Embracing the 3-Month Rule: A Pragmatic Approach to Technical Development
In the world of startups, we’ve all heard Paul Graham’s popular phrase, “Do things that don’t scale.” However, the conversation often stops there, leaving many developers scratching their heads on how to apply this wisdom effectively, particularly in the realm of coding.
After dedicating eight months to developing my AI podcast platform, I’ve established a straightforward methodology: every unscalable approach gets a trial period of three months. At the end of this timeframe, I assess its value and decide whether to enhance it into a robust solution or discontinue it altogether.
The Challenge of Scalability in Startups
As engineers, we’re typically trained to opt for “scalable” solutions right from the start. This mentality leads us to design complicated architectures involving microservices and distributed systems—the kinds of frameworks geared toward accommodating millions of users. However, this represents the mindset of larger enterprises.
In a startup environment, building scalable solutions can often equate to needless delays, as it forces us to optimize for prospective users who may never arrive and solve issues we might never encounter. By adopting my 3-month rule, I focus on creating straightforward and sometimes suboptimal code that gets products shipped quickly and reveals what users genuinely require.
The Value of Unconventional Infrastructure Hacks
Here are some of my unorthodox yet effective infrastructure choices, paired with insights that prove their merit:
1. Consolidated Operations on a Single VM
Everything from the database to the web server and background jobs operates on a single, cost-effective $40/month Virtual Machine (VM) with no redundancy and manual backups to my local device.
Why is this approach beneficial? It has allowed me to gauge my actual resource needs more accurately than any theoretical capacity document would. For instance, my AI-centric platform has peak usage at just 4GB of RAM, rendering the elaborate Kubernetes setup I nearly implemented unnecessary since I would have been managing idle containers.
When crashes occur—yes, there have been a couple—I gather vital information on what actually fails, and it often surprises me.
2. Hardcoded Configuration Values
plaintext
PRICE_TIER_1 = 9.99
PRICE_TIER_2 = 19.99
MAX_USERS = 100
AI_MODEL = "gpt-4"
In my setup, there are no configuration files or environment variables—just constants spread throughout the codebase. Any changes necessitate a
One Comment
Thank you for sharing your practical approach to balancing speed and practicality in startup engineering. The 3-month rule strikes me as a refreshingly disciplined way to avoid over-engineering early on—allowing teams to focus on learning and real user feedback rather than theoretical scalability from the outset. Your emphasis on embracing “unscalable” solutions temporarily reminds me that early-stage products often need to prioritize rapid iteration and validation over perfection.
I particularly appreciate your examples, like consolidating operations on a single VM and using hardcoded configurations. These strategies exemplify the importance of simplicity and cost-efficiency during initial development phases. However, I wonder how you plan to transition from these hacky solutions to more robust infrastructure as your platform grows. Do you have a threshold or criteria for when to revisit and refactor these elements?
Overall, your framework offers a compelling mindset—one that encourages agility, learning, and pragmatism, which are crucial for early-stage success. Thanks again for sharing your insights!