Embracing the 3-Month Rule: A Pragmatic Approach to Building in Startups
In the world of startups, the adage “do things that don’t scale” is frequently echoed, yet there’s often little guidance on how to effectively apply this principle in technical contexts. After eight months of developing my AI podcast platform, I’ve devised a straightforward framework: any unscalable approach I adopt has a three-month lifespan. At that point, it must either demonstrate its value through successful implementation or be retired.
As engineers, we are conditioned to seek scalable solutions from the outset. We delve into design patterns, build microservices, and architect distributed systems—all to accommodate vast numbers of users. However, this mindset often aligns more with large corporations than with agile startups. In reality, focusing on scalable code early on can be a costly form of procrastination. By optimizing for future users who may never materialize, we risk losing sight of our present needs. Hence, my three-month rule compels me to produce simple and direct code that is not necessarily perfect but allows me to learn what users truly require.
Current Technical Strategies: Insights from My Approach
1. Consolidated Operations on a Single Virtual Machine
My entire infrastructure—including the database, web server, background jobs, and caching—is hosted on a single, budget-friendly $40/month virtual machine. While this setup lacks redundancy and relies on manual local backups, it has provided invaluable insights. In just two months, I’ve gathered more data about my actual resource requirements than any planning documentation could offer. My findings revealed that my “AI-heavy” platform operates efficiently on just 4GB of RAM. Had I pursued a complex Kubernetes setup, I would have found myself managing idle containers.
Experiencing downtime (which has happened a couple of times) allows me to analyze real scenarios and identify unexpected points of failure.
2. Simplified Configuration Management
In my codebase, configuration values are hardcoded directly:
python
PRICE_TIER_1 = 9.99
PRICE_TIER_2 = 19.99
MAX_USERS = 100
AI_MODEL = "gpt-4"
This approach means I don’t use configuration files or environment variables; simply changing a value requires redeployment. Surprisingly, this simplicity offers a significant advantage. I can swiftly search my codebase to identify any configuration value, and all modifications are documented in version control. Instead of investing a week in building a configuration service
