Embracing the 3-Month Rule: A Pragmatic Approach to Technical Development
In the startup world, the mantra “do things that don’t scale,” popularized by Paul Graham, often gets thrown around. However, there’s surprisingly little discussion on how to practically apply this concept in coding practices. Having spent the last eight months developing my AI podcast platform, I’ve discovered a straightforward yet effective framework: each unscalable solution is granted a lifespan of three months. After this period, it must either demonstrate its worth and be transformed into a robust implementation or be discarded.
As software engineers, we are typically conditioned to focus on building scalable systems from the outset—thinking about design patterns, microservices, and distributed architectures that can handle massive user bases. While these approaches are essential for larger companies, for a startup, pursuing scalable solutions too early can equate to costly procrastination. My three-month rule emphasizes swift iterations using simplified code that genuinely gets deployed, allowing me to ascertain what users actually need.
Key Infrastructure Strategies and Their Insights
1. Utilizing a Single Virtual Machine
Currently, all core elements of my platform—database, web server, background jobs, and Redis—are hosted on one $40/month virtual machine. This effectively eliminates redundancy and involves manual backups to my local system.
What’s beneficial about this approach? It has given me more insight into my actual resource usage in a short span than any capacity planning documentation could provide. For instance, my AI-centric platform peaks at 4GB of RAM. Had I opted for an elaborate Kubernetes framework, I would have likely found myself managing empty containers more often than not. When the system crashes, I gain practical insights into failure points, which often diverge from my original assumptions.
2. Hardcoded Configuration Settings
My configuration is entirely hardcoded, with values directly embedded in the codebase rather than managed through configuration files. For example:
python
PRICE_TIER_1 = 9.99
PRICE_TIER_2 = 19.99
MAX_USERS = 100
AI_MODEL = "gpt-4"
The immediate advantage of this structure is the ease of access; I can quickly search my entire codebase for any configuration value. Changes are tracked in version control and require a straightforward redeployment rather than a lengthy setup. Over three months, I’ve made only three changes, which took a mere 15 minutes to implement.
**3. SQLite for Production Use
