Embracing the Unscalable: My 3-Month Rule for Agile Development

In the dynamic landscape of startup development, one timeless piece of advice often echoed by Paul Graham is to “do things that don’t scale.” However, the challenge lies in executing this philosophy in practical ways, especially concerning coding and infrastructure decisions.

Over the past eight months, as I’ve embarked on creating my AI podcast platform, I have formed a straightforward yet effective framework: every unscalable approach I implement gets a lifespan of three months. At the end of this period, each hack either proves its worth and evolves into a robust solution or gets retired.

As engineers, we are often conditioned to prioritize scalable design from the outset. We focus on polished architectures featuring design patterns, microservices, and distributed systems, all tailored to accommodate millions of users. Yet, this is a mindset more suited for large corporations than agile startups. In reality, scalable solutions can often serve as costly delays that divert attention from immediate demands, ultimately hindering learning and growth.

The essence of my three-month rule is to challenge myself to write simple, straightforward, and sometimes “bad” code that can be deployed quickly. This approach not only accelerates the development process but also provides invaluable insights into user needs.

Current Hacks: Simple Choices with Serious Benefits

1. Consolidated Running Environment

I’ve opted to run my entire infrastructure—database, web server, background jobs, and Redis—on a single $40/month virtual machine. This means no redundancy and manual backups to my local machine.

Why is this a smart move? In just two months, I’ve gained more clarity on my resource requirements than I ever would have through extensive planning documents. It turns out my “AI-heavy” platform only utilizes around 4GB of RAM at peak times. The elaborate Kubernetes setup I nearly implemented would have been unnecessary, as it would have required managing idle containers.

When this setup has crashed (twice so far), it has provided real-time data on the true points of failure—information that often contradicted my expectations.

2. Hardcoded Configurations

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

Forget about configuration files and complex environment variables. With constants embedded throughout the codebase, making any adjustments necessitates a redeployment.

The clever aspect of