Embracing the 3-Month Rule: A Framework for Technical Growth in Startups
In the world of startups, the wisdom of Paul Graham rings true: “Do things that don’t scale.” While many consider this advice, there is often a lack of discussion surrounding its concrete implementation—especially in software development.
Over the past eight months, as I’ve been diligently working on my AI podcast platform, I have crafted a straightforward yet effective framework: I give every unscalable hack a lifespan of just three months. At the end of this period, each solution must either demonstrate its value and be transitioned into a more robust system or be discarded.
What’s the underlying principle here? Engineers are typically trained to design scalable solutions from the get-go. We think in terms of complex architecture like microservices and distributed systems that can handle thousands, or even millions, of users. However, this mindset is often more suited to larger corporations.
For startups, investing energy in scalability too early can be a form of procrastination. It’s like fixing a problem that may not even exist yet or optimizing for users who aren’t onboard yet. My three-month rule pushes me to focus on writing straightforward, albeit imperfect, code that actually gets deployed while teaching me about my users’ real needs.
Smart Hacks in My Current Infrastructure
1. Centralizing Everything on One Virtual Machine
Currently, my setup runs on a single $40/month virtual machine that encompasses the database, web server, background jobs, and Redis. There’s zero redundancy, and I rely on manual backups to my local machine.
Why is this approach valuable? In two months, I’ve gained insights into my resource requirements far beyond what any capacity planning document could provide. My “AI-heavy” platform only peaks at 4GB of RAM. The complex Kubernetes infrastructure I almost implemented? It would have been handling empty containers. When my setup crashes—and it has twice—I receive genuine insights into what fails—and it’s never what I initially anticipated.
2. Embracing Hardcoded Configurations
python
PRICE_TIER_1 = 9.99
PRICE_TIER_2 = 19.99
MAX_USERS = 100
AI_MODEL = "gpt-4"
All my configurations are hardcoded—no config files or environment variables, merely constants scattered throughout the code. While this may seem unprofessional, it has its advantages. A quick grep command allows me to
