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

In the realm of startup development, one piece of wisdom that often stands out is Paul Graham’s mantra: “Do things that don’t scale.” Yet, the actual execution of this philosophy in coding practices is seldom discussed. After eight months of refining my AI podcast platform, I’ve cultivated a straightforward framework: any hack that isn’t built for scalability is given three months to prove its worth before determining its future.

As software engineers, we’re often conditioned to pursue scalable solutions from the outset. We dive into intricate design patterns, microservices, and distributed systems, all aimed at accommodating millions of users. However, this mindset frequently aligns more with established enterprises than with the nimble nature of startups.

Scalable code can represent an expensive form of procrastination; it focuses on hypothetical users and future challenges rather than immediate needs. Thus, my three-month rule prompts me to write simpler, less refined code that is not only deployable but also clarifies user requirements in real time.

Current Infrastructure Experiments: Worth the Risk

1. Consolidation on a Single Virtual Machine

I operate my database, web server, background jobs, and Redis on a single, cost-effective $40/month VM. This approach has zero redundancy and relies on manual backups to my local system.

This is more than just a stopgap; it has allowed me to gain insights into my actual resource usage far more effectively than any theoretical capacity planning would have. I learned that my AI-driven platform’s highest RAM usage reaches only 4GB. The extensive Kubernetes setup I once considered would have only served to manage unused containers.

When my system has crashed—twice—I’ve gained invaluable data on the actual points of failure, which often surprise me.

2. Hardcoded Configuration Approach

My codebase features hardcoded values such as:

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

With no reliance on configuration files or environment variables, alterations require a redeployment.

This method yields a unique advantage: I can swiftly search my entire codebase for any configuration value. Each change is documented in Git history and reviewed—albeit by myself—ensuring accountability. Instead of spending a week on a configuration service, I’ve made only three changes in three months