The Three-Month Rule: A Framework for Non-Scalable Solutions in Tech Development
In the tech world, especially within startups, the principle of “doing things that don’t scale” frequently echoes, yet practical strategies for implementing it in coding remain under-discussed. Drawing inspiration from this idea, I’ve devised a straightforward approach while developing my AI podcast platform over the past eight months: any unscalable solution is given a lifespan of three months. After this period, the solution is either validated and optimized or phased out.
As engineers, we often feel pressure to create scalable solutions right out of the gate—think of intricate design patterns, microservices, and distributed systems that can handle vast user bases. However, this perspective is generally suited for larger enterprises. In the context of a startup, prioritizing scalability can sometimes equate to paving the way for costly delays. By adhering to my three-month rule, I am encouraged to deploy straightforward, even suboptimal code that can ship quickly, providing invaluable insights into what users truly require.
My Ingenious Infrastructure Hacks
1. Consolidated Resources on a Single Virtual Machine
Currently, all aspects of my platform—from the database and web server to background jobs—are hosted on a single virtual machine costing $40 per month. There’s no redundancy, and I perform manual backups to my local device.
Why is this approach effective? After just two months, I gained more clarity about my actual resource consumption than I would have from extensive capacity planning. I discovered that my platform peaks at a modest 4GB of RAM. The sophisticated Kubernetes architecture I almost implemented would have simply managed empty containers. Each crash I’ve experienced has yielded critical data about inefficiencies—something I could not have anticipated.
2. Simplified Configuration Management
Instead of using configuration files or environment variables, I employ hardcoded constants throughout my codebase:
python
PRICE_TIER_1 = 9.99
PRICE_TIER_2 = 19.99
MAX_USERS = 100
AI_MODEL = "gpt-4"
This choice might appear primitive, but it enables rapid access and alterations. For example, changing a pricing tier takes mere moments and is easily traceable in my git history, eliminating the need for a lengthy configuration service build-out.
3. Using SQLite for Production
I’ve opted for SQLite for my multi-user application, yielding an efficient, 47MB database capable of supporting 50 simultaneous users. This
