The Three-Month Framework: A Pragmatic Approach to Non-Scalable Coding
In the world of startup development, Paul Graham’s famous adage, “Do things that don’t scale,” resonates with many. Yet, few discuss the practical application of this wisdom within the realm of coding. After dedicating eight months to building my AI podcast platform, I’ve honed a straightforward methodology: every temporary solution receives a trial period of three months. At the end of this timeframe, it must either demonstrate its worth and be fully developed or be discarded.
As engineers, we often gravitate toward crafting “scalable” solutions from the outset, conjuring visions of elegant architecture capable of managing vast user bases. While impressive, this large-scale mindset does not always translate to the startup environment. In many cases, preemptively optimizing for future users can lead to unnecessary complexity and costly delays. By adopting my three-month rule, I compel myself to create simple, somewhat “imperfect” code that delivers results and helps me understand my users’ true needs.
Key Infrastructure Strategies: Why They’re Smarter Than They Appear
1. Consolidation on a Single VM
My web server, database, background processes, and Redis are all housed on a single $40/month virtual machine—an approach that many would deem overly simplistic. However, this setup has proven invaluable for identifying my actual resource requirements quickly. Within just two months, I’ve learned that my AI-driven platform only needs a peak of 4GB of RAM. The more intricate Kubernetes architecture I toyed with would have simply been managing idle containers.
When my system does crash (which has happened twice), I glean critical insights about the failure points—insights that often surprise me.
2. Hardcoded Configurations
Instead of using config files or environment variables, my code features hardcoded constants for essential parameters such as pricing and user limits. While this entails redeployment to make changes, it also allows me to swiftly search my codebase for any configuration value. Not only does this track every adjustment through Git history, but it also ensures thorough code reviews for each update, even if I’m the only reviewer.
Creating a dedicated configuration service could have consumed a week of development time, but I’ve only altered these values three times in three months. That translates to 15 minutes of redeployment compared to potentially 40 hours of development.
3. SQLite in a Production Environment
Surprisingly, my web app operates efficiently on
