Embracing the 3-Month Rule: My Approach to Non-Scalable Solutions in Software Development
In the startup world, the mantra often heard is Paul Graham’s well-known advice: “Do things that don’t scale.” While this nugget of wisdom resonates with many, implementing it within the realm of coding remains largely unexplored. After eight months of developing my AI podcast platform, I’ve devised a straightforward framework that has proven invaluable: every unscalable tactic is allotted a mere three months to demonstrate its worth. If it doesn’t, it’s time to say goodbye.
As software engineers, we often find ourselves caught in the trap of crafting “scalable” solutions from the get-go. We meticulously design systems with advanced architecture like microservices and distributed systems, aimed at serving millions of users. However, this thought process can be more fitting for large corporations than for startups. In the early stages of a venture, investing time and resources into scalability can simply act as a costly distraction. My three-month rule compels me to create straightforward, sometimes “imperfect,” code that can be deployed quickly, allowing me to learn what my users genuinely require.
Optimizing My Infrastructure with Purposeful Hacks
1. Streamlined Operations on a Single VM
The foundation of my setup runs on a sole $40/month virtual machine where everything resides—database, web server, background jobs, Redis. While this may seem risky due to a lack of redundancy and manual backups, it has been incredibly insightful. Within two months, I’ve gained a clearer understanding of my resource needs than I could have from comprehensive capacity planning. Contrary to initial assumptions of needing an elaborate Kubernetes configuration, it turns out that my AI-driven platform only peaks at 4GB of RAM. Each crash has provided actual data about the moments of failure, often surprising me in the process.
2. Adopting Hardcoded Constants
In an effort to keep things simple, I’ve opted for hardcoded constants in my codebase, eliminating complex configuration files or environment variables. This method may seem outdated, but it offers me impressive efficiency. With a simple grep command, I can track down any configuration value in seconds, and changes are quickly documented through Git history. Instead of spending a week building a configuration management system, I have redeployed my application just three times to adjust various settings, totaling a mere 15 minutes compared to countless hours of engineering.
3. Utilizing SQLite for Production
Yes, you
