Embracing the 3-Month Experiment: A Practical Approach to Unscalable Solutions
In the ever-evolving tech landscape, the mantra of “doing things that don’t scale” has been echoed far and wide. While this concept, famously popularized by Paul Graham, is often discussed in theory, the practical steps to implement it—especially in the realm of software development—remain elusive. This is where my unique framework comes into play, one that I’ve honed while building my AI podcast platform over the past eight months: the 3-Month Rule for unscalable hacks.
Understanding the 3-Month Rule
As engineers, we typically approach development with scalability in mind, focusing on elegant architectures that can accommodate millions of users. However, in a startup environment, such thinking can often lead to premature optimization. By imposing a strict three-month timeline on unscalable solutions, I ensure that these experimental hacks either demonstrate their value and evolve into robust systems, or are discarded entirely. This methodology allows me to create functional and relevant code while gaining insights into user behavior.
My Unscalable Infrastructure: A Smart Experiment
Here are a few current infrastructure decisions that may seem unconventional but have provided invaluable lessons along the way.
1. One Virtual Machine for Everything
Hosting my database, web server, background jobs, and caching solutions on a single $40/month virtual machine has turned out to be a strategic choice. The lack of redundancy isn’t a downside; rather, it has enabled me to grasp my real resource requirements in an incredibly short timeframe. For example, my “AI-heavy” platform has revealed a resource peak of only 4GB of RAM. Instead of diving into complex configurations with Kubernetes, I’ve learned firsthand what causes my system to crash—information that no theoretical model could provide.
2. Hardcoded Configurations
In my codebase, config values such as pricing tiers and user limits are hardcoded directly into the application. While this method sacrifices the flexibility of external configuration files, it brings its own advantages. Changes are minimal—typically only three adjustments in the last three months—allowing for rapid deployment with minimal overhead. Tracking these changes through version control offers insights into my decisions, essentially consolidating my process without excessive engineering time.
3. Using SQLite for Production
Surprisingly, my multi-user web application runs on SQLite. With a lightweight database size of merely 47MB, it seamlessly supports 50 concurrent users. An analysis of
