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

In the realm of startups, the advice from Paul Graham to “do things that don’t scale” is often echoed. However, a glaring absence of actionable guidance on how to integrate this ethos into software development practices leaves many engineers puzzled.

Having dedicated the last eight months to developing my AI podcast platform, I’ve established a straightforward methodology: every non-scalable workaround is given a lifespan of three months. After this period, each solution must either demonstrate its value and evolve into a more robust framework, or it is discarded.

The Challenge of Scalability

As software engineers, we often find ourselves pressured to create scalable solutions right from the outset. With the focus on design patterns, microservices, and distributed architectures, we can easily lose sight of the fundamentals. Yet, in the startup environment, aiming for immediate scalability can become a form of costly procrastination. It’s all too common to develop code to accommodate future users who may never materialize, neglecting the genuine needs of current users. My three-month rule forces me to prioritize straightforward, albeit imperfect, code that gets deployed and provides valuable insights into user behavior.

Current Infrastructure Hacks: A Smart Strategy

Let me share the specific strategies I’m using and the rationale behind them:

1. Unified VM Usage

All essential components of my infrastructure—database, web server, back-end jobs, and Redis—are consolidated onto a single $40/month virtual machine. This setup results in zero redundancy and manual backup to my local machine.

Why is this approach smart? It has illuminated my actual resource requirements in just two months far more effectively than any capacity plan could. My “AI-heavy” platform typically requires only 4GB of RAM, which suggests that my planned Kubernetes setup, designed for a situation that doesn’t exist, would have led to wasted resources managing idle containers. Each crash (which has occurred twice) has provided unexpected insights into failure points—often entirely different from my assumptions.

2. Hardcoded Values for Configuration

Instead of using configuration files or environment variables, I rely on hardcoded constants sprinkled throughout my codebase.

For instance:
python
PRICE_TIER_1 = 9.99
PRICE_TIER_2 = 19.99
MAX_USERS = 100
AI_MODEL = "gpt-4"
This method may seem primitive, yet it allows