The 3-Month Rule: A Practical Approach for Non-Scalable Solutions in Development
When it comes to startup advice, few principles resonate as strongly as Paul Graham’s famous directive: “Do things that don’t scale.” However, the application of this mantra in the realm of coding often goes overlooked. After months of developing my AI podcast platform, I have devised a straightforward framework: any unscalable solution gets assigned a three-month trial period. Following this timeframe, it either proves its worth and gets refined, or it is discarded.
As software engineers, we typically aim to implement scalable solutions from the outset, thinking of robust design patterns and microservices that can support millions of users. However, this approach can lead to unnecessary complexity for startups, where building scalable architecture may simply delay addressing current user needs. The 3-month rule has compelled me to craft simple and effective code that can be deployed quickly, providing real insights into what users genuinely require.
Current Infrastructure Strategies: Smart Choices for Rapid Development
1. Unified Virtual Machine Strategy
Currently, my entire infrastructure operates on a single virtual machine (VM), hosting databases, web servers, background jobs, and caching solutions—all for just $40 a month. While this might initially seem reckless, this setup has provided me with invaluable insights into my resource consumption. I’ve discovered that my platform only peaks at 4GB of RAM, which would have rendered a complex Kubernetes setup unnecessary.
When the system experiences downtime (which has occurred twice), I gain concrete data about failures—often surprising me with where issues truly lie.
2. Hardcoded Configurations
I’m operating with hardcoded constants throughout my code, such as:
python
PRICE_TIER_1 = 9.99
PRICE_TIER_2 = 19.99
MAX_USERS = 100
AI_MODEL = "gpt-4"
This approach allows me to quickly search through my codebase for any configuration values and easily track changes through version control. Instead of dedicating a week to create a separate configuration service, I’ve made just three adjustments over three months, saving immense amounts of time and allowing for efficient redeployment.
3. Utilizing SQLite in Production
Despite being a multi-user application, I’ve opted for SQLite, which currently weighs in at just 47MB. This choice has proven effective, even under light concurrent usage. By evaluating my access patterns, I’ve found a predominance of read queries, perfectly
