Manual highlight clipping does not scale. If you produce three long-form videos a week, you are spending hours scrubbing through footage to find 60-second segments worth sharing. AI-powered moment detection automates this by analyzing signals humans use intuitively but cannot process at machine speed.
The Three Signal Categories
Audio Signals
Speech energy is the strongest predictor of an engaging moment. When a speaker gets excited, their pitch rises and their pace increases. Sudden silences followed by speech often indicate a punchline or reveal. Background reactions (laughter, applause in multi-person recordings) are direct engagement markers.
Visual Signals
Rapid visual changes indicate action. For screen recordings, this means code being written, terminal output appearing, or UI interactions. For camera footage, it means gestures, movement, or visual demonstrations. Static frames with talking heads score low.
Semantic Signals
The transcript reveals intent. Phrases like "watch what happens," "here is the result," or "this is the key insight" are explicit markers. Topic transitions indicate segment boundaries. Questions followed by answers are natural clip structures.
Scoring Algorithm
Each signal produces a time-series score. Combining them requires weighting:
function computeMomentScore(timestamp: number, signals: Signals): number {
const audioScore = signals.speechEnergy[timestamp] * 0.3;
const visualScore = signals.visualChangeRate[timestamp] * 0.25;
const semanticScore = signals.keyPhraseMatch[timestamp] * 0.35;
const noveltyScore = signals.topicNovelty[timestamp] * 0.1;
return audioScore + visualScore + semanticScore + noveltyScore;
}Semantic signals get the highest weight because they directly indicate value. A speaker calmly explaining an important concept should score higher than someone excitedly discussing something irrelevant.
Peak Detection
After scoring every second of the video, run peak detection to find local maxima. Each peak represents a candidate highlight. Set a minimum distance between peaks (at least 90 seconds) to avoid overlapping clips. Set a minimum score threshold to filter out low-quality candidates.
VidNo's Detection Approach
VidNo adds a fourth signal category specific to developer content: code change detection. Using OCR and git diff analysis, it identifies moments where meaningful code changes occur -- a function being completed, a bug being fixed, a test passing. These moments are inherently interesting to a developer audience and score high automatically.
From Detection to Publication
Detected highlights feed directly into the rendering pipeline. Each clip gets cropped, captioned, and formatted as a Short. The pipeline generates metadata from the transcript segment and queues the clip for upload. The entire process from detection to published Short can run unattended.
The result: every long-form video automatically produces its own promotional clips. No scrubbing, no manual cutting, no decisions about what to clip. The AI handles selection, and you review the output.