Short answer: Sam Nadler says he built it in Claude Code in the terminal with Supabase, deploying via Amplify, AWS and GitHub, in about six hours without writing a line of code. Opus 4.5 powers every analysis.
Watch this answer from Episode 29 (starts at 7:35) · Episode 29 notes and transcript
From Episode 29: We Built an AI Recruiter Coach in 6 Hours (Plus Claude Cowork in Real Time), starting at 7:35.
Sam Nadler [7:34]: So, generally speaking, this is not live yet. It's, a day or so from going live and getting tested by our recruiters. There will probably be additional fixes and features after that. But this was built in Claude Code, Supabase, and that's pretty much it.
Jordan Metzner [7:51]: Oh, man. It's amazing. Honestly, it really is amazing. Obviously, the thoughts of just automating putting the notes in here so that this all gets automated as well. Jumping back into it's incredible. And if you could just go back to where it gave you the assessment, do you have, like, yeah, that one? Yeah. So, I mean, just this key evidence on, like, why the candidate seems good, seems amazing. I mean, this is an incredible demo, Strand. Cool. Yeah. And then, obviously, here you're seeing other candidates who are similar to that candidate. I mean, maybe there's a little bug there, but, for the most part, that looks pretty accurate. And, yeah, I think, as I'm thinking of this out loud, probably the next step is just to provide this feedback in real time while the interview is taking place. So let's, maybe replace Granola with our own custom built solution and, essentially provide real time feedback to recruiters to help them provide an even better interview experience to candidates.
Jordan Metzner [8:40]: So this is really, really awesome. Tell us a little bit, like, what code what tools you used to build it, not, like, kind of how what it's posted on.
Sam Nadler [8:48]: Yeah. I mean, it was done via Claude Code in terminal. I think I started, which we will demo here in a second, Cowork. And I really do like Cowork, but I think, like, pretty quickly, it just makes sense to keep it in the terminal. And then, I had Supabase. Supabase, I find, is is very easy to understand. That was literally it. I mean, then once I got to the point of, like, about to deploy it, which is where I'm at now, you know, our stack is Amplify, AWS, and GitHub and but, really, once it's all connected, you it's pretty simple to get it live on a Ryz Labs subdomain. So, it does take I would say, I started this Monday afternoon. Today's Wednesday. And probably a total, like, working between meetings, somewhere in between six hours to complete. So, like, really pretty reasonable. Unbelievable. And we're not done yet. Right? Like, there's gonna be feedback from the recruiters. They're gonna catch bugs. I've pushed it, like, a similar product on Monday to a different team, and, I've had three or four bug fixes from, live testing in, a live environment. But it's pretty incredible we could build this or I could build this with who I haven't written one line of code in my life, full stack web app development in six hours that has some pretty cool features.
Jordan Metzner [10:11]: That's honestly, it's incredible. And so, yeah, I guess, like, we get the feedback from the interview that we didn't get previously. Recruiters get feedback on how they think the candidate did versus how well the system believes the candidate did. And what models are you using to power that, like, LLM, like, AI analysis?
Sam Nadler [10:28]: Opus 4.5 for everything.
Jordan Metzner [10:31]: Okay. So even that LLM, like, the score is coming from Opus? Yes. Wow. Okay. Full on Anthropic Stack. Alright. Sounds good. Full on. Yeah. I mean, I guess, like, how could somebody build something like this themselves? Like, how long you know, how easy is it?
