The 3-Month Strategy: A Pragmatic Approach to Unscalable Solutions
In the startup world, there’s a well-known piece of advice from Paul Graham: “Do things that don’t scale.” While many entrepreneurs and developers understand the sentiment behind this, few delve into the practical aspects of applying it, particularly in coding.
Over the past eight months, while building my AI podcast platform, I have developed a straightforward strategy: allow any unscalable solution to operate for a maximum of three months. After this period, the solution must either demonstrate its value and undergo proper development or be abandoned.
As engineers, we often feel pressured to create scalable solutions right from the start. We’re trained to think in terms of elegant architectures like microservices, distributed systems, and extensive design patterns — frameworks that can support thousands or even millions of users. However, this mindset is more suited to established companies than to startups. In fact, pursuing scalable code too early can lead to unnecessary complications and become an expensive form of procrastination.
Through my three-month rule, I have learned to prioritize straightforward, albeit “rough,” code that focuses on launches rather than theoretical optimizations, allowing me to grasp my users’ real needs more effectively.
Current Infrastructure Strategies: Why Simplicity Works
1. Consolidated Architecture on a Single VM
My entire platform — including the database, web server, background jobs, and caching — runs on a single virtual machine costing just $40 per month. While this setup lacks redundancy and requires manual backups, it has provided invaluable insights into my actual resource demands over the past two months. This experience has revealed that my “AI intensive” platform peaks at around 4GB of RAM, indicating that the complex Kubernetes configuration I had considered would have been an unnecessary over-engineering effort. Each time the system crashes (which has happened a couple of times), I gain insights about what genuinely fails, often revealing unexpected issues.
2. Hardcoded Configuration for Agility
Instead of cluttering my setup with configuration files or environment variables, I’ve opted for hardcoded constants such as:
python
PRICE_TIER_1 = 9.99
PRICE_TIER_2 = 19.99
MAX_USERS = 100
AI_MODEL = "gpt-4"
This method allows me to quickly search through my codebase for any configuration value using simple commands. Each adjustment is tracked through version control, ensuring that changes are documented and revisited
