Embracing the 3-Month Rule: A Practical Approach to Non-Scalable Solutions
In the entrepreneurial landscape, Paul Graham’s adage to “do things that don’t scale” resonates deeply. However, putting this advice into practice—especially in the realm of coding—remains a challenge many face. After eight months of developing my AI podcast platform, I’ve devised a straightforward strategy: I give every non-scalable hack a lifespan of just three months. Within that timeframe, each hack must either demonstrate its worth and evolve into a more robust solution or be discarded.
Engineers often have a tendency to lean towards scalable architecture from the outset—think of intricate design patterns or distributed systems that could accommodate millions of users. While such frameworks are fantastic for larger organizations, in a startup environment, pursuing scalable solutions can lead to costly delays. This is because we typically focus on problems that don’t yet exist and anticipate users who are not yet in the picture. My self-imposed 3-month rule encourages me to create simple, even “bad,” code that can be quickly deployed, ultimately revealing what my users truly need.
My Strategic Hacks and Their Value
1. Unified Infrastructure on a Single VM
Currently, my entire platform—from the database to the web server—is running smoothly on a singular $40/month virtual machine, with no redundancy and manual backups to my local system. While this might seem reckless, it has provided invaluable insights regarding my resource requirements in just a couple of months, far surpassing what any extensive capacity planning document could convey. I discovered that my AI-focused platform frequently utilizes only 4GB of RAM; the complex Kubernetes cluster I almost set up might have only resulted in managing empty resources.
When server crashes do occur (and they have, twice), I gain real insights into the actual points of failure. Spoiler alert: It’s not what I anticipated.
2. Simplified Configuration Management
Instead of offers through configuration files or environment variables, my code contains hardcoded constants:
python
PRICE_TIER_1 = 9.99
PRICE_TIER_2 = 19.99
MAX_USERS = 100
AI_MODEL = "gpt-4"
The benefit of this straightforward approach? Quickly searching for configuration values across my codebase takes seconds. Each price adjustment is documented in my git history, ensuring that any changes are reviewed, albeit by me alone. Building a configuration management service would require a week of
