We're two coffee nerds who built an AI app to track beans and recipes

Hacker News

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 coffee tool really captures the flavor, emotion, and aesthetic of great coffee—from a design perspective. So we built BeanBook—a coffee notebook log beans, extract recipes, and organize your coffee life in one place with just a snap, powered by AI Here’s what it does: - Snap a bag → Auto-detects roaster, origin, process, roast date, notes, producer, farm, and more - Paste a YouTube link or photo → Extracts a structured recipe automatically - Log grind size, roast timeline, ratings & notes → All saved in a clean, elegant UI - See your coffee year in review → Track habits, trends, and favorites - Ask BeanBook AI → From brew temps to bean facts, get instant answers My co-founder and I built everything ourselves—branding, code, and UX design. If you’re into coffee (or trying to get more into it), we’d love your feedback. - Rokey & Eric

Visit product View on Hacker NewsJun 30, 2025

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Indie HackersFits the IH revenue-focused audience · Strong signals: ios · Missing: supports, reddit linkedin, podcasting

88%88% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

Product HuntOn track for Day 1 leaderboard · Strong signals: apple, notes, code · Missing: mac, agents, macos

83%83% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

TrustMRRFits verified-revenue profile · Strong signals: ios, answers, way · Missing: mobile apps, personal, entrepreneurs

57%57% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Hacker NewsStrong engagement from HN community · Strong signals: io · Missing: https docs, excited, just released

52%52% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive

41%41% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue

18%18% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto

0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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