Embracing the 3-Month Rule: A Pragmatic Approach to Learning Through Code
In the tech world, the mantra of “doing things that don’t scale” is frequently espoused, particularly by thought leaders like Paul Graham. Yet, the challenge lies in how to effectively apply this principle within our coding practices. After dedicating eight months to the development of my AI podcast platform, I’ve crafted a straightforward framework that I call the 3-Month Rule. This guideline stipulates that each unscalable hack is given a trial period of three months. At the end of this timeframe, the hack either demonstrates its value and receives further development or is discarded.
As software engineers, we are often conditioned to prioritize scalable solutions right from the outset—think complex design patterns, microservices, and distributed systems designed to accommodate massive user traffic. However, this mindset tends to align more with larger organizations than with startups. In fact, striving for scalability too early can amount to costly delays, as it diverts attention to hypothetical future users rather than current pressing needs.
My 3-Month Rule compels me to create straightforward, no-frills code that can be deployed quickly, allowing me to gather real-time data on user behavior and preferences.
Insights from My Current Infrastructure Hacks
1. Consolidation on a Single Virtual Machine
Currently, my entire platform operates on a single virtual machine for just $40 per month. This setup includes the database, web server, and background jobs all rolled into one. While this may seem reckless due to the absence of redundancy, I’ve gained valuable insights into my actual resource needs in a mere two months. I’ve learned that my application peaks at only 4GB of RAM usage.
When issues arise—an occurrence that has happened twice—I gather tangible data about points of failure, revealing unexpected areas of vulnerability.
2. Hardcoded Configuration
My configuration setup is straightforward: prices and limits are hardcoded, with no separate configuration files or environment variables. While this may seem cumbersome, it allows me to quickly search my entire codebase and effortlessly track changes through Git history. Each adjustment takes moments to deploy, significantly saving development time and resources.
3. SQLite as a Production Database
Yes, SQLite is the backbone of my multi-user application. Despite its lightweight nature, it manages to smoothly accommodate 50 concurrent users—a statistic that informs my ongoing understanding of access patterns. Unlike a more complex database system, SQLite allows me to
