Offline AI Photo Search (local VLM and semantic search)

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I built a small offline photo search tool that uses a local vision-language model (NexaAI Qwen3-VL-4B) to describe images and sentence-transformers to generate embeddings for semantic search. Everything runs 100% on-device (no cloud, no API keys). GitHub repo: https://github.com/pankaj4152/smart-photo-finder You can try it with: 1. python app.py 2. Choose "Process images" 3. Choose "Search images" Would love feedback on architecture, performance, and improvements.

Visit product View on Hacker NewsNov 26, 2025

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best fitHighest predicted score across all platforms for this description.

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49%49% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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42%42% 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

33%33% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Indie HackersIH features products with proven revenue · Missing: supports, reddit linkedin, podcasting

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

20%20% 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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