Embracing the 3-Month Rule: A Practical Approach to Non-Scalable Solutions in Software Development
In the world of software development, especially within startups, there’s a piece of wisdom often shared by entrepreneur Paul Graham: “Do things that don’t scale.” While the concept might be widely acknowledged, implementing this approach in coding practices remains a topic largely unexplored. Over the past eight months, while developing my AI podcast platform, I’ve crafted a straightforward framework: each unscalable solution is granted a lifespan of three months. After this period, it either proves its worth with effective implementation or is phased out.
The startup Dilemma: Scaling Too Soon
As engineers, we are typically conditioned to seek scalable solutions from the get-go. We often dive headfirst into sophisticated design patterns and distributed systems, envisioning applications that can handle millions of users effortlessly. However, in the context of a startup, striving for scalability too early can often turn into costly procrastination. We find ourselves optimizing for hypothetical users and solving issues that may never materialize.
My three-month rule encourages the creation of simple, straightforward code that allows me to ship quickly and gain insightful feedback on what my actual users need.
Strategic Infrastructure Decisions: Understanding the Rationale
Here’s a breakdown of my current non-traditional infrastructure strategies and the rationale behind them:
1. Consolidated Operations on a Single VM
All critical components—database, web server, background jobs, and Redis—operate on one cost-effective virtual machine. This setup lacks redundancy and relies on manual backups.
Why this approach is beneficial: Within two months, I’ve garnered more understanding about my resource needs than any extensive planning document could provide. My AI platform’s peak usage has revealed that it operates comfortably within 4GB of RAM, illuminating the futility of the complex Kubernetes structure I almost erected.
Whenever unexpected crashes occur (and they have twice), I gain firsthand data about failure points, often surprisingly different from my initial assumptions.
2. Hardcoded Configuration Values
Instead of employing configuration files or environment variables, I’ve opted for hardcoded constants throughout my codebase.
The hidden advantage: This approach enables me to quickly search through my entire code for any configuration in seconds, and changes are meticulously tracked in the version history. Those few configuration changes I’ve executed have required mere minutes of deployment time compared to investing extensive hours in establishing a dedicated configuration service.
3. Using SQLite in Production
Yes,
