Embracing the 3-Month Rule: A Practical Approach to Unscalable Solutions in Software Development
In the world of startup development, the guiding principle from Paul Graham—“Do things that don’t scale”—is frequently cited but rarely unpacked for practical application, particularly in software engineering. After spending the last eight months cultivating my AI podcast platform, I’ve devised a straightforward framework: every unscalable solution gets a trial period of three months. After this timeframe, the approach is evaluated to either solidify its value through proper implementation or be discarded.
Traditionally, engineers are inclined to create scalable architectures from the outset. They often prioritize complex design patterns, microservices, and distributed systems that are meant to accommodate vast user bases. However, these strategies may not be beneficial when launching a startup. In many cases, focusing too heavily on scalability can lead to inefficiencies, as it often involves crafting solutions for hypothetical users and problems that may never materialize. My three-month rule encourages me to write straightforward, albeit “less refined,” code that can be deployed quickly, allowing me to uncover the real needs of my users.
Current Infrastructure Hacks: Smart Choices for Growth
1. Consolidated VM Usage
Everything—including the database, web server, background jobs, and Redis—is operated on a single $40/month virtual machine. This approach lacks redundancy and relies on manual backups to my local system.
Why is this a smart strategy? Within just two months, I’ve gained invaluable insights into my actual resource requirements—far beyond what any capacity planning document could provide. My platform, which I initially deemed “AI-heavy,” peaks at merely 4GB of RAM. The intricate Kubernetes architecture I nearly created would have only served to manage dormant containers. Moreover, in the event of a crash (which has occurred twice), I receive real-time data regarding the failures—an experience that consistently reveals unexpected outcomes.
2. Simplistic Hardcoded Configurations
With parameters like:
python
PRICE_TIER_1 = 9.99
PRICE_TIER_2 = 19.99
MAX_USERS = 100
AI_MODEL = "gpt-4"
I have opted for hardcoded constants over complex config files or environment variables. Any change necessitates a redeployment.
The advantage? I can rapidly search my codebase for configuration values within seconds. Additionally, every price change remains traceable in my git history, and I conduct thorough reviews of all updates.
