The 3-Month Framework: A Pragmatic Approach to Non-Scalable Solutions
In the realm of startup culture, there’s a well-known adage from Paul Graham: “Do things that don’t scale.” Yet, when it comes to implementation—particularly in the coding arena—this advice often lacks clarity. After dedicating eight months to developing my AI podcast platform, I’ve devised a practical framework: each unscalable tactic receives a trial period of three months. At the end of this timeframe, these tactics must either demonstrate their worth and evolve into robust solutions or be phased out.
As engineers, our instinct often drives us to create for scale from the outset. We delve into sophisticated design patterns, microservices, and efficient distributed systems—hallmarks of a larger corporate environment. But in a startup context, focusing on scalability can become a form of costly procrastination. We’re often investing time and resources to cater to users who may not even exist yet, and my three-month framework encourages me to embrace more straightforward, albeit imperfect, code that can be deployed quickly and reveals genuine user needs.
My Current Infrastructure Strategies and Their Strategic Value
1. Single VM Architecture
My entire stack—including the database, web server, background jobs, and caching—runs on a singular $40/month virtual machine. This approach lacks redundancy and relies on manual backups to my personal storage.
What appears to be a reckless strategy has proven to be insightful. I’ve gleaned more about my actual resource requirements in just two months than I could have from detailed capacity planning documents. Surprisingly, my AI-centric platform only requires about 4GB of RAM during peak times. The intricate Kubernetes setup I nearly implemented would have led to wasted resources managing idle containers. Each time the system experiences a crash (which has happened twice so far), I gain concrete insights into failure points—revealing that the causes are rarely what I anticipate.
2. Hardcoded Configuration
python
PRICE_TIER_1 = 9.99
PRICE_TIER_2 = 19.99
MAX_USERS = 100
AI_MODEL = "gpt-4"
Instead of configuration files or environment variables, I use hardcoded constants throughout my codebase. This means modifying any setting necessitates a redeployment.
The silver lining? I can perform quick searches for any configuration value across the entire codebase within seconds. Changes to pricing have only occurred three times in three months, translating to about 15 minutes
