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
One Comment
This is a compelling approach that resonates with a lot of startups and early-stage projects. The 3-month rule effectively balances rapid experimentation with disciplined evaluation, ensuring you don’t get bogged down in premature optimization. I appreciate how intentionally simple infrastructure choices—like consolidating everything on a single VM—provide valuable insights and keep costs manageable, especially when learning what your actual needs are.
One thought I have is that, as your platform evolves, it could be helpful to establish a clear process for when to transition from these quick and dirty solutions to more scalable architectures. For example, capturing metrics during each cycle can help determine whether a shift to microservices or more sophisticated infrastructure is justified, rather than a hasty overhaul. Also, incorporating lightweight configuration management early on—such as environment variables or YAML files—could reduce redeployments for configuration changes, letting you maintain agility while reducing friction.
Overall, this pragmatic, cycle-focused strategy exemplifies the importance of validating ideas quickly and iteratively—key principles for sustainable growth in startups. Looking forward to seeing how this framework continues to evolve!