Embracing the 3-Month Rule: A Practical Approach to Non-Scalable Solutions in Development
In the world of software development, a common adage often surfaces: “Do things that don’t scale,” a notion championed by entrepreneur Paul Graham. However, the challenge usually lies in translating this advice into effective coding practices. Having spent the last eight months building my AI podcast platform, I’ve created a practical framework that prioritizes experimentation: any hack deemed unscalable is given a lifespan of three months. After this period, it must either demonstrate its value and evolve or be discarded.
As engineers, our conditioning often nudges us toward developing scalable solutions right from the outset. We focus on intricate architectures like design patterns and microservices, all of which are undoubtedly crucial for large-scale applications. Yet, in a startup environment, optimizing for users who aren’t on board yet can merely be a costly delay. My three-month rule encourages me to craft straightforward, albeit “imperfect,” code that yields tangible results and offers insights into user needs.
Current Infrastructure Hacks: Smart Choices for Real-World Learning
1. Single-VM Infrastructure
Running my entire stack—including the database, web server, background jobs, and caching solution—on a single $40-per-month virtual machine may seem reckless. However, this setup has been exceptionally revealing. Within just two months, I have gained invaluable insights into my actual resource needs. For instance, my platform experiences peak memory usage at just 4GB. The sophisticated Kubernetes configuration I nearly implemented would have left me managing idle containers.
The crashes I’ve experienced have provided concrete data on failure points, which consistently surprise me, reinforcing the importance of hands-on learning over theoretical planning.
2. Hardcoded Configuration Values
Instead of relying on external configuration files or environment variables, I’ve adopted a straightforward approach of utilizing hardcoded constants throughout my codebase. This may seem primitive, but it allows me to quickly locate any configuration value using basic search commands. I’ve only changed these parameters a handful of times in three months, making the minor inconvenience of redeployment a far more efficient option than investing time into building a comprehensive configuration management system.
3. Utilizing SQLite in Production
Yes, I have opted for SQLite for my multi-user application. Surprisingly, my entire database footprint is only 47MB, seamlessly accommodating 50 concurrent users. This choice reveals that my access patterns are predominantly read-heavy, making SQLite an optimal fit. Had I deployed with
