The 3-Month Rule: A Technical Strategy for Innovation in startup Environments

In the tech world, especially within the startup ecosystem, there’s a well-known maxim from Paul Graham: “Do things that don’t scale.” Yet, discussions around practical implementation—particularly in coding—are surprisingly scarce. After dedicating eight months to building my AI podcast platform, I have devised a straightforward approach: every unscalable hack I employ is granted a lifespan of three months. At the end of this period, it either demonstrates its worth and transitions to a well-structured solution, or it gets phased out entirely.

This concept may seem counterintuitive; as engineers, we’re often instilled with the notion of creating scalable solutions from the outset. We become enamored with sophisticated architectural concepts like design patterns, microservices, and distributed systems. However, this is often a mindset befitting larger organizations rather than agile startups.

In the early stages of a startup, focusing on scalable solutions can end up becoming an expensive form of procrastination. It’s easy to find ourselves optimizing for potential users who may never materialize and attempting to resolve theoretical issues that remain unexamined. By adhering to my three-month rule, I’ve learned to craft straightforward, unrefined code that successfully ships and reveals genuine user needs. Here are some of the unconventional infrastructure strategies I’ve employed, each of which underscores the importance of learning through doing:

1. Consolidating Resources on a Single VM

Currently, my entire infrastructure operates on one virtual machine that costs just $40 a month. This setup includes the database, web server, background jobs, and Redis—there’s no redundancy, and backups are done manually.

This approach may seem risky, but it has been instrumental in clarifying my actual resource requirements. Within just two months, I learned that my platform, which I initially assumed would be resource-intensive, peaks at 4GB of RAM. The Kubernetes architecture I almost implemented would have resulted in maintaining idle containers. When my VM crashes (it has happened twice), I gather crucial insights about unexpected breaking points.

2. Hardcoded Configuration for Simplicity

Instead of using configuration files or environment variables, I have hardcoded crucial values directly into my code. This includes elements like pricing tiers and user limits.

Here’s the advantage: I can quickly search my entire codebase for any configuration value and track changes easily through Git history. Since these constants rarely change (only three times over three months