Embracing the 3-Month Rule: A Practical Approach to Non-Scalable Solutions
In the tech world, the iconic advice from Paul Graham—”Do things that don’t scale”—is often discussed, yet rarely explored in depth, especially in the coding realm. As I have been developing my AI podcast platform for the past eight months, I have stumbled upon a straightforward framework that has significantly influenced my approach: the 3-Month Rule. This concept hinges on the idea that every unscalable “hack” I implement receives a lifespan of three months. At the end of that period, the solution either proves its worth and is refined, or it gets eliminated.
It’s crucial to recognize that, as engineers, we are often conditioned to focus on scalable solutions from the outset. Our training emphasizes elaborate architectures, like microservices and distributed systems, designed to handle enormous user bases—thought processes ingrained during our time with larger corporations. However, in the fast-paced startup environment, pursuing scalability prematurely can lead to wasted resources and delayed progress. My 3-Month Rule compels me to create straightforward, albeit imperfect, code that gets deployed, allowing me to truly understand user needs.
Current Infrastructure Hacks: Insights from Practicality
1. Simplified Infrastructure on a Single VM
I have opted to run my entire platform—database, web server, background processes, and Redis—on a single virtual machine costing $40 a month. While it lacks redundancy and relies on manual backups, it has taught me more about real resource demands in two months than any extensive capacity planning exercise ever could. Surprisingly, my “AI-heavy” platform only peaks at 4GB of RAM. The complex Kubernetes setup I nearly deployed would have resulted in managing empty containers.
When the system crashes—a scenario that has occurred twice—I obtain tangible insights into what fails, often revealing unexpected outcomes.
2. Hardcoded Configurations
In my codebase, parameters like pricing and user limits are hardcoded directly into the files. Alteration requires a redeployment, but the benefits are noteworthy. This approach allows me to quickly search configuration values and keep track of changes through version history. Instead of spending over 40 hours creating a configuration service that I only needed a few times, I save precious development time with just minutes of redeployment.
3. Employing SQLite in Production
Yes, you read that right: I’m using SQLite for what effectively is a multi-user application. With a
