, which is, "Wait a minute, should I have even built this in the first place?" Because once you figure out that we solved for the how, which is AI assistant or rapid engineering, call it whatever you want, you can call it vibe coding if you want to, but we solve for that. Now we got to solve for everything else. And everything else is what matters. Good design, good taste, good user experience. When you think about who you're building stuff for with these tools, you're building it for humans. Humans are emotional beings and we all make our purchasing or any kind of decisions on an emotional basis. So I think that the core skill there to work on and develop today isn't, again, coding, although I have nothing against traditional engineering. And I'll say later why. I'm actually a big fan of it, of elite engineering, but people like me, people watching that are like, "Should I start learning how to code?" If you haven't done it yet, I'd honestly say no. You're optimizing for the wrong skillset. We won't be rewarded in the world of AI for faster raw output; we will be rewarded for better judgment. So I think that better judgment comes with, again, to go back to your question, how are you solving for that? How are you solving for this? Well, it starts with exposure.
00:20:40 · Lazar Jovanovic
So I'm deliberately exposing myself to people and resources that I know I need to consume to level up. And then a lot of it just comes from building as well. If we're honest, it's a muscle. Everything is a muscle. You need to practice. You need to see what's possible. And though that's where some of the techniques and mindset shifts that I want to also use an opportunity today to ingrain into people's minds later down the call may be useful.
00:21:10 · Lenny Rachitsky
[00:21:10] S1: Okay. So what I'm hearing here is because coding is now essentially a solved problem, I love that you don't look at the code. You've never coded, you don't want to look at the code, you don't care about what's happening there. Instead, you're watching this agent output. I want to actually ask you about that. But what I'm hearing here is the areas you are investing in, building in yourself is at the front end, clarity around what it is and I want to hear how you actually do that, what you do there. You have a really cool system there. And then there's the taste and judgment of knowing, is this the thing I want? It feels like those are the two sides now that are more and more important. And on the taste judgment side, you share this concept. There's something Guillermo Rauch shared in our conversation, this idea of exposure time, exposure hours, being exposed to great stuff. Here's a great user experience. Here's a great onboarding flow. Here's a great, I don't know, website. So I really like that advice because it's so actionable. Okay. I'm going to spend more time with stuff that's great to inform my taste and judgment. And then on the clarity piece, let's actually talk about that, just what do you do there to be clearer with Lovable and other AI tools to help it build the right thing?
00:22:22 · Lazar Jovanovic
[00:22:22] S0: This is the first mindset shift that I want to put into people's minds. If you just have a vague idea, let that be your first version of the project, Open, Cursor, Lovable, whatever it is that you're using, and just input a brain dump prompt. Just talk into it. Lovable specifically, I don't know about the other tools, has a really cool voice function. You click it, you just dictate the hell out of it and just press send. Don't even wait for it to finish. Open a new window. Again, lovable.dev. In here, you're like, "Okay, as I was brain dumping, I think I found a good thread. I think things are getting clearer. So let me start another project now with more clarity, more deliverability. I know which features I want, which pages I want, and maybe I can even find a good reference. Maybe I can go on Mobbin, maybe I can go on Dribbble, maybe I can go wherever, get a good screenshot, get a good animation and attach it," because most of these tools accept files as a part of the input. So you have the second project started. Now things are even more clear. Now you expose yourself to quality and now you're like, "Well, what if I found a template that actually is already out there? Why reinventing the wheel? I'm building a platform that somebody else built. Why not expose AI to what quality looks like?" So what I'll do is I'll go and find a library, 21st.dev or a DotBuild or whatever, places which allow me not to export screenshots, but export code snippets. Because guess what? Even though English is the number one programming language, Lovable and all other tools still communicate in code the best. If you want to get pixel perfect results, just give them code. It will interpret it better than your English or Spanish or whatever language that you use in these tools. So that's the third way. You're like, "Okay, now I'm even more deliberate. I'm not even going as wide as giving it vague concepts. I'm giving it code snippets like, 'I want this exact design. I want this exact type of functionality.'" So that's your third project. And then by the time you do all of these three, you're already at a level of clarity that you wouldn't have if you just sat with an empty piece of paper or maybe chatting just with ChatGPT, but not taking action. I think taking action is so, so cheap these days and free, by the way. All the tools I mentioned have free plans.