Embracing the Three-Month Rule: A Practical Approach to Non-Scalable Solutions
In the world of tech startups, Paul Graham’s famous mantra—”Do things that don’t scale”—is often echoed, yet the methods for its effective implementation remain largely unspoken. After eight months of developing my AI podcast platform, I devised a straightforward yet powerful framework that I like to call the Three-Month Rule: any non-scalable solution I implement is given a lifespan of three months. If it proves its worth, it gets the upgrade it deserves; if not, it’s time to say goodbye.
As engineers, we’re often conditioned to prioritize scalability from the outset. We delve into complex architectures, utilizing design patterns and microservices suited for handling millions of users. However, this mindset usually aligns more closely with large corporations rather than the agile environment of a startup.
In a startup context, focusing too heavily on scalability can lead to unnecessary delays and over-engineering. My Three-Month Rule compels me to write straightforward and sometimes “imperfect” code that gets deployed; it allows me to truly understand my users’ needs in real-time.
Exploring My Current Infrastructure Hacks and Their Benefits:
1. Single Virtual Machine Setup
Everything—database, web server, background processing, and Redis—exists on a single $40/month virtual machine without redundancy and with manual backups.
This approach has been revealing. Within just two months, I gained invaluable insights into my resource requirements—far beyond what any capacity planning document could provide. I’ve learned that my “AI-heavy” platform efficiently operates at a mere 4GB RAM. Had I opted for a sophisticated Kubernetes setup, I’d be managing empty containers rather than focusing on genuine user needs.
When the system crashes—yes, it has happened twice—I gain real insights about failure points, which often surprise me.
2. Hardcoded Configuration Values
Instead of utilizing configuration files or environment variables, I have hardcoded constants throughout the code. This means redeploying whenever adjustments are needed, but it comes with its benefits:
For instance, I can quickly grep my codebase for any configuration value, and all changes are tracked through git history, ensuring accountability. Building a dedicated configuration service would take considerable time, yet in three months, I’ve altered these values just three times—spending 15 minutes redeploying rather than 40 hours developing.
3. Utilizing SQLite in Production
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