090°Open data
Open datasets, fully documented — searchable here, and readable by any LLM.
2 datasets
Stack Overflow data/ML question intelligence (agent-curated)
Every distinct Stack Overflow question surfaced by per-tag sweeps of a curated ~38-tag data/ML universe over the trailing 7 complete UTC days, deduplicated on question_id (first sweep wins as primary_tag; all observed tags unioned into tags). The official keyless Stack Exchange /questions API (sort=creation, fixed fromdate/todate window) supplies the raw material; the transform derives day-granular ages from the as-of stamp, scores each question with a documented 0-100 attention composite (60% min-max-normalized log10(1+views_per_day) + 40% min-max-normalized non-negative score_per_day), ranks it as attention_rank (ties: views desc, question_id asc), and flags unanswered_pain_flag when a question has zero answers with at least 2x the snapshot's median view count (visible community demand, no resolution). Titles + metadata only (CC BY-SA 4.0, attribution required) — no bodies, comments, or user data. Primary key: (week, question_id). Cadence: weekly. Nullability: scores and ranks are never null; age is at least 1 day. Caveats: counts tick intraday, so same-day re-runs refresh values; attention scores are within-snapshot relative (recalibrated every week). Sample use: order by attention_rank for the week's hottest data/ML questions, or filter unanswered_pain_flag for under-served demand.
Per-tag aggregation of the same weekly data/ML question universe as so_questions_weekly: for each of the ~38 curated sweep tags, the questions carrying it in the trailing 7 complete UTC days, with questions_7d, total_views, answer_rate (answered / total), median_views_per_day, unanswered_count, and pain_count (questions with zero answers at >= 2x the snapshot median views). heat_score is a documented 0-100 composite: 50% min-max-normalized log10(1 + questions_7d) (volume) + 30% min-max-normalized log10(1 + median_views_per_day) (engagement) + 20% min-max-normalized pain_share (unmet demand), ranked as heat_rank (ties: questions_7d desc, tag_name asc). Tags are classified with the shared curated 14-category taxonomy (same as so_tag_signals). Primary key: (week, tag_name). Cadence: weekly. Nullability: scores and ranks never null. Caveats: questions carrying several swept tags count toward each tag's totals; heat scores are within-snapshot relative. Sample use: order by heat_rank for the week's hottest data/ML topics.
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