The 3-Month Rule: A Practical Approach to Non-Scalable Solutions in Tech Development
In the world of startups, the advice from tech luminary Paul Graham resonates: “Do things that don’t scale.” However, translating this principle into actionable steps in software development is rarely discussed. As I reflect on my eight-month journey building an AI podcast platform, I’ve devised a straightforward framework: every non-scalable method is allotted a three-month trial period. After this duration, we either elevate its status based on proven value or phase it out.
The startup Dichotomy: Scalability vs. Immediate Needs
As engineers, we’re trained to immediately think in terms of scalability. We focus on sophisticated solutions—design patterns, microservices, and robust architectures—that are capable of supporting millions of users. However, this mindset is often more suitable for established companies rather than startups in their infancy.
In many cases, creating scalable solutions early on can lead to unnecessary delays, effectively turning your project into costly procrastination. The 3-month rule encourages me to pursue simpler, more direct coding solutions that can be implemented quickly, allowing me to gather valuable insights into genuine user needs.
Current Strategies and What They Teach Us
Here are some of my current strategies that might seem unconventional yet have proven to be quite effective:
1. Consolidated Operations on a Single VM
My entire infrastructure—database, web server, background jobs, and caching—runs seamlessly on one $40/month virtual machine, with no redundancy and manual backups to my local device.
Far from being a reckless choice, this setup has enlightened me about my actual resource needs more than any theoretical document could. For instance, I discovered that my platform, which I originally deemed “AI-heavy,” peaks at 4GB of RAM. This revelation made it clear that my plans for an intricate Kubernetes environment were premature, as I would have been managing resources that weren’t even in use.
Whenever my system crashes—which has occurred a couple of times—I gain real data on the specific issues at play, providing insights that often defy my initial expectations.
2. Simplified Hardcoded Configuration
My codebase features values defined directly within the files, like:
python
PRICE_TIER_1 = 9.99
PRICE_TIER_2 = 19.99
MAX_USERS = 100
AI_MODEL = "gpt-4"
No complex configuration files or environment variables—just constants interspersed throughout
