Embracing the 3-Month Rule: A Practical Approach to Building an AI Podcast Platform
In the world of startups, there’s a prevalent piece of wisdom that often circulates: “Do things that don’t scale,” as famously articulated by Paul Graham. While this advice is well-known, the challenge lies in translating it into actionable strategies, especially in the realm of software development.
After eight months of creating my AI podcast platform, I’ve crafted a straightforward approach to apply this principle effectively: each unconventional, non-scalable solution is granted just three months to prove its worth. At the end of that period, we either refine and enhance it based on what we’ve learned, or we phase it out entirely.
The Reality of Startup Engineering
As engineers, we tend to gravitate toward scalable architectures from the outset—think microservices, distributed systems, and elegantly layered designs that cater to millions of users. However, in a startup environment, the ambition to create scalable solutions can turn into costly delays. We frequently dedicate time to optimizing for user scenarios that might not even exist yet.
To counter this, my 3-month rule encourages me to write straightforward, albeit imperfect, code that delivers results and accumulates insights about actual user needs.
Current Infrastructure Approaches and Their Value
1. Consolidated Services on a Single VM
All essential components—database, web server, background jobs, and Redis—exist on a single $40/month virtual machine, with no redundancy and manual backups conducted by me.
What might seem like a reckless move has actually proven to be a brilliant decision. Within two months, I learned my true resource requirements more effectively than any exhaustive capacity planning report could have revealed. My platform’s peak RAM usage is merely 4GB. The intricate Kubernetes setup I had initially contemplated would have involved manage-empty containers without real data.
When faults occur (and they’ve happened twice), I get valuable insights into what actually goes wrong—usually not what I anticipated.
2. Simplicity Through Hardcoded Configurations
Instead of relying on complex configuration files or environment variables, I use hardcoded constants for key values—such as pricing tiers or user limits—
python
PRICE_TIER_1 = 9.99
PRICE_TIER_2 = 19.99
MAX_USERS = 100
AI_MODEL = "gpt-4"
While this means redeploying the entire system for adjustments, it also allows for quick searches across the
One Comment
This is a compelling and pragmatic approach to early-stage product development. I appreciate how the 3-month rule encourages rapid experimentation and learning, rather than being paralyzed by overly ambitious infrastructure planning. Your example of consolidating services on a single VM highlights the value of starting simple and letting real-world usage inform necessary improvements—this aligns well with the “fail fast” philosophy.
Additionally, using hardcoded configurations for rapid iteration can be invaluable in the initial phases, so long as there’s a clear plan to refactor when scaling becomes imminent. It’s a balanced reminder that, sometimes, embracing imperfection is the most scalable strategy in the short term.
Overall, your framework underscores the importance of tangible outcomes and iterative learning in startup engineering—something that can save both time and resources while building a deep understanding of user needs. Thanks for sharing these insights!