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,