Embracing the Unscalable: A Three-Month Approach to Learning in Development
In the world of startups, the mantra “do things that don’t scale,” made popular by Paul Graham, is essential advice. However, the challenge lies not in understanding this philosophy but in applying it effectively, particularly in the realm of software development. After eight months of diligently building my AI podcast platform, I’ve discovered a straightforward and impactful approach: the three-month rule for implementing unscalable solutions.
The Essence of the Three-Month Rule
As developers, we are often conditioned to focus on scalable solutions from the outset—design patterns, microservices, and distributed systems are staples of our training. While these concepts are undoubtedly valuable in larger organizations, they can lead to unnecessary complexity in a startup environment. The reality is that optimizing for future users who may never come can become an expensive form of procrastination.
My three-month rule revolves around the principle that any unscalable solution I deploy will have a lifespan of just three months. During this period, it must either demonstrate its value and evolve into a robust solution or be phased out entirely. This approach encourages me to write straightforward, albeit imperfect, code that provides insights into what users genuinely need.
Current Infrastructure Hacks and Their Value
Let me share some unorthodox practices I’ve adopted, each with a rationale that reveals their underlying wisdom.
1. Unified Virtual Machine Setup
All my services—database, web server, background jobs, and caching—run on a single $40/month virtual machine. This setup, devoid of redundancy and relying on manual backups, has provided invaluable insights. Within just two months, I learned about my resource requirements far more effectively than any capacity planning document could convey. It turned out that my “AI-heavy” platform only peaks at 4GB of RAM. Had I implemented a complex Kubernetes architecture, I would have been managing idle containers rather than focusing on real issues.
2. Simplistic Hardcoded Configuration
My configurations are hardcoded directly into the codebase:
python
PRICE_TIER_1 = 9.99
PRICE_TIER_2 = 19.99
MAX_USERS = 100
AI_MODEL = "gpt-4"
While it may feel old-fashioned, this method allows for quick searches and easy tracking of changes through version control. The time saved—15 minutes for a redeployment versus 40 hours building a
One Comment
Great insights! The three-month rule offers a pragmatic balance between action and learning, helping developers avoid getting bogged down in premature scalability overengineering. I particularly resonate with the emphasis on lightweight, flexible infrastructure—often, simpler setups like your unified VM reveal real user patterns more effectively than elaborate architectures. It’s a reminder that in early stages, speed and adaptability often trump perfection.
Additionally, embracing unorthodox practices, such as hardcoded configs or minimal infrastructure, can accelerate feedback loops, allowing us to iterate quickly based on actual usage rather than hypothetical scenarios. Of course, as you point out, the key is to evaluate whether solutions prove valuable within that three-month horizon and to be ready to pivot or scale up accordingly.
This approach fosters a Lean mindset—prioritizing learning over perfection—crucial for startups aiming to validate ideas efficiently. Thanks for sharing your experience; it’s a compelling reminder that sometimes doing “less” well, temporarily, sets the foundation for smarter scaling later.