Embracing the 3-Month Rule: A Pragmatic Approach to Rapid Development
In the tech startup world, Paul Graham’s mantra to “do things that don’t scale” often resonates, yet implementation can seem elusive, especially in programming. After dedicating eight months to the development of my AI podcast platform, I’ve devised a straightforward framework: allow any non-scalable solution a lifespan of just three months. In that timeframe, it either justifies its existence and evolves into a robust feature or gets abandoned.
As engineers, we are typically conditioned to craft scalable systems from the outset—thinking in terms of design patterns, microservices, and distributed architectures that can accommodate a surge of users. However, this mindset can be counterproductive in a startup context, where such scalability often translates to costly delays. My 3-month rule compels me to write straightforward yet effective code that delivers results and reveals genuine user needs.
My Current Infrastructure Hacks: Smart Solutions in Action
1. Unified Infrastructure on a Single VM
Rather than spreading resources thin, I’ve opted to run everything—database, web server, background jobs, and caching—on a single $40/month virtual machine. While this approach lacks redundancy and involves manual backups, it has provided invaluable insights into my actual resource usage. Within two months, I’ve learned that my platform only requires about 4GB of RAM during peak times. The elaborate Kubernetes setup I nearly pursued would have meant managing an array of empty containers.
When the system crashes (which has happened twice), I gain real-time insights into failure points that I would not have anticipated.
2. Simplicity with Hardcoded Configurations
Instead of relying on complex configuration files or environment variables, I utilize directly coded constants across my files:
python
PRICE_TIER_1 = 9.99
PRICE_TIER_2 = 19.99
MAX_USERS = 100
AI_MODEL = "gpt-4"
While it may seem crude, this simplicity allows me to locate configuration values with ease. Each price modification is logged in git history, and every change undergoes code review—even if it’s just me assessing my own pull requests. The trade-off is minimal: three adjustments in three months translate to just 15 minutes of redeployment versus 40 hours of development for a complex configuration service.
3. Utilizing SQLite in Production
I’ve taken a bold leap by opting for SQLite in a multi-user
