Embracing Imperfection: The 3-Month Rule for Agile Development

In the startup world, the advice to “do things that don’t scale,” famously shared by Paul Graham, is widely acknowledged but often misunderstood in practice, particularly when it comes to software development. As I embarked on the journey of creating an AI-focused podcast platform over the last eight months, I formulated a practical approach to implementing this principle: every unscalable method is given a lifespan of three months. After that period, it either evolves into a fully-functional solution or is discarded.

The Challenge of Scalability

As software engineers, we are conditioned to prioritize scalable architecture from the outset. We become fascinated with design patterns, microservices, and distributed systems, all engineered to handle immense numbers of users. However, this is often a mindset best suited for larger enterprises.

In contrast, at a startup, striving for scalability too early can lead to unnecessary complexities and costs. Instead of optimizing for future possibilities, I’ve found value in focusing on what I can build now. My three-month rule encourages me to write straightforward, even “imperfect,” code that can actually be deployed, while also providing insight into real user needs.

My Current Strategies: Unconventional Yet Effective

1. Centralized Operations on a Single Virtual Machine

I’ve consolidated my database, web server, background jobs, and Redis onto a single $40/month virtual machine. With zero redundancy and backups manually managed, this setup might seem risky, but it has allowed me to gain unparalleled insights. I’ve learned that my platform, which I initially thought would require extensive resources, only peaks at 4GB of RAM. What I presumed would be an elaborate Kubernetes architecture turned out to be unnecessary for my current needs.

When the server has faced crashes (which it has twice), the data I’ve gathered has been invaluable, often yielding insights that surprised me.

2. Hardcoded Configuration for Simplicity

Instead of using configuration files or environment variables, I opted for hardcoded constants in my codebase:

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

While this approach may seem rudimentary, it gives me quick access to configuration values through a simple search. Any necessary changes mean a quick redeployment, which has occurred only three times in the past three months