Millnew AI
Explaining time-series models using local LLMs and Captum
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HN · launched Aug 27, 2026 · 5 upvotes (top 87%) · 2 views
Niche: machine learning developer tools · 303 launches, 11 in the last 7 days ▼ -45% · Explore this niche →
Category and tags
- Horizontal
- Analytics & BI
- Developer
- AI-native
- Hobby / open-source project
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More like this: all 303 launches in machine learning developer tools →
Could you build this?
Partial The UI and local LLM orchestration are straightforward, but correctly computing feature attribution using PyTorch Captum on non-standard time-series deep learning architectures requires ML domain expertise.
What it would actually take: A production version requires a Python backend integrating PyTorch, PyTorch Captum (for Integrated Gradients, DeepLIFT, or SHAP algorithms), and local LLM runtimes like Ollama or vLLM. The challenging aspect is translating gradient-based feature attribution across multi-channel temporal dimensions into structured prompt representations that an LLM can accurately interpret without hallucinating spurious correlations. This demands ML expertise in time-series modeling and model explainability techniques.
Discussion
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Competitors
Other products that read as similar to this one : 1895 launches clear the similarity bar, closest 8 shown.
Attention rank: #1599 of 1896 (itself plus its competitors, highest first, normalized so YC and Product Hunt are compared fairly).
Launched 302 days after the earliest competitor.
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