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I built a local Elixir/Python pipeline to curate 14,000 RAW photos

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I had 14,000 photos sitting on a drive and wanted an excuse to play with local vision models and Elixir/Phoenix. I originally tried to get LLaVA to tell me if a photo was 'good' or matched my style, but quickly learned that LLMs have terrible taste. I ended up demoting the LLM to just extract metadata, and built a custom CLIP/Ridge Regression pipeline to actually learn my preferences based on how I rate things.The stack is Phoenix/Oban on the orchestrator side, and Python/FastAPI/Instructor for the AI workers. Happy to answer any questions about the architecture, fighting with local RAW file ingestion, or the pains of Pydantic validation with open weights.

HN · launched Apr 20, 2026 · 5 upvotes (top 89%) · 1 view

Niche: image processing and editing tools · 260 launches, 21 in the last 7 days ▼ -28% · Explore this niche →

Category and tags

  • Media & entertainment
  • Workflow automation
  • Prosumer
  • Not AI
  • Hobby / open-source project

Visit website ↗ Source listing ↗

Could you build this?

Yes The project is a personal local script pipeline combining an Elixir/Phoenix gallery with a Python worker script running local CLIP and LLaVA models via Ollama or PyTorch.

Discussion

2 comments analyzed.

Concerns raised: Network latency impact, RAM constraints for metadata extraction, RAW file processing overhead

Feature requests: Native RAW file conversion in Elixir

Competitors

Other products that read as similar to this one : 280 launches clear the similarity bar, closest 8 shown.

Attention rank: #235 of 281 (itself plus its competitors, highest first, normalized so YC and Product Hunt are compared fairly).

Launched 171 days after the earliest competitor.

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