render_digest

Inputs

Outputs

from spl import SPLClient

result = SPLClient.embedded().call(
    "splime://@splime.project/case4-hn/render_digest@3",
    kwargs={'analysis': {}, 'full_document': False, 'limit': 0},
    trust=True,
)

Review before running. Public bundles contain executable code. Verify the publisher and exact content hash; a virtual environment isolates dependencies, not the operating system.

Evidence

Evidence-backed concepts

• Render digest
• Optional standalone document
• Attention summaries
• Collection coverage
• Scoped CSS
• Full document
• Render observed hn topics
• Real story links

• v331 Oct 20266 downloads
• v214 Oct 20264 downloads
• v113 Oct 20269 downloads

• hn_topic_digestFetch 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.
• evaluate_topics_temporallySplit 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.
• predict_topicsApply 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_titlesAssign 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.
• fetch_snapshotCollect 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.