Embracing the 3-Month Rule: A Pragmatic Approach to Early Development
In the world of startups, advice often floats around that encourages founders to “do things that don’t scale.” While many entrepreneurs nod in agreement with Paul Graham’s well-known mantra, few delve into how this can be integrated effectively within the realm of coding. After eight months of developing my AI podcast platform, I’ve crafted a straightforward framework that I call the “3-Month Rule.” This principle allows any unscalable workaround to thrive for a duration of three months, after which it either demonstrates its worth and is properly developed, or is discarded.
One critical insight I’ve gained during this journey is that developers are typically inclined to construct scalable solutions from the outset. We obsess over elegant design patterns and robust architectures, often tailored for businesses ready to handle millions of users. However, in the startup environment, prematurely focusing on scalability can lead to excessive delays, making the elegant solutions cost-prohibitive and sometimes irrelevant. The 3-Month Rule encourages me to create straightforward, albeit less idealized code that can be deployed quickly, enabling me to learn directly from user interactions.
Practical Infrastructure Insights: Small Hacks That Yield Big Lessons
1. Single VM for All Operations
I decided to run my entire operation on one virtual machine, which hosts the database, web server, background services, and Redis. This setup, costing only $40 per month, intentionally avoids redundancy and relies on manual backups to my local machine.
This straightforward approach might seem reckless, but it has provided invaluable insights into my actual resource requirements. In just eight weeks, I’ve discovered that my “AI-heavy” platform effectively utilizes only 4GB of RAM. The complex Kubernetes architecture I nearly implemented would have simply resulted in managing empty containers. Each crash (of which there have been two) has given me unexpected insights into what truly fails in the system, which often isn’t what I anticipated.
2. Hardcoded Configurations
Instead of employing environment variables or configuration files, I’ve opted for hardcoded constants, such as:
python
PRICE_TIER_1 = 9.99
PRICE_TIER_2 = 19.99
MAX_USERS = 100
AI_MODEL = "gpt-4"
While this might be seen as a bad practice, it’s equipped me with speed and clarity. With a simple grep command, I can quickly find any configuration value in my codebase.
