Embracing the 3-Month Rule: A Practical Approach to Building Unscalable Solutions
In the world of tech startups, the mantra “Do things that don’t scale,” popularized by Paul Graham, resonates with many aspiring entrepreneurs. However, the application of this advice, particularly in coding, is often overlooked. After dedicating eight months to developing my AI podcast platform, I’ve formulated an effective framework I call the “3-Month Rule.” Here’s how it works: every unscalable solution I implement is given a lifespan of just three months. After this period, each hack must either validate its worth and be transformed into a robust solution, or it will be discarded.
As software engineers, we often feel the pressure to create scalable systems from the outset. We envision sophisticated architectures involving design patterns, microservices, and distributed systems capable of supporting millions of users. This is the mentality of larger enterprises, but in the startup environment, prioritizing scalability can sometimes lead to unnecessary complications.
At this early stage, I believe that investing time in creating highly scalable code may merely be a costly form of procrastination—ultimately preparing for users that don’t yet exist and addressing problems that may never surface. By adhering to my 3-Month Rule, I’m compelled to write straightforward, albeit imperfect, code that allows me to rapidly test ideas and understand user needs.
My Current Strategies for Managing Unscalable Hacks
1. Consolidated Infrastructure on a Single VM
For my platform, everything operates on a single $40/month virtual machine that hosts the database, web server, background jobs, and more. While this setup lacks redundancy and relies on manual backups, it has proven insightful. Within just two months, I gained a clearer picture of my resource requirements than any planning document could provide. I’ve learned that, despite my platform’s AI capabilities, it typically utilizes a mere 4GB of RAM. The complex Kubernetes architecture I nearly implemented would have resulted in managing unused resources.
When unexpected crashes occur—twice so far—I receive valuable insights about actual failure points, which often surprise me.
2. Hardcoded Configuration Using Constants
In my codebase, I use hardcoded values like:
plaintext
PRICE_TIER_1 = 9.99
PRICE_TIER_2 = 19.99
MAX_USERS = 100
AI_MODEL = "gpt-4"
Though lacking in flexibility, this approach offers a unique advantage: I
One Comment
Thank you for sharing this practical framework—it’s a refreshing reminder that sometimes the best way to move forward is to embrace quick, unscalable solutions as learning tools rather than delaying progress for perfect architecture. The 3-Month Rule strikes a healthy balance between agility and intentionality, encouraging startups to validate ideas rapidly without getting bogged down by premature optimizations.
I particularly appreciate your emphasis on gaining real-world insights through simple setups, like consolidating everything on a single VM. This approach often reveals bottlenecks and user behaviors that theory can’t predict, which is invaluable at early stages. It also echoes the concept of “launch fast, iterate faster” but with a structured timeline that prevents local optimizations from lingering too long.
A potential extension of this idea is integrating regular retrospectives at the end of each 3-month cycle to evaluate what worked, what didn’t, and what infrastructure or processes can be scaled up or refined. This ensures that the momentum from these rapid experiments continually informs your long-term architecture, rather than abandoning unscalable hacks abruptly.
Overall, your framework offers a pragmatic path for startups to build quickly, learn efficiently, and scale thoughtfully—thanks for sharing!