Embracing the 3-Month Rule: A Unique Approach to Building Solutions in startup Development

Navigating the startup landscape often involves a blend of innovation, risk-taking, and the necessity to adapt rapidly. One piece of timeless advice from Paul Graham resonates with many entrepreneurs: “Do things that don’t scale.” But how do we actually apply this concept in the realm of technology and coding?

After dedicating eight months to building my AI podcast platform, I’ve formulated a practical framework that drives my development process: each unscalable solution I implement gets exactly three months of active testing. After this period, I evaluate its performance—if it proves beneficial, it gets further development; if not, it’s phased out.

The startup Mindset: Navigating the Need for Scalability

In the world of software engineering, the instinct is to create scalable solutions from the outset, with concepts like design patterns, microservices, and distributed architectures prominently celebrated. However, in startup environments, focusing exclusively on scalability can often lead to unnecessary complexities and, frankly, a stalling of progress.

My three-month rule encourages me to produce straightforward code that prioritizes shipping over perfection. Here’s how I’ve implemented this philosophy and the valuable lessons I’ve learned through some unconventional strategies.

Current Hacks in My Infrastructure: Strategies That Work

1. Consolidated on a Single Virtual Machine

Currently, my entire infrastructure—database, web server, background jobs, and caching—is running on one $40/month virtual machine. This setup, while lacking redundancy, has provided insightful data regarding my resource consumption in just two months, far beyond what extensive capacity planning documents could offer.

The result? I realized my platform, which I initially expected to require large resources, actually peaks at merely 4GB of RAM. The overly complex Kubernetes architecture I contemplated would have been managing idle containers.

2. Hardcoded Configuration Values

Instead of utilizing external configuration files or environment variables, my codebase contains hardcoded constants—like pricing tiers and user limits. Though this might appear primitive, it has significant advantages: I can quickly search for any config value across the codebase and track changes in git history effortlessly. In the past three months, I’ve only needed to make three updates, translating to a mere 15 minutes of redeployment compared to the hours I would have spent setting up a robust configuration management system.

3. SQLite for User Management

Surprisingly, I am successfully running a multi-user