Embracing the 3-Month Rule: A Practical Approach to Non-Scalable Solutions
When it comes to startup wisdom, Paul Graham’s recommendation to “do things that don’t scale” is frequently cited. However, the practical application of this principle in coding is less often discussed. After dedicating eight months to building my AI podcasting platform, I’ve adopted a framework that allows me to meaningfully engage with this advice: every unscalable solution I implement is given a lifespan of just three months. At the end of that period, the solution either demonstrates its value and warrants further investment, or it is promptly discarded.
As developers, we are often conditioned to prioritize scalable solutions from the outset, focusing on design patterns, microservices, and robust architectures suited for millions of users. While this approach is admirable, it often reflects the mindset of larger companies rather than the agile nature of startups. In reality, developing scalable code at an early stage can be a costly form of procrastination; it often means addressing future issues that may never arise. This is where my 3-month rule comes into play, compelling me to produce straightforward and imperfect code that can be deployed quickly, allowing real user feedback to inform future iterations.
My Current Infrastructure Choices: Smart Hacks for Growth
Let me share a few of the unconventional solutions I’ve implemented, highlighting their unforeseen advantages:
1. Consolidated Systems on a Single VM
I’ve centralized everything—database, web server, background jobs, and Redis—on a single virtual machine costing about $40 a month. While this lacks redundancy and relies on manual backups, here’s why it’s not just a reckless choice: I’ve acquired invaluable insights into my actual resource needs in just two months, far more than any formal planning documentation could provide. For instance, I learned my “AI-driven” application only peaks at 4GB of RAM. The complex Kubernetes setup I nearly adopted would have meant managing a lot of idle containers. Each crash I’ve experienced has supplied real data about the core issues at play—insights I never would have expected.
2. Hardcoded Configurations
Instead of employing configuration files, I’ve chosen to hardcode essential values directly into my application:
PRICE_TIER_1 = 9.99
PRICE_TIER_2 = 19.99
MAX_USERS = 100
AI_MODEL = "gpt-4"
While this may seem overly simplistic, it provides an unexpected advantage. I can
One Comment
This is a fantastic approach that highlights the importance of iterative experimentation and learning in early-stage development. The 3-month rule serves as a practical discipline, enabling founders and developers to validate ideas quickly without getting bogged down by over-engineering. Your insights on consolidating resources on a single VM and hardcoding configurations resonate deeply—often, pragmatic solutions at the outset can reveal critical user behavior and technical bottlenecks that more “robust” setups might obscure. It’s a reminder that, in startups, speed and flexibility often trump perfectionism, especially when backed by real-world feedback. I’d also add that periodically reviewing these experiments within your 3-month window encourages continuous learning and adaptation, essential qualities for sustainable growth. Looking forward to seeing how these strategies evolve as your platform scales!