Embracing the 3-Month Rule: A Practical Approach to Non-Scalable Solutions in Tech Development

In the realm of startup culture, a mantra by Paul Graham resonates: “Do things that don’t scale.” While this advice is often discussed in theory, practical application in software development remains less explored. After dedicating eight months to building my AI podcast platform, I’ve derived a straightforward framework that I call the “3-Month Rule,” designed to guide the implementation of those non-scalable tactics.

Understanding the 3-Month Rule

As engineers, we are frequently conditioned to prioritize scalability from the outset. We focus on design patterns, microservices, and sophisticated distributed systems, all intended to accommodate vast user bases. However, this mindset can be counterproductive, particularly in the startup world where many optimizations may be premature, focused on future users who haven’t yet arrived. My 3-month approach challenges me to use simple, direct solutions that may not conform to conventional best practices but yield actionable insights about user behavior.

Current Infrastructure Tactics and Their Value

1. Consolidated Single VM Architecture

Everything, from the database to the web server and background jobs, operates on a single $40/month virtual machine. While this may seem reckless due to the lack of redundancy and reliance on manual backups, it has granted me profound insight into my resource consumption.

Within just two months, I understood my peak resource needs—4GB of RAM—without needing to build a complex infrastructure. Each time the system has failed (twice so far), I’ve garnered valuable data about the failure’s cause, which has often been unexpected.

2. Hardcoded Configuration Values

In my codebase, all configuration values—pricing tiers, user limits, and AI model references—are hardcoded constants. While this approach omits sophisticated configuration management, it has a hidden advantage: organization. I can swiftly search my entire codebase for any configuration, and any changes I make are captured in Git history.

Building a separate configuration management service would have consumed a significant portion of my time—time that I can instead spend on more pressing tasks. In three months, I’ve adjusted these configurations only three times, which took mere minutes to redeploy versus the potentially extensive engineering efforts to develop a complex system.

3. Using SQLite for Production Needs

Yes, I’ve chosen SQLite for what is essentially a multi-user web application. With a database size of just 47MB, it comfortably accommodates up