Embracing Imperfection: My 3-Month Framework for Building Non-Scalable Solutions
In the world of tech startups, the advice of renowned entrepreneur Paul Graham resonates deeply: “Do things that don’t scale.” However, what often gets overlooked is the practical implementation of this philosophy, especially in software development. After eight months of building my AI podcast platform, I’ve formulated a clear strategy: any non-scalable solution gets a trial period of just three months. At the end of that time frame, if the approach hasn’t demonstrated its worth, it gets discarded.
As engineers, we are often conditioned to strive for scalability right from the beginning. We immerse ourselves in intricate design patterns, microservices, and distributed systems—architectures that are built to support a vast number of users. But in the startup environment, chasing after scalable solutions can turn into a costly form of procrastination. We end up optimizing for hypothetical users instead of addressing the immediate needs of our current audience. My three-month rule compels me to focus on straightforward, less refined coding that can be deployed quickly, allowing me to directly engage with user feedback and better understand their requirements.
Current Hacks in My Infrastructure and Their Benefits
1. Single VM Deployment
All components of my application—database, web server, background processes, and Redis—run on a single $40-a-month virtual machine. There’s no redundancy, and I perform manual backups to my local machine.
Why is this a smart move? In just two months, I’ve gathered more insights about my resource requirements than I could from any planning document. It turns out that my “AI-heavy” platform only peaks at 4GB of RAM. If I had gone ahead with a complex Kubernetes setup, I would have merely been managing idle containers. When the system crashes (which has happened twice), I gain valuable insights into what actually fails—often the results are unexpected.
2. Hardcoded Configuration
Instead of using configuration files or environment variables, I rely on hardcoded constants scattered throughout my code. Simple variables like PRICE_TIER_1 = 9.99 and MAX_USERS = 100 allow for straightforward adjustments.
This approach has unexpected advantages. I can quickly search my entire codebase for any configuration value. Changes are easily traceable through git history, ensuring accountability. Building a dedicated configuration service would have taken a week, yet I’ve only needed to modify these variables three times in three months—a fraction
