Embracing the 3-Month Rule: A Pragmatic Approach for Early-Stage Startups

In the world of startups, achieving rapid growth often entails embracing unconventional wisdom. Paul Graham famously advises entrepreneurs to “do things that don’t scale.” But how can one effectively apply this principle in software development? After eight months of developing my AI podcast platform, I have created a framework to navigate this dilemma: every hack or temporary solution is given a three-month lifespan. At the end of this period, we either build on its success or let it go.

Rethinking Traditional Development Practices

Often, engineers are educated to focus on scalable architectures from the outset—think microservices, complex design patterns, and robust systems that can accommodate vast numbers of users. While this mindset is excellent for large organizations, it may prove to be an obstacle for startups. Scalable code can become a form of procrastination, as you’re preparing for problems that may never arise and optimizing for users that do not exist yet. My three-month rule compels me to write straightforward, albeit imperfect, code that delivers real insights into user needs.

Observations from My Current Infrastructure Hacks

1. Consolidation into a Single VM

I’m currently running my database, web server, background jobs, and caching on a single virtual machine that costs just $40 a month. There’s no redundancy, and I perform manual backups locally.

Why is this a smart choice? In two months, I have gained critical insights into my actual resource needs—far more than any other planning document could have provided. My AI-focused platform, it turns out, peaks at just 4GB of RAM. The complicated Kubernetes system I nearly built would have simply managed idle containers.

When the server crashes (which has occurred twice), I gather real data on what went wrong. Spoiler: none of it is what I initially anticipated.

2. Directly Hardcoded Configuration

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

No environment variables or separate config files here—just constants sprinkled across my codebase. Any changes require a redeployment.

The benefit? I can quickly search my entire project for configuration values and see a clear history of changes in Git. I’ve only adjusted these values three times in three months; the time investment has been just 15 minutes compared