Embracing the 3-Month Rule: A Pragmatic Approach to Non-Scalable Solutions in Tech Development

In the fast-paced world of startups, it’s common to hear the adage from Paul Graham: “Do things that don’t scale.” However, the implementation of this advice, especially for engineers and developers, often goes unaddressed. After dedicating eight months to refine my AI podcast platform, I’ve harvested a useful methodology: every unscalable solution is given a lifespan of three months. At the end of this period, if the initiative hasn’t demonstrated its worth, it’s time to let it go.

The Common Mindset of Scalability

As engineers, we’re trained to construct solutions with scalability in mind from the outset. We lean into methodologies like design patterns, microservices, and distributed systems, focusing on architectures that can potentially handle millions of users. While this approach is ideal for large organizations, it can be a trap for startups. Opting for scalable solutions too prematurely can lead to costly procrastination, addressing future user needs that may never materialize. My three-month rule compels me to write simple, direct code that ships quickly, allowing me to discover what users genuinely need.

Current Hacks I’m Utilizing and Their Strategic Value

1. Single Virtual Machine for All Operations

I’m running my database, web server, background jobs, and caching on a single $40/month virtual machine—with no redundancy and manual backups to my local machine. While this setup might appear reckless, it has provided invaluable insights into my resource requirements. Within two months, I’ve learned that my AI-centric platform only peaks at 4GB of RAM. Had I opted for a complicated Kubernetes setup, I would have ended up managing empty containers rather than optimizing resource usage.

2. Hardcoded Configurations

With constants like:

python
PRICE_TIER_1 = 9.99
PRICE_TIER_2 = 19.99
MAX_USERS = 100
AI_MODEL = "gpt-4"

I’ve abandoned the use of config files or environment variables. This means that any changes trigger a redeployment, but there’s a silver lining: tracking changes in my code history becomes effortlessly quick through a simple grep command. Since I’ve modified these values only three times in the last three months, this approach has saved me considerable engineering time.

3. SQLite for Database Management

Despite SQLite’s limitations,