Embracing the 3-Month Rule: A Practical Framework for Unscalable Solutions in Tech Development
In the world of startups and tech innovation, the mantra “Do things that don’t scale” resonates with many, yet the practicalities of implementing this advice often go unaddressed. Drawing inspiration from Paul Graham’s principles, I’ve devised a straightforward framework during my 8-month journey of developing my AI podcast platform. This approach centers around one key premise: each unscalable solution gets a trial period of three months. At the end of this period, it either earns a permanent place in the architecture or is phased out.
As engineers, we’re accustomed to pursuing scalable solutions from the outset—design patterns, microservices, and robust architectures are touted as the gold standard that can accommodate vast user bases. However, for startups, this often leads to unnecessary complexity and delays. My three-month approach compels me to create simplified, direct coding solutions that actually deliver, while also providing invaluable insights into user needs.
Current Non-Scalable Infrastructure Hacks and Their Unexpected Benefits
1. Consolidation onto One Virtual Machine (VM)
I currently operate my database, web server, background jobs, and Redis on a single VM costing $40 a month. While this setup lacks redundancy and relies on manual backups to my local machine, it’s proving to be a brilliant choice. Within just two months, I gained a clearer understanding of my actual resource requirements, revealing that my “AI-heavy” platform only peaks at 4GB of RAM. An overly complex Kubernetes setup would have required management of empty containers instead of focusing on meaningful metrics. Moreover, when issues arise—twice already—I gather real-time data on what fails, often revealing surprises.
2. Hardcoded Configuration for Simplicity
In my code, configurations are hardcoded directly—think constants like PRICE_TIER_1 = 9.99 and MAX_USERS = 100. While this may appear outdated, it allows for rapid updates. Since I can search my codebase swiftly for any configuration value, every price change is meticulously tracked in Git history. In three months, I’ve only needed to update these values three times; that’s a mere 15 minutes of redeployment compared to the potential 40 hours spent on a configuration service.
3. Utilizing SQLite in a Multi-User Environment
SQLite is my choice for database management, successfully supporting 50 concurrent users with a total size of
