Embracing the 3-Month Experiment: A Practical Approach to Non-Scalable Solutions
In the tech startup world, the mantra “Do things that don’t scale,” attributed to Paul Graham, resonates deeply. However, translating this advice into actionable coding practices is often overlooked.
Over the past eight months, while developing my AI podcast platform, I’ve crafted a personal guideline that I like to call the “3-Month Rule.” Essentially, any non-scalable workaround I implement is given a three-month trial period. By the end of that timeframe, it must either demonstrate its worth and be transformed into a robust solution or be retired.
As engineers, we tend to gravitate toward scalable designs from the get-go—think intricate design patterns, microservices, and distributed systems. While such architectures enable large operations to manage vast user bases efficiently, they often stall innovation in startups. Focusing on scalability too early can feel like costly procrastination, as we’re often crafting solutions for hypothetical users while missing the mark on real needs. The insights I’ve gained through my 3-month rule have led me to produce straightforward, albeit “imperfect,” code that launches quickly and reveals what users genuinely require.
My Current Non-Scalable Hacks and Their Unexpected Wisdom
1. Centralized Operations on a Single Virtual Machine
All critical components—my database, web server, background processing, and caching—operate on a single, $40/month VM. This setup lacks redundancy and relies on manual backups to my local device.
Why is this a sound strategy? In just two months, I’ve gained invaluable insights into my actual resource consumption, easily discernible through this simplified infrastructure. For instance, I found that my platform caps at 4GB of RAM, negating the need for the complex Kubernetes architecture I initially contemplated.
When failures occur—yes, they have twice so far—I collect real-time data to diagnose breakdowns, often revealing surprises in the failure points themselves.
2. Hardcoded Constants throughout the Codebase
Configurations like pricing and user limits are simply embedded in the code rather than stored in separate files or environment variables.
This approach appears rudimentary, but it has its advantages. I can quickly search through my code to locate any configuration value, ensuring that every price alteration is logged in the git history. In three months, I’ve modified these constants only three times, wasting mere minutes on redeployment instead of spending weeks creating a dynamic configuration service.
**3. SQLite:
