Embracing the 3-Month Rule: A Practical Approach to Unscalable Solutions in Tech Development
In the entrepreneurial landscape, the advice from Paul Graham to “do things that don’t scale” resonates deeply, yet the execution of this principle often goes unexplored, particularly in the realm of software development. After eight months of cultivating my AI podcast platform, I’ve crafted a straightforward framework to implement this idea: each unscalable solution is granted a lifespan of just three months. This allows me to assess whether it will evolve into a robust feature or be phased out entirely.
As engineers, we are frequently inclined to craft scalable solutions from the outset. We’re schooled in advanced design patterns, microservices, and distributed systems tailored for extensive user bases. However, that mindset often aligns more with large enterprises than startups. In my experience, striving for scalability too soon can be a costly exercise in procrastination—focusing on hypothetical users and challenges that may never materialize. By adhering to the 3-month rule, I’m encouraged to create straightforward, even rudimentary code that promptly delivers value and reveals authentic user needs.
Creative Infrastructure Hacks—Why They Work Wonders
1. Consolidating with a Single VM
Everything operates on a singular $40/month virtual machine, encompassing my database, web server, background jobs, and Redis. There’s no redundancy, and I perform manual backups locally.
This strategy has proven to be insightful rather than reckless. Within two months, I’ve gained a clearer understanding of my resource requirements than any elaborate capacity report could provide. It turns out that my “AI-centric” platform typically utilizes 4GB of RAM. The complex Kubernetes infrastructure I nearly implemented would have been managing dormant containers.
When outages occur (which they have on two occasions), I gather invaluable data about real points of failure—often surprising ones.
2. Hardcoded Configuration for Efficiency
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 hardcoded constants scattered throughout the code. Updating any of these requires a redeployment.
The brilliance of this approach lies in its simplicity: I can search my entire codebase in seconds for any configuration value. Changes in pricing are logged in version control, and I can review each update promptly.
Creating a dedicated configuration service would consume a week of development time.
