The 3-Month Rule: A Pragmatic Approach to Understanding What Truly Works in Tech

In the world of startup development, the advice from Paul Graham resonates widely: “Do things that don’t scale.” However, the challenge lies in translating this philosophy into actionable technical practices. This revelation has come to me as I’ve been working on building my AI podcast platform over the past eight months. To effectively navigate the uncharted waters of coding at a startup, I’ve adopted a straightforward approach: every non-scalable strategy or hack is given a lifespan of three months. After that period, it either proves its worth and is transformed into a durable solution, or it is eliminated from the codebase.

The Startup Reality: Embracing Non-Scalability

As developers, we are primarily conditioned to think in terms of scalable solutions from the get-go. We envision complex systems: design patterns, microservices, and robust architectures capable of supporting millions of users. However, this mindset can often be an expensive distraction in the startup environment.

The reality is that dedicating time to scalable code may just lead to delaying crucial iterations. My three-month rule compels me to craft straightforward, albeit imperfect, code that can be shipped swiftly. This approach provides invaluable insights into what my users genuinely need and how they interact with the platform.

Smart Infrastructure Hacks That Foster Learning

Here are some unorthodox approaches I’ve implemented, along with the reasoning that underscores their effectiveness:

1. Consolidated Infrastructure on a Single VM

All components of my platform—database, web server, and background processes—are operational on a single, $40/month virtual machine with no redundancy. While this setup lacks typical safety measures, it has afforded me practical insights into my resource requirements. In just two months, I’ve learned that my platform only peaks at 4GB RAM, rendering my initial thoughts of a complex Kubernetes architecture unnecessary.

Whenever the system crashes (and it has twice), I gather concrete data on the actual points of failure, revealing trends that were far from my initial assumptions.

2. Simplicity in Configuration Management

Instead of intricate configuration files or environmental variables, I’ve opted for hardcoded constants within the code. This method might seem rudimentary, but it allows me to quickly trace back any configuration change through my version history. In three months, I’ve altered these settings only three times, saving significant development time when compared to building a dedicated configuration service.

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