The 3-Month Experiment: A Pragmatic Approach to Non-Scalable Solutions
In the startup arena, the mantra “do things that don’t scale” coined by Paul Graham is widely recognized, yet the practical application of this advice in coding is often overlooked. Over the past eight months developing my AI podcast platform, I’ve devised a straightforward yet effective methodology that I call the 3-Month Rule. The premise is simple: every quick-and-dirty hack that I implement is given a lifespan of three months. After this period, each solution must either demonstrate its worth and be properly implemented, or it gets discarded.
Rethinking Our Engineering Mindset
As software engineers, we are conditioned to prioritize scalable solutions right from the start. The allure of elegant design patterns, microservices, and distributed systems can often cloud our judgment, leading us to craft sophisticated architectures suitable for millions of users. However, in the startup phase, chasing scalability could merely translate to expensive delays. This is where my 3-Month Rule comes into play, compelling me to create straightforward, even suboptimal, code that can be shipped out and, crucially, teaches me about real user needs.
Smart Hacks That Drive Learning
1. Consolidated Infrastructure on One VM
Currently, my entire tech stack—including the web server, database, background jobs, and Redis—is hosted on a single virtual machine costing $40 per month. With no redundancy in sight and manual backups being the norm, some might label this approach reckless. However, I see it as a valuable learning opportunity. In just two months, I’ve gathered insights on my actual resource requirements, debunking assumptions about my “AI-heavy” platform and learning that 4GB of RAM was sufficient. This setup has allowed me to identify the exact reasons for my crashes—providing data that’s often counterintuitive.
2. Hardcoded Configurations
Instead of relying on configuration files or environment variables, I’ve adopted a straightforward method: hardcoding essential values directly into the code. For example:
plaintext
PRICE_TIER_1 = 9.99
PRICE_TIER_2 = 19.99
MAX_USERS = 100
AI_MODEL = "gpt-4"
Though this may seem primitive, it has its perks. I can quickly search my codebase for any configuration and trace changes through git history effortlessly. Moreover, I’ve only altered these values a handful of times
