Embracing the 3-Month Rule: A Pragmatic Approach for Startups
In the world of startup development, the phrase “do things that don’t scale,” coined by Paul Graham, often comes up. However, the challenge lies not just in understanding this advice but in effectively applying it, especially in the realm of software development. After several months of working on my AI podcast platform, I’ve created a straightforward framework: each unscalable solution gets a trial period of three months. At the end of this timeframe, the solution either proves its worth and evolves into a sustainable build or is discarded.
As software engineers, we are typically conditioned to create scalable architectures from the outset—think design patterns, microservices, and intricate systems designed to accommodate millions of users. While such frameworks are essential for larger enterprises, they often represent unnecessary complexity for startups. In fact, pursuing scalability too early can be a costly distraction, compelling us to develop solutions for hypothetical users and problems. My three-month rule compels me to focus on simplicity and rapid implementation, allowing me to directly address the actual needs of users.
Current Infrastructure Strategies: Simple Solutions with Significant Insights
- Single VM Architecture
My entire application stack—including the database, web server, and background jobs—resides on a single $40/month virtual machine (VM), with no redundancy and backups manually saved to my local system. This approach has been surprisingly illuminating; in just two months, I’ve gained more insight into my resource requirements than any conventional planning document could provide. I discovered that my “AI-heavy” platform only peaks at 4GB of RAM, meaning the intricate Kubernetes setup I considered would have been a case of over-engineering. When issues arise, such as server crashes, they reveal valuable data that directly inform my development efforts.
- Hardcoded Configurations
I’ve opted for hardcoded constants across my codebase for pricing and feature limits:
python
PRICE_TIER_1 = 9.99
PRICE_TIER_2 = 19.99
MAX_USERS = 100
AI_MODEL = "gpt-4"
This approach eliminates the need for configuration files or environment variables, making it simple to track changes via Git. Although creating a dedicated configuration service might take substantial time, the reality is I’ve only modified these constants a handful of times over three months, resulting in minimal redevelopment efforts.
- **Utilizing SQLite
