Embracing the 3-Month Rule: A Pragmatic Approach to Rapid Development
When it comes to startup growth, most of us have heard the advice from influential figures like Paul Graham: “Do things that don’t scale.” However, there’s often a lack of guidance on how to actually put this principle into practice, especially when it comes to coding.
After spending the last eight months developing my AI podcast platform, I’ve adopted a straightforward framework to facilitate this: every unscalable solution has a lifespan of just three months. At the end of this period, the effectiveness of each solution is evaluated; if it proves its worth, it gets a more robust build. If not, it’s eliminated from the system.
As engineers, we often focus on constructing scalable solutions from the outset. We’re trained in creating intricate architectures—think microservices, distributed systems, and design patterns—that can handle vast numbers of users. While these practices are essential for larger organizations, they can lead to unnecessary complexity in a startup environment. Scalable solutions can sometimes equate to costly postponements. Why invest in an architecture for users who aren’t even on board yet, or tackle problems that might never arise? In contrast, my 3-month rule encourages me to write lean, efficient code that delivers real results and reveals the authentic needs of my users.
My Current Development Strategies and Their Hidden Benefits
1. Consolidated Infrastructure on a Single VM
My entire application runs on one $40/month virtual machine, hosting everything from the web server to the database. This approach may seem reckless, yet it’s proving to be invaluable. By simplifying my setup, I’ve gained insights into my actual resource demands in just two months—far more than any elaborate capacity planning could provide. For example, my AI-heavy platform requires only 4GB of RAM at peak usage. All those plans I had for a complex Kubernetes environment? They would have turned out to be a way to manage empty containers.
Crashes (which have occurred a couple of times) have provided useful data about what genuinely fails, helping me understand the system better.
2. Hardcoded Configuration for Rapid Changes
Instead of complicated configuration files, I’ve hardcoded constants throughout my application. For instance:
python
PRICE_TIER_1 = 9.99
PRICE_TIER_2 = 19.99
MAX_USERS = 100
AI_MODEL = "gpt-4"
While this may seem primitive
