The 3-Month Rule: A Pragmatic Approach to Building an AI Podcast Platform
In the tech world, there’s a well-known mantra from startup guru Paul Graham: “Do things that don’t scale.” Yet, the challenge for many developers lies in applying this principle effectively, particularly when it comes to coding practices. After eight months of working on my AI podcast platform, I’ve devised a straightforward framework: every unscalable approach I implement is given a lifespan of three months. If it proves to be valuable, I’ll refine it and transition it into a robust solution; if not, it gets the axe.
Understanding the Dilemma
As software engineers, we’re often conditioned to develop scalable systems right out of the gate. We’re trained in sophisticated design patterns, microservices architecture, and distributed systems that are capable of accommodating vast user bases. However, this is a mentality more suited to larger enterprises, where resource allocation is typically less restrictive. In a startup environment, focusing exclusively on scaling can lead to inefficiencies, often leading us to optimize for hypothetical users rather than addressing the needs of the users we currently have.
My three-month rule compels me to prioritize simplicity and directness in my coding practices. Rather than seeking perfection from the beginning, I aim to learn from real-world user interactions and needs.
Practical Infrastructure Hacks That Yield Insights
1. Single-VM Operations
I run my entire infrastructure on a single virtual machine that costs just $40 a month. This includes everything from the database to the web server and background operations. While it lacks redundancy and I have manual backups, this approach has empowered me to accurately assess my resource demands. Through practical experience, I discovered my platform, which leverages AI, peaks at a mere 4GB of RAM. In hindsight, the complex Kubernetes setup I nearly constructed would have only managed idle containers.
When the system encounters failures — which it has a couple of times — I gain valuable insights into the real points of failure, which often aren’t what I anticipated.
2. Hardcoded Configurations
In my code, configuration values are directly hardcoded, for example:
python
PRICE_TIER_1 = 9.99
PRICE_TIER_2 = 19.99
MAX_USERS = 100
AI_MODEL = "gpt-4"
While this method may seem cumbersome, it affords me the ability to quickly search my entire codebase for any configuration
