Embracing the 3-Month Rule: A Practical Approach to Non-Scalable Solutions in Tech Development

In the world of tech startups, advice often echoes in the halls of entrepreneurial wisdom: “Do things that don’t scale.” While Paul Graham’s guidance is well-recognized, the nuances of translating this philosophy into concrete coding practices are seldom discussed. After eight months of developing my AI podcast platform, I have crafted a straightforward framework: every non-scalable solution I implement is given a lifespan of three months. After this period, it must either prove its worth and be properly integrated into the architecture or be discarded.

A start-up Mindset: The Cost of Over-Engineering

As engineers, we are trained to implement scalable solutions right off the bat. Concepts of microservices, distributed systems, and polished design patterns dominate our training as we envision handling countless users seamlessly. However, in a startup environment, this fixation on scalability can lead to unnecessary expenditure and time away from immediate user needs. Frequently, we find ourselves optimizing systems for hypothetical users or issues that may never arise.

To combat this, my 3-month rule compels me to produce straightforward, albeit imperfect, code. This approach facilitates expedient releases and allows me to gather the data necessary to understand my users’ needs.

Current Infrastructure Tactics: Insight Over Complexity

1. Consolidation on a Single Virtual Machine

Currently, my database, web server, background jobs, and caching are all functioning on one $40/month virtual machine with no redundancies. Manual backups are stored locally.

This strategy may seem flawed at first glance, but it’s been a goldmine of information. In merely two months, I’ve gained insights into my resource requirements that most capacity planning documents wouldn’t have provided. I discovered that my “AI-heavy” platform only peaks at 4GB of RAM. The complex Kubernetes setup I nearly pursued would have done little more than manage idle containers.

When the system experiences crashes—twice at the time of writing—I gain invaluable insights into real-time issues. Contrary to my assumptions, it’s rarely what I anticipated.

2. Hardcoding Configuration

python
PRICE_TIER_1 = 9.99
PRICE_TIER_2 = 19.99
MAX_USERS = 100
AI_MODEL = "gpt-4"

All configurations are hardcoded without external files or variables, necessitating redeployment for any updates.

The advantage