Embracing the 3-Month Rule: A Practical Approach to Building Expedited Solutions

In the startup ecosystem, the conventional wisdom from thought leaders like Paul Graham is to “do things that don’t scale.” While this advice is widely acknowledged, a roadmap for its implementation in technology development often goes unexplored. As I navigate the creation of my AI podcast platform—now eight months into the journey—I’ve cultivated a straightforward yet effective framework: every non-scalable solution receives a lifespan of three months. Following this period, it must either validate its worthiness for further investment or be phased out.

The Challenge of Scalable Solutions

As software engineers, we are conditioned to prioritize scalability from the outset. We focus on sophisticated architectures, employing design patterns, microservices, and distributed systems that are capable of supporting vast user bases. However, this mindset can lead startups to invest resources in future users that do not yet exist, effectively delaying critical learning processes.

The three-month rule compels me to embrace straightforward, if imperfect, coding practices that deliver quick results and illuminate genuine user needs—an essential aspect of early-stage product development.

Current Infrastructure Innovations: Smart Hacks with Purpose

1. Consolidated Server Management

My entire platform—including the database, web server, background jobs, and caching—runs on a single $40/month virtual machine (VM) without redundancy. This setup, while seemingly risky, has provided invaluable insights into my actual resource utilization over the last two months, far beyond what any theoretical capacity planning could offer. I learned, for instance, that my platform’s peak usage hovers around 4GB of RAM, proving that an intricate Kubernetes architecture would have been premature and unnecessary.

2. Simplifying Configuration

Instead of employing configuration files or environment variables, I opted for constant values directly in my code. For example:

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

While some may criticize this approach as outdated, the speed and transparency it offers are unparalleled. I can quickly search through my codebase for any configuration term, and each adjustment is meticulously tracked. Creating a separate configuration service could have consumed a week of development time, but I’ve made only three changes in three months, demonstrating the efficiency of this method.

3. SQLite: An Unexpected Champion

I have chosen to use