Embracing the 3-Month Rule: A Practical Approach to Unscalable Development
In the realm of startup development, a well-known piece of wisdom from Paul Graham rings true: “Do things that don’t scale.” Yet, the uncharted territory lies in applying this philosophy to the world of coding.
After dedicating eight months to building my AI podcast platform, I’ve distilled my approach into a straightforward framework: every unscalable solution I implement gets an experimental lifespan of three months. After this period, it must either validate its worth through measurable results or be discarded.
As engineers, we often default to thinking about scalability from the outset. We gravitate toward grand architectures like microservices and distributed systems, ideally aimed at supporting millions of users. However, this mindset can lead to costly delays for startups, as we frequently optimize for a user base that may not even exist yet. The three-month framework encourages me to focus on rapid, functional code that genuinely addresses user needs rather than theoretical expectations.
The Smart Infrastructure Hacks I’m Implementing
1. Consolidating into a Single Virtual Machine
Currently, my entire stack—database, web server, background jobs, and caching—is housed on a single $40/month virtual machine. This setup lacks redundancy and relies on occasional manual backups.
Why is this a strategic choice? In just two months, I’ve gained invaluable insights into my resource requirements—far more than any capacity-planning document could offer. I discovered that my platform, driven by AI, peaks at 4GB of RAM. The complex Kubernetes architecture I nearly built would have merely managed idle containers.
When my VM crashed (which has happened twice), I gathered concrete data on failures—often revealing surprises about what truly causes issues.
2. Utilizing Hardcoded Configurations
Instead of using external configuration files or environment variables, my code features constants like:
python
PRICE_TIER_1 = 9.99
PRICE_TIER_2 = 19.99
MAX_USERS = 100
AI_MODEL = "gpt-4"
While this might seem archaic, it enables quick searches through my entire codebase and tracks changes in Git history. Each tweak requires minimal redeployment while avoiding the lengthy setup of a configuration service—a trade-off I’ve found to be overwhelmingly in my favor.
3. Running SQLite for a Multi-User Application
Yes, I’m primarily using SQLite for my platform, which encompasses a mere 47
