aeon-huggingface-trending
Trending Hugging Face models, datasets, and spaces — filtered by license sanity, dedup vs same-week quantizations, with a "why notable" line per pick (architecture shift, size step, license change, notable author). Surfaces what's actually shifting rather than just popular. Triggers: "trending on HF", "what models are hot", "huggingface trending", "new spaces today", "best new datasets".
What this skill does
# aeon-huggingface-trending Daily filtered scan over HF's three trending surfaces — models, datasets, spaces — cluster-ranked rather than raw-download ranked. ## Endpoints ```bash curl -s "https://huggingface.co/api/models?sort=trending&direction=-1&limit=30" curl -s "https://huggingface.co/api/datasets?sort=trending&direction=-1&limit=30" curl -s "https://huggingface.co/api/spaces?sort=trending&direction=-1&limit=30" ``` ## Filters - Empty repos, no commits, no model card → drop. - Same-week quantization of an already-trending model → demoted to "Quantizations" tail. - Fork without README delta vs upstream → drop. - Authors with > 5 trending entries in 24h → demoted (typically aggregators). - License unclear or missing → flagged inline, not dropped. ## "Why notable" line Per surfaced entry, a one-sentence tag: new architecture / size step / context-window jump / notable author affiliation / license change. If no concrete reason exists, the entry says "no clear why — popular but unremarkable" rather than inventing one. ## Output Three sections — Models, Datasets, Spaces — each with the surviving picks, "why notable", license, and download/view count. Tail section for quantizations. ## Rules - "Why notable" is a hard requirement. No reason → no surface. - License flags appear inline (Apache 2.0, MIT, OpenRAIL-M, custom-no-commercial). Operators trading on model output care. - Spaces section often beats Models for builder-tier signal — a working demo is stronger validation than a card claim.
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