Embracing the 3-Month Rule: A Practical Framework for Scaling Down in Development
In the tech startup world, it’s widely known that Paul Graham famously advised: “Do things that don’t scale.” However, the challenge lies in translating that wisdom into actionable strategies, especially when it comes to coding.
After spending eight months developing my AI podcast platform, I have implemented a straightforward yet effective framework: every unscalable hack gets a trial period of three months. After this duration, each approach is either validated and enhanced, or it gets the ax.
The startup Dilemma: The Trap of Scalability
As engineers, we’re often conditioned to focus on “scalable” solutions from the outset. We dream of pristine design patterns, microservices, and distributed systems capable of supporting millions of users. However, this type of planning suits larger companies and can prove perilous for startups, where scalable code frequently becomes a costly form of procrastination. Too often, we end up optimizing for a user base that doesn’t yet exist, addressing hypothetical problems that may never arise. The three-month rule pushes me to create straightforward, albeit imperfect, code that delivers real results and helps me understand user needs more effectively.
Current Infrastructure Hacks: Why They Work
Here are a few of my current infrastructure strategies that may initially seem unorthodox but offer remarkable insights.
1. Consolidated Resources on a Single VM
My entire stack—including the database, web server, background jobs, and even Redis—operates on one $40/month virtual machine. There’s zero redundancy, and backups are managed manually.
While this setup may appear risky, it has provided me with more insight into my actual resource requirements in just two months than traditional capacity planning ever could. I discovered that my “AI-heavy” application peaks at merely 4GB of RAM. The complex Kubernetes architecture I nearly implemented would have meant managing unused resources. Every time the system crashes (which has happened twice), I gain real data about the underlying issues—almost always different from what my assumptions suggested.
2. Hardcoded Configurations
I maintain hardcoded configurations like so:
python
PRICE_TIER_1 = 9.99
PRICE_TIER_2 = 19.99
MAX_USERS = 100
AI_MODEL = "gpt-4"
There are no config files or environment variable dependencies; it’s all about constants scattered throughout my code. Changes may require a rede
