The 3-Month Framework: Navigating Unscalable Solutions in Software Development
In the world of startups, there’s a well-known piece of advice from entrepreneur Paul Graham: “Do things that don’t scale.” While this guidance is often shared, the actual application of it within coding practices remains relatively under-discussed. After eight months of developing my AI podcast platform, I’ve crafted a practical approach: every unscalable solution is allowed a lifespan of three months. At the end of this period, it’s either validated through demonstrated value and subsequently refined, or it gets scrapped.
As engineers, we often focus on scalability from the outset. We’re trained in advanced architectures, from microservices to complex distributed systems designed to manage millions of users. However, this mindset can be a hindrance in a startup environment where anticipating future user demand may lead to ineffective procrastination. Instead of laying the groundwork for future users who may never arrive, I have embraced a three-month timeframe that compels me to create straightforward code that delivers immediate results and reveals users’ actual needs.
My Strategic Hacks: Insights over Complex Architecture
1. Unified Infrastructure on a Single VM
Currently, my entire setup—database, web server, background jobs, and caching—operates on a single $40-per-month virtual machine. While this approach lacks redundancy and relies on manual backups, it has proved to be a learning goldmine. In just two months, I’ve better understood my resource requirements than any theoretical planning document could provide. My platform, defying expectations, peaks at only 4GB of RAM. The elaborate Kubernetes infrastructure I nearly implemented would have been a burden for managing empty containers. Each crash (and there have been two) offers valuable insights into what truly fails—often unanticipated components.
2. Simplified Configuration Management
Instead of utilizing configuration files or environment variables, I’ve opted for hardcoded constants throughout my codebase:
python
PRICE_TIER_1 = 9.99
PRICE_TIER_2 = 19.99
MAX_USERS = 100
AI_MODEL = "gpt-4"
This method, while unorthodox, allows for rapid changes, as I can quickly grep my code for any configuration value. Since these constants have only changed three times over three months, the time commitment for redeployment has been minimal—merely 15 minutes compared to the 40 hours required for a more formal configuration system.
