The 3-Month Rule: A Practical Approach to Non-Scaling Solutions in Tech

In the startup realm, advice from industry leaders often circulates like gospel. One piece that stands out is Paul Graham’s famous suggestion: “Do things that don’t scale.” While many nod in agreement, few explore how to effectively incorporate this ethos into the world of coding. After eight months of developing my AI podcast platform, I have established a straightforward yet powerful framework: every unscalable solution has a life span of just three months. At the end of that period, it either proves its worth and is properly refined or is promptly phased out.

The Challenge for Developers

As engineers, the instinct to build scalable applications from the outset is ingrained in our training. We focus on design patterns, microservices, and distributed systems—architectures that are designed to accommodate millions of users. Yet, this mindset can become detrimental, particularly in a startup environment where scaling preemptively often leads to costly delays. In my experience, the 3-month rule has encouraged me to write straightforward, though perhaps suboptimal, code that gets deployed rather than stalled while waiting for an ideal solution. This approach not only accelerates my development process but also provides valuable insights into user behavior and needs.

Smart Infrastructure Hacks I’ve Implemented

Here are some of my current infrastructure strategies that may seem unconventional at first glance, but have proven to be surprisingly effective:

1. Unified Virtual Machine Usage

I’ve consolidated my database, web server, background jobs, and Redis all onto a single $40/month virtual machine without any redundancy and with manual backups that I handle personally. This setup has enlightened me on my actual resource requirements, revealing that my AI-driven platform only needs 4GB of RAM at peak usage. The complex Kubernetes infrastructure I once contemplated, had I proceeded, would have meant managing redundant containers that were largely unnecessary.

2. Simplistic Hardcoded Configurations

Instead of using configuration files or environment variables, I’ve opted for hardcoded constants spread throughout the application:

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

While this seemingly hinders flexibility, it allows me to quickly scan the entire codebase for any configuration value and track changes efficiently via version control. In just three months, the necessity to update these parameters has arisen only