The 3-Month Rule: A Pragmatic Approach to Unscalable Solutions

In the realm of startups, the iconic advice from Paul Graham to “do things that don’t scale” often sparks engagement, yet the practical implementation of this concept in software development remains less discussed. After eight months dedicated to developing my AI podcast platform, I’ve crafted a straightforward framework that involves embracing unscalable hacks for a limited duration—three months, to be exact. At the end of this period, each hack must either demonstrate its value for further expansion or be consigned to oblivion.

Embracing a Learning-Centric Model

As software engineers, we’re often conditioned to prioritize scalable architectures right from the start. We dive into advanced design patterns, microservices, and distributed systems, crafting intricate infrastructures designed to accommodate millions of users. Yet, this ‘big company’ mentality can often lead to inefficiencies in the startup ecosystem, where scalability might simply represent a costly diversion.

The three-month rule compels me to adopt a more straightforward coding style that prioritizes shipping over perfection, guiding me to understand my users’ genuine needs.

Insights from My Current Infrastructure Hacks

1. Unified Virtual Machine Setup

Instead of spreading my database, web server, and background jobs across multiple instances, I operate everything on a single virtual machine costing $40 per month. While this setup lacks redundancy and necessitates manual backups, it has provided invaluable insights into my actual resource demands. Within two months, I discovered that my “AI-intensive” platform peaks at just 4GB of RAM, something that a more elaborate Kubernetes architecture would have obscured. Each crash (and there have been a couple) has revealed critical lessons about system failures—none of which aligned with my expectations.

2. Hardcoded Configurations

My configuration philosophy is simple: keep everything hardcoded, from pricing tiers to user limits. This means that updates require a redeployment, but the trade-off is highly efficient tracking. I can swiftly search my codebase for any parameter and have a complete history of changes at my fingertips. What might take a week to build into a configuration service ends up being a 15-minute redeployment task that I’ve only needed to execute three times in three months.

3. Leveraging SQLite for Multitasking

Despite the conventional wisdom against using SQLite for multi-user applications, my decision to do so has proven fruitful. My entire database is a mere 47MB, effortlessly managing