Embracing the 3-Month Rule: A Pragmatic Approach to Rapid Prototyping
In the startup landscape, there’s a popular piece of wisdom from Paul Graham: “Do things that don’t scale.” While many acknowledge this advice, few explore how to effectively apply it in the realms of programming and development. Over the past eight months of building my AI podcast platform, I’ve crafted a straightforward framework that can be beneficial to aspiring developers: every quick and unscalable fix is given a lifespan of just three months. After this period, I evaluate its effectiveness; it either evolves into something more robust or is discarded.
As software engineers, we often prioritize creating scalable solutions from the outset. The allure of modern architecture—think microservices and distributed systems—can distract us from the immediate needs of a startup. In reality, focusing on scalable solutions too soon can translate to wasted resources on problems that might never arise. My three-month rule encourages me to write straightforward, sometimes imperfect code that can be released quickly, allowing me to gain insight into what users actually require.
My Current Implementation Strategies and Their Value:
1. Consolidating on a Single Virtual Machine
I operate my entire application—database, web server, background jobs, and caching—on a solitary $40/month virtual machine (VM), with no redundancy. Backups are manual, stored on my local machine.
This approach may sound imprudent, but here’s the silver lining: I’ve gained more insight into my actual resource needs in just two months than any planning document could provide. I discovered that my “AI-centric” platform peaks at only 4GB of RAM, which means the complex Kubernetes setup I initially considered would have been wasted on managing unused resources.
When failures occur (and they have twice), I receive immediate data on the root causes. Surprisingly, the issues never align with my initial expectations.
2. Utilizing Hardcoded Configurations
Rather than implementing configuration files or environment variables, I’ve opted for hardcoded constants like:
plaintext
PRICE_TIER_1 = 9.99
PRICE_TIER_2 = 19.99
MAX_USERS = 100
AI_MODEL = "gpt-4"
By scattering these constants throughout my codebase, modifying any value necessitates a redeployment. The benefit? I can quickly search my code for any configuration in seconds. Every price adjustment is logged in the git history, with code reviews occurring even if it’s just
