Embracing the 3-Month Rule: A Pragmatic Approach to Non-Scalable Solutions in Tech Development
In the world of tech entrepreneurship, advice often circulates around the idea of “doing things that don’t scale,” a principle famously championed by Paul Graham. However, there’s a critical lack of discussion on the practical application of this concept within the software development realm.
After spending the past eight months building my AI podcast platform, I’ve devised a straightforward strategy for navigating the non-scalable terrain: every unscalable approach is granted a three-month lifespan. At the end of this period, it must either demonstrate its worth through tangible results or be phased out.
As tech professionals, we are conditioned to focus on scalable solutions from the outset. We’re drawn to intricate design patterns, microservices, and distributed systems designed to accommodate millions of users. However, this perspective often leads startups to engage in costly procrastination, investing time and resources to optimize for hypothetical future scenarios rather than addressing immediate needs. My 3-month framework encourages me to create straightforward, albeit “imperfect,” code that expedites learning about actual user requirements.
My Current Infrastructure Approaches and the Rational Behind Them
1. Consolidated Operations on a Single Virtual Machine
I host everything—databases, web servers, background tasks—on a single $40/month virtual machine, opting for zero redundancy and manually backing up data to my local machine.
This choice has proven insightful; in just two months, I’ve gained a clearer understanding of my resource needs than any planning documentation could provide. My platform—more AI-centric than I anticipated—peaks at 4GB of RAM, which means the elaborate Kubernetes infrastructure I nearly set up would have been managing underutilized containers. Moreover, experiencing two crashes has offered valuable insights into what truly fails—surprise: it’s never what I expected.
2. Hardcoded Configuration for Ease of Use
Instead of using configuration files or environment variables, I utilize constants scattered throughout the codebase like so:
python
PRICE_TIER_1 = 9.99
PRICE_TIER_2 = 19.99
MAX_USERS = 100
AI_MODEL = "gpt-4"
While this may seem haphazard, the benefit is clear: I can quickly search my entire codebase for configuration values. Each price change is meticulously tracked via git, and every update receives a code review—albeit primarily by me
One Comment
This is a compelling framework that emphasizes speed, learning, and adaptability over premature optimization—a mindset that’s often overlooked in favor of scalable architecture from the outset. The 3-month rule resonates well with the concept of “test fast, iterate faster,” especially in early-stage products where understanding user needs and system limitations is crucial.
Your practical approach—living with simpler infrastructure and intentionally short-lived unscalable solutions—helps prevent analysis paralysis and facilitates rapid validation. The insights gained from such setups often inform smarter scaling decisions later on. Additionally, your use of hardcoded configurations for agility is a smart tactic during initial development phases, provided it’s paired with disciplined tracking.
Ultimately, this methodology strikes a healthy balance: build just enough to learn, then adapt or scale based on real-world feedback. It’s a reminder that sometimes, the most effective engineering is the one that’s “good enough” to learn quickly and evolve intentionally. Thanks for sharing this pragmatic perspective!