Embracing the 3-Month Rule: A Practical Approach to Building an AI Podcast Platform

When it comes to startup advice, few phrases resonate as deeply as Paul Graham’s mantra: “Do things that don’t scale.” While this concept is widely accepted, the challenge lies in its execution—especially in the realm of coding.

After dedicating eight months to creating my own AI podcast platform, I’ve developed a straightforward framework based on a pivotal principle: each non-scalable hack is granted a lifespan of three months. Following this period, each hack must either demonstrate its worth and transform into a more robust solution or be discarded.

The Dilemma of Scalable Solutions

In the engineering world, there’s a strong inclination to focus on building scalable solutions from the outset. We often delve into design patterns, microservices, and distributed systems, envisioning infrastructures capable of accommodating millions of users. However, this perspective is often too aligned with big corporate thinking.

In a startup environment, pursuing scalable solutions can frequently lead to costly delays. You find yourself optimizing for future users that may never materialize, all while leaving pressing issues unresolved. The three-month rule compels me to craft straightforward, albeit imperfect, code that can be swiftly deployed—allowing me to discern what my users truly require.

My Innovative Infrastructure Hacks

1. Single-VM Setup

2. My entire platform—database, web server, background jobs, and even Redis—runs on one $40/month virtual machine. While this might seem reckless, the insights I’ve gained in just two months have been invaluable. I’ve identified that my “AI-heavy” platform peaks at 4GB of RAM, saving me from the unnecessary complexity of setting up Kubernetes to manage idle containers. Each crash has provided critical data about real issues, which often deviate from my expectations.

3. Hardcoded Configuration

4. Configurations are hardcoded directly into my codebase, eliminating the need for extensive configuration management systems. This approach may seem primitive, but it allows for rapid tracking of changes through version control and quick adjustments with minimal downtime. In three months, I’ve made only three changes, saving me countless hours.

5. SQLite in Production

6. My choice to deploy SQLite for a multi-user application seems unconventional, yet it has proven effective. With only 47MB of data, the database efficiently handles 50 concurrent users. This choice has highlighted my access patterns, which skew heavily towards reads rather than