The 3-Month Rule: A Pragmatic Approach to Unscalable Development
In the realm of startup development, many are familiar with Paul Graham’s iconic advice: “Do things that don’t scale.” While this notion often sparks discussions, the implementation in the world of coding is frequently overlooked. Over the past eight months while working on my AI podcast platform, I’ve crafted a straightforward yet effective approach: any unscalable solution receives a lifespan of three months. After this period, it must either demonstrate its value convincingly and be refined or face elimination.
As software engineers, we are conditioned to prioritize scalable solutions right from the outset. We delve into intricate design patterns, microservices architectures, and distributed systems—concepts that are essential for managing vast user bases. However, in a startup environment, focusing solely on scalability can often become a costly form of procrastination. We find ourselves catering to potential users who may never materialize, addressing challenges that might never arise. My three-month rule compels me to write straightforward, albeit less-than-perfect code that gets deployed. This, in turn, allows me to understand precisely what my users require.
Current Infrastructure Hacks: Insight Through Simplicity
1. Unified VM Setup
All my essential components—database, web server, background jobs, and Redis—operate on a single $40/month virtual machine (VM) with zero redundancy. Manual backups? I perform these locally.
This arrangement has revealed far more about my actual resource needs over the past two months than any theoretical planning document could have. In reality, my “AI-heavy” platform has a RAM peak of just 4GB. The intricate Kubernetes infrastructure I nearly established would have simply managed vacant containers. Each time the system crashes (and it has twice), I gain insightful data about what truly fails—an experience that consistently surprises me.
2. Hardcoded Configurations
python
PRICE_TIER_1 = 9.99
PRICE_TIER_2 = 19.99
MAX_USERS = 100
AI_MODEL = "gpt-4"
Forget the configuration files and environment variables. By embedding constants within my code, any changes necessitate a deployment. The benefit? I can quickly search my entire codebase for configuration values in moments. Each pricing alteration is documented in my git history and subjected to my own code review.
The creation of a configuration service would consume a week of my time, yet I’ve modified these
