Embracing the Three-Month Rule: A Practical Approach to Non-Scalable Solutions in Software Development

In the world of startups and technology, it’s common to hear the mantra, “Do things that don’t scale,” attributed to Paul Graham. However, the challenge lies not in knowing this advice, but in operationalizing it, especially in software development. Over the past eight months while developing my AI podcast platform, I’ve adopted a straightforward framework: every non-scalable solution has a lifespan of three months. After this period, it must either demonstrate clear value and be improved, or it will be discarded.

As programmers, we often feel compelled to prioritize scalable solutions from the start. We immerse ourselves in the latest design patterns, microservices architectures, and distributed systems capable of supporting massive user bases. While these paradigms are essential for larger companies, in the startup environment, they can often lead to procrastination— we find ourselves optimizing for potential future users while neglecting the needs of our current ones. My three-month rule compels me to produce straightforward, albeit less polished, code that enables me to better understand what my users truly require.

My Experimental Infrastructure: Simplifying Complexity for Insight

1. Consolidation on a Single VM

At the heart of my project, I run everything—from the database to the web server—on a single $40-per-month virtual machine. This approach intentionally lacks redundancy, with all backups done manually.

Why is this effective? In just two months, I gained valuable insights into my resource demands, learning that my AI-powered platform only requires 4GB of RAM. The complex Kubernetes setup I envisioned would have led me to manage idle containers instead of actively engaging with user needs. Moreover, when crashes occur (which they have, a couple of times), I receive actionable data about system failures—often illustrating that things break in unexpected ways.

2. Hardcoded Values for Configuration Management

In my code, configuration details such as pricing tiers and user limits are hardcoded directly into the application:

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

This choice may seem primitive, as it lacks separate config files or environment variables, but it offers advantages. I can quickly search for any value within my codebase and track changes through version history. The time saved by avoiding