Embracing the 3-Month Rule: A Pragmatic Approach to Learning through Unscalable Solutions
There’s a well-known piece of advice from entrepreneur Paul Graham: “Do things that don’t scale.” While this concept is widely acknowledged, its application within the realm of coding remains less discussed. As I reflect on my journey building an AI podcast platform over the past eight months, I have crafted a personal framework: each unscalable workaround is allotted a lifespan of three months. After this period, it either demonstrates its value and gets the necessary infrastructure to back it up, or it’s discarded.
As engineers, we often find ourselves conditioned to construct scalable solutions from the outset. We dive into design patterns, microservices, and distributed systems, imagining how our architecture will handle millions of users. However, this ‘big company’ mentality tends to overlook the realities of startup life. At this stage, striving for scalability can often mean delaying progress and wasting resources on issues that may not even arise.
The 3-Month Rule: Key Insights from My Infrastructure Hacks
Here’s how I’ve applied my three-month philosophy to infrastructure decisions, proving that sometimes, less is indeed more:
1. Consolidated Operations on a Single VM
I run my entire infrastructure—including the database, web server, background jobs, and even Redis—on a single $40/month virtual machine. Lack of redundancy and reliance on manual backups might seem reckless, but this approach has unfolded invaluable lessons about my resource needs. Within just two months, I discovered that my so-called “AI-heavy” platform only peeks at 4GB of RAM. Had I built a complex Kubernetes setup, I would have simply been managing empty containers. When crashes occur—twice so far—I gain immediate insights into unexpected failures rather than theoretical scenarios.
2. Simplistic Configuration Management
Instead of using separate configuration files or environment variables, I’ve hardcoded constants directly into the code:
python
PRICE_TIER_1 = 9.99
PRICE_TIER_2 = 19.99
MAX_USERS = 100
AI_MODEL = "gpt-4"
Though some may scoff at this lack of sophistication, the reality is that it allows me to search my entire codebase for values in seconds. Each price adjustment is neatly recorded in Git history, and any code updates are reviewed—by me, at least. Instead of a week spent creating a configuration service, I dedicate
