The 3-Month Rule: A Pragmatic Framework for Rapid Learning in Development
In the world of software engineering, we’ve all heard the advice from entrepreneur Paul Graham: “Do things that don’t scale.” However, translating this wisdom into practical steps during software development can sometimes be overlooked. After eight months of building an AI podcast platform, I implemented a straightforward yet effective strategy: any unscalable hack is allowed a lifespan of three months. Following this period, each hack must either demonstrate its value and undergo proper development, or be discarded.
The Challenge of Scalability in Startups
As technologists, our instincts typically push us toward designing scalable solutions right from the get-go. We delve into employing sophisticated design patterns, microservices, and distributed systems, all aimed at accommodating millions of users. However, this mindset often feels more suited to larger companies than to the nimble nature of startups.
In many cases, prioritizing scalability too early leads to unnecessary complexity and can delay real progress. My three-month rule encourages the development of straightforward, albeit rudimentary, code that can be deployed quickly. This approach allows me to uncover valuable insights regarding user needs without the burden of speculative architecture.
Current Infrastructure Hacks: Smart Decisions Under Constraints
1. Centralized Operations on a Single VM
My entire setup, including the database, web server, and background jobs, operates on one $40/month virtual machine. While this configuration has zero redundancy and relies on manual backups, it’s proved to be an enlightening experience. Within two months, I gained more insight into my resource requirements than any elaborate planning document could provide. I discovered that my “AI-heavy” platform only requires a peak of 4GB RAM. Had I implemented a complex Kubernetes configuration, I would have been managing unutilized containers instead.
When the server experienced downtime, which occurred a couple of times, I received real data on failure points—information that consistently surprised me.
2. Simplified Configuration Management
In my codebase, configurations are hardcoded directly, such as:
python
PRICE_TIER_1 = 9.99
PRICE_TIER_2 = 19.99
MAX_USERS = 100
AI_MODEL = "gpt-4"
There are no configuration files or environment variables—just constants across the files. While this might seem inefficient, the benefit lies in simplicity. Retrieving any configuration value is instantaneous through search commands, and any changes are meticulously tracked through version
