Embracing the 3-Month Approach: A Framework for Innovating Without the Pressure of Scale
When it comes to growing a business, many of us have heard Paul Graham’s famous mantra, “Do things that don’t scale.” However, translating this wisdom into practical coding strategies is often overlooked. Over the past eight months of developing my AI podcast platform, I’ve devised a straightforward yet effective framework that prioritizes unscalable techniques: every non-scalable hack earns a three-month lifespan. After this period, it either demonstrates its worth and earns a polished redesign, or it is shelved for good.
As engineers, we are traditionally conditioned to construct solutions that are scalable from the outset, focusing on intricate architectures suited for handling millions of users. Yet, in a startup environment, this approach can often lead to costly delays. By adhering to my three-month rule, I’m encouraged to write straightforward and perhaps “imperfect” code that gets implemented quickly, allowing me to truly understand user needs.
My Ingenious Infrastructure Hacks and Their Strategic Benefits
1. Consolidated Operations on a Single Virtual Machine
Currently, my entire platform—comprising the database, web server, and background jobs—operates on just one virtual machine costing $40 per month. This means I forgo redundancy and depend on manual backups.
Why is this method so effective? In just two months, I’ve gained invaluable insights into my resource needs that any detailed capacity planning document would struggle to provide. My platform, which I initially thought would demand extensive resources, has proven to peak at only 4GB of RAM. That elaborate Kubernetes setup I nearly implemented? It would have meant managing containers without any real workload.
Each time it crashes (which has happened a couple of times), I glean concrete data about the real issues. Spoiler alert: they’re rarely what I anticipated.
2. Explicit Configuration in Code
python
PRICE_TIER_1 = 9.99
PRICE_TIER_2 = 19.99
MAX_USERS = 100
AI_MODEL = "gpt-4"
In my codebase, configuration values are hardcoded rather than stored in environment files, which means any modification requires a redeployment.
The genius here lies in simplicity: I can quickly search my codebase for any configuration value. Each price update is meticulously recorded in Git history, and each adjustment undergoes my personal code review process.
The time saved is
