Embracing Imperfection: The 3-Month Rule for Scalable Learning in Software Development
In the realm of software development, the mantra “Do things that don’t scale,” popularized by Paul Graham, often gets tossed around but rarely examined in detail. It’s easy to say, but how do we effectively implement this approach, particularly in coding?
As I embark on my eight-month journey of building an AI podcast platform, I stumbled upon a straightforward yet powerful framework: give every unscalable hack a lifespan of three months. After this period, each solution must either prove its worth and receive a comprehensive build-out or be phased out entirely.
The Challenge of Scalability
In the world of engineering, we are conditioned to prioritize “scalable” solutions right from the outset. The allure of design patterns, microservices, and distributed systems is undeniable—these serve to accommodate millions of users seamlessly. However, this perspective is often characteristic of larger companies with established user bases.
In the early stages of a startup, writing scalable code can frequently turn into an expensive exercise in procrastination. We tend to optimize for hypothetical users and address challenges that might never materialize. My 3-month rule compels me to produce straightforward, albeit imperfect, code that is deployable and provides genuine insights into user needs.
Exploring My Current Infrastructure Hacks
Here are a few of my current projects and the wisdom they impart:
1. Consolidation on a Single VM
I have consolidated my entire tech stack—database, web server, background jobs, and Redis—onto one $40-per-month virtual machine. Despite this simplistic setup lacking redundancy and relying on manual backups, it has taught me invaluable lessons about my resource requirements. Within just two months, I understood that my “AI-heavy” platform typically operates efficiently with just 4GB of RAM. Initially, I considered creating an elaborate Kubernetes setup, yet I would have spent time managing unnecessary empty containers.
When the system crashes— which has happened twice—I gather real data about what fails, revealing unexpected insights into my setup.
2. Hardcoded Configuration Values
Instead of employing configuration files and environment variables, I’ve utilized hardcoded constants, such as:
plaintext
PRICE_TIER_1 = 9.99
PRICE_TIER_2 = 19.99
MAX_USERS = 100
AI_MODEL = "gpt-4"
While this approach seems crude, it boasts a hidden efficiency: I can quickly locate
