Embracing the 3-Month Rule: My Framework for Non-Scalable Solutions in Tech

In the world of entrepreneurship and technology, the advice from prominent figures often carries significant weight. Paul Graham’s mantra, “Do things that don’t scale,” is frequently cited, yet the implementation of this philosophy in coding practices remains largely unexplored. After eight months of developing my AI podcast platform, I have crafted a practical approach: every hack that lacks scalability will be evaluated after a three-month trial period. If it demonstrates its worth, it will be developed further; if not, it will be retired.

As engineers, we often lean towards creating solutions designed to scale from the outset. This includes utilizing established design patterns, microservices, and distributed systems—all ideally suited for accommodating vast amounts of users. However, such big-picture thinking can be a liability in a startup environment. Focusing on scalable solutions too early can lead to unnecessary overhead, addressing issues that may never arise while delaying immediate progress.

My three-month rule compels me to write straightforward and occasionally “messy” code that not only launches swiftly but also provides insights into my users’ actual needs. Below, I outline some current aspects of my infrastructure that may seem unconventional yet serve a strategic purpose.

Current Infrastructure Strategies: Smart Hacks for Growth

1. Consolidated Resources on One VM

I have opted to run my database, web server, background jobs, and caching systems on a single virtual machine costing me $40/month. While this approach lacks redundancy and relies on manual backups, it has provided invaluable insights into my immediate resource requirements. My platform’s peak usage revealed a need for just 4GB of RAM. The elaborate Kubernetes architecture I once considered would have gone to waste managing mostly idle resources.

Experiencing the occasional crash has also offered real-time data on system failures—insights that would have eluded me with a more complex setup.

2. Hardcoded Configuration for Efficiency

By maintaining all configuration values—such as pricing tiers and user limits—as constants in the code rather than in separate config files, I streamline my development process. This method allows me to swiftly locate and modify values throughout the codebase without the need for complicated configuration services.

In three months, I’ve only needed to make three adjustments, significantly reducing engineering time. This straightforward approach has proven efficient and effective.

3. Leveraging SQLite for Production

Despite its reputation, I am utilizing SQLite for a multi-user application, with the database