Embracing the 3-Month Rule: A Pragmatic Approach to Unscalable Development
When it comes to startup advice, few concepts resonate as powerfully as Paul Graham’s mantra: “Do things that don’t scale.” However, many developers struggle with translating this principle into actionable steps—not just on paper, but in their actual coding practices.
Having spent the last eight months developing an AI-driven podcast platform, I’ve devised a practical framework that I call the “3-Month Rule.” This approach allows every unscalable solution I implement a three-month trial period. At the end of this window, if the solution demonstrates its value, it will be worthy of a more scalable rewrite; if not, it will be discarded.
The Challenge of Scalability
As engineers, we often default to creating scalable architectures from the very beginning—think design patterns, microservices, and deploying distributed systems that cater to potentially millions of users. While this is undeniably impressive, it often reflects a mindset more fitting for large corporations rather than agile startups.
In a startup environment, focusing excessively on scalability can become a costly form of procrastination. You’re often crafting solutions for users who don’t even exist yet, and tackling problems that may never arise. By implementing my 3-Month Rule, I’m encouraged to write straightforward and sometimes “imperfect” code that can be quickly deployed. This hands-on approach reveals what users genuinely need, avoiding assumptions.
Insights from My Current Infrastructure Hacks
Let me share some of the infrastructure hacks I’ve adopted, illuminating why they are, in fact, intelligent decisions:
1. All-In-One Virtual Machine
Currently, my database, web server, background jobs, and caching are all running on a single $40/month virtual machine. There’s no redundancy and I perform manual backups to my local machine from time to time.
The upside? In just two months, I’ve gathered invaluable insights into my actual resource requirements—much more than any theoretical capacity planning report could provide. My platform, centered on AI, typically peaks at around 4GB of RAM. The complex Kubernetes architecture I almost deployed would have resulted in managing idle containers.
When the system crashes (which has occurred twice), I gain real data on the root causes—usually surprising ones.
2. Hardcoded Configuration Everywhere
Instead of relying on external configuration files or environment variables, I have constants defined directly within my code:
“`python
PRICE_TIER_1 = 9.99
