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hn_topic_digest
Fetch real HN stories, normalize them, apply the required immutable embedded trained model, count observed attention and render notebook-safe HTML. Training is a separate operation. Published implementation executes locally in embedded mode.
render_digest
Render observed HN topics, tags, attention summaries, collection coverage and real story links as an escaped notebook-safe HTML fragment with scoped CSS; optional standalone document.
evaluate_topics_temporally
Split HN stories chronologically, fit vocabulary and topics only on earlier titles, then inspect later-title coverage and reconstruction. No supervised accuracy or future-popularity forecast is claimed.
fetch_snapshot
Collect real HN stories from date-filtered Algolia search or current Firebase feeds. Explicit time range, overlapping feeds, request/item/time budgets and collection timestamp; no substitute data. Returns normalized rows and coverage diagnostics.
normalize_stories
Normalize HN records or a snapshot, preserve missing popularity values, merge overlapping feed membership and deduplicate story IDs. Returns acquisition provenance and quality counts.
summarize_topics
Measure observed topic counts, shares, scores and comments separately from topic assignment; compare equal-duration windows under one fixed model and report overlapping feed coverage.
analyze_titles
Apply explicit topic rules and compare document shares in adjacent equal-duration windows. Counts describe the supplied snapshot, not all Hacker News activity.
hn_topic_model
Return the immutable fitted JSON topic model included as a native Scalar dependency in this signed release. No model file, caller model input or retraining is required.
predict_topics
Apply a supplied fitted HN topic model without fitting vocabulary or components. Returns topic assignments, automatic keyword tags, unknown-topic diagnostics and unchanged observed popularity values.
tag_titles
Assign HN titles to topics with the immutable trained model embedded as a required native graph dependency. Automatic names use learned top terms; no caller model file or retraining. Returns hn-topics-v1 predictions.
train_topic_model
Fit a simple TF-IDF and nonnegative matrix factorization topic model on supplied training titles only. Returns portable JSON vocabulary, IDF weights, learned components, automatic top-term names and a content identity; no rule-generated labels.