Embracing the 3-Month Rule: A Practical Approach to Non-Scalable Solutions
In the world of entrepreneurship, a well-known piece of advice floats around: “Do things that don’t scale.” While this wisdom resonates strongly, especially for startups, the challenge lies in implementing it effectively — particularly in the realm of coding.
After dedicating eight months to constructing my AI podcast platform, I’ve developed a straightforward framework: every unscalable hack is assigned a lifespan of three months. At the conclusion of this period, the hack must either demonstrate its value and transition to a robust implementation or be discarded.
As engineers, we’re often conditioned to design scalable solutions from the outset. Concepts like microservices, design patterns, and distributed systems are all vital in constructing infrastructures that can support millions. Yet, this is often the mindset of larger organizations. In startups, focusing too much on scalability can lead to excessive groundwork that caters to hypothetical future users rather than actual needs. This is where my 3-month rule comes into play, allowing me to write straightforward, albeit imperfect, code that is deployable and enlightening in terms of real user requirements.
Current Infrastructure Hacks: Practical Wisdom in Simplicity
1. Consolidated VM Usage
My entire stack—database, web server, background jobs, and caching—operates on a single $40/month virtual machine, sans redundancy, with manual backups conducted locally.
This approach has proven to be insightful. Within just two months, I’ve acquired a clearer understanding of my resource demands than any planning document could provide. The reality is that my “AI-driven” platform rarely exceeds 4GB of RAM usage. I was close to implementing a complex Kubernetes architecture, but in reality, I would have been managing idle containers instead of addressing actual needs.
When the system fails (which has happened a couple of times), I gain valuable insights into the root causes of the breakdown, which are often surprising.
2. Hardcoded Configurations
Consider the following example:
python
PRICE_TIER_1 = 9.99
PRICE_TIER_2 = 19.99
MAX_USERS = 100
AI_MODEL = "gpt-4"
I’ve opted to hardcode values directly into my codebase rather than utilizing configuration files or environment variables. This means any alteration necessitates a redeployment.
The upside? I can quickly search my entire codebase for any configuration value. Every update is logged through Git,
