The 3-Month Rule: A Pragmatic Approach to Experimentation in Tech
In the startup world, advice like “Do things that don’t scale” frequently comes up, often attributed to Paul Graham. However, implementing this idea effectively in software development tends to be overlooked. After dedicating eight months to creating my AI podcast platform, I’ve established a straightforward framework: every unscalable approach gets three months to prove its worth. If it doesn’t deliver results by then, it’s time to let go.
As developers, we’re conditioned to prioritize “scalable” solutions from the get-go, focusing on impressive architectures designed to accommodate millions of users through complex systems like microservices and distributed databases. While this concept is essential for large corporations, in a startup environment, chasing scalability too early may just be a form of costly procrastination. My three-month rule encourages me to utilize straightforward, sometimes even rudimentary, coding methods that actually get deployed while also revealing the true needs of my users.
My Current Development Hacks That Actually Make Sense
1. All Services on a Single Virtual Machine
My entire stack—database, web server, and background jobs—operates on a single $40/month virtual machine with no redundancy and manual backups to my local system.
You might be surprised, but this setup has allowed me to gauge my actual resource requirements far better than any capacity planning document ever could. It turns out my resource needs peak at around 4GB of RAM for my “AI-heavy” platform. The intricate Kubernetes environment I almost implemented would have been wasted on empty containers. When I do experience crashes (which have happened twice), I receive invaluable insights into what genuinely fails—often, it’s nothing I anticipated.
2. Hardcoded Configuration Settings
Consider the way I handle configurations:
python
PRICE_TIER_1 = 9.99
PRICE_TIER_2 = 19.99
MAX_USERS = 100
AI_MODEL = "gpt-4"
Instead of using configuration files or environment variables, I’ve opted for constants throughout the code. Any adjustment requires a quick redeployment.
The beauty of this approach lies in its speed: with simple search commands, I can trace any config value across my entire codebase in a few seconds. Each pricing adjustment is reflected in my Git history, and every change is reviewed (albeit by myself). Building a dedicated configuration service could take a week, yet I’ve altered these values
