Embracing the 3-Month Rule: A Pragmatic Approach to Building Non-Scalable Solutions
In the ever-evolving landscape of technology, one piece of advice rings clear: “Do things that don’t scale.” While this sentiment, famously articulated by Paul Graham, serves as a guiding principle for many entrepreneurs, the real challenge lies in its execution—especially in the realm of coding.
After dedicating eight months to developing my AI podcast platform, I’ve established a straightforward strategy: every non-scalable solution gets a trial run of three months. At the end of this period, if a hack demonstrates its value, it receives a proper architecture overhaul; otherwise, it’s time for it to go.
As engineers, we are often ingrained with a mindset that prioritizes scalable solutions from the onset—think intricate design patterns, robust microservices, and complex distributed systems capable of handling vast user bases. However, in the startup environment, pursuing scalability too early can often lead to costly delays. My three-month mandate compels me to embrace simplicity, allowing me to deploy straightforward, albeit imperfect, code that reveals what users genuinely need.
Insightful Infrastructure Hackery
1. Unified Virtual Machine Infrastructure
Instead of spreading my resources across multiple systems, everything—database, web server, background jobs, and Redis—operates on a single $40/month virtual machine. There is no redundancy; backups are done manually on my local machine.
Why is this approach beneficial? In just two months, I’ve garnered more insights about my resource requirements than any theoretical capacity planning document would have provided. My AI-heavy platform peaked at 4GB RAM; the elaborate Kubernetes architecture I almost built would have been a waste, managing empty containers. Each time the system crashes (and it has twice), I learn something new about failure points—details I wouldn’t have anticipated.
2. Simplicity in Configuration
Configuration is hardcoded throughout the codebase:
python
PRICE_TIER_1 = 9.99
PRICE_TIER_2 = 19.99
MAX_USERS = 100
AI_MODEL = "gpt-4"
This means that every change requires a redeployment, eliminating the complexity of config files and environment variables. The advantage? I can swiftly search my entire codebase for any configuration value, and each price change is neatly documented in the git history. Opting to build a configuration service would have consumed a week’s worth of engineering time; in three
