Embracing the 3-Month Rule: A Pragmatic Approach to Non-Scalable Solutions in Tech Development
In the realm of technology entrepreneurship, the mantra “Do things that don’t scale,” famously voiced by Paul Graham, resonates deeply. Yet, we often overlook the practical implementation of this idea during the coding phase. Over the past eight months, while constructing my AI podcast platform, I’ve developed a straightforward yet effective framework: any unscalable quick fix gets a three-month trial period. If it proves its worth, it will be developed into a more robust solution; if not, it will be phased out.
In the startup environment, the common inclination is to design for scalability from the start. We focus on idealized solutions—distributed systems, microservices, and sophisticated architecture—that cater to a hypothetical audience. However, this often leads to costly procrastination, optimizing for users who have yet to materialize and solving fictitious problems. Adopting my 3-month rule encourages me to write straightforward, albeit imperfect, code that delivers real value and reveals genuine user needs.
My Current Technical Workarounds and Their Rationale
1. Single VM Setup
I’ve opted to run my database, web server, background jobs, and caching on a singular, $40-a-month virtual machine. This means a lack of redundancy and reliance on manual backups.
Why is this considered smart? Within just two months, I’ve gained vital insights into my resource requirements that no amount of capacity planning would have provided. For instance, my AI-focused platform peaks at just 4GB of RAM. The complex Kubernetes architecture I nearly implemented would have been unnecessarily maintained—sometimes for empty containers.
When the system does crash (which has happened twice), I receive tangible data on its vulnerabilities, and surprisingly, the failures are not what I initially predicted.
2. Hardcoded Configuration
Configurations like pricing tiers and user limits are hardcoded throughout my project rather than stored in external files or environment variables. This methodology means that modifying any value necessitates a redeployment.
The hidden advantage here? I can quickly search my codebase for any configuration value in mere seconds. Each price adjustment leaves a trace in the git history, and every change undergoes a personal review—admittedly, one I conduct myself.
Establishing a configuration service would take about a week, but I have adjusted these constants three times in the last three months. This translates to a quick 15 minutes of deployment versus
