The 3-Month Experiment: A Strategic Approach to Unscalable Solutions in Coding
In the startup world, it’s common knowledge that adopting the mantra of “do things that don’t scale” is essential for early success. However, the real challenge lies in the execution, especially in software development. Drawing from my experience over the past eight months while building my AI podcast platform, I’ve devised a straightforward framework for navigating unscalable solutions: the 3-Month Rule. This approach allows me to assess each temporary hack based on its performance after three months, determining whether it should be fully implemented or scrapped.
Why Unscalable Solutions Matter
As software engineers, we often feel pressured to create scalable solutions right off the bat. With the allure of sophisticated design patterns, microservices, and distributed systems, it’s easy to get caught up in the architecture ideal for a large user base. Yet, in a startup scenario, pursuing scalability too early can result in counterproductive decisions, wasting resources on problems that don’t yet exist. My framework prioritizes simplicity, encouraging me to develop straightforward and practical code that can ship quickly, providing a clearer understanding of actual user needs.
My Practical Infrastructure Hacks: Insights and Justifications
1. Everything Consolidated on One Virtual Machine
Currently, my entire setup—database, web server, background jobs, and cache—operates on a single $40/month virtual machine. While this setup lacks redundancy and requires manual backups, it serves a critical purpose. In just two months, I’ve gained invaluable insights regarding my resource requirements. My so-called CPU-heavy platform only peaks at 4GB of RAM. The complex Kubernetes architecture I contemplated would have led to managing empty resources. Each crash offers real data about failures, revealing surprises about what truly causes issues.
2. Hardcoded Configuration Values
Configurations are hardcoded directly in my codebase, eliminating the use of configuration files or environment variables. Although it seems cumbersome, it allows for rapid tracking and modifications through Git, ensuring every change is documented. In three months, I’ve only adjusted these values three times. The time spent on redeploying is minuscule compared to the hours it would take to implement a separate configuration service.
3. Using SQLite for a Multi-User Environment
Despite its limitations, SQLite has proven effective in my application, with a database size of just 47MB. It handles 50 concurrent users effortlessly, and my read-to-write
