Embracing the 3-Month Rule: A Practical Approach to Building Non-Scalable Solutions
In the world of software engineering, the mantra “Do things that don’t scale,” as espoused by Paul Graham, is commonly recognized yet seldom elaborated upon in a technical context. After dedicating the last eight months to developing my AI podcast platform, I’ve established a straightforward framework to serve this advice: every unscalable hack I implement is given a three-month trial period. At the end of this timeframe, it’s either refined into a fully developed solution or discarded.
As engineers, we often gravitate toward building scalable solutions from the outset—focusing on design patterns, microservices, and distributed systems that are capable of supporting millions of users. While this approach is appropriate for large organizations, it can be a hindrance in a startup environment. Here, pursuing scale prematurely can translate into time-consuming and costly procrastination, optimizing for users who may never materialize. My three-month rule encourages me to adopt a more straightforward coding style that prioritizes functionality over perfection, providing insight into genuine user needs.
Current Infrastructure Strategies: Unconventional Yet Effective
1. Unified Virtual Machine Approach
At present, all components of my platform—including the database, web server, background jobs, and caching—operate on a single virtual machine costing just $40 per month. This approach, which lacks redundancy and relies on manual local backups, has proven to be a strategic advantage. Within two months, I’ve gained a deeper understanding of my resource demands than I would have through any traditional capacity planning methods. For instance, my AI-centric platform peaks at merely 4GB of RAM, raising questions about the necessity of the intricate Kubernetes setup I almost implemented. By allowing my system to crash (which has happened twice), I’ve learned precisely what fails—an experience that defies expectation.
2. Simplified Configuration Management
In my codebase, configuration values—like pricing tiers and user limits—are hardcoded instead of housed in complex configuration files. While this may seem counterintuitive, it possesses a unique advantage: rapid access and tracking. I can swiftly search for any configuration value across the code, with all changes documented in Git history. In just three months, I’ve altered these values three times, making the trade-off of 15 minutes for a redeployment far more efficient than the 40 hours it would take to create a configuration management service.
