Embracing the 3-Month Rule: A Practical Approach to Learning Through Unscalable Solutions
In the world of startups, we often hear the insightful mantra from Paul Graham: “Do things that don’t scale.” However, the challenge lies not just in understanding this advice, but in implementing it effectively in the realm of software development. Having spent the last eight months building my AI podcast platform, I’ve developed a practical framework that I call the 3-Month Rule. This guideline states that any unscalable approach I implement will be evaluated after three months—either it will prove its worth and be transformed into a robust solution, or it will be retired.
The Challenge of startup Engineering
As engineers, we typically aim to create scalable systems from the get-go. We invest in complex architectures—microservices, distributed systems, and elaborate design patterns—intended to accommodate potential millions of users. This mindset often reflects a big company mentality that doesn’t always suit the agile environment of a startup. On this journey, I found that focusing on scalability upfront often leads to costly delays, as efforts are wasted on users that may not even exist yet.
By adhering to my 3-Month Rule, I have been compelled to write simpler, more direct code. This “imperfect” code gets shipped, allowing me to observe real user behavior and refine my understanding of their needs.
My Unique Infrastructure Strategies
Here’s a closer look at the unorthodox choices I’ve made and how they have proven to be insightful rather than reckless:
1. Consolidated Services on a Single VM
All my core services—database, web server, background jobs, and Redis—operate on a single $40/month virtual machine. While this lacks redundancy and relies on manual backups, it has taught me about my actual resource requirements more effectively than any theoretical planning document. I’ve discovered that my platform’s peak memory usage is only 4GB, which saved me from investing time in an overly complex Kubernetes setup that would have had little utility.
2. Hardcoded Configuration Values
Instead of traditional config files or environment variables, my settings are hardcoded as constants in my codebase. This allows for rapid access to configurations via simple search commands, enabling me to track changes efficiently. I’ve modified values just three times in three months, making the redevelopment time minimal compared to the extensive effort it would take to create a dedicated configuration service.
