Embracing the 3-Month Rule: A Pragmatic Approach to Unscalable Solutions in Tech Development
In the world of startups, the mantra “do things that don’t scale” is often shared, yet the path to achieving this in the realm of software development remains largely uncharted. Inspired by the insights of Paul Graham, I have developed a practical framework during my eight-month journey building an AI podcast platform: the 3-Month Rule. This approach not only encourages experimentation but also sets a definitive timeline for evaluating the viability of unscalable hacks.
As engineers, we’re primarily trained to favor scalable solutions from the outset. We gravitate towards intricate architectural designs, such as microservices and distributed systems, that are prepped for a multitude of users. However, in a startup environment, such foresight can morph into costly procrastination, addressing potential future needs while neglecting current user demands. My 3-Month Rule compels me to write straightforward, albeit imperfect, code that facilitates immediate deployment and teaches me valuable lessons regarding user requirements.
Proven Hacks: Why They Make Sense
1. Consolidation on One Virtual Machine
All components of my infrastructure—including the database, web server, background jobs, and Redis—operate on a single virtual machine costing just $40 per month. While this setup lacks redundancy and relies on manual backups, it has illuminated my actual resource requirements within just two months.
Instead of getting lost in the complexities of an elaborate Kubernetes setup, I’ve discovered that my “AI-heavy” application requires a maximum of 4GB of RAM. When server crashes occur—as they inevitably do—I gain genuine insights into the points of failure, which are often unexpected.
2. Hardcoded Configuration
Using constants directly in my code, such as:
python
PRICE_TIER_1 = 9.99
MAX_USERS = 100
eliminates the need for cumbersome configuration files. While some might view this as shortsighted, this method allows me to instantly locate and track configuration changes through Git history. In just three months, I’ve redeployed these constants only three times, saving an immense amount of engineering hours.
3. SQLite as a Production Database
Running SQLite in a multi-user web environment may raise eyebrows, yet my entire database is a mere 47MB and handles 50 concurrent users effortlessly. This experience revealed that my access patterns predominantly consist of reads rather than writes, making SQLite the ideal
One Comment
This post beautifully highlights the importance of embracing quick, unscalable solutions in the early stages of development to achieve rapid learning and iteration. The 3-Month Rule acts as a pragmatic boundary—it allows startups and engineers to experiment freely without the paralysis of over-optimization.
One insight that resonates is the value of concrete, time-bound experimentation—like consolidating infrastructure on a single VM or using hardcoded configurations—that enables real-world validation of assumptions. Such approaches not only reduce initial complexity and cost but also surface essential pain points that might otherwise remain hidden in overly complex architectures.
A key takeaway is that the goal isn’t to dismiss scalable solutions altogether but to prioritize immediate learning and user feedback. Once these foundational insights are gained, then you can transition toward more scalable and robust architectures. It’s about pragmatism: balancing the urgency of launch with the strategic planning for growth.
For those navigating the early days of product development, adopting a similar framework could provide clarity and focus, ensuring that efforts are aligned with validated needs rather than speculative assumptions. Thanks for sharing this insightful approach!