The 3-Month Rule: A Pragmatic Approach to Unscalable Solutions in Software Development
In the startup ecosystem, the mantra “Do things that don’t scale,” attributed to Paul Graham, is widely recognized, yet the practical application of this principle in coding often goes unaddressed.
After dedicating eight months to the development of my AI podcast platform, I’ve established a structured approach that revolves around a concept I aptly call the “3-Month Rule.” This framework dictates that every unscalable hack I employ is given a three-month lifespan. Within this timeframe, a solution must either validate its worth and be enhanced for long-term use or be discarded.
As engineers, our training often steers us towards creating scalable systems from the outset—think complex design patterns, microservices, and robust architectures capable of supporting millions of users. While this may suit larger organizations, startups often find that striving for scalability too early translates into costly delays. By adhering to my 3-month rule, I prioritize straightforward, albeit imperfect, code that can be deployed swiftly and provides real insight into user needs.
My Practical Hacks That Are Actually Strategic Choices:
1. Single Virtual Machine Strategy
By hosting everything—database, web server, background processes, and caching—on a single $40/month virtual machine, I have embraced simplicity. This setup lacks redundancy and relies on manual backups to my local machine.
But here’s the insight: In just two months, I’ve gained invaluable knowledge about my actual resource requirements. Instead of drowning in complicated capacity planning, I’ve found that my “AI-focused” platform peaks at just 4GB of RAM. The extensive Kubernetes cluster I contemplated building would have only complicated matters further, managing virtual containers that weren’t needed. Whenever I experience a crash, which has happened twice so far, I receive critical data on where the real issues lie—often in unexpected areas.
2. Utilization of Hardcoded Configuration
plaintext
PRICE_TIER_1 = 9.99
PRICE_TIER_2 = 19.99
MAX_USERS = 100
AI_MODEL = "gpt-4"
Laying out my configurations as hardcoded constants rather than using external configuration files means that any change necessitates a redeployment.
The unanticipated advantage here is efficiency—I can quickly search my entire codebase for config values. Each price adjustment is recorded in Git history, and every update undergoes my own review process. The time investment for a configuration service
