Embracing the 3-Month Rule: A Framework for Effective Experimentation in Tech Startups
In the world of software development, renowned entrepreneur Paul Graham has famously suggested that we should “do things that don’t scale.” However, the implementation of this principle within the coding realm often goes unaddressed. After dedicating eight months to constructing my AI podcast platform, I’ve created a straightforward framework I call the “3-Month Rule.” Under this rule, each unscalable approach I try is given a limited lifespan of three months. At the end of this period, I evaluate whether it deserves further investment or if it should be retired.
As developers, we often feel pressured to craft solutions that are scalable from the outset—think microservices, distributed systems, and intricate architectures designed to handle immense user loads. While these concepts are essential for larger companies, they can be impractical for startups. In many cases, focusing on scalability prematurely only leads to costly delays and inefficiencies, as we may be optimizing for problems that have yet to materialize.
The 3-Month Rule compels me to pursue simpler, more direct coding methods that deliver tangible results. Here’s a glimpse into the infrastructure hacks I’m utilizing, which may seem unconventional but have proven remarkably effective.
1. Consolidation on a Single Virtual Machine
Currently, my entire infrastructure—including the database, web server, background processes, and caching—is hosted on a single $40/month virtual machine. This approach has its risks; there’s no redundancy, and I perform manual backups to my local machine. However, this choice has allowed me to gain unparalleled insights into my resource requirements. In just two months, I learned that my “AI-heavy” platform peaks at only 4GB of RAM. I realized that a complex Kubernetes setup would have merely been managing idle resources while not addressing actual issues.
When the system encounters a crash (which has happened twice), I receive genuine feedback on what fails—and the results are often surprising.
2. Hardcoded Configuration
My configuration is hardcoded directly within the codebase, with constants like:
python
PRICE_TIER_1 = 9.99
PRICE_TIER_2 = 19.99
MAX_USERS = 100
AI_MODEL = "gpt-4"
While there are no configuration files or environment variables, the benefit is clear: I can quickly search through my code for any configuration value. Each price change is documented
One Comment
This is a compelling approach that truly underscores the value of rapid experimentation and learning through low-commitment solutions. The 3-Month Rule serves as an excellent framework to balance agility with reflection—preventing the trap of over-engineering from the outset. Your emphasis on consolidating infrastructure into a single VM resonates with the idea that early-stage startups benefit from simplicity and direct feedback, rather than premature scalability efforts.
The practice of hardcoding configurations, while seemingly risky from a best-practices standpoint, offers immense agility—especially when rapid iteration is critical. It’s a reminder that sometimes, less formal methods can accelerate clarity and decision-making in the short term.
Overall, your insights highlight that embracing “unscalable” tactics temporarily can provide valuable insights, reduce costs, and help founders and developers align on what truly matters before scaling. Thanks for sharing this practical framework—it’s a great reminder that initial simplicity often paves the way for sustainable growth.