Embracing the 3-Month Experiment: A Practical Approach to Building Scalable Solutions

In the startup world, we often hear the mantra from Paul Graham: “Do things that don’t scale.” However, translating this wisdom into actionable coding strategies is rarely discussed. After eight months of developing my AI podcast platform, I’ve devised a straightforward framework that keeps my experimentation focused and effective: each unscalable hack is given a three-month trial. If it fails to demonstrate its worth, it gets discarded.

As engineers, we’re conditioned to chase after scalability right from the onset. We obsess over design patterns, microservices, and other architecture meant to support millions of users. But for startups, this approach can sometimes feel like a costly delay, as we’re often optimizing for hypothetical users and solving problems that may never come to fruition. By adhering to my three-month rule, I find myself leaning into simpler, more direct coding practices that provide genuine insights into user needs.

Current Infrastructure Hacks and Their Value

Let me share some of my current infrastructure shortcuts and why they’re actually more effective than they might seem at first glance.

1. All-in-One Virtual Machine

My setup consists of a single $40/month virtual machine running everything—database, web server, background jobs, Redis—with no redundancy and manual backups stored locally.

Why is this a smart move? In just two months, I’ve gained more understanding of my resource requirements than any complex planning document could provide. It turns out my platform, which I initially anticipated would be resource-heavy, only peaks at 4GB of RAM. The elaborate Kubernetes architecture I almost implemented would have solely involved managing empty containers. Every crash (yes, there have been a couple) has offered valuable data about failure points—always unexpected.

2. Hardcoded Configuration Throughout

In my code, you’ll find constants like:

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

There are no config files or environment variables, just straightforward constants embedded in the code. Modifying any value requires redeployment.

The upside? I can quickly search my entire codebase for configuration values, and every change is logged in my Git history. Over the past three months, I’ve only adjusted these values three times, which took about 15 minutes, compared to the 40 hours it would have taken to build a