Embracing the 3-Month Rule: A Pragmatic Approach for Startups
In the entrepreneurial landscape, it’s common to hear Paul Graham’s advice to “do things that don’t scale.” However, the challenge arises in translating that guidance into actionable steps, particularly within the realm of coding. After spending eight months developing my AI podcast platform, I discovered a straightforward framework that I now apply: each non-scalable hack is given a lifespan of three months. Beyond this period, it either proves its worth and is properly built out or is scrapped.
As engineers, we’re often conditioned to conceive scalable solutions right from the start—think meticulous design patterns, microservices, and distributed systems. While these frameworks are essential for large organizations handling large user bases, they can become pitfalls for startups.
Focusing on scalability too soon can lead to unnecessary complexities, often delaying progress. My three-month guideline encourages me to write simpler, albeit imperfect code that allows for quicker deployment and real-time learning about user needs.
How I Implement This Framework: Current Infrastructure Hacks
1. Consolidating Resources on a Single VM
My entire stack—database, web server, background jobs, and Redis—operates from a single virtual machine (VM) at a reasonable $40 per month. This setup intentionally lacks redundancy, relying on manual backups to my local machine.
The brilliance of this approach lies in the insights I’ve gained regarding my resource usage within just two months. I discovered that my AI-centric platform only requires 4GB of RAM at peak times. Had I pursued a more complicated Kubernetes arrangement, I would have merely ended up managing idle containers. Moreover, any crashes provide valuable lessons about what truly fails—often surprising elements that I wouldn’t have predicted.
2. Hardcoded Configurations
I maintain crucial configurations directly within my codebase, such as:
python
PRICE_TIER_1 = 9.99
PRICE_TIER_2 = 19.99
MAX_USERS = 100
AI_MODEL = "gpt-4"
By avoiding complex configuration files or environment variables, making changes necessitates a redeployment—but this has proven efficient. In three months, I’ve only altered these constants three times, resulting in a mere 15 minutes of redeployment versus a potential 40 hours of engineering effort to create a configuration service.
3. Using SQLite for a Multi-User App
Surprisingly, I’m running SQLite in a web
One Comment
This is a compelling approach that balances the realities of startup engineering with the need for agility. The 3-month rule provides a practical framework to iterate quickly and validate assumptions without over-investing in scalability too early. I particularly appreciate the emphasis on simplicity—using a single VM, hardcoded configs, and lightweight databases like SQLite—these choices enable rapid learning and adjustment.
It’s worth noting that this mindset aligns well with the “move fast and break things” philosophy, but with a clear, time-bound review process to ensure that only proven components are scaled up. As startups grow, transitioning from these basic setups to more robust architectures can be smoother because the team has already validated core functionalities in a real-world setting.
One potential addition could be establishing clear milestones at the three-month mark: if a hack proves effective, plan for refactoring into scalable solutions; if not, scrapping it prevents waste. This disciplined yet flexible approach helps avoid the trap of premature optimization, ultimately fostering a lean development cycle.
Thanks for sharing this insightful framework—it’s a practical reminder that often, the simplest solutions coupled with disciplined iteration yield the best results in early-stage startups.