Deedy Das, Engineer-Turned-Investor

Deedy Das is a engineer-turned-investor who brings deep technical expertise to the venture capital world. After a 10-year career in engineering and product, including roles at Google, Glean, and other tech companies, he transitioned to investing because he felt the industry needed more investors who could "nerd out on tech."

He's known for his viral technical content on Twitter, where he frequently shares hands-on demonstrations of AI tools, coding projects, and technical insights.

Insights

• Technical Revolution & Historical Parallel: Deedy compares today’s AI revolution to past industrial revolutions. Just as factories transformed clothing manufacturing, AI is transforming knowledge work, automating previously manual or tedious tasks.

• Investor Approach: Tech Nerds Needed: Coming from a 10-year engineering background, Deedy believes more “tech nerd” investors are essential. Rather than focusing purely on financial metrics, these investors bring deep product and technical insight to discover genuine breakthroughs (and call out snake oil).

• AGI Perspectives & Model Scaling: Deedy points out that if you asked someone 4 years ago what AGI looked like, many would say today’s large language models already qualify. The real question he’s interested in is: “How much further can these models go?”

• Full Self-Driving Analogy: He likens the current stage of LLMs to “full self-driving” in cars—impressive demos exist, but hitting 100% reliability remains an unsolved frontier. This gap is evident in tasks like coding, where LLMs get you far but still stumble.

• Meta-Prompting for Better Results: A practical trick Deedy uses is to break down big requests into smaller instructions—often called “meta-prompting.” He’ll ask the model to output a series of sub-prompts and handle each one individually, greatly improving reliability and debuggability.

• Three AI Startup Categories: In his view, current AI startups often fall into:

  • Foundational Model Companies (trying to build the next big model, e.g., Descartes, Carticia),
  • Workflow Automation (replacing back-office tasks, vertical or horizontal),
  • Advanced Agents (e.g., AI Site Reliability Engineers, AI coding agents).

• Call BS on AI Snake Oil: In a hot market like AI, many startups over-promise. Deedy emphasizes looking for real customer value, renewals, or working demos. If claims are all buzzwords (like “RAG but better!”) without a real product or proof, it’s probably snake oil.

• Coding Agents & Tool Use: Deedy frequently uses Claude’s “computer use” (and custom forks) to generate code, automate tasks, or even create waveforms in audio generation. Though it’s powerful, it’s also prone to getting stuck if not carefully prompted or monitored.

• Diagramming for Better Comprehension: A favorite tactic is generating diagrams (using Graphviz/DOT files) to summarize transcripts or codebases. Seeing relationships and flow visually reduces overwhelm and offers quicker insights than reading raw text.

• Building & Sharing: Deedy’s prolific online presence stems from building interesting demos (like automated scrapers or music generators), then sharing them via short videos on Twitter. This cycle—idea > build > post—keeps him motivated to experiment.

• Virality & Thick Skin: Going viral on social media (via Elon Musk or Paul Graham retweets) can open new doors but also invites criticism. Deedy advises staying open to feedback, yet remembering that negative reactions often blow over—keep creating and learning.

Transcript

00:00:01 Deedy: We are in the middle of a technical revolution.

00:00:01 Deedy: There was a time when people made machines to make manufacturing clothes better. We're in that revolution for knowledge work. I felt like there needed to be more investors who just nerded out on tech.

00:00:13 Deedy: I think the biggest unlock for me personally

00:00:13 Deedy: is it's really simple. I I use I personally is it's really simple. I I use for like half of the stuff and it buzzes and it says something something you want to tweet it some. But then my phone just starts blowing up like notification I've been on. I'm like what is going on? When I do play with Claude, I noticed, like, this one small, like, one weird trick that makes it so much better.

00:00:48 Greg: Hey crew. What is going on today? We have DD Doss with us today. Very excited to have him on board today. We are gonna be talking not only about his builder hat but also his investor hat, how he's evaluating different companies. But the coolest part, which I love and I'm excited to share with you, is he's actually gonna screen share, and he's gonna show us how he builds with Claude Computer Use and MCP. So let's jump into it. So, Didi, you're a builder and you're an investor. What are the cool people or what are the smart people in AI talking about right now?

00:01:18 Deedy: I think the one question on everybody's mind is how far does this go? Like scaling or just Whether it's I think scaling is more of a tactical thing. It's just how good can these models become?

00:01:30 Deedy: Mhmm.

00:01:31 Deedy: Can it do we get to a world where this solves problems we could never solve ourselves? Yeah. I think that's the frontier. Because the when people talk about AGI, it's quite it's it's this amorphous thing I I love to tell. Exactly. You know

00:01:44 Greg: what blows my mind is that us as an industry, we don't have a solid definition of intelligence that we formally rely on. It's wild that we're still arguing about what is intelligence. It's like a weird side product of this whole, kind of revolution.

00:01:54 Deedy: I was telling a buddy yesterday. I was like, if we ask somebody 4 years ago what AGI was, by all practical purposes, we're there.

00:02:01 Greg: We'd probably we'd probably agree that you open up literally GPT 4 0, start chatting, and you're like, well, yep. We're here.

00:02:07 Deedy: Yeah. Yeah.

00:02:08 Greg: Yeah. Okay. So, how far does this go? So you're it sounds like you're talking about the application side, like, value providing. Right?

00:02:15 Deedy: Oh, well, there is the value side. I'm less concerned about that. Obviously, as an investor, I concern myself about that. As more of a technical nerd, I just think about how good can these models get. Like, I still have flaws today. Right? Like, there's still limits. You have you have the ARC prize and everything. There's limits to what it can do. Sure. But what are those limits? Where how far does it go?

00:02:35 Deedy: Yeah.

00:02:36 Deedy: And I love the alpha zero, alpha go analogy. We'll see a world, hopefully, 2025 Uh-huh. Where these models give it a general task. Yeah. It'll figure out the rules. It'll figure out what it needs to do. It'll take some time. It'll take as much time as it wants Sure. And it just gets superhuman level intelligence Yeah.

00:02:54 Deedy: Yeah. On arbitrary tasks.

00:02:54 Deedy: And in tasks. And in some ways, depending on how far you want to take that, I think that that is reasonably close to AGI.

00:03:00 Greg: Yeah. It's pretty wild. So that sounds like that's your builder hat definition of AGI which is pretty cool. So while we're on this topic, I gotta ask about your investor hat of AGI. So how do you approach what the next 5 years are gonna look like from an investor side?

00:03:14 Deedy: I've only been investing for a year. The the reason I wanted to get into investing after a 10 year career in in engineering and and and product Yeah.

00:03:23 Deedy: Yeah. Yeah.

00:03:23 Deedy: Is because I felt like there needed to be more investors who just nerded out on tech. Sure. There's a lot of investors who are really good at what they do, analyzing businesses, but I wanted to be that voice of, like, just show them what's cool and show me what's possible.

00:03:39 Greg: Yohei comes to mind. Yeah. Fantastic. Builder. Like, it's so cool to be, like, here's my pipeline, here are the tools I built for myself, and here's how it's actually providing value.

00:03:47 Deedy: Value. Exactly. I love that. I love that. Exactly. So as an investor, I also take, like, at least 50% is that view

00:03:53 Deedy: Nice.

00:03:53 Deedy: Which is just show me what's possible.

00:03:55 Deedy: Yeah.

00:03:56 Deedy: And we'll figure out a way to make it work. Yeah. We'll figure out a way to make money.

00:03:59 Deedy: Sure.

00:04:00 Deedy: Sure. That's the that's much easier than Yeah. Groundbreaking technology. And then the other 50% is, yeah, how do you take what exists today and apply it in in useful and meaningful

00:04:11 Greg: Yeah. It's also nice because you can call bullshit on a lot of the tech when you need to. Oh, man. You could just, like, call them out and, like, ask real questions.

00:04:17 Deedy: I was literally I don't wanna name the technology. Maybe I can't name the technology. I was talking to a couple of, other investors the other day, and they were like, you know, I had this pitch and these guys said, you know, they do, like, rag but better, and they threw all these fancy terms at me. Like, what do you think? And I'm like, look. I feel like that's every pitch, almost every every other pitch I've had for the last 3 months.

00:04:42 Deedy: Yeah. Yeah.

00:04:43 Deedy: And when it comes to things, especially that are hot like AI, there's gonna be a lot of snake oil sales. Sure.

00:04:48 Greg: Yeah.

00:04:49 Deedy: And it is pretty important to call BS on a lot of these things.

00:04:53 Greg: Well, so I'm I get a lot of pitches myself too, and one of the easiest ways to go through it is don't tell me what your tech does. Show me the value or show the customer who's paying for it or, like, just show me something that's an output that comes from it because it's hard to bullshit value. Like, that's proof of work almost right there.

00:05:08 Deedy: True. I would argue that it is still it's it's the reason investing is especially hard with AI is if you can bullshit value. Like, if I've seen so many start ups, 1,000,000 ARR because, you know, CIOs and CTOs are saying go buy a gen AI tool.

00:05:22 Deedy: Sure.

00:05:22 Deedy: Does that mean they created any value yet?

00:05:24 Greg: The the revenue's there in the beginning year 1, but the renewals may not be there. And even further than that, you dig down to the user metrics, the intention of engagement and all inactivation. It's like, well, if that's not even there either, then you're gonna get yourself in trouble. Okay. So I love the question, how far does this go? What are the obvious gaps that are limiting us going further from a, tech, AGI perspective?

00:05:46 Deedy: There's a couple of different ways to to break that down. I think let's talk just about text models. Yeah. I I look at text models today, and I'm like, okay. Well, this is good for a lot. Uh-huh. I use Claude almost all the time. Nice. And what I and I played with all the other models too, like all like, o o one and and llamas and everything. Yeah.

00:06:08 Deedy: Yeah. Yeah.

00:06:09 Deedy: And what I really wanna know is, okay, I I've seen people have all had this comment is when you use it for a very specific task, like a a task that has a correct outcome, an objective task, it goes pretty far and it can amaze people, but it doesn't go a 100% of the way. And I I love to call this, like, the full self driving problem where it's kinda easy to get a demo of full self driving to kinda work in Sure. Most situations. It's really, really hard to get an actual full self driving

00:06:40 Deedy: Yeah.

00:06:40 Deedy: Vehicle that can do everything in real roads everywhere. Yeah. And let's just take, say, coding for example. That's one example of a very objective thing. You can also take math. You can also take any other form of logical reasoning, biology, which combines memory and reasoning. Sure. And I think about those things and I and I wonder, like, okay. We're clearly at a gap. People love Cursor, but there's also some people who are like, okay. Cursor is awesome, except I hit them all. Yeah. I can't do beyond a certain thing, and I don't know what went what went wrong.

00:07:12 Greg: Imagine how how entitled or, like, selfish those people must be to think that, oh, it's this AI is not good enough to do my job for me. You know what I mean? It's just funny that they're critical of AI when it's, like, literally so helpful for so many things. But no because it's not throwing you a touchdown at the very end of the end zone that you're giving it a hard time.

00:07:29 Deedy: Oh, yeah. We're all humans are infamous. I think Louis CK of all people has this great joke about how people complain in flights and they're like, guys

00:07:36 Deedy: Yeah.

00:07:37 Deedy: Flying in the middle of the sky. Yeah. Yeah. Yeah. This is the most incredible thing that could ever happen. Like, have some have some you know, be thankful. Be grateful.

00:07:44 Deedy: Yes. Yes.

00:07:44 Deedy: And so I think about that too, obviously, but, you know, we also wanna think about the frontier, what's possible. And, you know, when I've been coding with Cursor and things like this, there does come a time where I'm like, damn. It did a lot for me. But now I kinda wonder there's clearly something it can't do no matter how much I prompt it, and it goes, like, 5 cycles, and it's just not working.

00:08:03 Deedy: Yeah.

00:08:03 Deedy: And then I'm in this weird spot where I don't even know where to look. It's generated, like, 10 files, 500 lines each. Like if I was writing it myself I would know how to debug this. Now I don't.

00:08:16 Greg: Like a vocabulary word for a shared feeling of I just had the AI generate a ton of stuff and now there's a bug, and I have no idea where it is. Exactly. I don't know what that word is, but I I hear people talk about that all the time. We need to come up with a word for it. I have a hypothesis that GPT 4 can do almost any task as long as it's sufficiently scoped down.

00:08:39 Deedy: Absolutely. And we'll maybe get to this later on, but I love this like that that anecdote because I play with computer use all the time and I wish I could do it with with GPT 4 and all the o one stuff as well, but I just don't. Like, they don't have the computer use version yet. But when I do play with Claude, I noticed, like, this one small it's like one weird trick that makes it so much better. And that really it was a really simple trick, and it was exactly what you said, which is I just went to Claude first and said, here's my high level 3 prompts. Uh-huh. You, Claude Yeah. Have to now give instructions to a computer

00:09:11 Deedy: Yeah.

00:09:11 Deedy: To break this down into tasks Yes. Yes. And then I feed that huge prompt

00:09:16 Deedy: Yes.

00:09:16 Deedy: Into computer use and say, now you do it. Huge improvement.

00:09:19 Greg: One of my when I work with clients and we talk about prompting, one of the most common problems I see is that they stuff too much freaking stuff inside the same prompt. They want 5 tasks. I wanted to make a mini little tool and it sounds like we have the same thought. It's like feed them the prompts and output what are the individual prompts you should actually be getting from your big one. Just scope it down even further because then that makes evals a lot easier too. It does. You know where it's messing up. It does.

00:09:42 Deedy: And they

00:09:42 Deedy: call it meta prompting and I think Yeah. It it it kinda alludes to this, like, fundamental CS thing which is everything is solved by one additional layer of abstraction. And in this case, it's just on you to to build that layer of abstraction and then and then, you know, give a prompt that gives you more prompts and then Yeah.

00:09:57 Greg: That's exactly it. On the investor side, you're seeing a lot of startup pitches come through. What are the trends in the past, let's call it 3 months? Not like the beginning stop, not the 2023, but what's the last 3 months look like from an investment side?

00:10:08 Deedy: Well, so so a lot of people are talking about agents. Right? And agents are one of those other words that people are like, what does that really mean when you say agent? And and so when when it comes to AI companies, let me let me sort of break break down the field.

00:10:21 Greg: Sure.

00:10:21 Deedy: There's foundational companies. Yeah. Foundational companies can be tough because sometimes you're really asking the question, are you really trying to compete with people with Sure. 1,000 of the money?

00:10:29 Deedy: What are

00:10:29 Greg: some examples of these?

00:10:31 Deedy: Well, a good example is actually this company Descartes. Okay. They bold, brilliant founder. It's just one of those guys and as an investor now, I've learned this. Like, just trust your instinct. You meet this guy. Dean, I think his name is. Just whiz. Like, you hear, like, oh my god. Like, this guy's so smart.

00:10:49 Greg: It's almost like you're back in the you're back in the person.

00:10:51 Deedy: Go get the idea. Yeah. You're almost always actually back in the person. But some people just have this energy where they're like, this guy is a genius. Yeah. And he was one of those Descartes taking a problem way out of their league, which is this idea of generative gaming. So Mhmm. You know, it's hard enough to generate good images. It's hard to do it real time and and then consistently. So what the question to ask is, you have a game where people which involves thousands and thousands of game designers, engineering different parts of the graphics, how things behave in that game. What if you could just throw a gen AI at it and just have each pixel just show up and travel in the game?

00:11:26 Deedy: Yeah. He has no business doing this. Okay. He's like, kind of a kid. Uh-huh. And it's with a small team, and these are things that, like, Google can't do in OpenAI. Now Google has a thing that does this OpenAI, I can't do. Anthropic can't do. But that was a beautiful one. That's foundational, and that's great. You have other companies. There's a company called Carticia. Mhmm. Carticia is a company doing a foundational model for audio. They came definitely after 11 Labs, and, you know, they had a different approach to it. And so good. Man, I know. Reasonable outcome

00:11:56 Greg: so far. Labs is crushing it right now too. They're freaking shipping and building right now. A ton

00:12:00 Deedy: of stuff.

00:12:01 Greg: Yeah. Okay. So foundational companies. Alright. What are the other trends we're seeing here too?

00:12:04 Deedy: Then you have the companies that are are very clearly on the application side that are doing workflow automation, I like to call it, but that's a really boring word. The way I would think about it is there we are in the middle of a technical revolution. There was a time when people made machines to make, like, manufacturing clothes better and other things better. Sure. We're in that revolution for knowledge work. And the question is, what machines can we make? Yeah. And and we're calling those machines agents, but this is everything and and people love to, like, take the extreme side and go like, woah.

00:12:38 Deedy: Well, my job is not automatable because it's so smart and my but, like, there's clearly jobs on the other side which are dumb and boring, and no one wants to do them.

00:12:48 Deedy: And a

00:12:48 Deedy: lot of them really involve look at this Google Sheet, look at this Excel, look at this PDF, get this data, write this data here, send this data to this person. If you would look at a response this person sends you and do this, that's most jobs

00:13:00 Greg: Yeah.

00:13:01 Deedy: And moving stuff around.

00:13:03 Greg: Sure.

00:13:03 Deedy: And if you have a job that's regular enough, you could kind of automate that today. Sure. So a lot of people doing automations there and that can mean voice stuff, that can mean pure, you know, text and

00:13:15 Deedy: Are we

00:13:15 Greg: talking about vertical AI? Are you talking about, like, the Lindy's and the Zapier's, like, the horizontal?

00:13:20 Deedy: I'm in this case, I think most of the ones in this category are are pretty vertical Sure.

00:13:24 Deedy: Yeah.

00:13:24 Deedy: Because at least at first. Because you have to start with a way to say, hey, company. I know you have a 100 people doing this job. Yeah. We think you don't need that and we can do it for you. Yeah. That's kind of the Yeah. The high

00:13:36 Deedy: level pitch.

00:13:37 Greg: It feels like there's an arbitrage opportunity right now for just going and selling AI plus your industry. Like, people will get over that eventually, and that's gonna get diluted and already is starting to get diluted. But if you go to a law firm and you say, hey. I'm I'm gonna get an AI lawyer. They're like, great. I need one of those without even knowing what the heck it is.

00:13:52 Deedy: Yeah. There is the that there is this idea of or this phenomena of people buy tools that overpromise and then underdeliver. Totally. Totally. And it doesn't work a lot of times. But on the other side, you know, Maxine, insurance, health care, finance, there are a lot of pretty tedious industries, a lot of industry that had back offices especially where you it's just labor that you don't wanna pay for basically and so you're like automate that away and let's move forward as basically to be grandiose as a species and not have to do Yeah. Dumb work. Yeah.

00:14:26 Greg: We can move on we can move on to other dumb work.

00:14:28 Deedy: Yeah. We want other dumb work. Every generation has dumb work.

00:14:31 Greg: One of my favorite consulting books, I freak I forget what it's called, but basically one of the main lessons is there's always a number one problem. Once number 1 is solved, well number 2 gets a promotion to number 1. Right? So there's always gonna be dumb work. There's always gonna be interesting work. It's just a it's a big long cycle that comes from there.

00:14:46 Deedy: True. But that yeah. The the to your point, like, this is this is there there's a whole thing of a workflow automation. Like, how do I automate people's jobs? Assist or automate whatever, people's jobs and make it a little bit easier to do. The 3rd sort of category I will touch on is it's very hard because there's so there's a big long tail of AI AI startups, but especially you asked last 3 months what I'm seeing more of. I'm seeing a lot more companies really talk about this idea of let me let me I'll I'll take 2.

00:15:19 Greg: Yeah. Totally.

00:15:20 Deedy: 1 is the idea of what they call AI SREs is a common bucket of things that people wanna solve, and I'll I'll get to what that is. And then the second one is a is is also in in a some semirelated space. It's just programming agents. Sure. And and that's not like that's a 3 month thing. It's been around for a while. But in the last 3 months, the the the evolution has

00:15:40 Greg: been pretty interesting. Yeah. Well, and there's a cursor competitor coming around now too. Yeah. I mean, I love competition. So I I'm glad to see it.

00:15:47 Deedy: Yeah. It's, well, cursor's doing really well. Well, the who's the competitor you're

00:15:51 Greg: Oh, Windsurf. That's yeah. Correct.

00:15:54 Deedy: So it's it's awesome to see. Yeah. It'll be interesting to see how this all goes out.

00:15:58 Greg: Alright. So 2 categories. So first one, SREs.

00:16:00 Deedy: SREs. You know, tons of companies doing various versions of of this. And I say SRE, but it's an also like, support is kind of included in that technical and nontechnical support. The idea of being look. I have a problem in the company. Code goes breaks in production. Things go wrong. That is money on the line, especially from a big company. Like, every you can do the math. Every hour can be, like, tens of 1,000,000 of dollars, and it's kinda crazy that right now, the best thing we have is we send a guy. Like, that's the the the the resolution is a guy goes and looks at it and tries to, like, figure this issue out.

00:16:37 Deedy: And it and I've done that at at Clean and at Google. It is one of the most nerve wracking things you can do as a software engineer, which is, like, thumb things down in production. There is time on the line and there are literally petabytes of data you can look into. Like, which dashboard do I look into? What do I check? What do I check next? What do I query for? How do I fix it? When does the release go out? Like, your brain's going bonkers. And this could be 3 AM in the in the in the Sure. And I understand

00:17:01 Greg: ping and it's like, oh, boy. It's crazy. Uh-oh. Yeah. It's crazy.

00:17:05 Deedy: So it's a really bad hard thing to do and there's ways to make make this easier. Sure. And there's like a couple of companies that are that are doing this pretty well. There's there's there's resolve. Sure. And there's like a couple of companies that are that are doing this pretty well. There's there's there's resolve. There's company Traversal. There's a bunch bunch of these other companies trying to do this. Super interesting sort of sort of category of problems and just to me. And then the last one, like, the the one I was talking problems and just to me. And then the last one, like, the the one I was talking about is coding agents.

00:17:31 Greg: Nice. Yeah.

00:17:33 Deedy: There's just so much more stuff we can build here.

00:17:35 Greg: Just so much more, and it's so cool. It's a it's a it's a place that's right too because everyone talks about, you know, code is easily verifiable. Does it work? It does not. Right? The hardest part is still just with the planning piece, and do you get the intention right? And can the human adequately express their intent to the coding agent in the first place?

00:17:50 Deedy: Exactly.

00:17:51 Greg: It's amazing. One of the side thoughts I've had is I bet the outside of just AI in general, like, epistemology as a study, like, the study of thought and the study of how knowledge works must get such tailwind from AI the AI move move that's happening. So people are thinking so much more about how do I express language? How do I formulate these thoughts? How do I think about planning? How do I think about communication? All that wouldn't have happened unless we had these text based LLMs that we now have a financial incentive to really dig into how we're dealing with these things.

00:18:22 Deedy: Absolutely. I mean I think it's incredible where we are in the world.

00:18:26 Greg: So let's move on to the builder side. Yeah. So what technologies are you personally building with right now that you're you're excited about?

00:18:34 Deedy: Right now, I think the biggest unlock for me personally is it's really simple. It it it I I use cloud computer use for, like, half of the stuff.

00:18:43 Greg: Oh, true.

00:18:44 Deedy: And I think there's with the caveat. Alright. There's 2 caveats, actually. There's a version of cloud computer use that came out. They wanna do it safely. Anthropic is all about safety Yep. And as they should be. Like, I don't think this is something that you should just let everyone willy nilly go and and run a mock in their computer. Everyone willy nilly go and and then run a mock in their computer. But, oh, I'd like to take some risk. And so I cloned the version that they have for Linux, and you have to do a couple of edits to that and go into as much detail as you want. Yeah. And and I'll show it to you later if you want to.

00:19:15 Deedy: You can get but you can make it work on your actual Mac, and it makes things a lot better. That's caveat number 1. Second thing that you can do is I realized over time that as cool as this is, it's cloud computer use did this really cool thing where it can can control your mouse and view your screen and then take an action. Yeah. But as cool as that is, it's kinda the most lame part in the sense this is something that takes a human a second, and now it takes this 20 seconds.

00:19:41 Deedy: Yeah. Yeah.

00:19:42 Deedy: And it's really, really frustrating when it does all this cool stuff and then it gets stuck trying to find a button and click it Sure. On your computer. So when I prompt it and I do a couple of alterations, I just say, can you not do that? Like, just try to stick to the command line.

00:19:59 Greg: Sure. Okay.

00:19:59 Deedy: You can pretty much run anything on the command line, pretty much.

00:20:04 Deedy: Just stick

00:20:04 Deedy: to that.

00:20:04 Greg: Okay.

00:20:05 Deedy: And I felt like the progress has been incredible.

00:20:08 Greg: That's cool. That's very cool. So is cloud computing use is that gonna be the product or is that gonna be the platform that other people are gonna build really cool products on top of?

00:20:16 Deedy: It's unclear. Like, now they have MCP, which is I like, I don't I don't wanna speak for anthropic but I feel like MCP is the version of safe cloud computer use. Sure.

00:20:25 Greg: Well, so can you define what what is the MCP? Could you define it in in layman's terms too?

00:20:31 Deedy: The way I see it, and this is probably not doing justice to everything it can do, is MCP is a way for you to safely say, hey, Claude. You can use these tools. I can give you some memory. You can go use these tools

00:20:42 Deedy: Uh-huh.

00:20:42 Deedy: And customize the way you can you you can do things with Claude, but it's very dev dev centric. It's not really, you know, consumer facing already.

00:20:50 Greg: There's this whole spec that comes with it. Right?

00:20:51 Deedy: Thing like that. Yeah.

00:20:52 Greg: Yeah. Yeah.

00:20:53 Deedy: So I don't know I don't know where Anthropic goes with this, but I do know coming back to the startup thing, it's a big thing that startups are thinking about. What's that? Computer use. How to use things like computer use to automate more kinds of tasks. Yeah. Like, the first era of automating tasks was all browser based.

00:21:13 Deedy: Yeah.

00:21:13 Deedy: And the general way it would work

00:21:15 Deedy: is

00:21:15 Deedy: scrape the DOM, everything on the HTML on on a web page, and then try to figure out various hacks on what I can click on.

00:21:23 Deedy: Yeah.

00:21:24 Deedy: And, you know, like, you could think about that for 2 seconds and go, like, that's a pretty shitty strategy because that's not how humans work. Uh-huh. It would be insane to read 1,000 and thousands of lines of, like, HTML code and then figure out what to do on a page. Yeah. The whole point of the page is you see something and you do something Yeah.

00:21:39 Deedy: Yeah. Yeah.

00:21:40 Deedy: And computer use makes that a lot better because you have the vision angle.

00:21:43 Deedy: Yeah.

00:21:43 Deedy: But it's slow.

00:21:44 Greg: It is.

00:21:45 Deedy: So but there is going to be a time where the vision latency comes down and you can start doing actual actions on pages

00:21:52 Deedy: Yeah.

00:21:52 Deedy: That get to automating a whole other boatload of tasks both on web and on your desktop.

00:21:58 Greg: So it's

00:21:58 Deedy: an exciting

00:21:59 Deedy: Yeah.

00:21:59 Deedy: Sort of high level technical thing that startups can do now.

00:22:02 Greg: One last question before we dig into computers. I'll pull all computers out here. So there's an open question on if the future of automation what's the interface for an AI type of thing with, call it, 3rd party software? Will it be more like cloud computer use where you're actually interacting through the UI, or are the API is the API layer gonna get built out? And that's where the automation's gonna happen. The I think the jury's still out. What what's your opinion on which way it's gonna go?

00:22:25 Deedy: There have been couple of startups I've seen trying to sort of have various approaches to how that layer should look. So there's one called Composio. Right. Composio is a way of saying,

00:22:37 Deedy: there are a bunch of tools that are out there for you to use. Mhmm. I'm going to toolify it for you so that Oh, yeah.

00:22:38 Deedy: I'm to use. I'm going to toolify it for you so that you can go and and the LLM can see a prompt and understand an API spec of, say, GitHub

00:22:49 Deedy: and

00:22:50 Deedy: then figure out what to do. So you can think about it

00:22:52 Deedy: almost like documentation, technical

00:22:52 Deedy: documentation for LLMs. Yeah. And then I will

00:22:52 Deedy: teach an LLM how

00:22:52 Deedy: to do element how to use the tools that it has. I will keep it up to date. Mhmm. And if you just feed it to the LLM, it can use my tool. That's one angle. There's another company called Anon. And what Anon does is it's more like, almost think of it as the plaid for APIs Okay. Which is, like, the same way you do bank authorizations via protocol or with plaid. This is, like, trying to do to extract authorization out to the user, but let an agent act on the behalf on behalf of you.

00:23:26 Greg: And the funny part about Plaid is it used to be just via scraping, and now it's, like, done proper. And it's almost like the metaphor for how this is probably gonna go too.

00:23:33 Deedy: Maybe. Yeah. I I I still think tool use has not got mainstream adoption yet. Yeah. So it's it's TBD where that direction goes. Obviously, a lot of start ups looking after it

00:23:44 Deedy: Yeah.

00:23:45 Deedy: Looking out for it. A lot of enterprise companies that do a good job there, but end users still kinda kinda haven't done it. Most end users are still just kinda talking to Claw, talking to Chat JBTs like hey.

00:23:55 Greg: What the heck's going on right now, right? They're just waiting for more builders to go build stuff to go use it from there.

00:24:00 Deedy: To me it's crazy, maybe maybe as good a sign there but you step out of Silicon Valley bubble Yeah. And nobody knows what's going

00:24:06 Greg: on. It's it's it's just it's almost baffling sometimes because when you spend so much time on AI Twitter and you're literally in San Francisco or the heart of the city right now, it's hard to get away from it. You know, when you go to these meetups and it's just like, oh my goodness, I'm so far behind because there's so many other people that are so far ahead of me. But like you said, you step outside of it and it's almost like you're speaking different languages when it comes to all of it.

00:24:24 Deedy: Yeah. Yeah. And and there's like a socio cultural element to this too. I don't know if you've been seeing this, but like a lot of the youth of of the world today like Gen z, I don't know what Gen and Gen Alpha. Yeah. There's, like, this hatred of AI Interesting. In in a in a huge way

00:24:43 Deedy: Wow.

00:24:43 Deedy: Where it's, like, it's not cool to talk about it, to use it. Interesting. It's it's the most weird like like, I feel like an uncle now even saying this, but I'm like, dude, when I was a kid, all new tech stuff was cool. It was really cool. Nerdy, but cool. Yeah. What is what is that reaction? And and maybe it came from the world of art where I know AI has had a really, really negative reaction from

00:25:06 Deedy: all artists.

00:25:06 Deedy: For sure. But now it's it's it's it's seeped into just AI at large. People are just like, ugh.

00:25:11 Greg: Yeah. Alright. Well, whatever. They're they're lost.

00:25:14 Deedy: They're lost.

00:25:15 Greg: Cool. Well, I tell you what, let's do some building. So, Didi, one of the cool things is that a lot of the projects you have surround or revolve around diagrams, and that's cool because it's not only very pretty for humans to look at, but it also actually helps AI understand the flow of something a little bit better that language may not express in the first place. So why do you love diagrams so much?

00:25:34 Deedy: I like diagrams are just way more expressive. It's it's bringing structure to unstructured data.

00:25:39 Deedy: Mhmm. And I

00:25:40 Deedy: think the caveat is, look, it's not gonna be perfect. You're gonna get things wrong, but just the fact that I'm a huge book nerd Yeah. And I love reading books and it was always just, like, thing in the back of my mind where I'm like, I wish I had a character diagram because my memory is terrible. Oh. Like, again, I watch Game of Thrones and every episode, I'm like, well, who's this? Yeah. Who's that? Why do they know them? Yes. And I just I don't wanna read a Wikipedia again. I want to see the relationships. That's cool.

00:26:03 Greg: And that

00:26:04 Deedy: kind of inspired that, you know, viral Harry Potter treat back in the day Sure. Where I was like, let me just throw all Harry Potter at, in that case it was Gemini for the context of this and just get a graph out of it. That's perfect. A diagram out of it. So maybe you can do that. That's one and I'll another one I'll talk about and we'll do it Yeah. Is getting diagram from code. He said, hey, the biggest problem in the world when you look at a new code base is it's not the time you spend reading it, it's the time you spend not doing anything because you're overwhelmed.

00:26:35 Deedy: Sure.

00:26:35 Deedy: You're looking at it, you're like, nah. I don't know.

00:26:37 Greg: That's because it's just a bunch of text files. It's like it's where's the insertion point? Where do I go start? Where

00:26:41 Deedy: do I start?

00:26:42 Greg: Right? Yeah.

00:26:42 Deedy: It's kinda crazy

00:26:44 Deedy: if you

00:26:44 Deedy: think about it. Where do I start? And so I oh, we'll do a demo. But what I did was I, like, like, dumped the entire repository into a plain text file Nice. Put it into an LLM and go like, hey, tell me tell me what the code looks like.

00:26:57 Greg: Yeah. Love it. Beautiful.

00:26:58 Deedy: So let's start with let's

00:27:00 Greg: start with number 1. Beautiful.

00:27:02 Deedy: We will what do

00:27:05 Deedy: you wanna summarize? What'd be cool? What's something relevant? Okay. I have a good one. I have a good one. Let's let's start with this. Can you you can see my screen. Right? Yes.

00:27:12 Greg: Yes. I can.

00:27:14 Deedy: It's funny that you mentioned the the work Oh, good.

00:27:16 Deedy: The the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the

00:27:17 Deedy: the the the the

00:27:22 Greg: that would be nice. Okay.

00:27:23 Deedy: Cool. Let's see what we can do here. This is all live. I have not done anything before. I have not prepped for this and I think that is going to be the beauty of it. So where are we?

00:27:34 Greg: So you like term like working in the terminal almost?

00:27:36 Deedy: Yep. I am a purist I guess they would call me at some point. So I'm going to use, I like to use Zed so That's cool. Sorry cursor guys but this is awesome. So this is a VTT file and what this means is I use this tool called YTDLP. YTDLP not only helps you download YouTube videos but it helps you download transcripts. So I'll actually show you a It's

00:28:00 Greg: just the transcript that comes straight from YouTube itself. Right? Yeah.

00:28:03 Deedy: Okay. So we got it. So what that did was I generated this previously with Claude. If you look at this code, it is basically trying to take out the timestamp artifacts which take a bunch of tokens and it gives me some plain text. And this plain text is not great and there's some repetition Uh-huh. But it's it'll get

00:28:21 Greg: you there.

00:28:23 Deedy: It'll serve our purpose. So let let's see if this one fits. We're we're gonna go we're gonna put it all in. Oh, great. It fit. And I'm gonna say draw a graph. I know mermaid is the default, but I don't like using mermaids. So draw a visually aesthetic graph.

00:28:41 Deedy: This is

00:28:42 Deedy: what I just like to do.

00:28:43 Greg: There you go.

00:28:43 Deedy: In as a dot file.

00:28:46 Greg: Why don't you of mermaid out of curiosity?

00:28:48 Deedy: I think mermaid is too simplistic and I'm just like a a more old school and mermaid comes from something called dot and dot or graph is is what I used to use in college and so I'm just more used to that.

00:28:57 Greg: Yeah. There you

00:28:58 Deedy: go. As a dot file

00:29:01 Greg: For those that don't know, Didi was a hardcore engineer before investing.

00:29:05 Deedy: I know.

00:29:05 Greg: We actually had our first conversation when you're still at Glean a a a long time back.

00:29:09 Deedy: Yeah. I I was an engineer. I was an engineer with with subgraphs demonstrating their relationship between various things that are talked are spoken about in this podcast.

00:29:28 Greg: What what about prompting techniques? Do you do you like to think out loud? Do you like tips? Or what what's your take on those?

00:29:33 Deedy: I wouldn't say, like, I'm a pro prompter. Sure. Like, my my advice is don't think. Do. Yeah. Just go

00:29:41 Greg: try something out.

00:29:42 Deedy: Just go try something.

00:29:42 Deedy: Yeah.

00:29:43 Deedy: You'll figure out in the end. Like, I a couple of these things that I've done, I've just figured out over time.

00:29:47 Greg: Sure.

00:29:47 Deedy: So here, it generated a bunch of stuff. Now what I do is I will go and take this to online graphviz, Paste it in and Oh, look at that. There's

00:30:02 Greg: one shot too. That's

00:30:03 Deedy: nice. Yeah. It it's just so cool. So we have some personal background, hearing impairment, social isolation, reading focus, talks about rabbit holes, patterns of theses. And it gets it gets it gets you some somewhere

00:30:19 Deedy: Yeah.

00:30:20 Deedy: And it looks pretty nice.

00:30:21 Greg: This is just such a nice, like, better way to look at a long body of text. Yeah. It just makes more so much more sense.

00:30:28 Deedy: Makes it so much easier to do. And Yeah. And we'll do a little bit more on this because I think this is a question I get asked a lot, which is how do I get out of this context window limitation a little bit? Correct. So I like to do this is one of my techniques, my prompting hacks. Assume all of these nodes and edges exist. Keep going. Add new ones and make it exhaustive.

00:30:52 Greg: And so you wanted to go you wanted to double click here?

00:30:54 Deedy: I just want it to go deeper. I think this graph was a good start.

00:30:57 Greg: Yeah.

00:30:57 Deedy: And let's see let's see where it can get me. And so it does regenerate some of the boilerplate here. Yeah. But it it's going on and on and on. Oh, look. We have some GLP drugs. Some China. Some great stuff. Great stuff. So now what I'm gonna do is I'm actually not gonna copy the whole thing. I'm gonna copy just the subgraph thing and let's see if it worked. So go back to this and oh, look at that. And it got even more complicated.

00:31:36 Greg: That's nice. So That's cool.

00:31:38 Deedy: So now it's like, okay. You're, like, if if you're a hyper productivity guy, you don't have that much time, you wanna listen to this podcast, but you don't have 3 hours.

00:31:47 Greg: Yeah.

00:31:47 Deedy: Download it, and now you just know.

00:31:50 Greg: It'd be so nice to have time stamps for each one of these and just click on it and then

00:31:52 Deedy: just listen

00:31:53 Deedy: to it.

00:31:53 Deedy: It's funny that you ask. It's, like, let's say I'm interested in AI and intelligence. Can you time well, it can't do that. It doesn't have the time stamps

00:32:04 Deedy: right now.

00:32:04 Greg: But it would be really cool.

00:32:05 Deedy: It would be really cool.

00:32:06 Greg: Yeah.

00:32:07 Deedy: Look, in a world where we could fit the timestamps and I've done that before too which is like just give me quotes with timestamps

00:32:13 Deedy: Yeah.

00:32:13 Deedy: For for these areas that I like.

00:32:15 Greg: That's cool. So then other than just looking at the diagram, will you use that diagram schema anywhere else for it? Like any other problems? Like, does that make its way to an input to a prompt for you?

00:32:25 Deedy: I haven't tried that yet.

00:32:27 Greg: Yeah. One example I talked to, I talked to another person on this interview series called Arjan, and he wanted to judge how well his support people were following the call script that they were instructed to do. So we first started with it with the diagram flow of the call script and what it should go here go here, if this then go here blah blah blah. You give that, pair it up with a transcript, and then the AI will LLM as a judge will judge how well he followed that script.

00:32:50 Deedy: I've seen some voices coming up to that and that's

00:32:53 Greg: very cool.

00:32:53 Deedy: That's pretty cool.

00:32:53 Greg: Yeah. Okay. So that's on the diagram side. Let's go into computer use.

00:32:58 Deedy: Let's do it. Yeah. So we're going to and again this is my fork of of of anthropic computer use. Totally unsafe. Do not use it.

00:33:06 Greg: Fork. Fork. Okay.

00:33:07 Deedy: Yeah. Do not use it. It can do all sorts of weird stuff. One thing that went wrong when I when I was trying to make this do something was they started a rogue process and just created, like, a 1,000,000 files in a directory which then stopped loading because the computer just went bonkers. So let's try to do something here. What I'm gonna do first is we're gonna go to Claude and we're gonna say instruct a computer to and, you know, there are some people who like to save prompts and they're, like, very meticulous. I'm I'm not that guy. I kinda I I instruct a computer in as much detail. Do not write the code now.

00:33:47 Deedy: If you tell Cloud to do that because it loves to write code. To do the following. Create a directory called dev slash we'll call it greg demo. Nice. And in that directory, create an you tell me, Greg. Let's we could interact.

00:34:13 Greg: Create for it app wise.

00:34:13 Deedy: We're gonna create an app with a front end and a back end. Give you some ideas, like, what kind of stuff do you think and I can push back. I think

00:34:19 Greg: it's a complicated

00:34:20 Deedy: but

00:34:20 Greg: For the front end and the back end. Let's I mean, let's just do it on brand topical. No. It's kinda lame. I was gonna do, like, VC inbound blah blah blah. Blah. How about, like, simple restaurants in San Francisco?

00:34:35 Deedy: Simple. Thinking about that one. What's the name? APIs APIs will be tough because you have to

00:34:41 Greg: Oh, you wanna pull some of my

00:34:43 Deedy: real data too? Oh, we could. I'm trying to think. You know what? I'll do one now. I wanna to get us started.

00:34:51 Greg: I don't know how complicated you wanna go. I wanna scrape OpenTable to know when reservations are opening.

00:34:56 Deedy: Oh, interesting. Because there's this one spot

00:34:58 Greg: in San Francisco that every time I look, it's always booked, and I'm never there early enough.

00:35:03 Deedy: You know what? It's not gonna be as visual, but let's let's see how far we can go. Alright. Let's see how we can do that. In that directory, create an app to scrape OpenTable. OpenTable.

00:35:19 Greg: And I have the restaurant

00:35:22 Deedy: reservations in San Francisco to find do you have a restaurant in mind?

00:35:30 Greg: I do. I I just put it in the Riverside Studio chat. It's called 4 Kings San Francisco.

00:35:39 Deedy: How do you spell that? I was just Oh,

00:35:40 Greg: just 4, like the the the number 4.

00:35:43 Deedy: To find 4 Kings availability, and let me no. I'll I'll I'll make it interesting and but tell I'll just keep it simple for now. Tell me when it opens up. Nice. And so let's see how this goes. And it might it might say something about

00:36:12 Greg: So are you making the prompt right now that you're gonna go then give to the computer use? Yeah.

00:36:16 Deedy: And then okay. Yeah. Man, this is if you could I mean Oh my goodness. This is a lot. I don't even know. This is a lot. This is a lot. So, yeah, this is gonna be fun because I have no idea if this is gonna work. We'll see how far it goes. Would you like me to proceed? And then let you know I don't want you to proceed. Try to use this is another one. Try to use command line only Nice. As much as possible and build an MVP. Yes. Boom. So now okay. Let me open up what's going on here. This is how I typically look at it. Okay. It is trying to it's creating all these files. It's trying to it's just trying to create a config. Mhmm.

00:37:07 Deedy: It's trying to download all the requirements for Python. This is where it's running at. And then real quick, let me just go into that demo. But I think for a lot of people, if you do prompt it the right way, absolutely.

00:37:20 Deedy: Yeah.

00:37:20 Deedy: Like, I have a friend, one of the one of the people who work at one of the companies that I I I named

00:37:25 Deedy: before Let's let's pause

00:37:27 Greg: there and just bring it up for a second. You had a tweet that said everyone thinks this is an exaggeration, but there are so many software engineers, not just Spang, who I know that literally make 2 code changes a month and they're getting paid 200 2 to $300,000 a year. It went viral. It popped off. You listed the name of companies. So who who's this friend at the company?

00:37:44 Deedy: There's many friends at many companies. Wow. Andre Karpathy tweeted back at that and I love like using other people to validate that this is a real thing. And he's like, people brag about this all the time.

00:37:55 Greg: Yeah. There's entire subreddits dedicated to

00:37:57 Deedy: it. Yeah. Overemployed is an entire subreddit dedicated to it. So I think like for me as somebody in the software engineering community, I don't think this was like a de novo type thought. Everyone knew this happened.

00:38:08 Deedy: Sure.

00:38:09 Deedy: I think outside that community, maybe people didn't know as much. Maybe the executives didn't know as much. And now maybe with the street, some people do. Without question, there are people who don't do anything. Okay. And is it their fault? Is it their fault? No. I mean, they're not sometimes Well,

00:38:23 Greg: it's just it's where incentives are.

00:38:25 Deedy: Right? Incentives and the incentives go down up the chain. Right? The managers aren't giving them work. They don't have anything to do because the managers don't expect them to do anything. Managers at any big company never have incentive to get rid of you. Yeah. They always have I have an I have a follow-up tweet about that. They always want to increase headcount because that's the only way that gets them promoted.

00:38:45 Greg: Sure.

00:38:46 Deedy: So no matter how bad you do, they have no way. They have no reason to get rid of you.

00:38:49 Greg: Sure.

00:38:49 Deedy: And and look, the the the nature of work is you're not gonna have work for 5 people. Let's say you're a team of 5. You're not gonna have consistent work for 5 people consistently. The problem happens is when you have basically no work for no people. Uh-huh. And then you're like, I can't like, I'm a manager. I can't tell my manager that I don't have any work for these 5 guys. So I have to then pretend that I have work so that he like, I keep my job.

00:39:10 Deedy: Mhmm.

00:39:10 Deedy: And then it's just this thing goes up the chain and then nobody does anything. Mhmm. But to get back to it, there's a follow-up. I'll I'll mention in that thread where somebody said something something. What does this have to do with like, does AI have anything

00:39:22 Deedy: to do

00:39:22 Greg: with it? Sure.

00:39:23 Deedy: And the answer was no. Like Yeah. The people who are doing these jobs have no idea what computer use or Claude or attach a p t. They're practically not even they don't care. They're in Mexico. They're in Hawaii. They are vacationing all the time. Yeah. So That's so crazy. But when you think about and I can can give one example of a couple of friends that work in a job like this. They were telling me I'm like, dude, what when you do work, what do you do? And it was like, we maintain the service. It's a big Java service and, like, I just had to do, like, one dependency upgrade once a

00:39:59 Deedy: month. That's

00:40:00 Greg: so crazy.

00:40:01 Deedy: A, that's ridiculous. Uh-huh. And b, let's talk let's talk about the dependency upgrade. Yeah. How much time do you spend on it? And he's like, oh, I spent a lot, dude. It was a hard one. I spent, like, 10 hours. And I'm looking at like, okay. Can you can you show it to me? Show me show me what you're doing. It was a buddy of mine, and we we met, and he pulls up his computer. He was showing me a little bit. I'm like, dude, why are you doing this manually? That's insane. You're really trying to resolve Java depths, and I know that this is insane.

00:40:26 Deedy: It's like

00:40:26 Greg: go to Stack Overflow or something.

00:40:28 Deedy: Yeah. He's basically googling a bunch of things to try to figure out how to use this dependency system that he does not know how to use. I'm like, dude, you can run this through, like, Claude and it will will fix it for you. Yeah. And and his response was, well, they don't let us use AI at work. Mhmm. And I'm like, okay. Well, that's a pretty shitty response because, like, you're the person who's gonna have to do it now.

00:40:50 Greg: Yeah. Exactly.

00:40:51 Deedy: Not not me. And so that's kinda the that's kinda where it is.

00:40:55 Greg: Alright. And so where we at now with this thing?

00:40:58 Deedy: Let's see. There are this is a problem that happens where here's what happened. It ran the scraper. The scraper ran and it blocked the thread.

00:41:09 Greg: Oh, okay.

00:41:09 Deedy: It waited 4 minutes and then it timed out.

00:41:12 Greg: Okay. Well, there you go. That's the work in progress with it.

00:41:15 Deedy: So we'll see whether and and there's a log. So there's a little bit of a log, and it said

00:41:23 Greg: Nice. At least we're at the right URL, which is good.

00:41:25 Deedy: Started a

00:41:25 Greg: reservation. Loading reservation bridge. Yeah.

00:41:27 Deedy: Oh, look at that.

00:41:28 Greg: Pretty good about scraping.

00:41:30 Deedy: Yeah. Yeah.

00:41:31 Greg: If any OpenTable engineers are listening to this.

00:41:35 Deedy: So it looked like the error was there was a time out, and then it schedules to check every 5 minutes and then, therefore, computer used timed out.

00:41:41 Greg: Schedules to check every 5 minutes. There you go. Nice, man.

00:41:46 Deedy: But, look, it wrote a lot of code. It somehow wrote wrote this, like, notification sending code, which

00:41:53 Greg: That's crazy. It just

00:41:54 Deedy: has to be emailed

00:41:55 Greg: from it.

00:41:55 Deedy: And emailed to you.

00:41:57 Greg: It's like sometimes when I look at AI code that's like this, I just get overwhelmed. It's like, man, I don't have any mental bearing or anchor points to to lock myself into this and it makes me a little nervous sometimes. But I'll tell you what, when you click run and it works, it's like, well, alright.

00:42:10 Deedy: It's true. I I feel a little bit better because I, like, sometimes understand what's going on. Mhmm. I'll give you one example now that we're that while we're at it. And I know I know we're

00:42:18 Greg: Yeah. Yeah. Yeah.

00:42:19 Deedy: At the time but I can I can go to to 1:30 flats? Don't worry. Beautiful. Here. I will do another one of these and actually, you know what? Let me do a simpler one. K.

00:42:34 Greg: And while you're doing that, I would love to hear about what's the stack you're using right now? Where do you like to deploy? What what's your DB? What's all that stuff?

00:42:40 Deedy: I keep it really simple. Yeah. My this was true of when I did this at companies which is do what you know. Yeah. Don't do what you don't know.

00:42:47 Greg: Sure.

00:42:47 Deedy: Sometimes I I wanna learn something new. I'll learn something new. I know Google Cloud really well. Nice. So I do Google Cloud auth Yep. That's one one command after I run on my command line and then I say go deploy it with Google Cloud. I ask computer use, tell me what to do. Yeah.

00:43:00 Greg: And it just does it. Nice. So GCP.

00:43:03 Deedy: GCP and then for what else? What what other

00:43:09 Greg: Let's see. I mean, GCP, you you're using zed? You're using a lot of clog?

00:43:12 Deedy: Oh, I use I use zed. I use cursor too. I I I use a lot of clog and computer use. Sometimes when claw doesn't work, I won't go to o one, and I'll try to, like, go back and forth and get different answers. I use Gemini when I need long context. Gemini's pretty useful for that. Yeah. That's that's kind of my stack. When it comes to languages, there's another really cool project that I that I was working on where in in Rust. And so sometimes I'll use Rust when I view it to be super fast. But I'm mostly doing stuff in Python and then if I do front ends, it'll be in React. Cool. That's mostly it.

00:43:48 Greg: Beautiful. Love it, man.

00:43:49 Deedy: Real quick, I wanna tell you about, like, the coolest example Yeah. Of something I learned when usually, it's one of the hard mental constructs with AI is you don't know what it can do. So there's, like, this unknown unknown problem where I know what I can do. Therefore, I ask it to do things that I know that I can do. Yeah. I don't know what I can't do. Therefore, I can't ask it to do that. But there was this one example where I I asked Claude again, meta prompting, to our point. I'm like, give me some random ideas. And I just got out their ideas. And then I took some of those ideas, and one of those ideas was get it to make a song.

00:44:22 Deedy: Yeah.

00:44:22 Deedy: And in my head, I'm like, well, that's stupid because obviously computers can't do that yet. And but I'm like, okay. Well Yeah. What would happen if I asked it to do that? And this one blew my mind because I didn't know it was possible, and I'll show you this example Sure. That you could create waveforms from scratch

00:44:40 Greg: Just like with code.

00:44:42 Deedy: Like with code. Yeah. And

00:44:47 Greg: Yeah. Looks like

00:44:48 Deedy: And I just built that from absolute scratch using a thing called socks, which is a library I had never heard of and I did not know. And it downloaded it and it used it. And this is a really simple one, but I've gotten to get some really, really

00:45:04 Greg: crazy That's wild. Okay. So say we were gonna take the, OpenTable example we just did. And it's like, okay. It works. It's cool. It's there. You're about to package it up and you're about to put it on Twitter.

00:45:15 Deedy: Yeah.

00:45:15 Greg: You're very good at this. What is the mental process you're gonna think, okay, here's how I package it up and everything up until you click send or a post.

00:45:23 Deedy: Okay. So step number 1 is GitHub repo. Usually you can get computers to take care of most of that for you. So put it on GitHub. Couple of general sanitary things such as get get some documentation going so people would, like, know what it's Yeah.

00:45:36 Deedy: Yeah. Yeah.

00:45:36 Deedy: So you can just spit out all the code and say, now create a read me for it. Yeah. Get a nice read me, whatever. That's step 1. Step 2 is I think about, like, videos and there's 2 ways to do this. There's, like, the video of the process which has a certain audience and there's a video of the output which has a certain other audience. Sometimes, just a picture works better than video. It's pretty laborious because what I have to do is QuickTime, screen record. I'm not super fancy with it. Yeah. Do a screen recording, run the entire process back, literally, like, load it into Imovie. Yeah. But sometimes I use some other video software and, like, clip it up to see like, make it faster.

00:46:11 Greg: Cut the ums. Cut the pauses.

00:46:13 Deedy: Yeah. And I I usually do no audio for this because that's a whole other

00:46:16 Deedy: Oh, okay.

00:46:16 Deedy: Thing, but, I just wanna, like, show the the demo Yeah. Because it's usually for Twitter or not YouTube. Mhmm. Cut it up. Make it 4 x faster if it's a computer used thing and export. Boom. I use an app called Type Fully to schedule tweets. So I'll put the video on of you never put a link on your first tweet. You put the link in the thread. So I link to my repo in a follow-up tweet and then I just compose. Like, what do I and how do I wanna talk about this? So one one example of that was say I I built a thing on on front of my friend's computer which is I always thought this would go which would be a really nice concept for people to understand which is everyone talks in SF especially is like make me a bot that swipes on Hinge.

00:46:54 Deedy: And especially is, like, make me a bot that swipes on Hinge. And iPhone screen mirroring came out, computer use, and, like, put 2 and 2 together. Like, let's make this happen. And so that was a cool one. And so take the whole video, get computer used to do it, and then package up the demo, get rid of PII, boom, tweet, and then go.

00:47:15 Greg: So you have an idea. You go and build it. You share it, and then it seemed like it goes viral every other post. In fact, when you were doing the pre call for this one, I said, I wanna talk about your viral post. Then you said, which one? And so it's like, yeah. You have so much good content out there. So how how do you build so much? Like, what what's the secret behind it?

00:47:32 Deedy: Firstly, I just wanna say well, I'll I'll tell you the story of how I got into Twitter because I think I think people misconstrue a lot of the the online stuff and I hate, like, the word influencer because that's not what I try to do at all. The reason why I'm able to output so much is because I I think I just genuinely love building stuff. And in the past, one of the biggest constraints I think mentally now that I introspect about it was what's the point of me building this? Yeah. No one's gonna see it and no one's gonna care.

00:48:01 Deedy: Yeah.

00:48:01 Deedy: It's just gonna be a little project that sits on GitHub with no views and I can tell one buddy of mine who cares and nobody else Yeah. Gives a shit. Uh-huh. And now that I have an audience, I'm so I I feel really, like, genuinely grateful that it lets me go, like, I'm gonna go build it because at least some people will see it. Yeah. And And and and it kinda brings me and that's kinda all I wanted to do and That's so cool. The reason I got into writing online in the first place was because I mean, the story is me and a friend were out at drinks. We were having a great time, and I gave him a like like, an idea. And he looks at me, and he goes like, dude, that's a terrible idea.

00:48:40 Deedy: And I'm not I'm arguing, and I'm like, what are you talking about? Like, no. It's a great idea. And he kinda does this flip on me, and he goes like, look. And this is before I really tweeted, but I've been right online a little bit and he's like, I trust your judgment. If you, sober, can go online and write about this publicly, I'll believe it's a good idea. And I'm like, oh, well, that's a that's an interesting thought right there.

00:49:05 Greg: It's literally just framing the idea.

00:49:07 Deedy: Exactly. And then I realized that gap between you having an idea and thinking you're awesome Yeah. Versus pressing the send button Yeah. So big for so many people.

00:49:17 Greg: That's so true.

00:49:18 Deedy: And I realized that that was stopping me from doing a lot. Yeah. I was I I thought I had great ideas, didn't put them into production or put them out there.

00:49:28 Deedy: Sure.

00:49:28 Deedy: And the second thing is everyone does this on the Internet. You go into rabbit holes. We learn things, but you don't remember much.

00:49:35 Greg: Yeah.

00:49:35 Deedy: And this was a good way I found. I promised myself I would go online. I would write one thing every day. Nice. One thing that's somewhat useful to other people that I learned. Sure. And that's how it all started. And I just promised myself to do that and it gave me a reason to go and learn stuff.

00:49:49 Greg: Yeah. That's very cool.

00:49:49 Deedy: Aimlessly exploring, I would say I learned a thing, here's a thing.

00:49:54 Deedy: That's right.

00:49:54 Deedy: And that's kind of how it evolved but then obviously, you know, there

00:49:57 Greg: It goes from there. I was just listening to the Darksh Goran interview, and I think Goran said that one of his north star metric for himself is to maximize the number of rabbit holes he goes down. So it's literally just getting in flow state into an esoteric topic and just going and having fun with it. So when you said Raval, it made me think of that. One thing I would love to do to inspire people to share more here. So going from the journey of having no audience all the way up to 100 of thousands of followers on Twitter. I'm sure there's a lot of, like, cool serendipitous moments that happen along the way.

00:50:27 Greg: Maybe somebody famous reaches out or an opportunity prevents itself or maybe a new career thing. What are some of the cool things that happened along the way of your journey that are due to having a bit more exposure?

00:50:38 Deedy: Oh, this is my favorite one which is I was on the first date with the the girl I've been seeing now for a while and I remember we were we're driving in my car together and across across the Bay Bridge going to Oakland from SF, and it's the first time we ever met. Yeah. And my phone starts buzzing, and it's like, I see a notification. Like, you know, I'm driving and the phone's up there on the on the dash. And it buzzes, and it says something something. Elon tweeted something. And and but it wasn't the normal Elon tweeted something. It was I couldn't tell what it was. I wasn't really looking at my phone, but then my phone just starts blowing up.

00:51:17 Deedy: It's like notification after notification. And I'm like, what is going on? And probably shouldn't publicly admit this, but then I put my car in self driving, and

00:51:24 Deedy: I'm like, I don't know

00:51:25 Deedy: what's going on.

00:51:25 Greg: You're like, babe, I gotta look at this.

00:51:27 Deedy: No. It's because my phone's blowing up. And I saw an Elon thing, and then it blew up. And I'm I looked at it, and it's like, Elon had retweeted and written something to me. And in my mind, I'm like, oh my god. What is going on? And to her, this is, like, meaningless. She's like, I don't know what you're so obsessed about. But I have friends reaching out to me going like, congratulations. Oh my goodness. And and I'm obviously, like, I'm a huge fanboy of of a lot of things Elon does. And so that was that was one moment where I'm like, damn.

00:51:56 Deedy: Like, you

00:51:57 Deedy: the richest guy in the world know I exist. Yeah. That's kinda, like, nice. So those are the positive ones. Like, yeah, the the Elon is I think we've had 10 replies and stuff now and then That's cool. Paul Graham, Mark Andreessen, a bunch of these, like, pretty esteemed

00:52:11 Greg: Yep.

00:52:12 Deedy: Tech people. Yeah. But I think it's worth, like, to take the positives with the negatives and I think the hardest part about writing online is that there's gonna be times people tear you apart and that's happened a ton of times too and

00:52:24 Greg: What what percentage of the energy when you when you when it ends up blowing up like that, what percentage of the energy is negative that comes up?

00:52:30 Deedy: It depends on the post. And I think, you know, there's some there's some threads where you're like, okay. 90% positive. There's a couple of people who are kinda dumb and they're saying, like, nonsense, and that's fine.

00:52:41 Deedy: You you

00:52:41 Deedy: you build thick skin to those things. Yeah. The hardest part is when you get a full blown cancel

00:52:47 Deedy: Yeah.

00:52:48 Deedy: Thing. Sure. And I don't think there's anybody I've met who has some scale online who hasn't had at least one of those. Course. Yeah. Yeah. But when it happens for the first time in a big way, it messes with you, man. Like, as much as you wanna say I have thick skin, you're just like I don't know. You're, like, looking at people on the street and going, like, do they know? Do they know? Do they know that I'm, like, I'm a villain online to millions of people? And and you get over it, but that's happened a couple of times too.

00:53:15 Deedy: So

00:53:16 Deedy: and I I will share that there's so many younger people especially that I've met who literally have had one of those happen and have stopped posting online for years. Wow. It kills people Yeah. Energy and motivation.

00:53:28 Greg: And what's the advice to them?

00:53:30 Deedy: For me, it's like it's the same advice and and, you know, in real life when people say you care too much about what other people think of you, they don't really care.

00:53:37 Deedy: Yeah.

00:53:37 Deedy: It's kind of that. It's like people aren't gonna say shit.

00:53:40 Deedy: Yeah.

00:53:41 Deedy: Keep doing you.

00:53:42 Greg: Just keep on doing it. And that's the other thing too is like the algorithm wants to promote good stuff. And if you post something that's not good, don't worry. Nobody's gonna see it. They're only gonna see the good stuff.

00:53:49 Deedy: There's only one caveat which is like take feedback. So if there is something you're actually doing wrong Yeah. Well, like, okay. Well, what am I actually doing here? But in many cases, that feedback is, you know, just keep doing what you're doing.

00:53:59 Greg: Didi, this is fabulous. Awesome. Thank you

00:54:01 Deedy: very much

00:54:01 Greg: for chatting.

00:54:01 Deedy: Thank you so much, Greg.