The 3-Month Strategy: A Practical Approach to Implementing Unscalable Solutions
In the world of startups, the words of Paul Graham resonate strongly: “Do things that don’t scale.” However, the challenge arises when we attempt to translate this advice into practical coding actions. After dedicating the past eight months to developing my AI podcast platform, I’ve crafted a straightforward framework that I call the “3-Month Rule,” which dictates that every temporary, unscalable solution will only be given three months to prove its worth. After this period, it either gets refined into a robust solution or is discarded entirely.
As software engineers, we often find ourselves drawn to the allure of scalability from the outset. We become preoccupied with sophisticated design patterns, microservices, and distributed systems—all of which are designed to manage significant user volumes. Yet, this approach often aligns more with the mindset of established companies than with the agile nature of startups.
In the early stages of a startup, focusing solely on scalability can turn into costly procrastination. We frequently end up optimizing for hypothetical users and addressing issues that may never arise. My 3-month strategy encourages me to write straightforward, if imperfect, code that is deployable—and ultimately reveals the genuine needs of my users.
Current Infrastructure Strategies: Why They Work
1. Unified Virtual Machine Setup
Currently, my entire architecture—from the database and web server to background jobs—is contained within a single $40/month virtual machine. This setup has no redundancy and relies on manual local backups.
Why is this a smart move? In just two months, I’ve gained invaluable insights into my actual resource requirements that no capacity planning document could provide. Surprisingly, my AI-intensive platform only peaks at 4GB of RAM. If I had opted for a complex Kubernetes infrastructure, I would have been left managing dormant containers. Each crash (which has happened twice) has offered real-world data about failures—unexpectedly, the points of failure were never what I anticipated.
2. Simplified Hardcoding of Configurations
python
PRICE_TIER_1 = 9.99
PRICE_TIER_2 = 19.99
MAX_USERS = 100
AI_MODEL = "gpt-4"
Instead of relying on configuration files or environment variables, I’ve opted for hardcoded constants scattered throughout my code. Altering any setting necessitates a redeployment.
The advantage here is clear: I can easily search my entire codebase
One Comment
Thank you for sharing this practical and insightful approach! The “3-Month Rule” is an excellent way to balance the need for rapid iteration with the reality that many unscalable solutions provide crucial learning opportunities early on. This strategy helps prevent startups from over-engineering from day one, which can slow down momentum and divert focus from delivering value to users.
I particularly appreciate your emphasis on starting simple—using a single VM and hardcoded configs—to gain real-world insights quickly. Often, teams get caught up in infrastructure and configuration management prematurely, which can lead to wasted effort and unnecessary complexity. Your approach reminds us that, in the early stages, “doing things that don’t scale” can be a powerful tool for discovery and validation.
It might be interesting to consider how this framework could be adapted as the platform grows, perhaps incorporating incremental scalability plans that align with observed usage patterns. Additionally, documenting each “failed” or “successful” experiment within the 3-month window can create valuable knowledge for future iterations.
Thanks again for sharing. This disciplined yet flexible approach can serve as a valuable blueprint for many founders and engineers navigating early-stage development!