Embracing the 3-Month Rule: A Pragmatic Approach to startup Development
In the world of startups, the maxim “Do things that don’t scale,” championed by Paul Graham, is often echoed but seldom broken down into actionable strategies, especially in the realm of software development. After eight months of building my AI podcast platform, I’ve established a straightforward yet effective methodology: every unscalable technique is given a lifespan of just three months. At the end of this period, the solution must either prove its worth and be fully developed or be abandoned.
As developers, we’re typically trained to chase scalable solutions right from the outset. We immerse ourselves in architectural elegance—design patterns, microservices, and distributed systems—crafting systems to support millions of users. However, in the startup ecosystem, this focus on scalability often leads to costly and unnecessary delays. We can find ourselves optimizing for a future that may never come, tackling challenges that simply do not exist yet. By adhering to my three-month rule, I am compelled to create straightforward, albeit imperfect, code that yields tangible results and reveals the true needs of my users.
Current Infrastructure Strategies: Why They Work
1. Consolidating onto a Single VM
My entire setup—database, web server, background jobs, and caching—all run on a single $40/month virtual machine. This arrangement, devoid of redundancy and bolstered by manual backups, has allowed me to gain insights into my operational needs faster than any capacity planning report could. Within just two months, I discovered my platform’s peak resource usage is only 4GB RAM. An intricate Kubernetes configuration I considered would have likely ended up managing idle containers.
When the system crashes (which has happened twice), I receive immediate, actionable insights about the true points of failure—rarely the ones I anticipated.
2. Hardcoding Configuration Values
Instead of relying on external configuration files or environment variables, I’ve embraced hardcoded constants throughout my application:
python
PRICE_TIER_1 = 9.99
PRICE_TIER_2 = 19.99
MAX_USERS = 100
AI_MODEL = "gpt-4"
This choice simplifies my workflow. Searching for configuration values is a breeze, tracking price adjustments through version history is straightforward, and reviewing changes—even if it’s just my own code—is quick. The development of a dedicated configuration service would have consumed a week of my time, while I’ve needed to update these constants
