Embracing the 3-Month Rule: A Practical Approach to Non-Scalable Solutions
In the world of startups, the age-old advice from Paul Graham resonates deeply: “Do things that don’t scale.” Yet, when it comes to implementing this wisdom in the realm of coding, there tends to be a gap. After dedicating eight months to developing my AI podcast platform, I’ve crafted a straightforward framework: every non-scalable solution is given a lifespan of three months. At the end of this period, we determine whether the approach holds value and deserves further development or if it should be abandoned.
As engineers, our instinct is often to create solutions that are scalable from the outset—idealizing elaborate architectures that can support millions of users. However, in a startup environment, this mindset can lead to costly delays, addressing issues that may never arise for current users. The 3-month rule compels me to write straightforward, perhaps even “messy,” code that gets shipped out quickly, allowing me to learn firsthand what users truly need.
Current Infrastructure Strategies: Intelligent Shortcuts
1. Consolidated Virtual Machine Operations
Currently, I run everything on a single virtual machine. From the database to the web server and background jobs, everything functions within a $40/month budget. There’s no redundancy and manual backups are done locally.
This setup has been enlightening; in just two months, I’ve grasped my actual resource demands better than any formal capacity planning report could. My AI-driven platform peaks at around 4GB RAM. The complex Kubernetes arrangement I nearly adopted would have had me managing idle containers.
When issues arise (and they have), I gain genuine insights into the failure points—revelations that frequently defy my expectations.
2. Simplified Configuration Management
Instead of using config files or environment variables, I directly hardcode values within my code. For instance:
python
PRICE_TIER_1 = 9.99
PRICE_TIER_2 = 19.99
MAX_USERS = 100
AI_MODEL = "gpt-4"
Though this method seems antiquated, it allows me to quickly search through my codebase for any value. Each modification is tracked within version control, and changes undergo my own review process.
The time saved is substantial; crafting a robust configuration service would consume significant time, yet I’ve adjusted these constants only three times over the last quarter. The ease of redeployment pales in comparison to the hours
One Comment
Great insights—thanks for sharing your practical approach to balancing speed and learning in a startup environment. I particularly appreciate the discipline of the 3-month rule; it ensures that quick, dirty solutions aren’t just thrown out without assessment, but are given a defined period to prove their value. This aligns well with the “fail fast, learn fast” philosophy, allowing teams to iterate rapidly without getting bogged down by over-engineering early on.
Your examples around consolidating infrastructure and hardcoding configuration values highlight an important principle: in early stages, simplicity often trumps scalability. This approach minimizes time-to-market and keeps the feedback loop tight, which is critical for startup success. As you gather user data within these 3-month cycles, it becomes clearer when and where to invest in more scalable solutions.
It’s also inspiring to see how embracing “messy” solutions temporarily can lead to valuable insights, ultimately guiding more deliberate growth. Perhaps a useful addition could be tracking data on which “messy” solutions persist beyond the initial cycle—informing decisions on where to refine or replace them as your user base grows. This disciplined experimentation, combined with intentional review periods, seems like a strong framework for sustainable, adaptive development in startups.