Scoring and history
Trend Finder combines current public evidence with deterministic signals and, when configured, validated AI analysis. Scores prioritize review; they do not certify a forecast or factual claim.
Score factors
Section titled “Score factors”Each topic carries six 0-100 dimensions:
| Factor | Question |
|---|---|
| Relevance | Does the topic match the current AI and creator scope? |
| Novelty | Is the idea meaningfully early or differentiated? |
| Evidence strength | Is there enough usable evidence to support inspection? |
| Source diversity | Does the signal appear across distinct source roles? |
| Niche fit | Does it match Creator Lens focus and audience? |
| Creator potential | Can it become a useful piece, workflow, product, or experiment? |
Named contribution rows, quality caps, corroboration, freshness, lifecycle, saturation, risk flags, and visibility bands make the final decision more inspectable. Source-local metrics never replace cross-source evidence.
Hidden gems and action verdicts
Section titled “Hidden gems and action verdicts”A hidden gem is low-volume and high-novelty, not low-quality. It must clear minimum opportunity, novelty, and evidence gates, then receives a continuous gem score for ordering.
Action verdicts such as act now, monitor, review, or ignore summarize the current evidence and risk posture. The next-step copy must remain consistent with the verdict and visible confidence.
Lifecycle and movement
Section titled “Lifecycle and movement”Lifecycle uses the bounded sequence unknown, whisper, builder,
creator, and saturated. Movement compares the stable topic identity with
prior local snapshots so renamed or reclustered topics can retain continuity
when the match is strong enough.
Daily series, sparklines, convergence, velocity, risers, coolers, and Story Log entries describe observed run history. They are not guarantees of future attention.
Prediction and retro loop
Section titled “Prediction and retro loop”Trend Finder can write bounded next-run predictions and later grade them when a target run arrives. Calibration summaries measure how those calls performed over time. Script-only historical backtests are a separate workflow and are not the same as Engine Replay or normal future-run retros.
AI analysis and fallback
Section titled “AI analysis and fallback”Analyst output is parsed against a structured contract. Invalid output gets a bounded repair attempt and then falls back deterministically. The fallback can cluster, score, and produce creator copy without claiming an AI-analyzed run.
Always pair the score with the current provenance labels and the public evidence linked from the topic.