The 3-Month Experiment: A Pragmatic Approach to Development in Startups

When it comes to startup culture, a common piece of advice often heard is Paul Graham’s mantra: “Do things that don’t scale.” But how exactly do you integrate this philosophy into software development? After eight months of building my AI podcast platform, I’ve developed a straightforward method to navigate this challenge: any unscalable quick fix is given a trial period of three months. After that time, it either proves its worth and is revamped into a sustainable solution, or it is discarded.

As engineers, we typically lean toward creating scalable solutions right from the outset. We think in terms of design patterns, microservices, and distributed systems—architectural frameworks designed for serving millions of users. While this approach is suitable for large enterprises, it can often lead to unnecessary complexities in a startup environment where scalable code can sometimes equate to costly delays. My 3-month rule compels me to design simple and direct code that actually gets shipped while revealing what users genuinely need.

Current Infrastructure Strategies: Clever Hacks That Work

1. Unified Virtual Machine Hosting

All elements of my application—database, web server, background jobs—operate on a single $40/month virtual machine (VM). This means zero redundancy and manual backups to my local device.

Why this makes sense: Within a mere two months, I’ve gained insights into my real resource needs that traditional capacity planning could never provide. My application, which I believed would be “AI-heavy,” only peaks at 4GB of RAM. The intricate Kubernetes setup I had considered would have meant managing unused containers.

When the system experiences downtime (which it has twice so far), I receive invaluable data on failure points—insights that often contradict my initial expectations.

2. Simple, Hardcoded Configurations

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

There are no configuration files or environment variables—only hardcoded constants scattered throughout my codebase. Updating any value requires a redeployment.

The hidden benefit: I can quickly search the entire codebase for any configuration value. Each price adjustment is captured in Git history, with every configuration modification subject to code review (even if that review is just my own analysis). A dedicated configuration service would demand a week of work, while