Embracing the 3-Month Rule: A Pragmatic Approach to Unscalable Solutions in Tech
In the world of entrepreneurship, there’s a well-known piece of advice from Paul Graham: “Do things that don’t scale.” Though this advice is often cited, the challenge lies in effectively applying it, particularly within technical environments. After eight months of developing an AI podcast platform, I have crafted a straightforward framework that I like to call the “3-Month Rule.” This approach allows every unscalable solution a three-month testing period—after which, we either upgrade it because it has proven its worth, or we discard it entirely.
As engineers, our instincts often push us toward building scalable solutions right from the start—think microservices, fancy architecture, and vast systems designed to accommodate millions of users. However, this mindset can lead to costly delays, especially in a startup where resources are limited. By implementing my 3-Month Rule, I consciously focus on developing straightforward, albeit “bad,” code to quickly learn what my users genuinely need.
Key Infrastructure Strategies and Their Benefits
1. Streamlining on a Single VM
Currently, my entire system—database, web server, and background tasks—operates on a single, affordable $40/month virtual machine. While it may seem reckless with no redundancy, this approach has given me invaluable insights into my actual needs. In just two months, I’ve realized that my AI-driven platform uses a maximum of 4GB of RAM during peak times. Had I set up a complex Kubernetes infrastructure, I would have ended up managing containers that went unused.
Experiencing system failures (which have happened twice) has provided me with real data regarding the causes—insight that no preliminary setup could have predicted.
2. Utilizing Hardcoded Configurations
Instead of using configuration files or environment variables, I rely on hardcoded constants like:
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PRICE_TIER_1 = 9.99
PRICE_TIER_2 = 19.99
MAX_USERS = 100
AI_MODEL = "gpt-4"
This approach simplifies tracking changes—each modification is noted in git history and is reviewed, even if it’s just by myself. While a comprehensive configuration service might take weeks to develop, the reality is that I’ve only needed to change these constants three times over three months. That’s a mere 15 minutes spent on redeployment instead of 40 hours on engineering.
**3. Opting
One Comment
Thank you for sharing these practical insights on embracing unscalable solutions through your 3-Month Rule. I really appreciate how you emphasize the value of rapid experimentation and learning over premature optimization—a perspective that can often be lost in the rush to build scalable systems from day one.
Your example of consolidating everything on a single VM resonates strongly; it’s a powerful reminder that simplicity can yield critical insights and save resources, especially in the early stages of a startup. The approach of using hardcoded configurations for quick iteration also highlights a pragmatic mindset—cutting through complexity to focus on what truly matters: understanding user needs and validating product assumptions.
One point that might add even more value is considering how to transition smoothly from these unscalable practices to scalable solutions once the product proves its value. For example, after gathering data on resource usage, you could gradually introduce configurable parameters or shift to managed services, reducing technical debt without sacrificing agility.
Overall, your framework champions a balanced approach—prioritizing speed and learning, which is often the key to successful product development. Thanks again for sharing such actionable strategies!