Embracing the 3-Month Rule: A Strategic Approach to Unscalable Solutions in Tech
In the world of startups, where innovation is key and resources are often limited, the time-honored advice from Paul Graham to “do things that don’t scale” is invaluable. However, translating this wisdom into practical coding strategies can be challenging. After working for eight months on my AI podcast platform, I have established a guiding principle: every unscalable solution is permitted a lifespan of just three months. At the end of this period, each solution must either demonstrate its worth and be refined or be discarded.
As developers, we often excel at creating scalable systems, focusing on design patterns, microservices, and intricate architectures capable of accommodating vast user bases. However, this mindset can be a double-edged sword, particularly in a startup environment where scalability may lead to costly delays. My three-month rule encourages me to write straightforward, bare-bones code that can be deployed quickly and helps reveal genuine user needs.
Current Infrastructure Strategies: The Benefits of Simplicity
1. Consolidated Operations on a Single Virtual Machine
My setup runs every essential component—database, web server, background tasks, and caching—on a lone $40/month virtual machine. While this approach offers no redundancy and relies on manual backups, it has provided me with invaluable insights about my resource usage.
In a mere two months, I’ve gained a clearer understanding of my platform’s demands, discovering that it peaks at only 4GB of RAM. The elaborate Kubernetes framework I nearly implemented would have faced empty containers. Each time the system crashes, I gather insightful data regarding failures—often surprising me by highlighting issues I hadn’t anticipated.
2. Hardcoded Configuration
Instead of relying on external configuration files or environment variables, I’ve opted for hardcoded constants dispersed throughout my code. For example:
python
PRICE_TIER_1 = 9.99
PRICE_TIER_2 = 19.99
MAX_USERS = 100
AI_MODEL = "gpt-4"
While this might seem unorganized, it streamlines modifications. With every price adjustment tracked through git history and reviewed by me, it has turned out to be an efficient process. Building a configuration management service might have consumed a week of development time, yet I have only needed to make three changes in three months, resulting in minimal overhead.
