Embracing the 3-Month Rule: A Framework for Rapid Development in Early-Stage Startups
In the startup world, the mantra of “do things that don’t scale” is often echoed by thought leaders like Paul Graham. Yet, what remains less discussed is how to practically apply this philosophy within technical development. After eight months of building my AI podcast platform, I’ve devised a personal framework that has revolutionized the way I approach coding: each unscalable hack is given a lifespan of three months. After this period, it either demonstrates its worth and gets refined, or it is discarded.
The Conventional Approach vs. startup Realities
As software engineers, we are ingrained with the principles of building scalable solutions right from the beginning. We think in terms of intricate design patterns, microservices architecture, and distributed systems capable of serving millions. However, this line of thinking often disconnects us from the immediate needs of a startup. Scaling too early can lead to inefficient use of resources, as we may end up optimizing for users that don’t yet exist, tackling issues that may never arise. My three-month rule compels me to write straightforward, yet effective “bad” code that actually gets deployed, providing me with valuable insights into what users genuinely require.
Key Insights from My Current Infrastructure Hacks
1. Consolidating Resources on a Single VM
My setup involves a single $40/month virtual machine that handles everything from the database to the web server and background tasks, all without redundancy. While this may seem imprudent, the benefits are clear: I’ve gained a profound understanding of my resource needs in just two months, revealing that my “AI-intensive” platform only peaks at 4GB of RAM. The elaborate Kubernetes structure I nearly implemented would have been wasted on managing empty containers. Each crash provides real-time data on failures, which have consistently surprised me.
2. Utilizing Hardcoded Configuration
In my code, configuration values like pricing and maximum users are hardcoded as constants scattered throughout the files. While it might sound risky, it actually simplifies the process of tracking changes since every adjustment necessitates a redeployment. Over the past three months, I’ve only updated these constants three times, saving me significant engineering hours that would have been spent developing a configuration service.
3. Choosing SQLite for Simplicity
Yes, I opted for SQLite in a multi-user setup, with my entire database size at a mere 47MB. Astonishingly, it manages to serve
