Embracing Imperfection: The 3-Month Rule for startup Development
In the entrepreneurial world, one recurring piece of advice resonates from Paul Graham: “Do things that don’t scale.” While this wisdom is popular among founders, the challenge often lies in its practical application—especially in the realm of coding.
After dedicating eight months to developing my AI podcast platform, I’ve crafted a straightforward yet effective strategy: every unscalable hack gets a three-month lifespan. By the end of this period, each method must either demonstrate its value, leading to a more robust implementation, or face the axe.
The Shift in Engineering Mindset
As engineers, we’re conditioned to construct scalable solutions right off the bat. We dream of design patterns, microservices, and distributed systems—spectacular architectures intended to support millions of users. However, such big-company thinking can be counterproductive for startups.
In a nascent environment, over-engineering scalable code is often a costly delay. By optimizing for users who may never arrive, we risk solving non-existent problems. My three-month rule compels me to write straightforward, even ‘messy,’ code that actually gets deployed, allowing me to uncover genuine user needs.
Practical Hacks: My Current Approach
1. Consolidated Resources on One VM
Currently, my entire stack—database, web server, background jobs, and Redis—runs on a single $40/month virtual machine. While this setup lacks redundancy and relies on manual local backups, it’s been illuminating.
After two months, I’ve gained better insight into my resource requirements than any capacity-planning document could provide. Remarkably, my “AI-centric” platform peaks at around 4GB of RAM, meaning a complex Kubernetes setup would have required maintenance of nearly empty containers. When things do crash (which they have twice), I gather valuable data regarding actual failure points—which are rarely what I anticipate.
2. Hardcoded Configurations
Constants such as:
python
PRICE_TIER_1 = 9.99
PRICE_TIER_2 = 19.99
MAX_USERS = 100
AI_MODEL = "gpt-4"
act as my configurations, spread throughout the codebase—the absence of separate config files certainly simplifies things. While changing these values requires redeployment, the benefit lies in the speed of access. I can instantly search for any configuration, and every price adjustment is tracked in Git history
One Comment
This post offers a compelling perspective on the importance of embracing imperfection in early-stage development. The “3-month rule” not only encourages rapid experimentation but also helps avoid the trap of over-engineering from the outset—something I believe is especially crucial when resources are limited. Your approach of leveraging simple, “messy” code and minimal infrastructure to gain real user insights is a powerful reminder that the best solutions often come from what works in practice rather than what’s perfectly designed on paper.
Additionally, your emphasis on data collection during failures is insightful—each crash is an opportunity for learning rather than a setback. It’s a great example of how deploying unscalable hacks with clear timeframes can accelerate validation and guide subsequent iterations. Would be interesting to see how you balance this pragmatic approach with the eventual need for scalability once user traction begins to grow. Thanks for sharing these practical strategies!