Embracing the Unscalable: A Three-Month Approach to Lean Development
In the startup world, the sage advice often quoted by Paul Graham rings true: “Do things that don’t scale.” Yet, implementing this principle in coding practices remains a less-discussed topic. After dedicating eight months to the development of my AI podcast platform, I’ve arrived at an insightful framework that I term the Three-Month Rule. This approach stipulates that each unscalable hack gets a trial period of three months. If a method proves its worth, it will be properly integrated; if not, it will be phased out.
The startup Mindset
As software engineers, we often feel compelled to design scalable solutions from the outset. We delve into complex architectures featuring design patterns, microservices, and distributed systems—all aimed at accommodating millions of users. However, this mindset can be counterproductive in a startup environment where scalable coding may simply delay important decisions. My Three-Month Rule encourages the development of straightforward, albeit imperfect code that can be deployed swiftly, allowing me to gather real user feedback.
Current Infrastructure Strategies: Unveiling the Logic Behind My Choices
1. Single Virtual Machine for Everything
I run all essential services—from the database to web servers—on one $40/month virtual machine. This approach offers no redundancy and relies on manual backups to my local system.
This strategy has been invaluable for understanding my actual resource requirements. Within just two months, I’ve learned that my platform peaks at 4GB of RAM, saving me from investing time and resources into an elaborate, unused container orchestration setup. Crashes have provided insights into unforeseen issues, allowing me to address real problems instead of hypothetical ones.
2. Hardcoded Configuration Settings
Instead of utilizing configuration files or environment variables, I have opted for hardcoded constants dispersed throughout the codebase:
python
PRICE_TIER_1 = 9.99
PRICE_TIER_2 = 19.99
MAX_USERS = 100
AI_MODEL = "gpt-4"
While at first, this may seem inefficient, it allows me to quickly grep for configuration changes across my entire code. Any adjustments are committed to the git history and code-reviewed, albeit by myself. Building a configuration service would consume a week of work, while I’ve only modified these values thrice over three months—meaning a 15-minute redeployment instead of 40 hours of development time.
**
