Embracing the 3-Month Rule: A Pragmatic Approach to Startup Development
In the world of startup innovation, the mantra “Do things that don’t scale” made famous by Paul Graham often echoes in the corridors of newly established enterprises. Yet, the question remains: how do we effectively translate this principle into our coding practices? Over the past eight months of developing my AI podcast platform, I’ve crafted a straightforward technical framework that I call the “3-Month Rule.” This method revolves around a critical timeline: any unscalable solution gets a trial period of three months. If it proves its worth, it will be built out comprehensively; if not, it will be discarded.
As software engineers, we are often conditioned to create scalable frameworks right from the beginning. We gravitate toward sophisticated design patterns, microservices, and distributed systems, crafted for handling extensive user bases. However, this mindset frequently belongs to the realm of larger corporations and can represent a form of costly procrastination for startups. Instead of preemptively optimizing for future user bases that may never materialize, my 3-Month Rule allows me to embrace simple, less refined code that can be delivered swiftly. Through this process, I gain invaluable insights into the actual needs of my users.
Practical Examples of My Infrastructure Strategies
1. Everything Run From a Single Virtual Machine (VM)
Currently, I’m operating my entire platform—from the database to the web server—on a solitary $40/month VM. While it lacks redundancy and relies on manual backups, this setup has allowed me to accurately assess my resource requirements in a way that exhaustive capacity planning documents could never achieve. The platform, which I anticipated would require extensive resources, peaks at a mere 4GB of RAM. The complex Kubernetes configuration I nearly implemented would have been unnecessarily burdensome.
When my system has crashed—twice, mind you—the breakdown provided crucial insights into where true vulnerabilities lie, and interestingly, it was never where I had initially predicted.
2. Hardcoded Configuration Values
Utilizing hardcoded configuration values instead of using config files or environment variables has its merits:
python
PRICE_TIER_1 = 9.99
PRICE_TIER_2 = 19.99
MAX_USERS = 100
AI_MODEL = "gpt-4"
While this may feel inconvenient, it allows me to quickly access and update values without the need for extensive groundwork. Every change I make is
One Comment
This approach vividly illustrates the value of embracing simplicity and rapid experimentation in early-stage development. The 3-Month Rule serves as an effective guardrail, encouraging founders and engineers to prioritize validated learning over premature optimization.
I particularly appreciate the emphasis on using straightforward infrastructure—like running everything from a single VM—to gain genuine insights before complicating the architecture. This pragmatic mindset not only reduces initial overhead but fosters a deeper understanding of real user needs and system behavior under load.
Additionally, the acceptance of practical shortcuts, such as hardcoded values, highlights a mindset of agility: recognizing when the efficiency gained from quick iterations outweighs the long-term benefits of cleaner configurations. Of course, as the platform scales, these elements can be refactored, but the core principle remains—the focus on “what works now” rather than “what could work.”
Overall, your framework supports a lean, learning-oriented approach that can help startups avoid unnecessary complexity and build truly user-centric solutions from the ground up. Looking forward to seeing how this methodology evolves with your project!