Embracing the 3-Month Rule: A Strategic Approach to Non-Scalable Coding
In the world of tech startups, the sound advice of Paul Graham—”Do things that don’t scale”—often rings true. However, what remains under-discussed is how to practically apply this principle in software development. After dedicating eight months to building my AI podcast platform, I’ve adopted a straightforward framework: every unscalable solution I implement has a lifespan of three months. Following this period, it must either demonstrate its value and get the necessary overhaul—or it will be discarded.
As engineers, we are typically trained to prioritize scalable solutions right from the outset. We become proficient in utilizing design patterns, building microservices, and creating distributed systems—all geared towards supporting millions of users. Yet, this mindset often belongs to larger organizations, and can lead to unnecessary complexity for startups.
In the early stages of a startup, creating scalable code can be equivalent to delaying crucial decisions. We often find ourselves optimizing for users who aren’t yet present, addressing hypothetical issues, and missing opportunities to learn from real user interactions. My three-month rule compels me to create direct, albeit imperfect, code that allows me to ship quickly and gain insight into actual user needs.
Current Infrastructure Hacks: Lessons Learned
Here are some of the unconventional practices I’m currently employing and why I believe they are beneficial:
1. Consolidation on a Single VM
I run my database, web server, background jobs, and caching on one $40/month virtual machine, sacrificing redundancy and depending on manual backups. This approach has proven enlightening; in the past two months, I’ve gained a clearer understanding of my resource requirements than any capacity planning document could offer. The platform, which I anticipated to be resource-intensive, only peaks at 4GB of RAM. Had I gone ahead with a complex Kubernetes setup, I would have spent time managing resources that weren’t being utilized.
When the system crashes—an event that has occurred twice—I receive genuine insights into what fails, and it consistently surprises me.
2. Simplified Hardcoded Configuration
Instead of using configuration files or environment variables, I’ve opted for constants throughout my code:
python
PRICE_TIER_1 = 9.99
PRICE_TIER_2 = 19.99
MAX_USERS = 100
AI_MODEL = "gpt-4"
The straightforward nature of this setup allows me to quickly search
One Comment
This approach of implementing a “three-month rule” for non-scalable solutions is a compelling reminder that agility and learning often outweigh theoretical perfection, especially in the early stages of a startup. By intentionally limiting the lifespan of these quick-and-dirty solutions, you’re effectively creating a feedback loop that prompts continuous evaluation and iteration, fostering both rapid validation and disciplined reflection.
Your emphasis on balancing the necessity of speed with the eventual need to refactor or replace makes eminent sense—particularly in avoiding the trap of unnecessary complexity before validating core hypotheses. I also appreciate how you’ve highlighted practical, low-cost infrastructure hacks, like consolidating services on a single VM and using hardcoded configs, which can significantly accelerate development without sacrificing learning.
In the long run, this mindset not only conserves resources but also keeps the team focused on real user insights rather than overly optimized infrastructure for hypothetical future growth. It would be interesting to see how you evolve these solutions as your product gains more users and the stakes become higher—perhaps extending the cycle or introducing more structured refactoring phases. Thanks for sharing such a pragmatic framework that bridges the gap between ideal scalability and startup agility!