Embracing the Unscalable: The 3-Month Framework for Startups
In the entrepreneurial world, a well-known piece of advice from Paul Graham resonates loudly: “Do things that don’t scale.” However, many often overlook how to effectively apply this concept to technical development, especially when coding solutions in a startup environment.
After dedicating eight months to building my AI podcast platform, I have devised a straightforward methodology: any unscalable approach I implement gets a defined time frame of three months. If it proves its worth within that period, it earns a transition to a more structured solution; otherwise, it’s phased out.
Why the 3-Month Rule is Crucial for Startups
As engineers, we frequently find ourselves striving to create scalable solutions from the onset. We dive into design patterns, microservices, and distributed systems, building architectures capable of supporting vast numbers of users. However, this mindset is typically suited for larger corporations rather than the agile nature of startup ecosystems.
At a startup, the pursuit of scalable systems can lead to excessive delays and unnecessary complexity. You’re often optimizing for a user base that isn’t even there yet and addressing challenges that might never arise. By adhering to my 3-month rule, I prioritize developing straightforward and perhaps “imperfect” code that enables me to learn what users genuinely require.
Current Infrastructure Hacks: Insights on Smart Simplifications
1. Single Server Setup
My entire stack—including the database, web server, and background processes—operates on a single virtual machine for just $40 a month. This approach avoids redundancy, relying on manual backups to my local system.
While this might seem reckless, it’s been a learning boon. In just two months, I have gained more insight into my resource requirements than I could have through formal planning. I found out that my “AI-heavy” platform typically uses only 4GB of RAM, which is far below what more complex setups would have required.
2. Hardcoded Constants for Configuration
python
PRICE_TIER_1 = 9.99
PRICE_TIER_2 = 19.99
MAX_USERS = 100
AI_MODEL = "gpt-4"
There isn’t a configuration file in sight. Instead, I’ve used hardcoded constants scattered throughout my files. While redeploying is necessary for any changes, the ease of tracking and searching these values provides significant advantages.
Instead of investing time in creating a configuration
