Embracing the 3-Month Rule: A Pragmatic Approach to Building an AI Podcast Platform

In the entrepreneurial landscape, Paul Graham’s mantra of “doing things that don’t scale” is widely recognized. However, few delve into how to effectively apply this philosophy within the realm of software development. Over the past eight months, while constructing my AI podcast platform, I have established a personal framework that I call the “3-Month Rule.” This approach involves allowing any unscalable hack to thrive for a maximum of three months. After this period, I evaluate its effectiveness: if it proves its worth, it is refined into a scalable solution; if not, it is promptly discarded.

As engineers, we are often conditioned to prioritize the creation of scalable architectures from the outset. We envision grand designs complete with microservices and distributed systems—ideal for handling massive user bases. Yet this perspective can be problematic, particularly in a startup environment where optimizing for non-existent users can lead to unnecessary delays.

Implementing my 3-Month Rule has encouraged me to embrace simpler, more straightforward coding practices that not only facilitate rapid deployment but also enhance my understanding of user requirements.

My Pragmatic Infrastructure Hacks: Smart Choices for Effective Learning

1. Simplified Architecture on a Single VM

I opted for an uncomplicated setup in which my entire platform operates on a single virtual machine (VM) costing $40 per month, hosting everything from the web server to the database and background processes. Although lacking redundancy and relying on manual backups, this configuration has provided invaluable insights into my actual resource needs sooner than any theoretical planning might have.

This set-up revealed that my platform peaked at just 4GB of RAM during user activity. Had I invested time in a complex Kubernetes architecture, I would have simply been managing idle resources. Additionally, observing system crashes has yielded critical data about failure points that defy my initial assumptions.

2. Hardcoded Configuration

Rather than utilizing configuration files or environment variables, I have chosen to implement hardcoded constants throughout my codebase:

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

By doing so, I am able to quickly locate any configuration value and track changes through version control history. This method allows for rapid deployments—just 15 minutes for a change—versus the weeks that could be spent developing