Embracing the 3-Month Rule: A Practical Approach to Unscalable Solutions in Coding
In the realm of startup culture, the iconic advice from Paul Graham, “Do things that don’t scale,” often resonates with founders and engineers alike. However, the challenge lies in translating this principle into effective coding strategies. After dedicating eight months to developing my AI podcast platform, I’ve crafted a straightforward framework that allows me to thrive in this paradox. Welcome to the “3-Month Rule,” where every unscalable approach is given a limited lifespan of three months. At the end of this period, each solution is either validated and supported by proper infrastructure or discarded.
As engineers, we are conditioned to pursue scalable solutions from the get-go. We admire sophisticated designs, microservices, and distributed systems that cater to millions of users—essentially the architecture of well-established companies. Yet, in the startup environment, chasing scalability too soon can often lead to unnecessary expenses and distractions. My approach compels me to prioritize direct, simplistic coding practices that yield immediate insights into user needs.
Current Infrastructure Experiments: A Smart Path to Understanding
1. Consolidated Operations on a Single VM
At the heart of my operations is a single $40/month virtual machine that manages everything: from the database and web server to background jobs and caching with Redis—there’s minimal redundancy and backups are manual.
Why is this strategy beneficial? Within two months, I’ve gained valuable insights into my actual resource requirements, far exceeding what a capacity planning document could provide. My platform, which I assumed would be heavily reliant on AI, only peaks at 4GB RAM usage. The complex Kubernetes architecture I contemplated would have left me managing idle containers. Each time my server fails (which has happened twice), I obtain precise data on what caused the issue—often surprising me with the actual points of failure.
2. Hardcoded Configurations
My configurations are hardcoded directly into the codebase as constants—prices, user limits, and model identifiers—without any external configuration files. Modifications require redeployment, which may sound cumbersome but holds hidden advantages.
The beauty of this approach lies in its simplicity: I can quickly search my entire codebase for configuration values without navigating complex structures. Each price change has been diligently logged via git history, and although I am the sole reviewer, this method allows for efficiency over elaborate systems—shaving development time substantially.
