Embracing the 3-Month Rule: A Pragmatic Approach to Non-Scalable Solutions in Software Development
In the realm of software development, there’s a widely recognized mantra from Paul Graham: “Do things that don’t scale.” While the philosophy is often discussed, the execution—particularly in coding—remains a less-charted territory. Over the past eight months of constructing my AI podcast platform, I’ve created a straightforward framework to tackle unscalable solutions: I allocate a three-month lifespan for every hack. At the end of this period, it must either demonstrate its worth and evolve into a robust solution, or it must be discarded.
As engineers, we are typically conditioned to aim for scalable solutions right from the start. We know the allure of design patterns, microservices, and distributed systems—architectures that can manage vast numbers of users seamlessly. However, this type of thinking is often geared more towards larger organizations than startups.
In a startup environment, pursuing scalability prematurely can result in costly procrastination. You’re optimizing for hypothetical users and encountering problems that may never arise. My three-month rule compels me to implement straightforward, albeit “imperfect,” code that is deployed quickly, allowing me to gather valuable insights into what users genuinely need.
Current Infrastructure Strategies That Prove Effective
1. Consolidated Operations on a Single VM
All essential components—database, web server, background jobs, and Redis—operate on a single $40 monthly VM. While this approach offers zero redundancy and relies on manual backups, the benefits have been enlightening.
In just two months, I have gained more awareness of my actual resource requirements than through any detailed capacity planning document. It turns out that my predicted “AI-heavy” platform only taps out at 4GB of RAM. The complex Kubernetes setup I nearly initiated would have had me managing empty containers instead of addressing real issues. Each time the system crashes—twice so far—I gather crucial data about what truly fails, which is rarely what I initially expected.
2. Hardcoded Configurations Across the Board
PRICE_TIER_1 = 9.99
PRICE_TIER_2 = 19.99
MAX_USERS = 100
AI_MODEL = "gpt-4"
With hardcoded constants scattered throughout the code instead of configuration files or environment variables, any change compels a redeployment.
This approach offers a hidden advantage: I can quickly search through my entire
