Embracing the 3-Month Rule: My Strategic Framework for Scalable Development
In the realm of startup development, the wisdom of Paul Graham’s principle, “Do things that don’t scale,” resonates profoundly. Yet, the practical application of this advice within coding is seldom explored. Over the last eight months while developing my AI podcast platform, I have established a straightforward yet effective approach: every unconventional hack I implement is granted a lifespan of three months. After this period, the hack must either demonstrate its worth and be refined or be discarded.
As developers, we are often conditioned to create scalable solutions right from the beginning. The allure of design patterns, microservices, and expansive architectures is hard to resist, especially when envisioning products aimed at millions of users. However, in a startup environment, overly focusing on scalability can lead to expensive delays, as it requires us to anticipate user needs that may not yet exist. My three-month rule compels me to produce straightforward and functional code that can be deployed quickly, ultimately teaching me the true demands of my users.
Current Infrastructure Hacks: Functional Approaches to Development
1. One Virtual Machine to Rule Them All
Imagine running your entire system—database, web server, background jobs, and Redis—on a single virtual machine costing only $40 a month. This setup comes with no redundancies and manual backups done locally.
This may seem reckless, but it has provided unparalleled insights into my resource needs. In just two months, I’ve learned that my platform’s peak usage only requires 4GB of RAM. The intricate Kubernetes architecture I once considered would have been managing unused containers. Moreover, each crash has revealed valuable data, showing me that the issues often differ from my initial assumptions.
2. Embracing Hardcoded Configuration
python
PRICE_TIER_1 = 9.99
PRICE_TIER_2 = 19.99
MAX_USERS = 100
AI_MODEL = "gpt-4"
Instead of employing configuration files or environment variables, I have hardcoded constants throughout my code base. The necessity for redeployment every time a change is made might sound cumbersome, but it actually simplifies tracking. I can swiftly search my code for any configuration value, and all changes are documented in version control. In three months, I have only altered these values three times. This translates to a quick redeploy rather than an extensive engineering effort.
