Embracing the 3-Month Rule: A Framework for Building Non-Scalable Solutions in Software Development

In the world of startups and innovative project development, one piece of advice frequently resurfaces: “Do things that don’t scale.” This wisdom, popularized by Paul Graham, echoes through the halls of entrepreneurial thinking. However, the practical application of this principle, especially within the realm of coding, often goes unaddressed.

As I embarked on the journey of creating my AI podcast platform over the past eight months, I found myself navigating through the complexities of software development. Through trial and error, I’ve established a novel framework that I call the “3-Month Rule.” This approach simplifies decision-making by giving each unscalable hack a defined lifespan of three months. After this period, each solution must either demonstrate its value and transition into a more sustainable form, or it will be discarded.

The Default Mindset: Building for Scale

Engineers are often trained to prioritize scalability from day one. We immerse ourselves in design patterns, microservices, and distributed systems – all geared toward accommodating millions of potential users. Yet, in a startup environment, this mindset can lead to costly delays. By investing time in optimizing for users who may not yet exist, we risk hindering our immediate goals.

My 3-month framework encourages me to focus on writing straightforward, even “messy” code. This iterative process not only enables efficient shipping but also helps me uncover the true needs of my users.

Exploring My Current Technical Insights

1. Single Virtual Machine Setup

I’m currently operating all components of my platform—including the database, web server, background jobs, and caching—on a single $40-per-month virtual machine. Despite the apparent riskiness of this arrangement, I’ve gained valuable insights into my actual resource requirements over just two months. It turns out that my “AI-heavy” application peaks at just 4GB of RAM, which saved me from embarking on an unnecessarily complex Kubernetes setup.

Moreover, each crash provides essential data regarding system vulnerabilities, informing my future architectural choices.

2. Simplistic Hardcoded Configurations

In my code, configuration is done through const variables like:

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

This approach eliminates the need for configuration files or environment variables. Though