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

In the world of startups, the mantra “Do things that don’t scale,” coined by Paul Graham, often serves as a guiding principle. Yet, while many grasp the concept, few delve into actionable strategies, particularly in the realm of software development. After eight months of refining my AI podcast platform, I’ve come up with a straightforward framework: each unscalable hack is granted a lifespan of three months. After this period, we assess its worthiness for further development or decide to phase it out.

Rethinking Scalability

Traditionally, software engineers are trained to architect scalable solutions from the outset, implementing sophisticated design patterns, microservices, and robust infrastructure capable of accommodating vast user bases. However, within the startup environment, focusing on scalability too early can lead to wasted resources, as it often means you are optimizing for non-existent users and addressing potential problems that may never arise. My three-month rule compels me to create straightforward, often imperfect code that actually gets deployed, allowing me to gain insights into what users truly require.

Innovative Yet Unconventional Infrastructure Hacks

1. Consolidated Resources on One Virtual Machine

My platform operates on a single $40/month virtual machine that hosts everything from the database to the web server and background tasks. While this setup lacks redundancy and involves manual backups to my local computer, it has provided invaluable insights into my resource needs. Within two months, I’ve learned that my “AI-heavy” platform only requires 4GB of RAM at peak usage — detail that could have easily been overlooked in elaborate preparatory documentation. When my system has faced downtime, I’ve gathered real data on its failure points, often revealing surprises.

2. Simplified Configuration Management

Instead of utilizing complex configuration files or environment variables, I’ve hardcoded key values directly into my code, like pricing tiers and user limits. While this may seem primitive, it offers a unique advantage: I can swiftly search the entire codebase for any configuration. Each modification generates a quick redeployment, which saves countless hours compared to implementing a full-fledged configuration service that I’ve adjusted only a minimal number of times.

3. Utilizing SQLite for Concurrent Users

Running SQLite for a web application may raise eyebrows, but my 47MB database handles 50 simultaneous users effortlessly. Through this setup, I discovered my usage patterns lean heavily towards reads rather than