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

In the startup world, the advice from Paul Graham to “do things that don’t scale” is well-known, yet the practical implementation of this advice within coding practices is often overlooked. After dedicated eight months to developing my AI podcast platform, I have crafted a straightforward framework: any temporary workaround is given a lifespan of three months. Following this period, each solution must either demonstrate its value and transition to a robust development stage or be discarded entirely.

As engineers, we often prioritize scalable solutions from the outset. We gravitate towards design patterns, microservices, and distributed systems that can cater to millions of users. However, this mentality can hinder innovation in a startup environment where scalable approaches may serve as costly procrastination. We tend to optimize for hypothetical users while neglecting to address real-time demands. My 3-month rule encourages the creation of simple, functional code that allows me to understand user needs better.

Innovative Infrastructure Hacks: Simplicity Leads to Insights

1. Consolidated Operations on a Single VM

All elements—database, web server, background processes, and Redis—are housed on a single $40/month virtual machine. While this may initially seem reckless, it has provided invaluable insights. Within two months, I’ve gained a clearer understanding of my resource needs than any complex capacity planning document could have offered. The current peak of my “AI-centric” platform—just 4GB of RAM—proved that the sophisticated Kubernetes architecture I almost implemented would have served no purpose. Since the system has crashed a couple of times, I’ve gathered actual data on failure points, which frequently deviates from my initial expectations.

2. Hardcoded Configuration Values

Instead of leveraging configuration files or environment variables, I rely on a series of hardcoded constants:

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

This approach, while seemingly outdated, offers distinct advantages. I can quickly search my codebase for any config value, and all changes are documented in version history. In total, I’ve revised these constants just three times in three months—resulting in 15 minutes of redeployment instead of the extensive hours required to create a dedicated configuration service.

3. Using SQLite in a Production Environment

Surprisingly, I