fetch_snapshot

Inputs

Outputs

from spl import SPLClient

result = SPLClient.embedded().call(
    "splime://@splime.project/case4-hn/fetch_snapshot@1",
    kwargs={'attempts': 0, 'budget_seconds': 0.0, 'feed': 'replace-with-value', 'max_items': 0, 'proxy': 'replace-with-value', 'timeout': 0.0, 'transport': 'replace-with-value'},
    trust=True,
)

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Evidence

Evidence-backed concepts

• Fetch snapshot
• Total time budget
• Requires network access
• No bundled dataset
• Official API
• Explicit feed
• Acquisition provenance
• Httpx caller s environment

• v113 Oct 20269 downloads

• hn_topic_digestTurn supplied Hacker News records into an explainable HTML digest in one call: normalize records, apply explicit topic rules, and render a reading list. Returns a notebook-safe HTML fragment by default; full_document=True produces a standalone document. No live fetch or bundled dataset. Runs in the caller's Python environment.
• analyze_titlesApply explicit topic rules and compare document shares in adjacent equal-duration windows. Counts describe the supplied snapshot, not all Hacker News activity.
• normalize_storiesValidate Hacker News records, normalize UTC timestamps and HTTP(S) links, and deduplicate by record identity. Input data stays with the caller.
• case5_first_pd1_predictApply the fitted first_pd1 rules embedded in this release. Returns application IDs, predicted classes, probabilities and segments. No caller-side model file or retraining is required.
• find_rulesSearch numeric classification rules with a dataset and target column. Skip unsupported columns and return their names and reasons in unusable_columns, alongside the model, predictions and holdout metrics.