The 3-Month Experiment: Embracing Non-Scalable Solutions in Tech Development
In the world of startup innovation, there’s a well-known principle by Paul Graham: “Do things that don’t scale.” However, the challenge lies in translating this advice into actionable steps, particularly in coding. Drawing from my experience of building an AI podcast platform over the past 8 months, I’ve adopted a straightforward strategy that I call the “3-Month Rule.” This approach allows any unscalable solution to exist for just three months. After that time, it either proves its worth and deserves further development or it’s phased out.
The startup Mindset: No More Premature Optimization
As software engineers, we’re often conditioned to prioritize building scalable infrastructures. We dream in terms of design patterns, microservices, and robust distributed systems that can accommodate millions of users. Such thinking is essential for established companies, but in the startup environment, this reliance on scalability can hinder progress. In fact, it can lead to unnecessary complexity and costly procrastination, targeting users who aren’t even in the pipeline yet.
The 3-Month Rule compels me to ditch the ideal and embrace the practical. It encourages me to write simpler, even “messy,” code that can be deployed rapidly. This process ultimately reveals what my users genuinely require.
Current Infrastructure Strategies: Ingenious Hacks for Smart Learning
1. Consolidated Infrastructure on a Single VM
What if everything—database, web server, background jobs, and caching—ran on one $40/month virtual machine? That’s exactly what I did, with no backups or redundancy. This choice might seem reckless, but it’s provided me invaluable insights into my actual resource requirements over just two months. I’ve discovered that my platform peaks at a mere 4GB of RAM. Had I embarked on a complex Kubernetes setup, I would’ve wasted time managing underutilized resources.
When the inevitable crashes happened (twice, to be exact), I gained vital data on real failure points—insights I never would have forecasted.
2. Hardcoded Configurations: A Bold Choice
Imagine every configuration variable solidified within the code itself:
python
PRICE_TIER_1 = 9.99
PRICE_TIER_2 = 19.99
MAX_USERS = 100
AI_MODEL = "gpt-4"
While it sidesteps the sophisticated use of configuration files or environment variables, it offers unmatched
One Comment
Thank you for sharing this insightful approach to balancing the urgency of startup experimentation with pragmatic engineering. The “3-Month Rule” effectively highlights the importance of rapid iteration and learning through hands-on experience, which is often overlooked in favor of premature scalability considerations.
I particularly appreciate the emphasis on using simple, consolidated infrastructure and even “messy” code—these tactics accelerate feedback loops and help us understand user needs more authentically. It’s a reminder that sometimes, building the simplest solution that works is the most valuable step before refining and scaling thoughtfully.
Your approach aligns well with the lean mindset, emphasizing that initial solutions should serve as prototypes for learning rather than perfect, scalable systems from day one. It would be interesting to explore how this method can be systematically integrated into team workflows, perhaps with regular “reflection points” at each 3-month interval to assess whether to pivot, improve, or sunset features.
Thanks again for sharing these practical strategies — they’re a great reminder that effective engineering often begins with simplicity and a willingness to experiment.