Embracing Imperfection: The 3-Month Rule for Scalable Learning in Coding
In the tech world, we’ve all heard the sage advice from Paul Graham: “Do things that don’t scale.” But how does that translate into concrete actions when it comes to programming? As I navigate the development of my AI podcast platform, I’ve implemented a straightforward yet effective framework that can dramatically enhance learning and agility in the startup phase: I give every unscalable approach a strict lifespan of three months. After this period, it’s either adopted into my core system or deprecated.
The startup Paradigm vs. Big Tech Thinking
As developers, our instincts often push us toward crafting scalable solutions right from the outset—think design patterns, microservices, and distributed architecture capable of managing extensive user bases. However, at the startup level, such ambitious plans can represent expensive procrastination. We may find ourselves optimizing for hypothetical users and addressing issues that may never arise. My 3-month rule compels me to produce simpler, more effective code that can be released quickly, ultimately revealing genuine user needs.
Current Hacks That Are Surprisingly Effective
1. Consolidated Operations on a Single VM
Currently, my application runs entirely on a $40/month virtual machine that accommodates my database, web server, background tasks, and caching—without redundancy and relying on manual local backups. This setup has proven insightful; I’ve learned more about my resource utilization in two months than any planning document could have conveyed. I discovered that my supposedly “AI-heavy” operations peak at just 4GB of RAM, saving me from the complexities of an elaborate Kubernetes architecture that would have gone largely unused.
When my system encounters issues (which has happened a couple of times), I’m provided with valuable insights into the specific points of failure, revealing patterns I hadn’t anticipated.
2. Hardcoded Configuration Values
Instead of using complex configuration management systems, I have opted for hardcoded constants throughout my codebase, such as:
plaintext
PRICE_TIER_1 = 9.99
MAX_USERS = 100
AI_MODEL = "gpt-4"
This approach means any adjustments require redeployment, but it offers advantages: I can quickly search for and modify any configuration value across the entire codebase while maintaining a precise git history of every change. While building a configuration management service might seem like a prudent endeavor, the reality is that I’ve changed these values a mere three times in three months.
