Embracing the 3-Month Rule: A Practical Approach to Scalable Solutions in Startups

When it comes to advice in the startup world, Paul Graham’s recommendation to “do things that don’t scale” often reigns supreme. However, translating this concept into practical, actionable coding strategies is rarely discussed. After eight months of building an AI podcast platform, I’ve devised a straightforward framework that has proven invaluable: I give every unscalable solution a maximum lifespan of three months. At the end of this period, it must either demonstrate its worth and warrant further development or be discarded.

In a startup setting, we often find ourselves caught up in the ideal of creating scalable solutions right from the outset. We learn about intricate design patterns, microservices, and distributed systems—all essential for handling significant user bases. However, in an early-stage startup, focusing on scalability can sometimes lead to unnecessary delays, tailoring our infrastructure to hypothetical users instead of real ones. The three-month rule ensures that I produce simple, agile code that swiftly moves to production, allowing me to identify actual user needs through genuine feedback.

Current Infrastructure Hacks: Why I Believe They Work

1. Single Virtual Machine Setup

I operate my entire platform—including the database, web server, background jobs, and caching—on a single $40/month virtual machine. While this setup lacks redundancy and relies on manual backups to my local machine, the insight it provides has been unparalleled. After two months, I finally grasped my true resource requirements, discovering that my AI-driven platform peaks at just 4GB of RAM. The sophisticated Kubernetes environment I nearly established would have involved managing unnecessary resources.

When my system has crashed—a couple of times—I’ve gained real-world insights into what truly fails. Surprisingly, it’s rarely what I initially thought.

2. Hardcoded Configurations for Agility

Configurations such as pricing tiers and user limits are directly defined within the code rather than managed through external files or environment variables. While this may seem archaic, it comes with the advantage of quick searches across the codebase. Every configuration change is documented through Git history, ensuring simplicity in tracking adjustments. Instead of investing a week in developing a configuration service, I’ve spent a mere 15 minutes redeploying changes that occurred just three times in three months.

3. Adopting SQLite for Production Use

Yes, I’ve opted for SQLite in a multi-user web environment, and surprisingly, it works like a charm with