Embracing the 3-Month Rule: A Practical Framework for Startups
In the world of tech startups, there’s a well-known principle from Paul Graham: “Do things that don’t scale.” While this advice is widely quoted, the practical application of it within the realm of software development often remains uncharted territory. Having spent the last eight months developing an AI podcast platform, I’ve adopted an approach I call the “3-Month Rule,” which has dramatically influenced how I build and refine my product.
Understanding the 3-Month Rule
The essence of the 3-Month Rule is straightforward: allow unscalable hacks a lifespan of just three months. At the end of this period, each solution must either validate its worthiness for further development, or it will be retired.
As engineers, we are conditioned to conceive systems that are scalable from the outset—microservices architectures, complex design patterns, and robust infrastructures meant to support upward of thousands of users. However, this mindset is often more applicable to established organizations than to startups. Early-stage startups can quickly find themselves entangled in creating solutions for hypothetical users, which may lead to inefficiencies and wasted resources.
By committing to the 3-Month Rule, I prioritize quick iterations and straightforward coding practices that yield tangible results and inform a better understanding of user needs.
Current Infrastructure Insights
Let’s delve into some of the unconventional strategies I’ve employed within my current setup that exemplify this philosophy:
1. Unified Infrastructure on a Single VM
Everything—from the database and web server to background jobs and caching—runs on one $40/month virtual machine. This setup lacks redundancy, relying on manual backups to my local system.
This arrangement has proven invaluable, as I have quickly assessed my actual resource requirements. The platform, which I anticipated to require significant capacity, peaks at just 4GB of RAM. If I had opted for a more complex Kubernetes architecture, I would have been juggling many idle containers instead of gaining insight into my needs.
When system failures occur (which they have a couple of times), I receive clear indicators of what truly goes awry—often surprising me in its simplicity.
2. Hardcoded Values Throughout the Code
Instead of maintaining separate configuration files or environment variables, I’ve opted for hardcoded constants across my code:
“`python
PRICE_TIER_1 = 9.99
PRICE_TIER_2 = 19.99
MAX_USERS =
