Embracing the 3-Month Rule: A Practical Approach for startup Developers
In the tech world, the advice “Do things that don’t scale” from entrepreneurial thought leader Paul Graham is commonly acknowledged. However, the implementation of this principle, particularly in coding, often remains unspoken. After spending eight months developing my AI podcast platform, I’ve discovered a valuable framework: every unscalable solution is given a lifespan of three months. At the end of this period, I determine if it has demonstrated its value and merits further development or if it needs to be discarded.
As engineers, we frequently approach problem-solving with a mindset geared toward scalability from the outset. We invest time designing intricate systems, utilizing design patterns, microservices, and distributed architectures to prepare for a future with millions of users. However, in the startup landscape, what we often call “scalable code” can be nothing more than costly procrastination, focusing on hypothetical users and non-existent issues. My three-month timeline encourages me to produce straightforward, albeit imperfect, code that can be promptly deployed, allowing me to better understand user requirements through real-world feedback.
My Key Infrastructure Decisions and Their Benefits
1. Single VM Deployment
At present, I run my entire application—covering the database, web server, background jobs, and caching—on a single $40/month virtual machine. This might seem reckless due to the lack of redundancy and reliance on manual backups, but it has provided significant insights into my true resource needs within just two months.
I learned that my platform, which I initially thought would be resource-intensive, peaks at just 4GB of RAM. The elaborate Kubernetes architecture I almost chose would have ended up managing idle resources instead. Each instance where the system crashed has delivered invaluable data, revealing unexpected points of failure that have informed my development strategy.
2. Simplified Configuration Management
Instead of utilizing configuration files or environment variables, I’ve opted for hardcoded constants dispersed throughout the codebase:
python
PRICE_TIER_1 = 9.99
PRICE_TIER_2 = 19.99
MAX_USERS = 100
AI_MODEL = "gpt-4"
This seemingly rudimentary approach allows me to quickly search the entire codebase for configuration values, while every adjustment gets logged in Git history. In three months, I have only needed to change values three times—saving me a considerable amount of engineering effort.
