(And I launched it on Reddit before I told anyone I knew)
The Problem
At 4AM, fueled by my third cup of Blue Tokai, I launched a product to help people find better coffee in India. The irony wasn't lost on me.
As someone who was just getting into specialty coffee in India, I found myself constantly asking: "Wait, is this a light roast or a dark roast?" "What's the difference between these two brands?" "Where the hell do I find Indian roasters who actually ship and don't just flex on Instagram?"
There wasn't a clear, curated directory of Indian coffee roasters and their products — just scattered Instagram posts, occasional Reddit comments, and vague blog roundups from 2019.
So I built one.
The Stack (a.k.a. Ctrl+C, GPT+Claude, Go)
This wasn't some beautifully architected system from the start. It was a chaotic cocktail of:
- Reddit + GPT + Claude — to find my first ~50 roasters
- Claude + GPT again — to quickly build the scraping logic (they helped me write code to extract basic info like name, website, and city)
- v0.dev — to throw together a frontend without starting from scratch
- Supabase — as my CMS/backend/data storage layer
- Vercel — for deployment in minutes
- Google Forms + Vercel Analytics — to track early usage and collect feedback
And yes, all of this happened between the hours of midnight and 4:15AM. Because that's when I get my real work done.
What It Does
IndianCoffeeBeans.com is a growing directory of Indian roasters and their products.
- 50+ roasters listed
- 600+ coffee products parsed (beans, blends, single origin, gear — working on cleaning that part)
- Direct links to their websites
- Plans to add: filtering (brew method, roast, etc), reviews, brewing guides, and a newsletter
Now instead of scrolling through dozens of Instagram posts, you can find that medium-roast Chikmagalur in 30 seconds.
The trickiest part? Getting consistent roast level data when every site formats it differently. My solution involved a mix of CSS selectors and letting Claude interpret ambiguous descriptions like "bright and balanced profile" into standardized categories.
It's built for people like me — curious home brewers in India who want better coffee, minus the Instagram noise.
How It Went Live
I posted it to r/IndiaCoffee and a specialty coffee WhatsApp group with 800+ members. People started responding within hours:
- Some bookmarked it immediately
- One asked if I planned to monetize it
- Another said they'd needed something like this for a while
- Someone pointed out I was missing a few roasters (true!)
That's when it hit me: this might actually be useful.
What's Next
- Work on the scrapers to pull correct additional data points
- Connecting the scraper to Supabase for live updates
- Building a real blog (WordPress on a subdomain — I'm not fighting SEO for now)
- Writing guides for brewing, beginners, and gear
- Exploring monetization (maybe roaster promos, featured listings, etc.)
- Creating filters for brew method, roast, and availability
And yes, all of this is still powered by too much coffee, GPT tabs, Claude rewrites, and the occasional Framer fiddle.
If You're Curious: Check it out: https://indiancoffeebeans.com
What Indian coffee roaster should I add next? Drop a comment or use the feedback form on the site. Your suggestion might save me from drinking bad coffee tomorrow morning.