Embracing the Imperfect: The 3-Month Rule for startup Development
In the startup world, advice often circles around the concept of doing things that don’t scale. While Paul Graham’s wisdom on this topic is well-known, the challenge lies in how to apply it effectively within the realm of coding and development. After eight months of building my AI podcast platform, I’ve adopted a straightforward approach: every unscalable solution gets a trial period of three months. If it demonstrates value during that time, it gets a solid foundation; if not, it gets phased out.
As developers, we often find ourselves gravitating toward scalable solutions from day one, focusing on complex architectures like microservices or distributed systems designed for a significant user base. However, at a startup, this forward-thinking strategy can sometimes turn into a form of expensive procrastination. You may end up optimizing for potential users who might never materialize or crafting solutions for problems that don’t exist yet. My three-month framework compels me to rely on straightforward, even “bad” code that can be deployed quickly, allowing me to identify the real needs of my users.
Practical Hacks that Keep Me Agile
1. Consolidated Operations with One VM
I run my entire platform on a single virtual machine – a $40/month investment that houses my database, web server, background jobs, and caching Layer using Redis. This setup lacks redundancy and depends on manual backups to my local system.
Here’s why this approach is smart rather than irresponsible: I’ve gained insights into my actual resource requirements far beyond what any capacity-planning document could reveal. My AI-driven platform peaks at just 4GB of RAM, which means that the intricate Kubernetes architecture I was considering would have necessitated managing empty containers. Each time it crashes (which has happened twice), I gather real-time data about the underlying issues — and it’s never what I thought it would be.
2. Simplified Hardcoded Values
python
PRICE_TIER_1 = 9.99
PRICE_TIER_2 = 19.99
MAX_USERS = 100
AI_MODEL = "gpt-4"
I employ hardcoded configuration values instead of using config files or environment variables. This means that changing any value requires a redeployment. While it might seem primitive, it offers a hidden advantage: I can perform a quick search across my entire codebase to find any config value instantly. Each adjustment is tracked in
