Embracing the 3-Month Rule: A Practical Approach to Non-Scalable Development
When it comes to the startup world, one often cited piece of wisdom comes from entrepreneur Paul Graham: “Do things that don’t scale.” Yet, translating this philosophy into tangible coding practices is often overlooked. Having dedicated nearly eight months to developing my AI podcast platform, I’ve created a straightforward framework that guides my approach: every unscalable workaround is given a lifespan of just three months. At the end of this period, these temporary solutions either prove their worth and evolve into robust features, or they are discarded.
The Startup Mindset: Rethinking Scalability
As engineers, we’re often conditioned to think in terms of scalability from the outset—embracing design patterns, microservices, and distributed architectures capable of supporting millions. However, this is a mentality more suited to larger corporations. For startups, investing heavily in scalable solutions can amount to costly procrastination, as it focuses on potential users that don’t yet exist and preempts challenges that may never arise. My three-month rule encourages me to embrace simpler coding practices that allow me to expedite delivery and gain real insight into user needs.
Ingenious Yet Simple Infrastructure Hacks
Here are some of the unconventional methods I’ve employed in my development process, demonstrating that “bad” code can be a strategic asset:
1. Consolidated Operations on a Single VM
My entire service—database, web server, background jobs, and caching—runs on a single $40/month virtual machine. While this might sound reckless and lacking in redundancy, it has provided me keen insights into my actual resource consumption. I’ve quickly discovered that my AI-driven platform peaks at just 4GB of RAM. The complex Kubernetes setup I nearly implemented would have been an exercise in futility, managing idle containers. The few crashes I’ve experienced offered valuable data on breaking points, often revealing surprises.
2. Hardcoded Configuration for Simplicity
Instead of using configuration files or environment variables, I’ve opted for hardcoded constants:
python
PRICE_TIER_1 = 9.99
PRICE_TIER_2 = 19.99
MAX_USERS = 100
AI_MODEL = "gpt-4"
Altering these values requires a redeployment, but this deliberate setup allows for unprecedented simplicity. I can swiftly locate any config value across my codebase, and all changes are tracked within my version history. A configuration service that might take
One Comment
This post offers a compelling perspective that resonates deeply with the startup mindset—prioritizing speed, experimentation, and learning over premature scalability. The 3-month rule is a practical framework that encourages developers to adopt a “go fast and iterate” approach, shedding perfectionism early on. I particularly appreciate the emphasis on intentionally “bad” or temporary solutions as strategic tools, which reminds me of Eric Ries’ Lean Startup methodology—validated learning outweighs perfect architecture in initial stages.
One insightful addition might be to consider a structured review process at the end of each 3-month cycle. For instance, tracking metrics on how the workaround performed, the insights gained, and whether it should evolve into a more scalable solution or be discarded entirely. This can help ensure that these temporary solutions are evaluated thoughtfully, preventing technical debt from accumulating unchecked.
Moreover, as your platform scales or user base grows, it’s valuable to plan for a transition from these hacky solutions to more robust systems. But starting lean, as you advocate, enables faster learning and pivots—key to startup success. Thanks for sharing these practical hacks and your thoughtful approach; they are highly instructive for early-stage product development.