Embracing the 3-Month Rule: A Pragmatic Approach to Non-Scalable Solutions in Development
In the startup world, the mantra often heard is Paul Graham’s famous advice: “Do things that don’t scale.” This principle challenges developers and entrepreneurs to engage in practices that may not be efficient in the long run but can provide valuable insights along the way. However, the implementation of such a philosophy in coding often lacks clarity. Over the past eight months of building my AI podcast platform, I’ve adopted a straightforward framework: every unscalable solution gets a trial period of three months. After this timeframe, it must either demonstrate its worth and be refined into a robust system, or be discarded.
As software engineers, we are usually conditioned to prioritize scalable architectures from the very beginning—using design patterns, microservices, and distributed systems to prepare for high user loads. However, this approach can lead to unnecessary complexity in the early stages of a startup. In reality, crafting scalable code too soon can result in costly delays, addressing concerns for problems that are non-existent at this stage. By enforcing my 3-month rule, I have permitted myself the freedom to write straightforward, albeit “imperfect,” code that can be deployed quickly, ultimately revealing what users genuinely need.
Practical Infrastructure Hacks: My Path to Essential Learning
1. A Unified Virtual Machine
My entire system—database, web server, background processes, and caching—runs on a single $40/month virtual machine with no redundancy and manual backups. While this approach may seem risky, it has provided me rich insights into my actual resource requirements. I discovered that my “AI-intensive” platform maxes out at just 4GB of RAM. Had I opted for a more complex solution like Kubernetes, I would have been managing empty resources instead of gaining practical data from real system failures.
2. Hardcoded Configurations
All of my fundamental configurations are hardcoded directly into the codebase—things like pricing tiers and user limits are set as constants. While this bypasses the convenience of config files and environment variables, it allows for quick searches and immediate deployment of any changes. Over the past three months, I’ve only modified these values three times, translating to minimal deployment effort compared to what would have been a time-consuming configuration management process.
3. SQLite as My Production Database
Yes, I’m using SQLite in a multi-user environment. Surprisingly, my entire database is merely 47MB
