Mastering the 3-Month Approach: A Practical Method for Scalable Solutions in startup Development

In the ever-evolving world of software engineering, there’s a well-circulated mantra by Paul Graham: “Do things that don’t scale.” While this advice resonates with many, the challenge often lies in executing it, especially within the realm of coding. After dedicating eight months to developing my AI podcast platform, I’ve established a straightforward yet effective framework—the 3-Month Rule.

Understanding the 3-Month Rule

The essence of my framework is simple: each unscalable solution receives a trial period of three months. Within that timeframe, it must demonstrate its utility or face discontinuation. For those of us in the startup ecosystem, where the objective is to pivot quickly and learn efficiently, this approach can be transformative.

As engineers, we often gravitate towards scalable solutions right from the start—think complex design patterns and elaborate infrastructures built for vast user bases. However, in a startup environment, such scalability can inadvertently become an expensive form of procrastination, focusing on problems that are yet to materialize. The crux of my methodology compels me to embrace straightforward, albeit imperfect, coding that directly addresses user needs and gathers valuable insights in real-time.

Innovative Infrastructure Solutions: My Current Setup

1. Unified Virtual Machine Operations

I’m currently hosting everything—from the database and web server to background jobs and caching—all on a single $40/month virtual machine. This setup offers no redundancy and involves manual backups to my local machine.

Why is this approach beneficial? In just two months, I’ve gained a clearer understanding of my real resource requirements than any capacity planning document could offer. My platform, although AI-centric, peaks at a modest 4GB of RAM usage. The intricate Kubernetes architecture I was contemplating would have been managing resources that were ultimately unnecessary. Importantly, when the system has crashed (which has happened twice), I’ve been able to collect genuine data on the failure points—lessons I certainly didn’t foresee.

2. Hardcoded Configuration

Instead of utilizing configuration files or environment variables, I’ve hardcoded values throughout my codebase. Examples include:

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

This practice, while unconventional, grants me the ability to search the entire codebase for configurations with