Embracing the 3-Month Rule: A Practical Approach to Non-Scalable Solutions in Software Development
In the domain of software engineering, a common piece of wisdom attributed to Paul Graham is the notion of “doing things that don’t scale.” While many acknowledge the concept, few dive into practical methods for implementing it, especially in coding. As I’ve spent the last 8 months developing my AI podcast platform, I’ve synthesized a straightforward system: every unscalable hack I employ is given a trial period of three months. After this time, the solution is either validated and turned into a sustainable model or discarded.
The startup Struggle with Scalability
As engineers, we are often conditioned to prioritize scalable solutions from the outset, focusing on design patterns, microservices, and distributed architectures capable of managing vast user bases. However, this mindset is more suited to established companies than to startups. For newer ventures, crafting scalable software can sometimes translate to delaying crucial decisions and over-engineering for problems that may never arise. My three-month rule compels me to prioritize simplicity, allowing me to produce functional code that delivers real insights into my users’ needs.
My Innovative Infrastructure Adjustments
1. Consolidated VM Utilization
My operation is entirely contained within a single $40/month virtual machine, running the database, web server, background jobs, and even Redis. While many would view this as a risky choice, it has proven invaluable. In just two months, I’ve gained a clearer understanding of my actual resource requirements rather than relying on theoretical capacity planning documents. Discovering that my platform typically peaks at just 4GB of RAM has spared me the unnecessary complexities of an advanced Kubernetes setup that would have managed idle containers.
2. Unified Configuration Constants
My approach to configuration is straightforward: hardcode values directly into the codebase. For example, pricing tiers and maximum user limits are simply constants. Although this method may seem primitive, I can instantly search my complete code repository for any configuration value and track changes through version control history. What looks like a cumbersome task actually saves significant engineering hours, especially since these values only require adjustments infrequently.
3. Utilizing SQLite for a Multi-User Environment
In a somewhat unconventional choice, I’ve deployed SQLite for my multi-user application, where the entire database is a mere 47MB. It efficiently accommodates 50 concurrent users. This decision revealed that my access patterns are overwhelmingly read-based, making SQLite an ideal fit. Had
