Stop saving your scans on 3rd party servers

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Hi HN, I built DocsOrb to solve a simple but stressful problem (and my own problem too since many years!): keeping track of important documents like passports, rental contracts, and insurance papers. Too often they're scattered across folders, emails, or piles at home... and you only realize it when you urgently need them. DocsOrb helps you: > Scan documents with auto-crop and enhancements (mobile camera or file upload) > Organize them around life's "moments" (travel, housing, insurance, etc.) > Search quickly using Key Information > AI extracts Key Information so the most important details are always at your fingertips > Export or share in one tap > AI Bulk organize: load up multiple images from your Photos to automatically organize them as documents, put them in the right folders, extract Key Information and also suggest a recommended name and description. Everything stays on your device by default, with optional cloud backup if you want it. Privacy-first, so you're always in control. Tech-wise: it's built with Nuxt + Capacitor, Supabase for structured storage, and a custom scanning flow (to avoid pricey SDK lock-ins). I'd love your feedback: > Does this flow make sense to you? > What's missing in how you manage important documents? > Any suggestions before I go full blast on Marketing?

Visit product View on Hacker NewsAug 26, 2025

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

87%87% 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: email, using · Missing: mac, agents, macos

63%63% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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.

Hacker NewsMay not resonate with HN audience · Strong signals: io · Missing: https docs, excited, just released

34%34% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

TrustMRRLess likely to generate early MRR · Strong signals: way · Missing: mobile apps, ios, personal

30%30% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

15%15% 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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