The Three-Month Rule: A Practical Approach to Building an AI Podcast Platform

In the tech world, we often hear the mantra from renowned startup investor Paul Graham: “Do things that don’t scale.” Although many applaud this wisdom, the conversation rarely addresses how to put it into practice, particularly in software development.

After dedicating eight months to create my AI podcast platform, I established a straightforward framework to navigate the chaos of scalability: any non-scalable strategy gets a trial period of three months. If it demonstrates its worth during that time, I commit to enhancing it; if not, it’s retired.

This perspective is crucial, especially for those of us in the startup ecosystem. As engineers, we’re trained to build for scalability from the outset, focusing on intricate design patterns, microservices, and distributed systems—all of which are essential for handling extensive user bases. However, this approach can often lead to delays, particularly in the early stages of development where scalability concerns can turn into expensive procrastination. My three-month rule encourages the creation of simple, direct, and perhaps “imperfect” code that allows me to gather vital insights into my users’ actual needs.

Current Infrastructure Hacks: Strategic Simplicity

1. Single Virtual Machine Operation

I opted for an all-in-one setup: my database, web server, background jobs, and caching run on a single $40/month virtual machine with no redundancy and manual backups to my local system.

This straightforward approach has proven invaluable. Over just two months, I gained insights into my real resource needs that no capacity planning document could have provided. Interestingly, my so-called “AI-heavy” platform’s peak usage only taps out at 4GB of RAM. The complex Kubernetes architecture I nearly implemented would have been entirely unnecessary—filled with idle containers.

Having faced a couple of crashes, I gleaned critical data about which components truly fail—an eye-opening experience that highlighted unforeseen issues.

2. Hardcoded Configuration

My configurations look something like this:

plaintext
PRICE_TIER_1 = 9.99
PRICE_TIER_2 = 19.99
MAX_USERS = 100
AI_MODEL = "gpt-4"

By avoiding config files and environment variables, I streamlined the process—changing values requires a redeployment. The beauty of this setup? I can quickly search the entire codebase for any configuration value while tracking every change in git history. Instead of a week-long