The 3-Month Experiment: A Pragmatic Approach to Non-Scalable Solutions in Software Development
In the ever-evolving world of technology, it’s common to encounter the advice from creators like Paul Graham: “Do things that don’t scale.” This statement resonates with many entrepreneurs, yet the mechanics for effectively applying it within the realm of coding remain relatively unexplored.
After dedicating eight months to building my AI podcast platform, I’ve crafted a straightforward framework that I call the “3-Month Experiment.” Under this system, each unscalable solution is given a lifespan of three months. At the end of this period, I assess its value: if it proves useful, it gets a more robust implementation; if not, it is discarded.
As developers, we often aim to build scalable architecture right from the start—think of microservices, distributed systems, and intricate design patterns intended for millions of users. However, this mentality tends to align more closely with large corporate processes than with the agile mindset required in a startup.
Indeed, pursuing scalable solutions too early can turn into an expensive form of procrastination. You may find yourself optimizing for hypothetical users and addressing problems that may never arise. This is why my 3-Month Experiment prioritizes efficient, direct coding practices that deliver products to market quickly, allowing me to grasp user needs in real-time.
My Current Infrastructure Strategies and Their Unexpected Wisdom
1. Operating on a Single Virtual Machine
I run my entire operation—from database management to web services—on a single $40/month virtual machine without redundancy. This minimalist approach has provided me with more insights into my resource requirements within just two months than any detailed capacity planning report ever could. My “AI-heavy” application doesn’t require the elaborate Kubernetes setup I previously considered; instead, I’ve learned its peak usage hovers around 4GB of RAM. When issues arise (as they have twice), I receive immediate feedback on real failures—often surprising lessons on what actually breaks.
2. Simplistic Hardcoded Configurations
I have eliminated environment variables and configuration files in favor of hardcoded constants sprinkled throughout my code base. While this approach may appear simplistic, it comes with the distinct advantage of easily tracking and updating configuration values. Changing a price or user cap only takes about 15 minutes of redeployment, compared to the extensive engineering time required to implement a configuration service.
3. Utilizing SQLite in Production
Despite the common practice of deploying more
