Embracing the 3-Month Rule: A Practical Approach to Non-Scalable Solutions in Software Development
In the tech world, particularly when it comes to startups, the advice from Paul Graham—”Do things that don’t scale”—is often echoed yet rarely fully utilized. While most discussions focus on the wisdom of this principle, they tend to overlook the nitty-gritty of implementation, especially when it comes to software development.
After dedicating the last eight months to building my AI podcast platform, I’ve adopted a straightforward framework: every non-scalable tactic I employ is given a lifespan of three months. At the end of this period, it must prove its effectiveness or be discarded.
As software engineers, we’re conditioned to build scalable solutions right from the start—design patterns, microservices, and intricate distributed systems, all that technical architecture meant to support millions of users. However, this is often an unsuitable mindset in a startup environment, where investments in scalability can lead to procrastination. The three-month rule compels me to create simple, straightforward, yet functional code that enables me to understand real user needs, rather than speculating on future demands.
Highlights of My Approach: Infrastructure Hacks That Make Sense
1. Consolidated Infrastructure
I run my entire stack—a database, web server, background jobs, and caching—all on a single $40/month virtual machine. This minimalist setup, devoid of redundancy and relying on manual backups, has proven smarter than anticipated. In just two months, I’ve gained invaluable insights into my actual resource requirements, revealing that my AI-centric platform peaks at 4GB of RAM. The elaborate Kubernetes system I nearly implemented would have merely led to the maintenance of idle containers. Each crash (two times so far) has also provided concrete data about the system’s failures, revealing insights I hadn’t expected.
2. Hardcoded Settings
Constants like
python
PRICE_TIER_1 = 9.99
PRICE_TIER_2 = 19.99
MAX_USERS = 100
AI_MODEL = "gpt-4"
are scattered throughout my codebase instead of being managed through configuration files or environment variables. While some may see this as a flaw, I consider it a hidden strength. I can easily search across my entire codebase to find any configuration variable, and every change is archived via Git. In three months, I’ve altered these values just three times, requiring mere minutes of redeployment
