Embracing the 3-Month Rule: A Pragmatic Approach for Unscalable Solutions
In the world of startups, the sage advice from Paul Graham—“Do things that don’t scale”—often resonates, but executing on that principle within the realm of coding requires a unique approach. Over the past eight months, while developing my AI podcast platform, I’ve adopted a straightforward framework: every unscalable stopgap solution is given a lifespan of three months. After that period, it must either prove its worth and be properly constructed or be discarded.
Why the 3-Month Rule Matters
As developers, we often feel pressured to design scalable systems. We get caught up in discussing sophisticated architectures that support millions of users right off the bat. However, in the startup environment, pursuing scalability from day one can turn into an exercise in futility—you’re essentially investing time into problems that may never arise, anticipating needs that don’t yet exist. By adhering to this three-month rule, I’m empowered to implement straightforward, albeit “imperfect,” code that prioritizes actual deployment over hypothetical scenarios. This approach allows me to derive genuine insights into user requirements.
Current Infrastructure Hacks: Smart Choices for Now
1. All-In-One Virtual Machine
My entire stack—database, web server, background jobs, Redis—is housed on a singular $40/month virtual machine. It lacks redundancy and relies on manual backups to my local system.
Why This Works: This setup has swiftly revealed my resource demands in just two months, far surpassing what any planning document could unveil. The reality was enlightening—I only require about 4GB of RAM for my ostensibly demanding AI platform. The complex Kubernetes infrastructure I nearly adopted? It would have merely served to manage idle containers. Each time my server has crashed (twice, so far), I’ve gathered valuable data about actual failure points, often contrary to my prior assumptions.
2. Hardcoded Configurations
PRICE_TIER_1 = 9.99
PRICE_TIER_2 = 19.99
MAX_USERS = 100
AI_MODEL = "gpt-4"
I use constants spread throughout my codebase without any configuration files or environment variables. Modifying these parameters mandates redeployment.
The Hidden Benefit: This method allows for rapid searches of any configuration value across my codebase. All pricing alterations are meticulously logged in Git history, and even though I’m the sole
