Embracing the 3-Month Rule: A Pragmatic Approach to Non-Scalable Solutions
In the entrepreneurial realm, Paul Graham’s slogan resonates widely: “Do things that don’t scale.” However, translating this wisdom into practical application, especially in coding, often remains uncharted territory. Throughout my eight-month journey building an AI podcast platform, I’ve cultivated a simple yet effective strategy: any unscalable workaround gets a three-month evaluation period. At the end of this timeframe, if it demonstrates tangible value, it can be refined; otherwise, it is left behind.
As developers, we are instinctively drawn to constructing scalable architectures from the outset—think microservices, distributed systems, and other intricate designs meant to accommodate vast user bases. However, this mindset can lead to unnecessary complexity and procrastination in startup environments. In fact, spending time developing scalable solutions prematurely often diverts focus from current users and their needs. My three-month rule encourages the use of straightforward, albeit “inefficient,” coding methods that not only get shipped but also provide insights into actual user requirements.
My Current Infrastructure Adjustments and Their Pragmatic Value
1. Consolidated Resources on a Single Virtual Machine
All crucial components of my application, including the database, web server, background jobs, and Redis, operate on a single $40/month virtual machine with no redundancy and manual backups.
Why is this strategy effective? It has allowed me to gain crucial insights into my resource requirements far quicker than traditional capacity-planning methods could offer. My platform’s peak usage turned out to demand merely 4GB of RAM, saving me from setting up an unnecessarily complicated Kubernetes environment that would have essentially been managing idle resources.
Each time the server crashes—which has happened twice—I receive valuable data on the specific failures and their causes, often surprising me with unforeseen issues.
2. Simplistic Hardcoded Configurations
Instead of relying on config files or environment variables, I’ve opted for hardcoded constants across my codebase:
python
PRICE_TIER_1 = 9.99
PRICE_TIER_2 = 19.99
MAX_USERS = 100
AI_MODEL = "gpt-4"
While it may appear rudimentary, this method enables me to quickly search for any configuration value using basic command-line tools. Each change is recorded in git, ensuring that even my minimal updates are version-controlled.
Creating a dedicated configuration service would consume significant time, yet a
