Parth Patil: Inside Reid AI — Building Digital Clones & Agent Workflows | Glasp Talk #60

This is the sixtieth session of Glasp Talk.

Glasp Talk delves into intimate interviews with luminaries from various fields, unraveling their genuine emotions, experiences, and the stories behind them.

Today’s guest is Parth Patil, an AI engineer, innovator, and technical advisor driving the future of AI agents and compositional software. Parth has worked at the cutting edge of generative AI, from building research automation systems and chatbot frameworks to teaching thousands of learners worldwide through Coursera. He previously worked at Clubhouse and is now shaping AI agent development alongside the Office of Reid Hoffman, where he famously built "Reid AI," an AI clone of the LinkedIn co-founder that sparked global conversations about identity and intelligence in the age of AI.

In this conversation, Parth shares his vision for how compositional software, systems built by combining AI agents and generative tools, will redefine the way we build and interact with technology. He explains the principles of "vibe coding," an experimental approach he pioneered since the GPT-3.5 era that emphasizes intuition and creativity over rigid structure, and reflects on its applications with platforms like Replit.

Watch the full episode on YouTube

Parth Patil: Inside Reid AI — Building Digital Clones & Agent Workflows | Glasp Talk #60 (YouTube)

• (11:11) Why AI-assisted coding is the future of software development

• (29:37) Training AI on personal game history to capture user preferences

• (30:38) Defining the social and business value of AI clones in future tech use cases

• (47:23) Using Graph-RAG to handle complex queries and track evolving user preferences

• (1:00:11) The overlooked power of structured outputs in language models (e.g., JSON, data extraction)

• (1:17:38) The critical problem of verifying authenticity in the AI era (provenance & blockchain)

• (1:31:13) Building “network intelligence” through conversations, diverse communities, and creativity

• (1:45:29) Brain–AI interfaces and the future of personal memory augmentation

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Key questions & insights

Q: What inspired Parth Patil to focus on autonomous agents rather than traditional AI tools?

Parth began by building AI-powered utilities for research, content generation, and automation. However, he realized that their fixed outputs and reactive behavior limited traditional tools. His curiosity led him to explore agents that could reason, take initiative, and evolve. Inspired by frameworks like BabyAGI and LangChain, he now focuses on building modular, composable systems that act more like collaborators than tools.

Q: What is “vibe coding,” and how does it change how software is built?

"Vibe coding" refers to a collaborative, improvisational approach to software creation. Instead of writing code line by line, developers provide high-level goals or constraints. The agent then suggests, refines, and adapts solutions in real time. This workflow prioritizes intuition, iteration, and co-creation, allowing users to build faster without being constrained by rigid specifications. It represents a shift toward ambient, conversational software development.

Q: How does Parth envision the future of human-AI collaboration?

Parth believes that AI’s greatest potential lies in enabling new cognitive workflows. Rather than replacing humans, agents will augment thinking, planning, and system design. He emphasizes a future where humans and agents operate in symbiotic loops, sharing context, delegating tasks, and learning together. This shift requires rethinking productivity, education, and even identity, as humans learn to collaborate with intelligent agents as thought partners.

Q: What kind of tools and frameworks does Parth use to prototype agentic systems?

Parth frequently uses tools like Replit for fast iteration, Airtable for structured knowledge storage, and LangGraph or custom frameworks to orchestrate agent behavior. He designs agents to be modular and composable, treating them as building blocks for larger workflows. His emphasis is on transparency, interoperability, and lightweight experimentation to enable fast, adaptable development cycles.

Q: What advice does Parth offer to builders entering the AI agent space?

He encourages builders to think in terms of systems, not just apps. Start with problems that benefit from continuous interaction and context retention. Don’t be afraid to launch imperfect prototypes and let them evolve. Parth stresses the importance of open-source collaboration, shared standards, and a mindset of curiosity. In his view, the future will be shaped by those who build with agents, not just for them.

Transcripts

Glasp: Hi everyone. Welcome back to another episode of Glasp Talk. Today, we are excited to have Parth Patil with us. Parth is an AI engineer and innovator, passionate about building compositional software and generative AI tools. He currently works with the office of Reid Hoffman, leading AI agent development, LLM Ops, and special projects in the Gen AI space. He also serves as a Technical Advisor at Blitzscaling Ventures, and previously founded Creatia.ai, where he built advanced AI solutions, such as chatbots, research automation systems, and generative coding assistance. As a Coursera Instructor, Parth has taught thousands of people worldwide on AI and programming, creating hands-on courses to help developers harness cutting-edge AI tools. Today, we will dive into his journey, his work shaping the future of AI agents, and his vision for making advanced AI tools more accessible to everyone. Thank you for joining Parth today.

Parth: Thanks. Thanks, Kazuki. Awesome. Thanks, Kei. Great to be here. Thanks. Yeah. Thanks so much.

Glasp: So now you are AI engineer at the office of Reid Hockman. I remember in the previous video, you said your official title is AI wizard at the company.

Parth: Yeah. Yeah.

Glasp: So could you tell us what you do at the office of Reid Hockman, and are there any AI projects you are currently working on?

Parth: Yeah. I mean, so many. I guess I'll give a little bit of context. So when... My last, like, normal job was, like, two years ago when I worked at a startup called Clubhouse, where we were doing audio conversations around the... Like, we had an app where we were... We created audio conversations on the internet. Kind of like Discord, but a little bit less gamer, more for everyone. And it was very popular during the pandemic. It was one of the fastest-growing apps of all time. And I was working there as one of the first data scientists. So my job was to figure out, like, why do people even use this? What is working? What's not working? How can we try to learn from that and get better at it?

And while we were working on Clubhouse, November of 2022, ChatGPT came out. And it became the most popular topic across every single language all over the world. Everyone was talking about it. And I was talking to people on the app, and I was just like, oh, how are you using it? And you just get so many different use cases. And, like, there were farmers from India that were using it for crop cycle planning. There's just, like, musicians using it to study music theory. There's all these different applications of general intelligence.

And for me, it was like, oh, my God, this is like a new computer. Like, this is, like, 100 years early. Like, I didn't imagine we would see this in our lifetime, that, like, the computer could speak every language and also write every programming language. And this was GPT 3.5, right? So initial ChatGPT launch. And then in March of 20... March 14th, 2023, GPT 4 came out. And one of my friends on the app... We used to be programmers. We were just programming, you know, chatbots, trying to figure out how to make the language model useful.

And one of my friends, he programs every single day and just makes projects and then teaches people. His name is EchoHive. He's on YouTube. He has a great, successful YouTube channel now and a Patreon. But he was saying, Parth, you got to be using GPT 4 because this is better than many of the engineers that we know. And you can just ask it to teach you how to program and you can make more interesting things. And in my career, I had been avoiding programming because I was more of a data analyst, data scientist. So I was using SQL. I'd use Python. But not a full-stack engineer at the time.

And then... So then I said, OK, I'll give it a shot. So I sit down with GPT 4 on a Sunday and I say, teach me how to clone my voice. And then it just writes the program, right? It writes the program. It uses an open source library, Tortoise Text-to-Speech. And then one hour later, I was like, oh, teach me how to run this program. Then one hour later, it's on my computer. I'm talking to... I have a program that sounds like me. And then I was like, wow, this is only one hour? I thought this was supposed to take forever. This was supposed to take a team of people a few months.

And then I was like, OK, teach me how to build a GPT-powered chatbot. And then it wrote the first version of that program. It's like 25 lines of code, just text-based script runs on your computer. And then I hooked it up together and I was talking to a program that had my voice, right? And it was like a voice assistant. And that was my first day of programming with language models. And for me, it was like, oh, my God, this is... Now I need to become a programmer. Now I need to learn, because it's never been easier. You have an expert. The model will teach you these things.

And then you can just... You have to still put it all together. But whatever you have in your mind might be possible. And it might be inside the model. And then you just have to be programming with the model. And then you'll see what's possible in concert with the model. And so I was working on that. And at the same time, Yohei Nakajima puts out Baby AGI. And it goes viral, like mega viral. And I look at the code and I was like, oh, my God, this is very straightforward. It's a fairly simple program. But it was so popular because I think at the time, it was the first...

The promise of the idea that you have a program that can just start doing meaningful work for you and just operates in a loop towards a goal that you give it was a very alluring problem. The idea of this agent, right? The LLM is good. Generating text is great. But can it actually be useful and do things? And Baby AGI was this spark of imagination that went through the community of what if we made these tools act on our behalf in a useful way? So I looked at his code and I forked it. And I started working on these agent loops.

And my friend who had introduced me, he was doing the same thing. So we were doing coding automation. Oh, go build 100 ideas. Come up with a list of 100 ideas. Go try to make those. And then I go and I'd sit by the pool and then the program will just be writing code. And then I come back and then I go grab the code and I go to the pool. I'm like, wow, I didn't know you could do that. This doesn't even work. But that's interesting. And so I was like, oh, my God, Python. Like that was my main programming language.

But then I saw because of the language model just how powerful Python was. And then I was like, oh, wow, like language model is useful. But now when you connect it to the ability to code, you are able to reach into every single programming language. So data science, you can do analytics through language models if you let them write SQL queries and write Python code. You can automate a lot of stuff like Excels, presentations.

And so that was like interesting to see how much automation you could get from language models. And as I was doing that, then we had layoffs at the company and I got laid off. And I was on my first day of layoffs, I was like, oh, this is good. Now I can just do this all day long until I run out of money. And I'm just going to do this until I run out of money. And then I'll figure out what to do after that. Like I was willing to.

And then and basically I was like my entire savings, I just put it all into open AI calls every single day. And my friends were like, wait, don't you want to get a job? Like I said, it's tricky because this, I think, is more interesting than getting a job. And a lot of the companies, a lot of the people I was talking to just didn't understand it. They were like afraid and they were banning GPT.

And I was kind of like, I can't work with people who don't see the value of this kind of technology. And I would rather just study this independently myself and just make things for fun than work for people that don't let me use this technology. So that was, so I did that for, and I had four months of severance. So I was like, okay, I have a little bit of buffer room here where I can just focus without having to recruit.

And then after the severance was over, because I was like, if I just sit here 14 hours a day, Saturday, Sunday, and I program with language models, I'll figure something out, like probably. And then worst case, even if I run out of money, like then I'll just go get a job after like, and maybe use the skill set, right? Like I might learn something and then use the skill set.

And then at the end of my severance, I was like, oh, I'm not done. I'm going to keep doing this. So then I just kept, I just kept burning my own money, building tools on top of language models, chatbots, retrieval systems, data analysis tools, data visualization, every single capability that I was like interested in, even just like music. Like I spent one month talking to language models about music and music videos and music theory and then music production.

And I was like, wow, this is so useful outside of work. Like this is just like the biggest, it's general knowledge, right? It's not just for work. It's actually just very generally applicable, super smart assistant. And then, and so I was like, okay, I'll just keep figuring these things out. And then I started getting contracting work. I started getting consulting work related to this skill set. They're like, oh, could you build a chatbot that retrieves context from a bunch of podcasts?

Could you, you know, like what kind of things can we automate now in the business? And because I had worked in startups my whole career and because I was a data analyst, I was very like, okay, this is, this is at least a huge amplification of analysis. Like that's obvious because you can automate SQL queries, right? Natural language. You can say who's our largest customer. Well, now the language model can write the SQL query. I don't have to write the SQL query. So now I can ask the next question. I can ask the next question. The model writes the SQL query. Now we have, we have high speed data analysis now.

And that's just like one part. Then it's like, you can also use structured outputs. You can figure out more. You can like create all the messy data can now be structured using language models. So I was like, okay, this is probably a skill set that is useful, even though I was like, so I started getting contracting work and that sounds like, okay, cool. Because once you get that first, once you get that first job, like the first contract, you're like, okay, there is a market demand for this skill set. So then I was like, okay, I'm going to be safe, I'll be fine.

And then, so that was nice. And then I kind of did that. I was, and then I did the Coursera, Coursera reached out and I created some courses on code generation and data analysis using language models. And that was great because I was just like, wow, like, I mean, being recognized and then my courses showcased at Davos and I was like, okay, cool. They are taking seriously that we need to update our curriculum. And even though I'm not like a full stack engineer, I do think that like the ability to generate code is an important message to send. It's not cheating.

It's actually the future of how we build software, right? So this was Vibe Coding. We were Vibe Coding before Vibe Coding was coined, right? Vibe Coding became a thing this year, but we were Vibe Coding with GPT 3.5. When you have to copy paste from chat GPT, copy paste the error back into chat GPT, it's like, oh, fix this. Oh, fix this. Okay. Why does this work? And so that was a, that was a, an interesting era.

And so I love code generation. That's my, one of my favorite things about language models. And then, then I, in about two years ago, I met, I was introduced by a mutual friend to Reid Hoffman and we were working on a project called Reid AI. So we built like an AI to represent Reid Hoffman, Reid Hoffman, the co-founder of LinkedIn and one of the earliest investors in open AI. And so we met, we talked for four hours and then the next day he was like, oh, you should come work for me. And I was like, yeah, let's do it. And so I've been working with him and so kind of projects, it's a lot of the same stuff, but now I'm not alone. Right. And I have like people I can, I can bounce the ideas off of. I have teammates and I'm like, oh, what if we wanted to like, you know, a lot of different stuff like translation, right. Translation is very useful, but now you can translate a speech into every single language. You can translate a podcast into every single language and then you can use avatars to get the lip sync right so there's a lot of stuff that I was like the tools that I was playing with now became useful and then now I have people I can like build the tools for, right? So building internal tools, vibe coding, using Replet with a teams plan is great because now I can make tools for my for my teammates and then they give me feedback and then I'm like okay cool now we can we it's a and it's like lower risk than starting a company like starting a startup because you have like you're building internal tools for an organization that already exists and people are like embracing AI so you get a very good feedback loop and sense of community and teamwork when you when you make things. So that's great.

Glasp: Yeah, it's really fascinating, and the end goal of like AI project in the office of Reid Hoffman is for the internal use, or have you or your team sort of thought about launching or releasing it to other external organizations and sell it to them.

Parth: That's a great question. I think we're not we're not opposed to it. I think we kind of just view it right now as like let's just learn and experiment. Let's see what you know some stuff does end up more publicly facing. I think and like Reid AI for example is like it's an internal tool but we are exploring what would it take like what would it be what would it take what do we need to build to make it something that we feel comfortable putting in your pocket right so releasing more widely as like a maybe a companion experience so we're open to it I think it's mostly like it's easier to move fast and experiment when you keep things internal I think this is the thing about vibe coding I recommend a lot of people are now getting into code generation and vibe coding And and I always say like, "Okay, you can get excited, it's good, but like start with things that are like safer and manageable." And if you know everyone who's using what you're making, then you can you can solve the problems before someone breaks it. And like, you know, you don't put you want you don't want to put it out there, have other people hack it, and then it starts becoming a nightmare to deal with. But if you're building internal tools, you have a higher bar, and you can kind of like internally break things, and that's fine because you're, you know, who's using it, and you get that feedback. So I think that even when you're building code generation like using code generation there's this like lower risk thing which is build things for your organization and then take those learnings and then you make the next like maybe your 15th idea you might be like okay let's make this for people right make this more externally facing and and it might not even be an application that reaches the other people. Might be like the output of gener like it might be generative media right it might be a video it might be yeah it might be a video it might be like a speech translated into 25 languages so the output of the model can still be be made externally available and I think that you learn you you want to learn things and you don't necessarily have to build and launch an app to learn things you can also learn things by just making making tools for like your first customers are just like your own teammates and you right and then if it's useful, you might find that there are other teams that you can you can partner with. So I do like branch out into the broader network and talk to some of the the firms in our network and it's like here's how we're using the tools. And then I and then it my big recommendation is everyone should have like a vibe coder in house, you know, like because we're kind of getting to a place I think that it is easier and faster to buy to build software than to buy it. And I met Amjad in from Replet in in San Francisco and I mentioned this to him. I said, "I have a feeling that buy versus build has flipped." It used to be traditionally it's like if it's not core to your business just buy the software and then move on. But now it's like you know you can tell AI to make the version of the the application that you need and you don't get all the extra features you don't use and you're not paying for a two-year subscription to some overly like oneizefits-all solution. Now you can just make custom software. And maybe that's just like my bias because I love generating code. But I love this this world we're getting into where I can just sit down with cloud code or cursor and just be like, "Hey, let's make this." And then two hours later, I have it. And I don't have to talk to a sales rep. I don't have to negotiate a two-year contract. This is something that's very interesting. And I think it's going to be very disruptive the the like personal software era.

Glasp: Yeah. Yeah. I was thinking, actually, the same. I mean, people will stop buying software outside because they can internally use you know bipe code tool, and also if you hire a boder and it's much cheaper than paying a million bucks every year to external software.

Parth: Yeah. Exactly. It's very disruptive and there are some I think the other there's like lasting effects. I think there are some businesses from the old world where you look at it and you're like I have a feeling that AI could build that you know like there are some gigantic uh you know like businesses that have kind of like relied now they have the network effect but you wonder it's like would I buy that if I knew that GPT-5 could build it in three prompts probably not you know and that's an interesting like they're going to have to adapt and then we have our own you know new options of uh ways to play the game and it's a lot cheaper right you can move faster, it's cheaper. There are definitely downsides, right? Security vulnerabilities. You know, people complain that the applications that you vibe code don't have, they're not, they don't scale, that people complain. I say, look, everything breaks once too many people start using it. But that's a good problem. You solve that problem, right? Like like when a million people showed up at Clubhouse, everything was on fire and stopped working. But now it's you have to try to solve that problem. You don't solve that problem when you have no one, right? And when you have no users, you're not trying to solve this like a scale problem. You're trying to get it working and trying to get something that people even want in the first place. So I think and also the models keep getting better. And I think that that's yeah cheaper so you can run more calls. The agent rappers are getting very good. And then you know even even on the cyber security side of things I think there's another way to think about it is just yeah maybe like vibe coded apps may be vulnerable but then you can also imagine a world where you have an agent and it's like hack just hack my app and then tell me and then my coding agent will patch it and so kind of we have that in human world right hackers and instead of ransoming your data back to you they just tell you and then you pay them you pay them to hack you and then and then you patch the vulnerability And then that becomes that's a good kind of hack, right? And so I think we're going to have AI that does that. I think Replet is starting to do that. They have before you deploy an application on Replet, you can click run a security scan on my app. Last week cloud code added a same feature which is like let's explore my application for possible vulnerabilities and then let's go patch them. The I think we're still going to see like people make mistakes. I think there's going to be a lot of uh entry-level vibe coders that don't know, right? Frankly, don't know better because they didn't go to an engineering like they don't have an engineering college experience. I didn't, right? So, so we're going to see these errors. but hope I think AI is a huge part of the solution and so we just need to models will get better. We're going to get better at using them, you know.

Glasp: Yeah, definitely. Yes. And I don't know if you if you involved in like a building did Reid AI at time but I mean I remember I still remember the last year last April the Reid AI and DMAN you know launched the video we yeah that was really amazing but did you did you work with the team?

Parth: That was me. That was actually the project that led me to working with Reid which was uh one of my teammate on the project I was just working out of a beach house in Los Angeles. And one of my friends at the house, Ben, he works for Reid. And he was like, "Parth, do you think you could build a chatbot on top of like a guy who has 30 years of podcast and books and everything?" And I was like, "Yeah, I think so. Give me because I think this was Oh, yeah. This was custom GPTs, right, from OpenAI. Remember custom GPTs? So you could take chat GPT and then you could give it a separate personality and then um upload a bunch of files and then say so we were like I was like okay yeah give me a couple hours. So in 3 hours I went I got his books I got a bunch of podcast transcripts and I uploaded them into a custom GPT. I said you are pretend you are Reid you are Reid AI you know digital avatar you know trained to represent the the body of work of Reid Hoffman. And so it was a little bit prompt engineering and RAG and custom GPT. So this was no code, right? So this is like a very simple it was like proof of concept and uh and we built that and then I showed it to him and then he showed it to his teammates and he was like and then and then that was when he was like oh yeah we should like we like you should come meet Reid and that was the beginning which was like a very simple prototype kind of proof of concept. But then when when I met Reid it was like no I build a lot of systems like this actually more you know on the with Python like closer to the baby AGI more agentic not just like retrieval systems but like tools that can act and so it was interesting because we both have this like theory that like these agents are just very useful even if they're not like fully autonomously endto-end they're still very useful co-pilots and I think the next 10 years we're going to see an explosion of these co-pilot type systems. for everything right and uh you know all business your medical like and it might be ChatGPT it might be a specialized application you know there's going to be therapy there are there's an AI agent for almost every single possible use case and we we kind of were like okay like Reid AI is cool but like what if we like we have this chatbot but what if we we used one of our portfolio companies at the time it was called Hour One and they they were doing avatars and so we were like What if we had and I was I don't know. Have you guys seen the movie Tron? Yeah. You know, Tron like 19 I think 1982. Tron is this movie where there's the old Tron and then there's Tron 2012 they made Tron 2 and then this year end of the year there's a third Tron movie Tron Aries coming out. But Tron is one of my favorite movies. And in Tron, Jeff Bridges is the main character and he gets stuck inside of a video game and he's talking to programs and he's in this like digital matrix and my dad watched it when my dad was in engineering college and he was like, "We didn't know what the computer looked like. This was awesome." And then then he showed us the movie, me and my brother. And then when we were working on Reid AI, I was like, "Oh, you know, we were thinking, how do we show this? Like how do we demonstrate this chatbot to the world?" Right? like if we wanted to like showcase this kind of experience and I had the idea I was like what if you could just talk to yourself, right? Like kind of like a mirror, you know, you have a copy of you and you could talk to it. And so the first video we put out, the one that went viral that I think that in April was Reid talking to his digital clone. And so under the hood was the custom GPT that was powering the voice and powering the the text generation, right? So 20 years of his knowledge and retrieval and then you have the video avatar which was by hour one. So it was a that was like we scanned Reid and then now you have this avatar and then the voice was ElevenLabs and so we were able to like you know clone his voice close clone his image and then we have this like chatbot that pretends to be his mind and then he he we had this video edited together of them talking to each other. The interesting thing is at the time there was no real-time avatar technology that was this good. So it was all pre-generated and edited together with pre-generated footage. But people were so excited by that that companies startups started coming to us and they were like we would love for you to try our real-time avatar technology. And so like then then we started you know when you build something and you put it out there especially if you're early right. So Yohei, right? Yohei built BabyAGI and now that went viral all around the world and it inspired so many people, me included, right? So you see something as possible and you're like, whoa, I didn't know that was possible. Like I want to get I want to try this. So the the builders also realize that the startups see that and they're like oh they're working on this. We're working on this. It would be cool if we teamed up. So a lot of startups come to us and we're like and they're like oh we're building an avatar technology. We'd love for you to try it out. So that's how we we kind of like use Reid AI as like kind of this magnet for people who are interested in avatars, people who are interested in agents, people who are interested in the concept of digital twins. And for me, it was just like I like Tron. I like science fiction. And it'd be very cool for us to like showcase this in a way that like reminds us of the movies and the video games, but actually is possible today with the technology that we have. And it's off-the-shelf technology. like it's not a like it's not you know the original Reid AI could could be made with like consumer grade applications state of you know like off the shelf regular subscriptions right so it was like here's what we can do with ChatGPT hour one and ElevenLabs now and then we've also done more advanced avatars where we actually go in and and we partner with u more you know like heavier grade avatar technology but it's really more like a bunch of related projects that are just like, "Oh, let's do another experiment. Oh, let's try this younger Reid interview." Like, he interviews his younger self. And so, we use Hedra or we do more of a real-time thing with with HeyGen. We're using HeyGen's real-time avatar right now. And that's a lot of fun because you know, every time we every month or every two months like some startup is like, "Oh, what if we teamed up and we tried this new thing?" And there's so many more capabilities of AI agents that we have yet to even tap into like vision for example or like doing more of a hologram lifesize. We've done a life-size hologram of him. so there's a lot of I think it's mostly for me it's like it's like how do you show people what agents are because you know agents are very like what are they? They're like invisible employees that just kind of like work. No, no, no. They can be also be characters, right? And so, we just like to you, we like to use Reid AI as a way to demonstrate the capabilities of language models in a way that is more interactive and intuitive and maybe like gets people inspired.

Glasp: Yeah. Yeah. Yeah. Definitely many startups are inspired by Reid AI for sure and you know Yeah. collaborate and come up with any new ideas. But it's one thing I never expected was that like how many I never expected it to be this popular like and like it you know it's been on the news it's been on many podcasts and now we have real time so it actually talks to people in real time when we we showcase it. It's gone on a speaking tour and I get messages on LinkedIn all the time. They're like, "Hey, it would be awesome if Reid AI could come and, judge our hackathon." And I'm like, "Yeah, I can judge your hackathon. Like, what am I the agent? What happened?" So, like, he gets a lot of invites to interesting events and I'm like, "Well, this is what happened." Like, I thought I thought I built an agent, but I feel like the agent now.

Parth: Yeah. Yeah. By the way, we're talking to real you, right? Not Parth AI.

Glasp: Oh, okay. Yeah, this is the year. This is the year I'm going to clone myself. I'm going to do something similar to that. I'm working on it. I think it's a lot of fun.

Parth: And my one of my theory, you know, I did it we did Reid AI for fun and I was because I was like, science fiction, this is cool. But now I'm like wait a minute, someone once asked me, she was like, "Oh, can I hire you?" And I was like, "I have a job." And then she's like, "Well, you cloned Reid, like what if you clone yourself? I pay to talk to your clone." And then I was like, "Wait a minute. If my clone can pay the bills, then I have to find out like that. That would be amazing, right? Like imagine I can just go to the beach and then play video games and like have fun and then the clone is just here doing all the work and thinking really hard." so I'm going to try it. Maybe it'll work, maybe it won't.

Glasp: But what if your clone goes to beach and start gaming for you? So you should work.

Parth: It might actually. I was talking to the so I'm working on like its knowledge base right now and so we really I have Claude Code and I talk to Claude Code and then it uses a graph RAG system under the hood and I say you ask me questions and then I'll talk and then you construct my knowledge graph and and it was like oh what games do you like and I was like hang on I go to my Steam I copy pasted all of my play history every single hour every single game and I pasted in these are the games that I play and now it's like oh I understand so it gets it gets a sense of my taste and when it asks me questions it's very it's much more like because it uses knowledge graphs to make the question much better because it's like oh you like you like Age of Empires you like Starcraft like tell me more about that and then I have this conversation with this AI and it's kind of like downloading some something like my philosophy right I hope I think and I don't think it's me I don't think you can actually like the more I offload to this the more I'm going to have fun and the more it can just wear the suit and be this like machine. I think it'll be fun. We'll see.

Glasp: And I'm always curious like creating AI clone like Reid is pretty obvious like it has social value also business value but so for people not like him, just ordinary people. Yeah. So what is social and business value and their use case in the future?

Parth: You know, it's funny. A lot of people ask me like, "What's the business?" Like, "What's the business?" And I'm like, "We were doing this because we are just having fun and exploring what's possible." Now, I'm starting to suspect there w there is a business. It's not validated yet. And actually, I don't think necessar necessarily that the video avatar is necessary. but I think that there is uh like Yohei, right? Well, okay. It's like Yohei, you have Yohei on and he has his unique perspective from his career and his his programming, his like everything that he does that's not inside GPT-5. Now, some people will say GPD5 is just more important than his perspective. I would I would disagree. I think that like especially with the knowledge cut off of the language models, they have this like they're kind of frozen in time a couple months behind humanity. And so people like us that are just making and living in the real world, we are learning and we have information. We do have insights from the real world that the models do not have. And that's your personal knowledge. I think that your personal knowledge is actually valuable. And I also think that it's not necess even if it's not necessarily clearly valuable to other people, which I think it is. Like I think if the if my friends could tap into my knowledge without me necessarily being there, I would love to offer that to them, right? Like people are always asking me, "Oh, they're like nowadays it's like par I'm having a hard time figuring out why GPT-5 is better than GPT-4." Now I can answer that question and I do that, right? like I'll like my best friends I'll answer that question but it I get that question a lot and I wonder like um and I don't have a blog the blog is not fully fleshed out but it would be nice if like they could get that answer without me having to be there and if it could be be at my level or even better than my level that would be nice and I think also for me my long-term memory is not as good as I wish it was but you know you can you can imagine your AI clone has perfect retrieval across 20 years of work, right? This is not hard to hard to engineer. So, you're kind of like, okay, there I think there is value even for yourself. I think the the most important value is for yourself. Like I talk to I talk to my own knowledge, right? I put a conversational assistant on top of my knowledge graph and then I talk to it and I feel like I'm bouncing ideas off of like a ghost in my like a version of me, like a spirit, right? And I think eventually maybe this is just a joke but probably is going to happen. Imagine like 100 200 years from now my great great grandkids are like about to make about to make a very stupid decision and then my digital twin my ghost comes back and it's like do not bring dishonor to the family. It's like it's like in Mulan, right? They send the ancestor spirits send Mushu to protect Mulan. I would like to do that if my, you know, if my great great grandkids need help. They It's like, oh, why don't you talk to your, you know, your the clone of your great great-grandfather? He might have some he might have some perspectives that people don't have, right? So, not everyone is like we're all different. And actually, I think that's why it's valuable. Like, if you're different from the rest of the people in the language model, you're definitely different from GPT-5. You have to I think that's valuable. I think that's valuable like you have a perspective right you have a unique life that's valuable that's by itself is valuable the question is do we make that perspective available to people and I would like to make that available to my friends for sure and maybe to strangers but most likely at least my friends should be able to access my my perspective even if I'm not there or maybe like maybe one day I get injured and I'm unable to like recall some of this there's so many I think that it is valuable I think right now it's weird weird but I think in the future it will be normal actually.

Glasp: Yeah definitely yeah that's exciting future and then use case so but when you are building AGI I mean having benchmark is like the how to say, the always I know a lot of startups are struggling with having benchmark and what metrics they should follow. So, did you have any metrics or numbers you your evaluation process to say oh this is really similar the response is maybe Reid would say oh you know similar to Reid yeah

Parth: so this is a continuous kind of thing first of all I think you're right every okay you cannot have an AI system you cannot reliably deploy it for anything serious unless you are evaluating it like unless you are giving it a grade on the things you want it to be good at you can't like you cannot improve what you don't measure. That's the age-old quote. If you if you're not measuring it, impossible to improve it. So now we have to measure conversational like accuracy. but that what does that mean? Right? So sometimes it's like retrieval. Okay, you ask a question and it is not in the context window of the model. It's in the knowledge base. So did the AI even retrieve the fact? Okay, that's one eval. Then the second eval is like did it interpret the fact? well enough to be you know as we would expect Reid to do so and on one hand you have like all the so it's like how do you create these these data sets I think one it's like you have all these Podcasts you have questions people are asking already to the real Reid and then you have you ask those same questions to the AI Reid and then you see okay it's like 60% there okay well now we need to know we need to improve prompt engineering or actually it's not even in the knowledge base we need to make the knowledge base more richer and this is a we're in the very beginning of this like this is when I say like internal tool is a good place to start because you have to build these evaluation criteria before you roll it out widely and also like there's a lot of human testing. So there my me and my teammates we we literally talk to this and we're like okay then there's evaluating the voice right we spend a lot of time just getting the voice to be like him and then you do like then all of a sudden you try the voice in Japanese and you're like wait a minute now we got to start all over like you it's like we want it to be good in different languages we want it to be good we want it to sound like him so then we have to like make sure we have the right raw data that we're using to train the clone and then we want it to say things that are in the vein of what he would he might say. And then also and this is character design. I think of this as this entire thing as character design because and I think it's up to the creator, right? In in Reid AI's case, you know, Reid, you know, Reid is like it shouldn't say I'm Reid Hoffman. It should just say I'm Reid AI. It should be transparent upfront. It's not like trying to deceive you, right? It's like this is a digital twin of Reid and it's not going to say I'm Reid Hoffman or if it does then I have to go and fix something because like that's an evaluation criteria, right? So when I say one of the questions that we ask it a thousand times is like what are you and then it's like you it's like 99% of the time it says I am Reid AI. I'm like good like you're not going to pretend to be Reid. But so we have a bank and it keeps growing the questions you ask it and then we also what what I started doing is I have like other I mean other LLMs are talking to him and those other LLMs are like personalities and so then they generate questions that that personality might ask. So it's like okay what might a Fortune 500 CEO ask Reid and then generate 15 questions and then you you plug that into the evaluation criteria you see the outputs and then you also have LLM you know judging that and then you also have humans judging that and we're in the very early phases but thanks to tools like Claude Code and um GPT-5 you can basically be like okay let's build an evaluation suite like this is the criteria we want to to to measure these are the types of things and then it just starts building this like tool for you to inspect the quality of the of the system and I think that like you have to build these evaluation things otherwise you won't be confident you can't and if you're not confident you're not going to put it in any serious application and but it's getting easier to do like build some of these like systems that allow us to build more trust definitely.

Glasp: Yeah. So do you remember the very first response of Reid AI? Was he satisfied with the response or was he surprised?

Parth: Oh, we once okay. I think it was like we asked it we asked it I think 10 questions and we asked Reid 10 question the same 10 questions and I think it was like five out of 10 were acceptable to him and his main feedback was like ah it's too much buzzword bingo it just uses too much of the like you know it sounds a little too ChatGPT but then I'm also like well Reid also uses a lot of big you know buzzwords for sure which is my feedback but it was it was very eye opening because it's Now you have a lot of the GPT the biases of like the underlying language model show up in the character and then it's like you can try to prompt engineer some of that you can try to do like few shot for like style and one thing I realized that was kind of helpful was in the prompt having examples from so I think of like the different sources of data so you have like podcasts you have books you have speeches you have um tweets etc like LinkedIn posts blog posts and actually they all have a different kind of purpose. I think the podcast on top of the fact that you get this like highquality voice audio that you can train a voice clone on which is great, right? Studio quality, no no noise, you know, I can feed this into ElevenLabs and clone myself at a pretty good like at least the voice level. But also what podcasts do which is different from books and blog posts and writing is conversational style. So, how you say something is often very different from how you write it because when you're writing, you're thinking about, you know, making it very structured and you're really compressing your idea into a form that's accessible. It's like more publishing. It's more editorial. You're spending a lot of time writing, but in conversation, it's more like you're it's closer to you in a stream of consciousness kind of way. So, I noticed that which was like, okay, if I have examples from the podcasts in the prompt, I can kind of like get, you know, iron out some of these like I can make it more conversationally like mimic his style. I'm sure if you want to go one step further, you could even fine-tune the model to get his voice like how he says something as opposed to like RAG I think of more as like what he says like what did he like what is the piece of information and then I think about fine-tuning and like the prompt engineering with examples as like how he said how he speaks right so a little bit like how it's like how he sounds the voice clone how he presents information which is more like style and then there's what he says which is more like the fact retrieval information retrieval and there's a whole extra layer like reasoning we can add right and I think about this like well what if like it's it feels not impossible that you could make a version of this that is like better at some of this stuff than even Reid imagine like something he an idea he had 25 years ago that he kind of forgot But the AI can retrieve it more quickly. So this is something that is interesting to me as we get more, you know, deeper into it and it's applicable outside of outside of this. I think this is just one example, but you can imagine like a lot of characters like this and similar and different video games, a lot of different possible. I think video games are going to see intelligent NPCs that are similar to this. I'm surprised we haven't seen it yet, but I think it makes sense because it might be the unit economics are not quite there. But I do believe that if you look at the games that people play, the games that I play, we love our, you know, the NPC characters in these games, imagine if they felt real, like very real. Even when you're playing Pokemon, like even if it doesn't speak English, like you get attached to this creature and it has this memory. I mean, it doesn't even have memory, but imagine you give it memory and then it'll feel more real, right? And I think that that's going to happen once these systems end up more inside entertainment and media um traditional entertainment and media.

Glasp: I see. And and I was curious the data set because you know you said you use podcast, book and you know like a public speak and so on. So but sometimes people change the perspective over time, right? Let's say if someone asks oh what's your thoughts on AI then early days oh I'm skeptical about AI but later they realize the value so they change oh AI is a future so in that case they have two opposite you know thoughts on the certain question so how did you I'm not sure it happened to lead and lead AI but if so how did you make sure the data is correct with his character

Parth: so I don't think it's even completely solved yet I have my own see I because I'm like a lonely developer. It's very much like I try to solve like as much as I can and then I understand that some things are not solved but eventually it will we may have time then we can get better at it. But for this my current interesting solution here is like like your perspective on something over time there's the latest perspective which is probably more relevant but the evolution of your perspective over time is a little bit beyond regular RAG. Like it's not like if you look at traditional like I call it naive RAG like top k similarity. let's go find the seven paragraphs that are most similar to the question that the user is asking and then assume the answer is in those seven paragraphs which is just like really it's naive and that's fine for like low stakes kind of stuff but then and this is something I've been using for like a year and a half is knowledge graphs specifically graph RAG so Microsoft graph RAG is the is my favorite framework for this but it basically is like Like some questions require multiple calls to the knowledge base and those questions they sometimes like for example you take Lord of the Rings like the book Lord of the Rings and if you say you have naive RAG you put the book you index the book and then you use a chatbot that has naive RAG so like custom GPTs and you ask what is the largest creature in this book like what happens is that the LLM is looking for the paragraphs that have text that is similar to the phrase largest creature and and it'll only look at top K. So it might look at seven, it might look at 20 depending on how many paragraphs you say it should look for. But if there are thousand creatures in this universe, how can you be confident in the answer to the question, what is the largest creature when it only looks at seven and it's like guessing like it's guessing basically. It's like, oh, I think it's probably this one because there's a mountain as a comparison of size. And then you're like, okay, that's not robust. That's just like you're lucky if it gets the answer right. And because actually that question requires like whole data set reasoning and you you need to know all the creatures to or at least like you need to know all the you need to know all the creatures to be able to ask that answer that question reliably. So I like graph RAG because you can you can basically take all this data and then construct essentially a Wikipedia of entities and relationships on top of that data and then when you ask a question that question gets split up into many queries and then it scans the graph and then you're like okay this is the largest creature and so you get a more reliable answer to any question that requires whole data set reasoning. It's not bulletproof, but it's the best idea I have so far for this kind of thing because it's like then you can see, oh, here's his perspective. Like, here's my perspective on this topic on on favorite favorite video game. But that changes over time. Like, well, back in the day it was Age of Empires, then it was like Pokemon, then it was like Roller Coaster Tycoon, Mario Brothers, now it's like Cyberpunk 2077. But it needs to kind of get that big whole picture to get a sense of like that evolution in preference over time. So, I like Graph RAG for this. and there's a trade-off. You takes like 20 seconds and like sometimes a dollar to answer a question and you're like, but I think then you can take that answer and you can put that into the naive RAG solution, right? So you can use graph RAG to create a richer u fast retrieval system.

Glasp: I see. You get this like new synthetic data that represents the answer, but like it's like, oh, we get this question a lot. Well, let's ask the full knowledge base. Even though that's not good for conversational speed, we can still take that and give it to the the fast retrieval system that we use for conversational speed. So that's my I mean this is just like my this is just like my me hacking through the this is vibe coding like this is me vibe coding and so like I would love if people have better answers to this like temporal change across large data sets like I would love to hear about that maybe like in the comments or whatever.

Parth: Yeah, we would love to learn that as well. Yeah, that's a great question. That's a great question. Yeah, thank you.

Glasp: Is Reid's character in public is different from his character in private office. And even though making a clone with public data but so if you fine tune with internally so maybe the character will be different. So it's not a problem or okay so how do you manage?

Parth: Yeah, the first version that I built was off of public data and the custom GT, right? I had his books and then his podcasts are available, but it gets a lot better. This is why I think like where it's like who's going to clone you. The person that can clone you best is going to be you because you have the primary sources, the best quality primary sources. And also like sometimes that information isn't even out there and then you just like you ask me 10 questions and I'm like oh here are my answers. Well, now we have a new piece of data that doesn't exist on the internet. We put it into this character. Now all of a sudden that is actually a unique like a unique proposition of this character is that its memory and knowledge is actually like based on some private data or like non-public data which might be more important in some ways than than everything that's publicly out there. And I think it's actually in my voice clone like my agent that I'm working on. I have my preferences for technology stack are in there because I had it look at a bunch of projects I've been working on for the last two years. I was like just read all my code and think about these technologies and put it in the knowledge graph like like what this is the kind of stuff that I like to play with and yes it's biased. It's my bias. That's actually the point, right? like it's my perspective is biased and I want that to be in this thing and while like chat will give you one answer this thing is going to give you a totally different answer because it is grounded in your perspective and I think that like yeah that's why I think like you should make your no one else is going to replace you you're going to replace yourself if you want to and actually even if you tried to do that you would realize that you are much more than what you thought like you you have this like expanding sense of self when you try to replace yourself. You're like, "Oh, I'm not just a data analyst. Actually, I have so many other things about me that I'm interested in that I like to think about as part of my identity." So, it's interesting. It's very it's like kind of a an expansive sense of self that happens when you try to do these things.

Glasp: Yeah. Yeah. And I'm a little bit afraid of the future where people are asking hey agent you know my life successful life and leave it for me or something like that. It won't happen.

Parth: So I mean it won't happen. Well you can choose to why would you give the fun things away like you go you could give the things that you don't want to do away. I think that's fine. Um, but sometimes I'm like, damn, if this thing could just do my job, but it just doesn't, right? Like we're not there yet. And I'm kind of just like, well, I'm gonna have a job for a while. but also I think it's like you I like to automate the things I don't want to do. And then the things I do want to do or if I have a very strong opinion, it's like, oh, this is a quality. This is my opinion what high quality looks like. And I don't think that these systems yet have that quality bar. They just don't they're not good at like we have that quality bar because we are in the real world. So I listen to a lot of music. I go to a lot of concerts. So I have my opinion on what highquality music is. And that's my opinion and I would not give that to an AI. Like I don't think an AI can do that because it's like can an AI get my opinions on this better than everyone else? Like no. Maybe it'll get like maybe I like to see it. But it's also like why would I give that up? Like it's like my taste in food, right? Like this is not something that I'm so eager to give away. Like I I think I'm going to keep all the most fun things for myself.

Glasp: Yeah, for sure. Yeah, definitely. And now you have worked with Reid Hogman closely and I'm curious did your impression change? I mean before you meet and work with Reid and you had an impression I think on him then now you have worked with him. Did your impression on him change and also what's the biggest lesson you learned from working with Reid if you could share?

Parth: Yeah, I read his books growing up and I listened to Masters of Scale the podcast when I was in college and no when I was in yeah when I was interning at my first startup and for me it was like gave me the confidence to go into startup because he's very much like he loves games board games and strategy kind of games and I also you know growing up love strategy games so when he was like oh the theory of the game like how do you he was kind of like using analogies to games as a way to make startup uncertainty easier to navigate, right? So, and I think for me it's like I love games and I think one time one way to kind of like games are fun because you get to be competitive but it's not going to it's not like life or death, right? So, we get to explore our personalities, how we work well with each other, how we compete with each other, what our strengths and weaknesses are through games. And then the it also just gives us sometimes it's like we're let's say I introduce us to a new board game. All of us are learning the new game at the same time. So some people learn more quickly. Sometimes you think oh this reminds me of that other game. This reminds me of that mechanic. And so you kind of are trying to pattern match to learn how to get good at a new game. That actually is very relevant in startups because I think in startup it's very much it's all new games. everything is a new game and you might be the first person or first company or first team to be attacking a problem and you're kind of like you have to form a theory of the game that you're in and that's what that's a common Reid phrase is like what's their theory of the game like how are they thinking about this competition how are they thinking about this ecosystem of problems and so I mean it was pretty incredible meeting him in person I was like oh wow that's him like that is this is how he kind of is like the most like it's very much the same guy that you you see. And I was like, "Okay, that's crazy." biggest thing I've learned, I think, is like this experimental like mindset of like we should just be figuring things out. Like he loves to experiment. this Reid AI, right? Like I remember after it went viral, I was meeting I was talking to him. I was like, "Man, I saw Reid AI on CNBC yesterday. What is happening?" And he's like, "Hey, you know, when you're like eventually maybe everyone will have something like this, but when you're the first to do something, you kind of get a dis you get a lot of attention, right? Because now it's same thing. Yay Nakajima, BabyAGI, perfect example of this. Everyone's building LLM rappers now." But BabyAGI was like it got all of this attention and momentum because he put it out there first even though it was not complete. Like it wasn't like it didn't work end to end. I remember I was using I was like wait it can't do everything. I was like obviously it can't do everything but that's crazy like people love it even though it's not perfect yet because of what it inspires them is possible. And so that was a huge lesson was like you know if you if you're first to something like there are advantages to being first sim similar like this philosophy of blitz scaling right if you think there's winner take all market like you have a different way to play the game you should play the game differently if you think there's a chance you could win it all. So that these kinds of like these kinds of like philosophies are and that's the other piece that I think I totally underestimated my whole career. Reid is a philosopher and I'm more of just like a hacker kind of programmer like you know tech tech guy but I realize now the value of philosophy much more deeply and I remember at the very beginning of this but he's always quoting philosophers Vitkinstein just like people I'm like who are these people like these they died 200 years ago why are we talking about them but actually like it's very relevant because things like language and the importance of language and language and cognition how you think how you speak how You're right. All of this is actually very important now. And a lot of the people who were thinking about this, the great thinkers of the past kind of were running into these ideas before they were relevant to technology. Especially now that we have programs that can emulate thought, right? How you think? And then if I think about philosophy, it's like do you like how well do you understand people? Because technology is not just like it's not just we don't just build machines for the sake of building machines. We build machines to solve problems that people have. So you have to actually understand people and like have deep empathy. Try to understand like what their you know what do people actually care about? What do they want? What are their desires? And that gives you a better lens with which to kind of build technology I think. Yeah. Yeah.

Glasp: Definitely making something people want and that's why she always say and that's right. Yeah. But you said you know like a fast to be the market is important but do you have any idea something it's unexplored yet but could have a huge potential I mean could be a

Parth: there's so many there's so many now right like I feel like I have these ideas every day honestly the question is what do you actually spend your time on but I think if you look at like just look at any new capability like let's take language models like this my bread and butter is language models Okay, now we have language models. Okay, what like make a list of all the things that you could not do two years like three years ago that language models let you do and then it's like well three plus years ago anyone that had messy data could not do data analysis. But one of the most powerful capabilities of language models is the structured outputs. the ability to take unstructured data and turn that into structured data, right? Because when you take unstructured data and turn it into structured data, you're able to connect it to traditional software, which traditional software expects structure. language models, they seem like this like random kind of black box, but actually you take like the like like if you take my bio and then you say extract every single company he's worked at and then like extract the title like his title as he self-defes it. Now you can put that into a CRM, right? But that might be unstructured at first. It might be the output of a deep research report, but then the LLM allows you to extract structure from that. I think this is the one of the most powerful capabilities of language models and I think people I think people a lot of people are just ignoring it. and that was one of the first things that actually I was working with before agents was structured outputs. Just trying to get the model to do JSON like that that I spent a month working with Microsoft research some library. I was like Microsoft guidance. I like it. It allows me to go from a blob of text into like fixed JSON and and I think people were just like whatever. And I'm like, "No, no, this is actually probably one of the most important things of all time." But you know, you kind of have to be a little like in the weeds to to realize it. But the implications like, "Okay, what's the second order effect here?" Well, one, it means that everyone that complains about not having clean data can go use LLMs to create a clean data set that represents the business logic. That's awesome. So now you can do like data analysis even though you have messy data. And then the second thing is like people do this with like transcripts right like the meeting transcript and they feed it into the LLM and then it's like well these are the action items that that's powerful right now we can just quickly move to the next thing. Same thing when you ask a question to the model it uses structured outputs to write the search query for the search engine to process. So it's happening under the hood how the model connects to the rest of the software. And then I think the one thing that this does that is still completely ignored by most people is generative UI, right? Like being able to and and people talk about is like, "Oh, we don't want like you shouldn't since people complain about vibe coding is like they're like at its limit generative UI is unfamiliar and then people won't build familiarity and patterns." But I think there's like an a middle ground here which is like what if you're talking to Reid AI and you know he's like oh I had this conversation last week like Reid had this conversation last week with Sethia Nadella and then the video comes up that that's like an application of generative UI right so within the constraints of the structured output the UI that we give it it is able to fill it in with the generative piece so I have a feeling that this is like largely untapped very important to me like user interfaces that are more just in time like the right user interface at the moment that you need is actually very interesting to me and like kind of not really explored. I really like graph RAG. I love the advanced retrieval stuff but and I think the applications are more like I think the applications of some of those systems is farther further reaching than people believe. But I think you have other people that are just so scale pilled that are just like ah we don't need this because eventually GPT7 infinite token context whatever and I'm like yeah okay fine but like until we have that like this is kind of cool like this allows me to create new artifacts in the meantime and so I like like yeah I think because memory will probably get I mean will memory will get solved but it's not solved knowledge structured knowledge is valuable in my opinion because there's not a Wikipedia page for everything on the planet. Like there's plenty of things that don't have a Wik I don't have a Wikipedia page like so but but like LLMs could make that now I it wouldn't be called Wikipedia because now it's not human review. It's not like humans wrote it, humans reviewed it. It's a different thing but that synthetic thing might actually be very useful. so I think there's a lot of artifacts that these models can create that will be valuable to us in ways that we can't expect. Let's see. Yeah, I think generative games. Generative games. That's an area that I'm like very excited and it's hasn't yet happened. It's like a genie 3. I think there's two versions of this that I'm very excited about. So one is the genie 3 kind of etched. they did this with Minecraft too which is like the model renders the whole game on the fly and then you interact with it but it's like diffusion model just like the game is just totally like dreamed on the fly. I think that's exciting. Uh I also am more interested in like like that's cool, but I think there's like generative games more like where uh like closer to Dungeons and Dragons. I don't know if you've ever played dun if you're familiar with Dungeons and Dragons, but it's like a game where you speak. You're kind of like talking. You're pretending to be a character and you talk and then the someone is conducting the game and then the game unfolds around you in conversation, but it's really more like improv. It's more like a conversation with friends. You're pretending to be characters. I think there's something there because there's that even can be automated using LLMs. And so like if that experience can be made easier then it can be made more fun and also it can be made more accessible. So it doesn't have to just be fantasy. It doesn't have to be just dragons and and witches and it can it can be anything. And so there's and then I think that applies to a lot of genres of games. So it's like so maybe the diffusion games are like where it ends up, but there are other things that we can do until then that like if you think about Pokemon and Yu-Gi-Oh, what does the generative version of Yu-Gi-Oh look like? I think about Yu-Gi-Oh a lot because it's like in the TV show, I don't know if you guys remember Yu-Gi-Oh, but like in the TV show, everyone has their own deck and like Kaiba has Blue Eyes White Dragon and then Yugi has Dark Magician and it's like this is my signature card. And I'm like, man, what if we had a card game where everyone had their own signature deck and it was just you had your deck, I have my deck, and maybe this is what NFTs was supposed to be, but like now it's possible, right? Like generative AI should make that possible. So I'm kind of like this feels obvious. Is it big idea? Who knows? So I'm kind of like, well, let's just go hack some version of it and then like if people are having fun, maybe we see what we see where it goes.

Glasp: Yeah. Very interesting. Yeah. But so when you mention about Wikipedia, I came up with a random question, but do you think in the future do we need Wikipedia to be honest? Because we have access to them. I was just curious about that.

Parth: I think we're going to need we're going to want some things where it's like humans. I think there's going to be humans and AIs. There's going to be a version of Wikipedia that's probably way bigger, which is made by humans with AIs. And I think that the feel the I'm a little bit like I think we need something because what's going to happen is it's already happening which is like there's going to be a lot of synthetic stuff out there and then it's like how do you tell what's real? What's what do we believe that's like fact and this is a hard problem and some people think that crypto is part of the solution maybe. I don't know. but if the internet ends up filled with a bunch of fake information, fake fake data, we're going to have like a lot of side effects. I think one of the side effects is that, if social media platforms keep getting filled with AI, we will have our group chats which are purely human. Maybe like we're going to have places we go where we only want to be with people and maybe in the real world, right? Because that's where it's easier to be. It's obviously like, yeah, you're real. I'm real, right? But like if it's Twitter, it's too easy for Twitter to be filled with bots. It's too easy for that to dilute the experience and then people lose trust in their information. And if the trust in information goes down, that's going to be really like it's one of the biggest problems that is not solved. And then I think like Wikipedia is Wikipedia this answer? I don't know. Is it like a version of Wikipedia that's built with AI, but like there's some kind of like verification system? biology talks about crypto as being a possible solution here. I'm like, hey, we need to try these things and like figure it out because the problem is only growing. But I think like every problem that AI creates also will create billion-dollar solutions like like AI is part of the solution in many of these in these problems. It's a problem of we need a system with the right incentive structure to preserve the values we we care about. So I think of it as like okay if that's the chaotic world we are headed towards how would we get a network of people where we actually agree on the values and we orient ourselves towards like playing a certain way so that we get better information streams maybe that's group chats maybe that's like a return to like the physical world right so totally like I don't think I have the answer but I'm optimistic that we will figure it out I think technology is a huge part of the solution in most to these cases as well. Same thing with cyber security, right? If vibecoded apps are vulnerable, then also AI is going to be a part of the solution. Like AI hacking your website and then telling you before in instead of telling like a bad actor, that's going to be I would pay for an I would pay for AI to hack my stuff and tell me and not tell anyone, you know.

Glasp: Yeah, definitely. Yes. So, yeah, that reminded me of the early days of Wikipedia. So when I was current student, high school student, I still remember teachers always say oh you know the trust in information of Wikipedia was way lower than I think current session today as we have today. So but now people say oh AI harass so we can't trust AI but people hallucinate but I think yeah that's I see the interesting how to say psychical change over time.

Parth: Yeah information we should trust in yeah sorry I feel the same way. I feel the same way. And then I think, man, there's so many topics I care about. And then there's no Wikipedia page. And I'm like, okay, I would rather there be an AI Wikipedia page at least. And then and then the people who care, we can go be like, whoa, whoa, whoa, whoa, whoa. Like, maybe it's human review. Maybe it's definitely going to be AI part of the review process. But like I think it's better than there not being a page, but maybe Wikipedia maybe won't do this. And like maybe like you and I like we might just make these like synthetic wikis. I do this a lot of times with graph RAG actually. I just make a bunch of wikis for my own favorite topics that do not have do not have pages and I'm like okay like it's fine I'll make my own. Me and my LLM will make my own for the games that I play for example, right? Like they may not have a wiki for that game and then I'm like oh we'll just I'll read the docs and then we'll have graph RAG go and like index everything. At least now I have a wiki. Is it accurate? Well, the alternative is it doesn't exist and I can see that the primary sources. So like same thing with Wikipedia. You can site Wikipedia but like you better look at the source that it's linking to because even that like people who write they they put stuff on there. It's not even like like they they're making the source and then they're putting it on there and there's like so there's abuse vectors even in human Wikipedia. So yeah it's going to be an interesting one but I think we're going to have some synthetic version of this. Yeah. Yeah. And I remember someone said you know if we show a citation the answer and people without checking it most people trust it but when we check you know that that information is generated by AI or someone you know it's not correct and they site another information then citing sighting citing the wrong information over time. So yeah, it's kind of

Parth: it's garbage in garbage out and then multiple layers of garbage and then you're like what are we doing? that's a but if you have a citation in answer we assume it lends credibility. Yeah. Yeah. Yeah. That's an issue. Yeah it is an issue. It is an issue. um So maybe reasoning helps with this. Maybe reasoning helps with this. But is it depends on how many how many layers deep does it go, right? Like if it's all AI generated citing AI generated then where what is real this is a huge question what is real and then we also have this problem in video but the video models are so good that you can't it's not a like two years ago it was like oh yeah six fingers it's clearly AI now it's like the bunnies are hopping on a trampoline and I'm like it looks real to me you know and then a week later Google is like that was Google Gemini and I'm My thing is like on video I'm like why don't we just generate things that are obviously not real first instead of recreating reality which is impressive but like deceptive or like don't do it for deceptive reasons like make some other you can generate any universe the video model is like an advanced simulation you can generate a candy universe no one's going to look at the candy universe and be like that is real and actually Maybe that's my preferred way to go is like, "Oh, let's do animated. Let's try some other style where like I'm not trying to deceive you here with like something that is pretending to be real." Of course, I worked on Reid AI, but he says he's at Reid AI, right? Like this is part of the design choice is like even if it would deceive you if you like it's like it's going to trick your grandma two years ago, now it's going to trick you because there's no skill in identifying whether it's AI. You're kind of just like you scroll through the comments and you're like hopefully this is real. And then it's like this is AI. This is AI. And then you it's like how do you trust it? Even if the comment is written by an AI, right? So it's a these are these are the I knew we would get here. I didn't think we would get here so quickly is for sure. Yeah. It's a crazy days we live in. Crazy days. And I was thinking about this idea. I mean to train humans to distinguish if it's AI or not. So let's say in early days we are kind of easier to get tricked by spams you know like oh you were chosen you have the right to get $1 million you know from I don't know like king or someone this is just totally email the email you get the email spam yeah you you get but now you right away distinguish oh this is fun but early days some people got tricked right so but no but now the spam can be generated by the LLM and you can't tell yeah yeah that that's right If we have a platform that teach people or hey show let's say two videos or two pictures two text or something then if they say oh this is spam or this is AI generated or this is human created then then we can train oh actually this is generated by AI so then so that we can like get used to the things that generated by AI or I don't think this is going to be possible and it's just like I don't think it's possible I mean look at V3, right? Um I can generate something in V3 and and like I mean I I did this with runway gen 3 runway and I made a flux I I created a Laura of myself. So I had an image model flux one and I fine-tuned a Laura on my face and then I made a music video. This isn't I did not put it publicly on my YouTube channel. It's a private video. Maybe I'll put it public after this now because it's like now it's it's like obvious. I'll just say it's totally AI. But I didn't put it up because I was like, "Oh my god." Because I showed it to my brother and I was like, "It's me in 15 different universes in the 1920s, me and going to space." And my brother's like, "The only reason I can tell that this isn't you is because I know you've never been to the moon." And I was like, "But otherwise it looks like I'm on the moon." And I'm like, okay, there's no skill in discerning this, right? For the person that doesn't know me, it's like, oh, you must have been in a movie about the moon. And I'm like, it's I mean, this is a movie that I made. It's like a little two-minute clip of me in different universes. But I didn't make it public cuz I was like, actually, like I I like I'm not sure like how I want to deal with this yet, like because it looks so real. And my brother was like, "No one can tell. Like it looks like you. I I grew up with you. It looks like you." So everyone else for all intents and purposes will think it is you unless you tell them it's not you. And so I think that um and and then it's like that's a lot of work like training. And also the models are just getting so good. The models are already at a point where I don't think you could train me to tell the difference. If someone wanted to make something that you could not tell was real, they're going to be able to do it. So then we need systems of like um the word is providence. systems of providence like systems of like this. We need new systems where we can ascertain like collectively like this is a network of of of content that is real and then actually everything else you have to assume it's fake. And unless it can prove that it's real somehow because of the new system we create which doesn't exist yet but people are trying to make it. You will just deny everything. Like you're just going to have to like it's like oh don't believe everything you read on the internet. Don't believe everything that you see is real on the internet. Isn't it a sad Isn't it a kind of dystopia? It is. It is. As long as we don't solve it, it's going to feel bleak. I Yeah. Yeah. Yeah. But on the flip side, whoever solves it is going to become a billionaire. Yes. So there's an opportunity, right? Yeah. Like Yeah. Maybe or maybe they'll make it like they'll do it just for the good. I don't know. We'll see. But people are working on it and I think it's a it's a it's probably one of the most important problems um that AI creates. AI creates some problems. It it's going to solve a lot of problems. It's going to create these problems and it's going to be a part of the solution. And then you have even crypto people are like, "Oh, we should make it so that if it's real, it must be verifiably on this blockchain." And I'm like, "Hey, man, if you can figure out how to get everyone to agree to that system, maybe you know." Interesting. Yeah. Yeah. And so, so what's next uh at the office of lead? Can you share? Oh, I don't know if you can share, but do you have any um future AA project coming out? Well, so I spent the last week, last Wednesday, I spent the whole day talking to 20B GPT OSS. um just like my personal project, right? Like I spent the whole day talking to GPT OSS and I was like, "Okay, wow." Like this model is very efficient in in terms of like it runs on a Mac Mini at 30 tokens per second and it's completely local. And I'm like, "Wow, this is crazy." And so now I have a new laptop coming and I'm going to be playing with the 120 uh billion parameter variant. So I'm going to be working on some local agents just to understand like I think I've kind of been mostly in the you know using the API and like using these um frontier models which you're kind of renting your intelligence but you know a lot of other people of course have been in open source this whole time building local models but I think the local models will be um agentic and you're going to have these this kind of like like local running agent smaller in scope but like useful for what it needs. needs to be. My dad always tells me he's like, "Parth, the fridge does not need super intelligence." So he he always says that he's like there is a version of this that doesn't need to be like trained on the entire internet for it to be useful. And and because he comes from like the last era of computers, right? So he thinks about like like he thinks about like the cloud, he thinks about like onrem, he thinks about a lot of this stuff and he's like you get every you get a lot of different sizes of uh of these things and you get the biggest ones. Yeah. It's going to be on the internet. It's the biggest smartest models are going to be the ones you rent. But also like you're going to have useful small models. I I think I think about my Roomba and it's like this Roomba is so behind. It's not even GBT1, right? Like it gets stuck in the corner and then I'm like oh my god. My cats are like this thing is not smart and I'm like this is but imagine a world where like the devices are actually intelligent because they have intelligent locally running small models. That's going to be interesting. um in terms of like so that was like an exploration from last week and then last couple days I've spent most of most of my time working with GPD5 and that has made a lot of my a lot of our projects internally a lot faster like there are things that you can make now in 3 hours that used to take me weeks and um I think the combination of claude code GPD5 and honestly me just getting better at using them like I think a huge thing is like adapting yourself um switching the way you work to being more agent friendly how you orchestrate these tools um you know you start by using one when you get good at using one can you use multiple and then when you use multiple can they work together on things that are even more advanced I think we're getting there it's very human in loop it's not like end to end automation is getting better at longunning tasks better at end toend automation on for you know better at zero shot but the the speed at which you can build tools is accelerating and I think we have some tools that um that I'm working on that are made possible by GPD5. Like yesterday I was like wow GPD5 built this like pipeline in in in two hours that I was going to spend a lot of time on but now I'm like wait we can go much bigger on this in a shorter time frame and I'll have more to share. We you know we're you know we always try to share how we do things like we we try to create content. Oh, here's how we made Reid AI. So, I think we're going to have some some more stuff like that that people can kind of like get the playbook, but really it comes down to just like play with the tools and then like use them and get really good at using them and then and then um share what you're learning with people and then that kind of is like a good feedback cycle.

Glasp: Yeah, definitely. And before you know start we starting this recording. So we talked about emo capture that's the idea we hacked you know during the last weekend weekend and actually I used GP5 and it worked in a single try but when I used uh code opus 4 4.1 or sonet it didn't work in a single try. So I was surprised by the the capability of GB5 and that's this is how I'm feeling right now man.

Parth: Like I'm like, "Oh my god." Cuz I call it like oneshot. Like, "Oh, did it oneshot the problem?" I'm like, "Oh my god." Because if it can oneshot the problem, then you're like, "We got to think bigger." Like we got to do way more stuff. We got to try more things because like turns out like this is not going to take a week. It's the first version might only take two or three prompts. And if that's the case, then like we need to be more ambitious. We need to be more creative because the models are really getting to this level of like code generation has never been this good. And now we have the CLI tools that are getting better, the the wrappers around them that are making them even faster and more reliable. The tools MCP is great. MCP is also a little bit you got to be careful, but it is very good. And then and I agree. I think what can the model oneshot? I think I'm like I look forward for me um my AGI test when it's like is it AGI? My test is I'm going to just go to the model and say clone clubhouse and then that's it. Can it build clubhouse in one shot? Take your time, maybe take two hours. I'm going to go for a walk and I come back and it better be an application that I can play with. But that mindset of experimentation is important because I think like you think about big companies and they sit around and it's like we should make this and then they write some document on like what the feature is going to be and then like six months of like planning and like building and I'm like no no no no no we're going to sit here replet is going to be here and we're going to tell it to make this thing and then we're going to see how far we get in this meeting and then that updates our own priors. We're like, "Okay, cool." Turns out we can get this far in two prompts, in three prompts. It's like really like there should be a hackathon where it's like you get 10 prompts and then whatever you submit at the end of that, that's the submission. Like how far do you go in 10 prompts? Like how far do you go in one prompt? Like that should be a hackathon category, right? Because then you start thinking about um Dexter Dexter Horthy, he he said he's like context engineering, right? Like the new phrase, which is true. It's like are you bringing the model? are you bringing the right context into the context window and then you can be much more efficient with what you get on that first try and I think it's it's awesome to hear that you're doing that and that that is a great story of GP5 one shot because I think there's so many more of these like we don't even know what it can do that quickly unless we try it. So that's why I say like don't judge a model until you've talked to it like unless until you prompted it a hundred times. Then maybe you can judge the model. But there are so many things we don't know yet that we will figure out just by playing with this and talking to each other and sharing what's working, you know.

Glasp: Yeah. I'm looking forward to joining prompt someday. This prompt. Yeah. Because because at the hackathon I know some people bring their startup project you know or pre-built project you know to hackathon and they win the prize and it's kind of unfair for some people who really build you know the project during the hackathon right yeah but prompt song you can't cheat it right you bring a prompt you got one box one prompt one attempt no thinking aloud no planning around it yeah it's funny it's fun yeah it's

Parth: But it's actually an important exercise. Even if you don't ship the application, it's an important exercise because when someone tells me, "Oh, it's going to take six months." I'm like, "What? Why is it going to take 6 months?" Like, is it because you need to meet actual people? Like, is there what is the bottleneck? If it's actually just software, we need to move way faster. If it's like hardware, if it's in the real world, if it's like brickandmortar stores, I get it. There's more work to do, relationships, human relationships. But if it's just like pure software, we should be going very fast. And we should be learning very quickly.

Glasp: Yeah. Yeah. And and this is a just a general question, but so where's a source of information and ideas? So what kind of source like on like X, LinkedIn, YouTube? What do you see and how do you correct ideas? Yeah.

Parth: Um, so I I I have like nine, no, I have 10 messaging apps and and I so for me it's like 10 messaging apps. So like people are always DMing me. They're like, "Oh, did you see this? Did you see that?" And I don't try everything, but I'm like, "Oh, interesting." But you have to have like your own network. You have to have a lot of people that are doing different things and in AI like or in technology or just creative, right? People who are creative and different. and then they you have to bounce ideas off of them. I think the the network intelligence is the most important thing. Um Twitter is good. I think that there is a the problem there are problems with Twitter. I think it can be kind of a time sync and also I think people tend to they tend to pay attention to some of the wrong things and they go deep in some of the wrong directions in my opinion. Um but it's good because you get a good feed. But I think that like I have a you know I have like my own all my favorite hackers. is I put them all into a Discord and then we have our own like oh here's what I'm making here's what I'm trying like oh and then it's like oh like someone might be hiring and it's like oh looking for an so there's opportunities and I think once you start networking the people who are uh you know really playing with the technology then the network intelligence benefits the whole group and that's important and it goes back to like what is real what is false like you got to create these like spaces where you have higher trust and you curate people so that it's like oh these are good people they are helpful they're generous with their information with their with their advice and and time and then they're also learning and by making things right so I think for me personally it's like I don't want to talk to people that don't make things that's just like I'm kind of getting biased like this now it's maybe it's a bad thing but like I prefer spending time talking to builders like it's like oh you made this yesterday I was like that's the kind of person I want to talk to because like that's the experimentation we need and also there's just too much there's too much happening right so no one person can keep up with all of this progress in AI um alone I I think it's singularity for me the definition of singularity is when information starts like technology starts advancing so quickly that you cannot keep up unless you're using AI and friends to keep up right like you have people that are covering different topic areas so I have friends that are just like really good at working with open source models and I just like learned from them. I just, you know, ask them dumb questions and you need to ask dumb questions like non-judgmentally just ask questions. No one is an expert in everything, but everyone can get very, you know, deep into a few things. And then when you share that, then the whole group benefits and then you get the interesting intersections, right? So I'll be like, "Oh, the voice, you know, the ElevenLabs V3 is actually very good for emotional control." And then my friend who's more of like a creative artist, designer, he's like, "We should do like a Dungeons and Dragons kind of experiment where we have LLMs." And then I'm like, "Oh, we can use the OpenAI agents SDK." And he's like, "And we can use these image models." Right? So the combination of two people that are actually very different because they're focused on different things, but if they agree that there's a cool interesting idea, that is actually the magic. The magic is the intersection of your intelligence and my intelligence. um So I don't think and I think also, yeah, I mean I do think I generate a lot of ideas. um But the the the consuming a lot of like having friends that are different is for me the most important thing cuz then I'm like, "Oh, what's your favorite LLM?" Like today like my friend's like, "Part, you're the only person I know that likes GPD5." And I'm like, "Wow." Then I have to like explain why I like GPD5 because otherwise he's going to assume that like maybe it's completely useless based on everyone else he's talking to. Maybe I'm wrong. Maybe I'm like maybe I am wrong. But but um that's the kind of thing, right? Like if I don't ask him why, it's like, well, what model are you using? Okay, Quen, why? What's your use case? Okay, interesting. Well, that makes sense then. Like why? It's better for your use case, worse for mine. But unless you're talking to people, and I mean like talking to people, like not Twitter. Twitter is not social. Twitter is like people kind of just blast stuff out there, but a dialogue is actually very powerful. So, I also use Clubhouse still. um Not a lot of people are on there, but for me, a lot of my friends are there and we just have conversations once a week. We're like, "Oh, this is very interesting. This is very useful. This is how I'm using it." And, you know, for 3 hours, a conversation like that ends up being very helpful to stay up to date with things and also to just get more ideas in the mix. And you have to have a lot of different perspectives. Like, I go to meet my friends in different cities because I want to I want to think differently. I live in LA instead of San Francisco because I like all the creative people here and uh they're just totally they're not in technology but for them they're like they're so interesting and creative. So when I ask them oh like what like learn more about what they're building I get so many so much inspiration. So you have to have people that are very different from you. I think this is network intelligence is key. AI is going to help but like network intelligence curate your network. choose interesting, you know, hardworking, creative, generous people.

Glasp: Yeah. Yeah. I like the word network intelligence and that's a great word. And also that's also a Reid concept. I mean the numbers guy, but now it's more important than ever because you know you have AI, I have AI. So like both of us are amplified, but now when we come together, we're even more amplified.

Parth: Yeah. We can come up with new ideas and then see things from different perspectives. Otherwise, you know, you can It feels lonely and you can feel like you're going a little crazy, but you that's why you need you need people and conversations.

Glasp: Yeah. Yeah. And that's why I love going to hackathons because I can meet a lot of great interesting creative developers trying or tackling some interesting ideas from different approaches. And yeah. Yeah.

Parth: Hackathons are I I recommend that for a lot of people like how do you get started? go to a hackathon and then you're going to be like you're going to realize like we can go fast, you can make things for fun, you're going to learn quickly and that people are using the state-of-the-art tools and you're like wow and and there's no experts, right? Like everyone's just learning. So, it's good to go to that environment where it's okay to be like just good. It's good to I went to a hackathon like two years ago and my mindset is like now I just I never left the hackathon. I'm still in the hackathon mode every single day now, right? So it's a very like I think it's a good exploratory uh set of like way of thinking and and making.

Glasp: Yeah. Yeah. And I have a designer friend and he I used to be asking him hey let's go to you know hakan together but he hesitated because he's designer he thought he couldn't contribute to coding or something but nowadays he can use AI tools to code. So now he asked me hey next time to go you you go to hackon please ask me. So I'm a designer but I can use AI tools now. So I I can code and build something.

Parth: Yeah. Yeah. Yeah. We I did the same thing. I brought my designers to hackathons. In LA we have hackathons, but our hackathons are like video model creative hackathons. It's like you're going to go, you get paired with three random people and then it's like make a music video in three hours, whatever tool you want. And so for us, the hackathons here are more like in the media side of things. Okay, what kind of story can we tell? This tool lets you do like, you know, style transfer. this tool like V3 plus like midjourney plus so you you people have they all have their own favorite tools and then you come together and you're like wow we can now make this new thing so here our hack we have technical hackathons too but I think our creative create even creative hackathons very useful right storytelling and just like learning how to use these tools because there's no like textbook there's no experts so the people who are the experts are those people right yeah definitely Yes. Yeah. We are living in the kind of golden age. Renaissance. Yeah. Renance.

Glasp: Yeah. One more question. So when you make you know your AI clone or AI you know digital clone so you need to have like your data set right? But yeah according to our conversation you talk to people on clubhouse which doesn't have transcript or you talk to people on discord which usually you know data will be deleted or removed eventually. So how do you keep you know your ideas also do you have any tips to you know save your real time data for your AI agent?

Parth: I don't do that level of um collecting data. I think and I think it's not going to be perfect copy because of that. But I think the goal is a little bit more like I don't need it to be a perfect copy of me. I'm I'm me. I need it to be like a like a like a different like a like a different kind of like I don't care that it's not exactly like me. I care that it's helpful to me and I think there are actually it's good to have places where things are not recorded because then people feel free to speak. Not everyone like we we're recording this conversation but I spend so much time just being authentically myself that I don't I'm like I'm pretty much you know recorded or not but not everyone's like that. Sometimes you put the recording button on and now people don't want to speak. And I think that's like do you want people to speak more? It's more important that we speak than for it to be recorded. And um so I'm not a fan of like auto always on recording. Uh I do use granola sometimes like but that's like you know double opt-in on the meeting transcription. That's more for work. It's like okay did I make sure I get everything? Um so that's different. But um if we lived in a world where everything was recorded I would not be happy.

Glasp: Okay. Do you don't like AI pin? I remember humane some

Parth: I don't like it. I don't like it. The the AI pin. Yeah, this concept is like um it's just like oh you didn't get my permission. And so then now I'm like well and and even the meta glasses sometimes I feel you know like people just I see the light but then now I'm like okay now I'm on camera right? So this is this this makes you it changes the way you act for some people I think. Um, so but that's going to be an interesting social norm we have to figure out. And I think for me this is it's more like I like talking to this system and be like here's hey here's what I'm thinking about and then ask me a question then I talk to it and then when I'm talking to it it is collecting data. I don't need it to follow me around forever in the real world because I think that like that some people want that and that's fine. Um I'm not going to judge them for it. Um, I think for me it's more like uh that's not I'm I don't want that relationship with technology yet. And I understand it might be there may be very huge benefits that I'm ignoring. It's just that I think the social trade-off is serious. Um it's pretty serious. Like if someone is has the Meta Ray ban on, some of my friends have them. When they engage it now, I'm like, "Okay guys, we're on a show." Like it's like we're now we're acting, right? It was just like I prefer to be like like you know chill, you know.

Glasp: So yeah, I was going to ask something but I forgot that. No worries. Yeah, we can cut. Yeah, we can cut out. Yeah. Maybe you already answered it but you use GPT5, also you use Claude Code and the codebase context so a different so if you switch so you need to have a context like

Parth: I'm so glad you asked this question.

Glasp: Ohm yeah thank you.

Parth: So the first month I used Claude Code, I was just learning it and I was like, "Oh my god." And I use it every single day. And I was like, "I burnt 560 million tokens." I was like, "How is this real?" Like this doesn't make any sense. Like who like, you know, like I'm like $10,000 worth of tokens, but I'm not paying $10,000. So it feels like it's very discounted. But then I and I was building like, "Okay, but you know, my laptop, my computer, my this is my gaming computer. So I this is like my main my desk but like some of my work is on my laptop my work is on then I have a Mac mini and I was like okay well what if Mac mini had coding projects on it and then I could just connect to that Mac mini from my phone from my laptop my other devices put it on a private network now we have a single chat and then but I can use that from any device so that was one thing that I was doing which is like um for like multiple clouds 24/7 on on a single device just that thing never sleeps I can, you know, connect to it from any other device. Very cool. Um, then, uh, you know, GP5 came out and Codex CLI is now good. Well, it's like I think Claude Code is better than Codex CLI. I think even the people at OpenAI would admit this, but and Codex CLI will improve. They're going to work very hard on it and um, you know, they're going to vibe code it with GP5. They're going to tell GB5 to add features, but you can also fork it and you can modify it yourself. And I think that the so now it's like you have multiple CLI agents and you want them to work on the same project. So then I was this is like last week I've been thinking about this a lot like I want to use both of them um and I want them to work on the same codebase. How do I do that? And I think so there's this thing called Claude Squad and uh if you just look up it's like a GitHub repo. It's it's a way to interact with multiple CLI tools and then they basically use their own work trees. So now I'm like okay well now I need to get I need to change the way I work with these tools. So instead of doing you know normal GitHub branching it's like okay work trees now now it's like can you work can they work on different copies of the same code and then when then triaging and and integrating the solutions becomes possible. So, Claude Squad is the most recent kind of tool that I've been using because it also allows me to go from Codex to Claude Code in the same tool and uh and you can fork it and then you can tell GPD5 to modify it and build a UI on top of it. Right? So remember everything everything can be modified with if you have the code you can modify it. You can make it even more personal. So I think we're at the very beginning of this. I think the CLI tools are a temporary thing. I think we will also want more traditional user interfaces than uh than CLI but CLI is powerful and you get raw access to the machine and then a lot of the tools have CLI uh support. So it's a good starting point. I have 17 MCPs uh that I use a lot and u one of them is this knowledgebased system the knowledge graphs but it doesn't it's not just one knowledge graph it's it's it's like an MCP that allows it to create new knowledge graph query them list them right so it one of the tools is the ability to create and query knowledge from long-term storage so there is a certain every I think there is a set of tools like that you want to give some of your agents Not all your agents need every tool and that can be risky, right? Um, but you know, access to your email, your calendar, it becomes so much more useful when it's like, "Oh, look at both my calendars, plan my next three months of travel." And I was using Claude Code to do that. Same thing with like like expense reporting. I get so many AI subscriptions, right? And they all have like Stripe, Stripe, Stripe, so many like emails of like receipts. And then I was like behind on my expense reporting. And then I and I got an email and they were like, "Part, you got to file your expenses." And then I was like, "Claude Code, we're late on my expense reporting. Go into my email, get every single receipt, then use GPD4 vision to categorize every single thing by vendor. Get the, you know, the the total of the the amount. use GPD4 Vision, look at the receipt, extract all the structured outputs, extract that information, put it in the name of the file, put them all into folders for every single month, and in 30 minutes, it does like 400 expense report um you know, it finds all of them. There's no way I'm going to do this manually ever again. But the realization here is that, oh wow, coding agents are very powerful for non-programming tasks that require structured logic, right? Just general automation. So Claude Code does my expense reporting, which a year ago I was like, "I wish there was an agent that did expense reporting," but now I'm like maybe that's too small. That's like a side effect of coding agents is that they can do this. So like we need to think even more deeper about these problems because actually a lot of that stuff is just, you know, GPD5 CLI code Claude Code might be able to do it. And um does that mean that there is no such thing as an expense reporting agent that it'll just be a feature? Like who knows? Like maybe that's just a feature, not a product. But this is like an early realization we're having now of like the coding co-pilots are very useful in non-programming tasks. So then actually we're I could see and I think that like that's probably going to play out more which is that like the the agents that non-technical people use will actually be under the hood coding agents that are just like guardrailed heavily and like these are the flows that they do really well right if an expense reporting agent is at its core like it's just a maybe it's just a cloud code wrapper right like that's what I'm using it as but then the same agent is also like the one because of 17 MCPS it's doing like web research and it's also you know helping me like file my own knowledge away. So I think it's funny that Claude Code for me for like two months is like an everything app. um and that's why I'm trying to do the Claude Squad which is like that way I can keep using Claude Code but I also benefit from GPD5 for a lot of programming. So I'm like, "Okay, I use both of them and I use this when I need personal assistant. This I use for programming and that way I can kind of like it's we keep adapting." I don't think three months from now it's probably going to be different but um it's my current approach.

Glasp: Yeah. Yeah. Interesting. Yeah. A couple years ago actually at Hackathon someone told me about the code squad and I was curious about that today idea.

Parth: Yeah. My buddy at OpenAI sent it to me. He was like, "Parth, Claude Squad work trees," and then I was like, "Huh, oh I should learn work trees." And now I'm like, "Okay, I need to learn work trees because this is actually very interesting and it might be a good way for multiple agents to work on the same project without undoing each other's progress." So this like coordination problem it might be a part of the solution.

Glasp: Yeah. Yeah. Definitely. Yeah. And I now remember my question. So sorry it's about cloning cloning yourself. So I think in the future I don't know if you really want to clone yourself and I think we should put something the brain to monitor your brain activities then AI pin and then because when you are exposed to some information a certain information you sometimes you don't pay attention to it you don't care but if you record everything the AI might assume oh you learned about this but you know your brain if your brain activity is lower means oh you didn't pay attention to it so but if you listen to something your brain activity is high. So okay, this person learns about oh listen at least resonate with this information. So we should use the information or weigh this information when we

Parth: maybe you might be right. I I I look I love it because like I'm a cyberpunk kid. I love cyberpunk. I don't know if you ever played Cyberpunk 2077 but that's one of my favorite games and they have a lot of like human augmentation and like the whole game you have a brain you have brain chips. Like brain chips are like the I play the game and I'm like buying brain chips, you know, but I think about like would I put a brain chip in? Yeah, maybe when I'm like 60 years old maybe if it's safe but and then run GPD8 on it like that, you know, maybe you know um but I think like more realistically like non-invasive non-invasive techniques would be a good like middle ground. um As long as I don't cuz I don't know like vision pro very powerful technology but no one uses it, you know, like it's so it's so clunky and like unwieldy and isolating. So I think it's like if they can make it and that's why Meta is winning on the because they made it cool, right? So if it's like cool and useful then um that'll be interesting. I think you might be some on something like if my glasses were like, "Oh yeah, Parth's paying attention to this. We should probably save this for later. It's like, "Oh, would you like me to like like you really like this person like do you want me to like, you know, remind you in six months to reach out to them?" Like that could be useful, right?

Glasp: Yeah. Yeah. Yeah. Definitely. Yes. So, yeah, I'm optimistic about the future in technology as well.

Parth: Yeah. Yeah.

Glasp: Anyway, yeah. Anyway, thank you so much for sharing a lot of, you know, insights and lessons, you know, you learned.

Parth: Yeah, absolutely. This is a lot of things. Yeah.

Glasp: Yeah.

Parth: No, thanks for bringing such like this is the kind of conversation that I love more than anything. So I appreciate the Yeah.

Glasp: But before but before anybody do you have any advice to people who are new to AI? Yeah. Or have never coded before wrote any single code.

Parth: Yeah. Yeah. Yeah. Definitely. um I think that like this is a great this is probably one of the most interesting moments in human history that we get to live through this transformation. um There are and and I think it's like it's a huge leveling of the playing field. We now have we have co-pilot systems that are smarter than many of our friends, smarter than most of the people that we know and they can help you learn any topic. I was not an engineer two years ago. Now I think I think I can safely say that I am an engineer. um you know I'm not perfect in any ways but because of language models I'm able to teach myself almost any topic I want. That means that like if you want to get good at something, you can. You can't get good at everything. You have to pick a few lanes, but it's really important to play, use the technology, figure out what you want to do like like your own. You might have you have to have a vision. You have to have an interesting life and a perspective. You have to like go live an interesting life so that you're like, "Oh, I want to do this. I want to make that. I want to." So when you realize what the things that you want to make, AI is going to help you make those things. And you would be surprised at how quickly you can get moving on your own ideas. And I think also don't only use it for work because that's a like what if your company fails? Like what if the company isn't around a year from now and then you wasted like all your energy was just trying to turn this into money when actually maybe it could have made your own projects, your own life better, your family, your friends, like your passions. And then the things that you're passionate about, when you apply AI to that, then it doesn't feel like work. Right now it's like, "Oh, I'm playing like now I get to do this. This is going to be fun." And then you go way further and then you figure things out. And that's like I think apply it to your passions. Use the technology. Like it's it's it's not enough to talk about it. Like that is not use the technology. Then that way you're like all the noise kind of fades away when you're like, "Oh, this is this is what it actually is. This is the part where it's very good. This is where it's not good." And the only people who are figuring that out are the people using it. And there's there's not really a textbook or expert. So don't look for certifications. The best certification is like you said hackathons like go make something and then just share that you're working on it. And you'll be surprised people will come to you with like oh yeah I'm also working on this. Now you have collaborators you have peers. So you know you want to make things share and talk about them. Not everything is going to be a company. That's fine. A lot of things are just like you know think of it as art. You know I think of sometimes like oh yeah this is like a beautiful thing that we just did for fun and then but you create a conversation and learn from who the conversations you have meet people then that's going to build your network then you get the network intelligence that's going to give you this sense of like okay cool we are going to be good like the it's it's uncertain everything is everything is changing very quickly so we have to adapt the more adaptable you are the faster you learn the better off you're going to be and sometimes you don't have energy or time to adapt, that's fine. You have friends, you can kind of do this. It's a team game. And I think that um yeah, that's that's like and and do things don't don't just apply it to work. Like you might find that the most interesting passions are now possible. Things that you were putting off are now possible because of these tools. So you should uh you should apply it to things that you care about.

Glasp: Yeah. Totally. 100%. And sorry this is a very last question. So since grasp is aware platform where people share you know what they're reading learning as the digital legacy and people can actually create the AI chrome through learning process but so we want to ask this question to you so what legacy or impact do you want to leave behind for future generations?

Parth: So, it's a tough question at the end but yes I hope that people who interact act with um anything that I have done for if you if I' if you've even watched just a video that I have put out um and I don't put a lot out but like if anything that you know something I made I hope that you hope I hope that you realize like it's like um just you should like it's it's like hopefully it inspires people to just make things and learn and to challenge the notion like there is no like we have so much to rebuild we have so much to make so much to create and make things I think like actually make things. It's very exciting to make things and to share those things. um And hopefully it makes people more creative and ambitious ambitious as well because a lot of the stuff that I've I've realized a lot of stuff is a lot easier now. I thought it was going to take 10 three years ago a lot of these capabilities were never I could not dream of these capabilities. Now you can do in three hours what used to take three years. And that means that we have to be more ambitious, more creative and more optimistic too. Like we have to actually like u it's not a it's not a 100% obvious that this will end out end well. We have to go make that we have to make the future that we want to live in. And so like you have a chance to be a part of making that future. This is it doesn't just happen to you. you you're like you are happening to the world around you. So, um hopefully hopefully like people realize that they are they are all like you're all the main characters, right? Like you're you're in the driver's seat like this is this is like your opportunity, right?

Glasp: Yeah, definitely. And yeah, thank you for the beautiful answer and yeah, thank you for joining today and we learned a lot and yeah, thank you so much.

Parth: Appreciate it, guys.

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