Local video search with Qwen3-VL: no API, runs on Apple Silicon, GPUs

Hacker News

Last week, I posted SentrySearch, a CLI for semantic video search using Gemini's embedding API. The #1 request was local model support. Turns out Qwen3-VL-Embedding can natively embed video into the same kind of vector space, no API, fully offline. Runs on Apple Silicon (MPS) and NVIDIA GPUs (CUDA). The 8B model needs ~18GB RAM, or use the 2B model on smaller machines. sentrysearch index /path --backend local Also added: similarity threshold to suppress weak matches, and a Tesla metadata overlay that renders speed/location onto matched clips. Details on the README.

Visit product View on Hacker NewsMar 30, 2026

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

96%96% 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.

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

67%67% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

49%49% 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

43%43% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

42%42% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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

7%7% predicted probability of success on BetaList, based on ML models trained on real launch data.

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