Embracing the 3-Month Rule: A Pragmatic Approach to Non-Scalable Solutions in Tech Development
In the world of startups and innovative tech solutions, we often hear the mantra: “Do things that don’t scale.” While this advice is widely understood, there is often little discussion about how to effectively implement it, especially in the realm of coding.
After eight months of developing my AI podcast platform, I’ve adopted a straightforward philosophy: every hack or workaround that doesn’t scale is given a lifespan of three months. Beyond that period, it must either demonstrate its worth and evolve into a robust solution or be discarded.
The Paradigm Shift: From Scalable Solutions to Practical Workarounds
As engineers, our training frequently emphasizes creating scalable systems from the outset. We learn to design complex architectures such as microservices and distributed systems tailored for millions of users. However, in the context of a startup, this approach can often lead to excessive complexity and expensive delays. We find ourselves optimizing for hypothetical users and problems that may never materialize.
My three-month rule compels me to focus on producing simple, albeit ‘messy,’ code that gets results and provides insight into what users genuinely require.
Current Infrastructure Hacks That Prove Their Worth
1. Unified Operations on a Single VM
My platform’s architecture relies on a single virtual machine to handle the database, web server, background jobs, and caching. Though rudimentary and lacking redundancy, this setup has been invaluable.
In just two months, I gained insights about my actual resource consumption that no theoretical planning document could have provided. I learned that my AI-focused platform requires only 4GB of RAM at peak usage. If I had implemented a complex Kubernetes infrastructure, I’d likely be dedicating resources to manage empty containers.
When this system crashes—it’s happened twice—I gather critical information about the real issues at play. Spoiler alert: it’s rarely what I anticipated.
2. Simplified Hardcoded Configuration
Throughout my codebase, configuration values are directly embedded within the code:
PRICE_TIER_1 = 9.99
PRICE_TIER_2 = 19.99
MAX_USERS = 100
There are no separate config files, meaning that any change necessitates a redeployment. This simplicity has a surprising benefit: I can swiftly search through my code for any configuration value and track changes through git history. In three months, I’ve only adjusted these values three times,
