Embracing the 3-Month Rule: A Pragmatic Approach to Non-Scalable Solutions in Software Development

The journey of developing an innovative AI podcast platform over the past eight months has led me to adopt a practical yet unconventional methodology inspired by Paul Graham’s mantra: “Do things that don’t scale.” While this advice is commonplace in entrepreneurial circles, its application in the realm of software development is rarely explored.

After months of experience and experimentation, I’ve established a straightforward framework: each unscalable solution gets a trial period of three months. If it proves valuable within this timeframe, it will be evolved into a scalable version. Otherwise, it will be discarded. This approach has transformed the way I view coding and has significantly influenced my development workflow.

The Dilemma of Scalability in Startups

As engineers, our instinct is typically to create systems that can effortlessly accommodate growth. We learn to rely on microservices, design patterns, and robust architectures capable of handling extensive user bases. Yet, in the context of a startup, these well-meaning plans can often result in costly delays. Focusing on scalability prematurely can lead to wasted resources on problems that don’t exist. By applying my three-month rule, I channel my efforts into crafting straightforward, sometimes “imperfect,” code that gets results and reveals insights into user behavior.

Here’s a Look at My Current Infrastructure Hacks

1. Consolidated Operations on One VM

Currently, all components—database, web server, background jobs, and caching—reside on a single $40/month virtual machine. While this might seem reckless, it has given me invaluable insights into my resource requirements. In just two months, I discovered that my platform, despite its AI focus, typically operates at only 4GB of RAM. Had I moved forward with a complex Kubernetes setup, I would have been managing idle resources instead of understanding my needs firsthand. The few times it has crashed have offered unexpected data, highlighting areas I didn’t anticipate.

2. Simplified Configuration Management

Instead of using configuration files, I’ve hardcoded essential values directly into my codebase. For instance:

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

Although this approach seems primitive, it allows me to quickly search for any configuration changes through my project’s history. Each update