Embracing Unconventional Development: The 3-Month Rule for Startups
In the world of startup development, there’s a popular mantra: “Do things that don’t scale.” While it’s a widely acknowledged principle from Paul Graham, its practical application in software engineering often gets overlooked. After eight months of building my AI podcast platform, I’ve crafted a straightforward framework to implement this idea: I give every unscalable hack a maximum of three months to prove its worth. If it doesn’t, it’s time to let it go.
As engineers, we’re often conditioned to create solutions that can grow indefinitely, focusing on scalable architectures right from the outset. We gravitate towards design patterns, microservices, and distributed systems—all intended to support millions of users. However, this approach can lead to costly procrastination for startups. We typically end up optimizing for a user base that may not even exist yet, addressing theoretical challenges instead of the real ones at hand. My three-month rule encourages me to write straightforward, albeit imperfect code that delivers real solutions and insights about user needs.
Ingenious Infrastructure Hacks: A Deep Dive
1. Consolidated Operations on a Single VM
My entire setup, including the database, web server, and background jobs, operates on a single $40/month Virtual Machine (VM). This approach features zero redundancy and manual backups to my local machine.
You might think this is reckless, but here’s the genius behind it: In just two months, I’ve gained more insights into my actual resource demands than I could have from any overly complicated capacity planning document. It turns out that my “AI-heavy” application only requires around 4GB of RAM at its peak. The elaborate Kubernetes configuration I almost implemented would have merely been managing idle containers. When the system crashes—and it has twice—I’m able to learn firsthand about the actual failure points, which often surprise me.
2. Hardcoded Configuration: Simplicity at Its Best
My configuration is straightforward. For instance:
python
PRICE_TIER_1 = 9.99
PRICE_TIER_2 = 19.99
MAX_USERS = 100
AI_MODEL = "gpt-4"
There are no external config files or environment variables, just constants embedded in the code. Modifying anything requires a simple redeployment.
The real benefit? I can quickly search my entire codebase for any config value whenever necessary. Each price adjustment is documented in the Git
