Embracing Imperfection: The Three-Month Experiment for Non-Scalable Solutions in Tech

In the realm of technology startups, one frequently cited piece of wisdom from Paul Graham is the encouragement to “do things that don’t scale.” However, while many are aware of this advice, few delve into practical strategies for its implementation within coding and development processes.

After dedicating the past eight months to developing my AI podcast platform, I have adopted a straightforward framework: each unscalable solution is granted a lifespan of three months. At the end of this period, it must either demonstrate its worth and transition into a more refined iteration, or it will be phased out.

The reality is that as engineers, we are often conditioned to prioritize scalable solutions from the outset. We dream in terms of design patterns, microservices, and robust architectures capable of handling vast user bases. This mindset is often tailored to larger enterprises, but in the startup environment, such scalability can lead to costly delays. My three-month rule compels me to deploy simple, sometimes ‘messy,’ code that facilitates actual product delivery, providing real insights into user needs.

Innovative Infrastructure Hacks: Why They Work

1. Consolidated Infrastructure on One VM

My infrastructure runs entirely on a single virtual machine, housing the database, web server, background tasks, and caching—all for a mere $40 a month. This approach means zero redundancy and manual backups to my local system.

This setup is far from reckless; in just two months, I’ve gained invaluable insights into my actual resource requirements, far exceeding what any capacity planning model could offer. Contrary to initial assumptions about needing extensive resources, my platform’s peak usage amounts to just 4GB of RAM. The complex Kubernetes architecture I initially considered would have ultimately led to more overhead without any actual user demand.

2. Simplified Hardcoded Configurations

My configuration is straightforward—no external files or environment variables, just constants embedded in the code. Adjusting a value necessitates redeployment, but it provides an unexpected advantage: I can easily search my codebase for any configuration parameter, making updates simple and transparent.

Creating a dedicated configuration service could take a week, yet I have modified these values just three times in three months. This approach saves me extensive engineering hours while still allowing for rapid iteration.

3. Utilizing SQLite in Production

Yes, my application runs on SQLite, serving a multi-user environment with a database size of just 47MB. It