Embracing Imperfection: The 3-Month Rule for Building Scalable Software
In the realm of software development, there’s a familiar mantra from startup guru Paul Graham: “Do things that don’t scale.” Yet, the challenge lies not only in accepting this wisdom but in applying it effectively, especially in coding practices. After eight months of developing my AI podcast platform, I’ve devised a straightforward framework to navigate this conundrum: every unscalable approach gets a trial period of three months. Post this period, we evaluate its effectiveness—either it evolves into a robust solution or it’s phased out.
As tech professionals, we often gravitate toward crafting scalable solutions from the outset, employing advanced architectures like microservices and distributed systems designed for high user volumes. However, this mindset is predominantly suited for larger organizations. In the startup environment, investing time in scalable code can often become a way of postponing real progress, as it directs our focus toward users that currently don’t exist and problems that may never arise. My 3-month rule compels me to produce straightforward, albeit imperfect, code that allows for real iteration and deep understanding of user needs.
Current Infrastructure Solutions: A Pragmatic Approach
Here are some of my current practices that might seem unconventional but have proven effective:
1. Consolidated on a Single VM
I’m running my entire platform—database, web server, background jobs, and cache—on one $40/month virtual machine. While it lacks redundancy and relies on manual local backups, this approach has been instrumental in revealing my actual resource requirements. Within just two months, I discovered that my so-called “AI-heavy” platform only needs 4GB of RAM. The complex Kubernetes setup I nearly implemented would have resulted in managing empty spaces.
When the system crashes—and it has twice—I gain valuable insights into the real limitations of my setup, often revealing unexpected vulnerabilities.
2. Hardcoded Configuration
Rather than creating a complex configuration management system, I’ve opted for simple constants like:
python
PRICE_TIER_1 = 9.99
PRICE_TIER_2 = 19.99
MAX_USERS = 100
AI_MODEL = "gpt-4"
With no separate config files or environment variables and everything hardcoded, changing a value requires a quick redeployment. This choice has allowed me to easily track changes and engage in code reviews, all while avoiding lengthy setup times. In three months, I’ve only
One Comment
This is a very pragmatic approach that highlights a crucial balance many early-stage developers and startups grapple with: prioritizing speed and learning over perfect architecture from the get-go. Your 3-month trial period for unscalable solutions provides a structured way to experiment, iterate, and learn without getting bogged down by unnecessary complexity—exactly what the startup environment demands.
I especially appreciate the emphasis on leveraging real-world insights—such as discovering resource needs through simple setups—as invaluable feedback that guides smarter scaling decisions later. Your choice to keep configurations straightforward and local, even if unconventional, underscores the importance of practical, quick-turnaround experimentation over immediate deployment of heavyweight systems.
This mindset fosters agility and a deep understanding of user needs—key ingredients for building truly scalable solutions down the line. Thanks for sharing your experience; it offers a lot of valuable lessons for anyone trying to balance speed, learning, and eventual scalability.