425: AI Best Practices for Bootstrappers (That Actually Save You Money)

Hey. It's Arvid, and this is the Bootstrap founder. I recently realized something while building PodScan, my podcast database system that does a lot of background data extraction and AI analysis for my users. I think I've stumbled upon a couple of AI integration best practices in all of these years now that a lot of people might not be fully aware of or just have never experienced. So today, I want to dive into the concepts that I found not just useful, but essential for maintaining and then operating this mission critical data handling with LLMs and AI platforms and AI tooling. A quick word from our sponsor here, paddle.com. I use Paddle as my merchant of record for all my software projects, the ones that involve AI and the ones that don't. They take care of all taxes, currencies, detract decline transactions, they update credit cards in the background so that I don't have to. It's really cool. And they allow me to focus on dealing with my competitors and my customers instead of banks and financial regulators. So if you think you would rather just wanna build your product, well, then check out paddle.com as your payment provider and merchant of record. So I was reminded of these AI practices that I have established in a tweet that I read from Greg Eisenberg. He said something along the lines of keeping up 100% with all the new AI tools and the models and their capabilities and the benchmarks and all that. It's pretty much impossible at this point. And that's something that works today might fail tomorrow. And that's very true. It's an observation that I learned. It's probably my biggest learning in building PodScan because I've built a couple of SaaS in the past, but AI is the new thing. And I think I realized that I turned all of this into not just a process, that's also true, but an implementation style. So that's what I'm gonna share with you here today. I'm gonna share what I've built and how I've built it. So whenever I use an AI call, be that to a local model that I have installed on a GPU enabled server somewhere, or a cloud model on OpenAI, Anthropic, Gemini, whatever it might be, there's so many things. And I'll get to the diversity here. I always have a migration pattern implemented in the code. So I extract all of my API calls into services. Right? That's just how I generally structure my code. I want these services to internally handle all the connection stuff, all the prompt massaging, prompt construction. And in addition to the specific prompt that I want for each API call, the service will format it in a way that makes it easy for the endpoints to consume. And all of these services always operate on what I would call a state of permanent migratability. That means they can always use the latest version and the latest …