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SnippetStash – Turn photos into searchable snippets with context

Hacker News

SnippetStash – Turn photos into searchable snippets with context

Hi HN, I wanted to share this app I built to scratch my own itch. I primarily read non-fiction and have a habit of highlighting certain snippets that stand out to me for later reference. At some point I realized that I rarely revisited these ideas and my effort was in vain. The issue was that these snippets were not easy to find across an entire bookshelf. Surprisingly, I couldn't find any existing tools to solve this problem. Copying everything into notes or a Word document might work, but it doesn't preserve the original context. If I wanted something like this I'd have to build it myself - so that's exactly what I did. Would love any feedback you have! Thanks!

Visit product View on Hacker NewsJul 25, 2024

Actual performance

1points

Did not reach leaderboard

Launch Intel predictions

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

77%77% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

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

55%55% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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

51%51% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

AppSumoStrong fit for a featured deal · Missing: plus, platform, intuitive

50%50% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

45%45% 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

17%17% 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.

Incorrect prediction on native model

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