RoastDB – A searchable database of 3,800 specialty coffee beans
Hi HN! We built RoastDB (https://roastdb.com) – think Discogs for specialty coffee. A searchable index of beans from independent roasters around the world. *The problem* We got into specialty coffee gradually. Whenever we tried something we liked — a washed Colombian, a natural Ethiopian — we'd save the bag. At some point we had drawers of empty coffee bags we couldn't bring ourselves to throw away. Our flow was simple: go to a cafe we liked, drink coffee, buy a bag of whatever they were roasting or stocking. Over time we started noticing patterns — we kept reaching for naturals,…
What it does
In the maker’s words, at launch
Hi HN! We built RoastDB (https://roastdb.com) – think Discogs for specialty coffee. A searchable index of beans from independent roasters around the world. *The problem* We got into specialty coffee gradually. Whenever we tried something we liked — a washed Colombian, a natural Ethiopian — we'd save the bag. At some point we had drawers of empty coffee bags we couldn't bring ourselves to throw away. Our flow was simple: go to a cafe we liked, drink coffee, buy a bag of whatever they were roasting or stocking. Over time we started noticing patterns — we kept reaching for naturals, for East African origins, for anything with fruity notes. We'd try to seek out similar beans next time. Occasionally we'd fall in love with something and start reordering it online. But discovery was limited to the few roasters we already knew. There was no easy way to find out that a roaster across town — or in another country — had something we were going to love. We knew great coffee existed out there. We just had no map. So we built one. RoastDB currently indexes 3,800+ beans from 420+ roasters — and growing every week. Search by origin, process, variety, tasting notes. Save beans you want to try. When you find something, you buy directly from the roaster — we're a discovery engine, not a store. *How it works* The hardest part isn't the scraping — it's finding roasters worth indexing. We spend a lot of time hunting for quality third-wave roasters: browsing coffee forums, following competition results, exploring roasters in new cities. The selection is the real work. Once we've found a roaster, the pipeline runs on a €5/month Hetzner VPS: 1. Scrapers fetch product pages from roaster websites 2. LLMs extract structured data (origin, variety, processing, price, tasting notes) 3. Normalization cleans up inconsistencies ("Äthiopien" → "Ethiopia", "84,25" → 84.25) 4. Non-English descriptions get translated 5. Deduplication scores beans and merges duplicates 6. Human review via an admin dashboard before publishing The scrapers rerun weekly with content hashing — we only re-extract pages that actually changed, which keeps the data fresh without burning through API costs. We built an internal tool that gamifies the review process, making it easier to keep up with new beans. And we control the whole pipeline through a Telegram bot — kick off scrapes, approve costs, get notified of failures, all from our phones. The web app is Next.js + SQLite. The database file is ~15MB and serves directly from disk, no complexity. *Feedback welcome* - Roasters we should add (especially outside Europe) - Filter combinations that would be useful - Anything broken or confusing
Does a similar job
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- WRWe're two coffee nerds who built an AI app to track beans and recipes2025 · beanbook.app · ▲60
It’s available on iOS now: https://itunes.apple.com/app/id6499280064 We got into specialty coffee during COVID and, like many others, fell deep down the rabbit hole. Along the way, we ran into the same frustrations: - A drawer full of empty coffee bags. - No simple way to track grind size, rest dates, notes—by bean. - My coffee history scattered across photos, screenshots, notebooks, and half-memories. - The unique traits, people, and stories behind each coffee disappearing from the internet once it sold out (since coffee is an agricultural good) - In our opinion, no…
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