The 3-Month Rule: A Practical Approach to Non-Scalable Solutions in Tech Development
In the realm of startup development, the well-known mantra of Paul Graham encourages entrepreneurs to “do things that don’t scale.” However, the challenge often lies in effectively translating this concept into practical coding strategies. After spending eight months building my AI podcast platform, I have devised a straightforward method: giving every quick-fix solution a lifespan of just three months. Post that period, the solution must either demonstrate its value and transition into a robust setup or be discarded.
As developers, we’re conditioned to strive for scalable solutions from the get-go, focusing on sophisticated architecture—think microservices and distributed systems capable of supporting millions of users. While impressive, this mentality frequently represents the thought processes of large enterprises rather than startups. For many startups, pursuing scalability too early can lead to unwarranted complexity and wasted resources, optimizing for nonexistent users and unresolved issues. Adopting the three-month rule encourages me to implement straightforward, albeit imperfect, code that is pragmatic and offers genuine insights into user needs.
Current Infrastructure Hacks: A Smart Approach to Simplicity
1. Consolidated Operations on a Single VM
I run my database, web server, background jobs, and Redis all on one $40/month virtual machine. While it seems risky with no redundancy and manual backups, this approach has provided invaluable insights. Within just two months, I learned the actual resource demands of my platform—peaking at merely 4GB of RAM. The complex Kubernetes setup I nearly implemented would have only led to managing idle containers. When it crashes—twice so far—I gather practical data illustrating the true points of failure, which often surprise me.
2. Hardcoded Configuration Across the Board
I have constants defined throughout my codebase—like:
python
PRICE_TIER_1 = 9.99
PRICE_TIER_2 = 19.99
MAX_USERS = 100
AI_MODEL = "gpt-4"
No configuration files or environment variables; just constants integrated within the source. Any change requires redeployment, but the efficiency gained is significant. With only three updates in three months, I avoid the overhead of developing a separate configuration service, which would consume a week of engineering time. Instead, I’ve invested a mere 15 minutes in redeployments, preserving my focus on product iteration.
3. Utilizing SQLite in a Multi-User Environment
Yes,
One Comment
This post thoughtfully highlights the importance of balancing pragmatism with scalability, especially in the early stages of startup development. I appreciate the emphasis on the three-month rule as a practical guideline—it’s a compelling way to prevent paralysis by overengineering and to prioritize real-world learning.
One aspect that resonates is the approach to infrastructure: starting small with consolidated operations on a single VM and keeping configuration simple through hardcoded constants. This hands-on method aligns well with lean startup principles, enabling rapid iteration and insightful metrics gathering. It reminds me that often, sophisticated solutions are not necessary initially and can be scaled or refined as product-market fit becomes clearer.
Additionally, I find the concept of intentionally limiting the lifespan of quick fixes quite valuable. It instills disciplined experimentation—presenting a clear deadline to validate or pivot. This mindset fosters agility, encourages learning from real user interactions, and avoids sunk cost fallacies.
Would be interesting to explore how to systematically evaluate when a quick fix has outlived its usefulness versus when to invest in more scalable infrastructure. Perhaps developing a simple checklist or key performance indicators associated with each three-month review could further enhance this approach.
Thanks for sharing these practical insights; they serve as a strong reminder that smart, lean experimentation often outperforms premature complexity in the fast-paced startup landscape.