Embracing the 3-Month Experimentation Rule for Software Development
When it comes to startup development, there’s a well-known mantra from Paul Graham: “Do things that don’t scale.” While this advice is often shared, what’s less frequently discussed is how to practically apply it, particularly in coding. After spending the past eight months developing my AI podcast platform, I’ve adopted a unique framework: every temporary, unscalable solution gets a 3-month trial period. At the end of this period, it either proves its worth and is transformed into a robust solution or is discarded.
As engineers, we’re trained to prioritize scalable architectures from the get-go. We imagine systems built with complex design patterns, microservices, and distributed protocols—ideal for catering to millions of users. However, this mentality often leads to premature optimization in a startup setting, focusing on hypothetical users and challenges that may never materialize. My 3-month rule has compelled me to create direct, straightforward code that can be deployed rapidly, allowing me to understand the true needs of my users.
Key Infrastructure Strategies That Are Surprisingly Effective
1. Consolidation on a Single Virtual Machine
Currently, my entire application stack—including the database, web server, background jobs, and caching—is hosted on a single $40/month virtual machine. While this setup lacks redundancy and relies on manual backups, it has provided invaluable insights into my actual resource requirements over the past two months, far exceeding what any theoretical capacity planning could have revealed. For instance, I’ve found that my platform’s memory usage peaks at 4GB. The complex Kubernetes architecture I initially considered would have resulted in management overhead for resources that I simply don’t need.
2. Hardcoded Configuration for Simplicity
I’ve opted for hardcoded constants for configuration rather than using external files or environment variables. This means that modification requires a simple redeployment. While traditional wisdom advocates for a dedicated configuration service—which would take significant engineering effort—my method allows me to trace historical changes easily. In three months, I’ve changed configuration values just three times, saving me roughly 40 hours of development time.
3. Using SQLite for a Multi-User Environment
Yes, you read that right—I’m running SQLite as the database for my web application. Its size is a mere 47MB, yet it smoothly accommodates up to 50 concurrent users. This approach has highlighted my usage patterns: 95% of my database interactions are reads. If I had
