Embracing the 3-Month Rule: A Practical Approach to Non-Scalable Solutions in Tech
In the realm of startups, one piece of wisdom stands out: the mantra of “doing things that don’t scale,” popularized by Paul Graham. Yet, translating this philosophy into actionable steps, particularly in the coding domain, often goes unaddressed.
After dedicating eight months to the development of my AI podcast platform, I have established a straightforward framework: every unscalable hack is given a lifespan of three months. At the end of this period, each solution is evaluated for its effectiveness—either it moves on to become a fully integrated feature, or it is phased out.
The startup Mindset vs. Scalability
As developers, our training typically leans towards crafting scalable solutions from the very beginning. This includes intricate design patterns, microservices, and distributed systems—the architecture that supports millions of users. However, this kind of thinking can lead to pitfalls in the startup world.
In a startup environment, such scalable solutions often turn into costly procrastination. They cater to hypothetical users and tackle problems that may never arise. Adopting my 3-month rule has pushed me to write simpler, more pragmatic code that facilitates shipping valuable insights, allowing me to understand user needs swiftly.
Highlights of My Current Infrastructure Solutions
1. Single VM Architecture
My complete infrastructure, including the database, web server, background jobs, and Redis, operates on a single, $40/month virtual machine without redundancy. Though this approach may appear reckless, it has provided invaluable insights into my resource requirements over just two months. Contrary to my initial expectations, my platform peaks at a mere 4GB of RAM. Had I implemented a complex Kubernetes architecture, I would have been managing empty containers instead of addressing real concerns.
When issues have arisen—and they have—I gained actual data about the failures, which have consistently surprised me.
2. Hardcoded Configuration Values
To manage configurations, I utilize hardcoded constants like so:
python
PRICE_TIER_1 = 9.99
PRICE_TIER_2 = 19.99
MAX_USERS = 100
AI_MODEL = "gpt-4"
By avoiding complex config files and environment variables, all necessary values are easily accessible. This simplicity allows me to quickly track changes via Git history, ensuring that updates are efficiently managed. Instead of investing a week in setting up a configuration service, I’ve
