Embracing the 3-Month Rule: A Pragmatic Approach to Non-Scalable Development
In the world of startups, the mantra “Do things that don’t scale” is often echoed, especially when it comes to innovation and growth. Yet, the practical application of this advice in software development is a topic rarely discussed. Drawing from my experience building an AI podcast platform over the past eight months, I’ve formulated a straightforward framework based on the “3-Month Rule.” This approach dictates that every unscalable solution is allotted a trial period of three months: after which it either demonstrates its value and is subsequently refined, or it is discarded.
Reassessing Our Approach to Scalability
As engineers, we are often conditioned to create scalable solutions from the start. We embrace the elegance of design patterns, microservices, and distributed systems—all designed to accommodate large user bases. However, this line of thinking may not serve us well in a startup environment, where scalability can sometimes translate into costly delays and distractions, addressing potential issues that may never come to fruition.
Under my 3-month rule, I focus on crafting simple, albeit imperfect, code that can be deployed quickly. This hands-on approach allows me to uncover genuine user needs while avoiding the pitfalls of over-optimization for users that may never materialize.
Infrastructure Hacks That Prove Their Worth
Here are some of my current unscalable hacks, each of which has provided significant insights into my development process:
1. Consolidated Operations on a Single VM
By hosting all components—including the database, web server, background jobs, and Redis—on a single $40/month virtual machine, I’ve gained valuable insights into my resource needs. This approach, though lacking redundancy, has taught me that my platform peaks at just 4GB of RAM. If I had implemented a complex Kubernetes setup, I would have been left managing idle resources instead of honing in on actual user demand.
2. Hardcoded Configurations
Instead of using configuration files or environment variables, I stick with hardcoded constants throughout the code:
plaintext
PRICE_TIER_1 = 9.99
PRICE_TIER_2 = 19.99
MAX_USERS = 100
AI_MODEL = "gpt-4"
The beauty of this approach lies in its simplicity. With rapid search capabilities at my disposal, I can quickly find and track any configuration changes in Git. Having updated these values only three times in three months
