The 3-Month Rule: A Pragmatic Framework for Unscalable Solutions in startup Development
In the entrepreneurial world, we often hear Paul Graham’s famous maxim: “Do things that don’t scale.” Yet, the practical application of this philosophy, especially in software development, remains largely overlooked. Having spent the last eight months developing my AI podcast platform, I have established a straightforward framework: any unscalable approach is trialed for a duration of three months. At the end of this period, each method either proves its worth and is refined, or it is discarded.
As engineers, our instinct is to create scalable solutions right out of the gate. We gravitate toward intricate design patterns, microservices, and distributed architectures intended to support millions of users. However, this mentality often overshadows the real needs of a startup environment, where the focus should be on agility rather than premature optimization. My three-month approach compels me to prioritize simple, straightforward, and even “messy” code that delivers real results and helps me better understand user requirements.
Insights from My Current Infrastructure Experiments
1. Consolidated Systems on a Single VM
All components of my application—database, web server, background jobs, and caching—operate on a single virtual machine costing only $40 a month. This setup lacks redundancy, and I handle backups manually.
Why is this smart instead of foolish? In just two months, I’ve gained more insights into my resource needs than any elaborate capacity planning document could provide. My “AI-centric” platform peaks at just 4GB of RAM. The complex Kubernetes framework I contemplated would have only meant managing idle containers.
When disruptions occur (which they have twice), I gather valuable data regarding failures—often revealing unexpected issues.
2. Simplified Hardcoded Configuration
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PRICE_TIER_1 = 9.99
PRICE_TIER_2 = 19.99
MAX_USERS = 100
AI_MODEL = "gpt-4"
Instead of utilizing configuration files or environment variables, I rely on constants embedded throughout my codebase. Changing any configuration necessitates redeployment.
The true advantage lies in accessibility: I can search my entire codebase for any configuration value in seconds. Any price adjustments are clearly traced in Git history, and all modifications undergo a review process, even if it’s a solo check by myself.
Constructing a dedicated configuration service would consume a week of my time. However, I’ve only adjusted
