Omni
Local-first multimodal file search on macOS
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Finally made something I've always wanted, using the model we built.• SOTA omni embedding model, fully local, indexes text, PDF, image, audio, and video • Swift-native app UI + mlx-swift-transformer core. No Python. • Tested on M3 Pro 18G / M3 Ultra 512G / M4 Pro 48G. All work fine. • HTTP server exposes search to local agents like OpenClaw & Hermes − Indexing still feels slow even on the latest M3 Ultra, ranging from 10K tps to 300 tps depending on file type − Fans go crazy, high power draw while indexing − Search is near-instant. Multimodal relevance is sometimes arguable, but the idea is recall (the agentic LLM takes the results and refines for the final answer), so maybe that's fine
HN · launched Jun 5, 2026 · 6 upvotes (top 69%) · 0 views
Niche: macos system utilities · 448 launches, 31 in the last 7 days ▼ -30% · Explore this niche →
Category and tags
- Horizontal
- Search & retrieval
- Prosumer
- AI-native
- Commercial product
Visit website ↗ Source listing ↗
More like this: all 448 launches in macos system utilities →
Could you build this?
Partial A developer can vibe-code the macOS Swift UI and local search orchestration, but developing a state-of-the-art unified multimodal embedding model from scratch requires deep ML research and training clusters.
What it would actually take: The production app uses Apple's MLX Swift runtime to execute matrix multiplications directly on Apple Silicon GPUs for local vector search. While the UI and SQLite/vector store indexing pipeline can be vibe-coded, the hard piece is the custom multimodal embedding model (spanning text, audio, image, and video in a single joint vector space), which requires large-scale contrastive pre-training across massive paired datasets using GPU compute clusters.
Discussion
2 comments analyzed.
Concerns raised: indexing performance is slow
Feature requests: run indexing as lower priority background task
Competitors
Other products that read as similar to this one : 92 launches clear the similarity bar, closest 8 shown.
Attention rank: #71 of 93 (itself plus its competitors, highest first, normalized so YC and Product Hunt are compared fairly).
Launched 185 days after the earliest competitor.
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