Scenery – an open-source photo-gallery with extended search

Hacker News

Hello HN! Sharing a pet project of mine. It started out as a dull photo-gallery then I tried to make it "smarter". In the process of development, I've learnt a lot about computer vision and programming in general. Current features: Search by tags (supports logical expressions, https://scenery.cx/search_syntax ) Semantic text search You can find images with similar tags, color palette or visuals/semantics Reverse image search Image anti-duplication mechanism Automatic image tagging and captioning IPFS support https://github.com/qwertyforce/scenery/blob/master/how_it_wo... https://github.com/qwertyforce/scenery https://scenery.cx

Visit product View on Hacker NewsSep 6, 2022

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

74%74% 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 · Strong signals: supports, started · Missing: reddit linkedin, podcasting, created

74%74% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

Product HuntOn track for Day 1 leaderboard · Strong signals: computer, visual, open · Missing: mac, agents, macos

54%54% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

48%48% predicted probability of success on TrustMRR, 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.

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

14%14% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

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

1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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