Embracing Imperfection: The 3-Month Rule for Scalable Solutions in Tech
In the tech world, we’re often told by thought leaders like Paul Graham to “do things that don’t scale.” But while this advice is widely acknowledged, the practical application—especially in coding and development—rarely gets the same attention. Over the past eight months, while building my AI podcast platform, I’ve developed a simple yet effective standard that I call the 3-Month Rule.
What Is the 3-Month Rule?
The essence of this framework is straightforward: any unscalable workaround I implement has a life span of three months. At the end of this period, each solution must either demonstrate its value and warrant a complete redesign, or it gets discarded.
As engineers, we are conditioned to construct solutions meant for scalability from day one. We immerse ourselves in best practices—design patterns, microservices, and distributed systems—all aimed at handling traffic from millions of users. However, such an approach may not be suitable for startups, where the need for scalable code can often lead to unnecessary complexities and delays. My 3-Month Rule compels me to focus on creating simpler, more immediate solutions that genuinely address user needs.
Practical Applications of My Current Infrastructure: A Closer Look
1. One Virtual Machine for Everything
All elements of my platform—database, web server, background processes, and Redis—operate on a single $40/month virtual machine, without redundancy and relying on manual backups.
This unconventional strategy has taught me more about my actual resource requirements in just two months than any comprehensive capacity planning report could. For instance, my AI-driven platform peaks at merely 4GB of RAM, revealing that the Kubernetes setup I almost implemented would have only been managing idle containers. Every crash—even the two I’ve experienced—provides valuable data regarding real breakpoints, which often surprise me.
2. Hardcoded Configurations
Instead of using configuration files or environment variables, I employ hardcoded constants throughout my codebase that dictate pricing tiers, user limits, and AI model types. This seemingly archaic approach comes with a hidden benefit: I can quickly search for any configuration value across my entire codebase.
In three months, I’ve made only three modifications, which took 15 minutes of redeployment compared to the potential 40 hours I would have spent creating a configuration service. This agile approach keeps my focus on what’s essential
