Embracing the 3-Month Rule: A Practical Approach to Non-Scalable Solutions in Tech Development
In the tech landscape, Paul Graham’s concept of “doing things that don’t scale” is often cited, yet the practical application of this philosophy is rarely discussed, particularly in the realm of coding. After eight months of developing my AI podcast platform, I’ve crafted a straightforward framework: any unscalable solution is given a lifespan of three months. At the end of this period, it either proves its worth and is fully developed, or it’s set aside permanently.
As engineers, our instinct is to create scalable solutions from the outset. We are entangled in design patterns, microservices, and distributed systems—all aimed at accommodating massive user bases. However, this mindset can be counterproductive in a startup environment, where prioritizing scalability often leads to unnecessary delays. By focusing on quick, simple solutions, I can better understand user needs and adapt accordingly.
Key Lessons from My Current Infrastructure Hacks
1. Consolidating Resources on a Single VM
All aspects of my platform—database, web server, background jobs, Redis—operate on a single virtual machine costing just $40 per month. While this decision lacks redundancy, it has provided invaluable insights. Within two months, I gained a clearer picture of my actual resource requirements. I found that my “AI-heavy” platform peaks at a mere 4GB of RAM rather than overhauling my system for Kubernetes, which I had initially considered. When my setup crashes—and it has happened twice—I obtain direct feedback, revealing issues I didn’t anticipate.
2. Utilizing Hardcoded Configuration
Instead of employing configuration files or environment variables, I’ve opted for hardcoded constants:
python
PRICE_TIER_1 = 9.99
PRICE_TIER_2 = 19.99
MAX_USERS = 100
AI_MODEL = "gpt-4"
This approach allows me to search through my entire codebase in seconds. With every configuration change tracked in version control and only three updates made in three months, I’ve saved countless hours I would have spent creating a separate configuration management system.
3. Running SQLite for Production Environments
SQLite might seem unconventional for a multi-user web application, yet with a database size of just 47MB, it smoothly accommodates 50 concurrent users. This experience has taught me that my access patterns are predominantly read-heavy, making SQLite an ideal
One Comment
This post offers some fantastic insights into the practical application of “doing things that don’t scale,” especially in the early stages of product development. I really appreciate your emphasis on quick experimentation and learning through lightweight infrastructure hacks.
The 3-month rule is a powerful framework—it creates a clear deadline to validate ideas without getting bogged down in unnecessary optimization or overengineering. It reminds me of the concept of “Good Enough” solutions as a step toward understanding real user needs before investing heavily in scalability.
Your choice to consolidate resources and use hardcoded configurations illustrates that simplicity can be a strategic advantage—saving time and exposing crucial bottlenecks early on. Additionally, experimenting with SQLite in a production environment challenges conventional wisdom, emphasizing that technology choices should be driven by actual usage patterns rather than industry dogma.
Overall, this approach fosters agility and learning—just what early-stage startups and innovative projects need. I’d be interested in hearing how you plan to evolve your infrastructure after the three-month period, especially regarding transitioning to more scalable solutions once you’ve validated the core model.