Embracing the 3-Month Rule: A Pragmatic Approach to Non-Scalable Solutions in Development
In the world of startups and tech innovation, the advice to “do things that don’t scale” is often repeated, especially in the context of growth and experimentation. However, the application of this advice in coding practices is frequently overlooked. After dedicating the past eight months to developing my AI podcast platform, I’ve established a simple yet effective framework: every unscalable shortcut is given a lifespan of three months. At the end of this period, the solution must either demonstrate its value and earn its place in the infrastructure or be discarded.
As software engineers, we tend to focus on building scalable solutions from the outset. We often chase after the idealized architectures—Kubernetes, microservices, distributed systems—designed to handle vast numbers of users. This approach, while relevant for larger corporations, can be detrimental in a startup environment. Early-stage companies often find that focusing on scalability can lead to costly delays, optimizing for potential users that may never materialize. My three-month rule encourages a different mindset: I prioritize straightforward, rudimentary code that gets shipped quickly, enabling me to learn what my actual users need.
Current Infrastructure Hacks: Why They’re More Insightful Than You Think
1. Consolidated Operations on a Single VM
I’ve opted to run my entire application—including the database, web server, background jobs, and caching—on a single virtual machine for just $40 a month. While there’s no redundancy and I handle backups manually, this approach has provided invaluable insights. Within two months, I’ve gained a thorough understanding of my resource requirements. It turns out my “AI-heavy” platform only peaks at 4GB of RAM. The complex Kubernetes framework I nearly implemented would have meant managing unnecessary overhead. When the VM does crash (and it’s happened twice), I get real data on system failures, often revealing surprises about where the actual issues lie.
2. Simplified Configurations Without External Files
In my codebase, configurations such as pricing tiers and user limits are hardcoded rather than stored in separate configuration files or managed through environment variables. Yes, redeploying is required with any change, but the simplicity allows for rapid tracking and easy retrieval via searches through my codebase. Each update is documented in version control, and I’ve only needed to change these values a handful of times in three months. This trade-off has saved considerable engineering
