The Three-Month Rule: My Proven Framework for Embracing Non-Scalable Solutions in Tech
In the world of tech startups, the adage from Paul Graham, “Do things that don’t scale,” is widely acknowledged. However, the conversation often omits practical guidance on how to implement this advice effectively in coding endeavors.
After eight months of developing my AI podcast platform, I’ve crafted a straightforward yet impactful framework: every unscalable workaround is granted a lifespan of three months. At the end of this period, the solution needs to either demonstrate its value and receive a proper implementation or be phased out.
As engineers, we are typically conditioned to pursue scalable solutions from the outset. We dig into design patterns, microservices, and intricate architectures intended to support millions of users. While this approach is crucial for larger corporations, in the startup environment, focusing on scalability too soon can become an expensive exercise in procrastination. My three-month rule compels me to write straightforward and sometimes “imperfect” code that facilitates actual shipping of the product while revealing genuine user needs.
Current Infrastructure Strategies That Work Wonders
1. Centralized Operation on a Single Virtual Machine
Everything from the database to the web server operates on a single, cost-effective $40/month VM. There’s no redundancy, and I conduct manual backups locally.
Why is this approach effective? In just two months, I’ve gained a clearer understanding of my actual resource requirements than any comprehensive capacity planning document could provide. I discovered that my “AI-heavy” platform peaks at 4GB of RAM—something I’d have missed while managing an elaborate Kubernetes setup.
When crashes occur (which they have, twice), I’m armed with firsthand data on what actually fails—often surprising me in the process.
2. Hardcoded Configuration for Simplicity
I have constants written directly into the code, such as:
python
PRICE_TIER_1 = 9.99
PRICE_TIER_2 = 19.99
MAX_USERS = 100
AI_MODEL = "gpt-4"
While this means no dedicated config files or environment variables, it allows for lightning-fast access to configuration changes. Any modification requires a simple redeploy, and I can swiftly track changes through Git history.
Creating a dedicated configuration service would have consumed a week of my time. Yet, I’ve only changed these values three times over three months—totaling an investment of just 15 minutes rather than 40
One Comment
This is an excellent approach that underscores the importance of pragmatic, rapid experimentation in early-stage development. Your three-month rule serves as a powerful mechanism to prevent overengineering and helps focus on delivering value quickly. I particularly appreciate the emphasis on gaining actual user insights and resource requirements through simple setups—sometimes, the most straightforward solutions reveal the most critical data.
One addition to consider is establishing a clear process for evaluating these solutions at the end of each cycle. For example, documenting key learnings, user feedback, and system performance during the three months can inform whether the solution should be maintained, improved, or replaced. Also, as your platform grows, you might revisit concepts like gradual decentralization—like moving from a single VM to more scalable architectures—only after validating core assumptions.
Overall, your framework aligns well with lean startup principles and can serve as a practical blueprint for startups aiming to balance speed with informed decision-making. Thanks for sharing such actionable insights!