How It Works

Scoring Formula

Each sub-metric is scored 0-10 by the LLM. Final video score is 0-100.

base = (title_similarity x 5) + (focus_ratio x 3) + (time_to_content x 2)

penalty = (deception x 2) + (sponsor x 1)

score = base - penalty   [0-100]

Channel score = average of its evaluated video scores.

What the Metrics Mean

Title-Content Similarity (0-10)Does the video deliver what the title promises? 0 = complete bait-and-switch, 10 = precise match.
Focus % + Time to Main Content (0-10)What fraction of the video stays on topic, and how quickly it gets there. Higher = more focused, less preamble.
Deception Penalty (0-10)Whether the title makes factual claims the video contradicts or never addresses. Up to -20 points.
Sponsor Penalty (0-10)Proportion of sponsor/ad content relative to total video runtime. Up to -10 points.

LLM Evaluation Process

Each video is evaluated using two parallel LLM calls:

1
Title Analysis - The LLM reads the video title and full transcript together. It identifies what the title promises in plain language, then scores title-content similarity and deception.
2
Content Analysis - The LLM analyzes the transcript with timestamps to measure focus ratio (% on-topic), time to main content, and sponsor interruption level.

For long transcripts that exceed the model’s context window, we chunk the transcript and aggregate metrics deterministically across all chunks.

Video Selection

15 videos are evaluated per channel - a mix of recent uploads and all-time popular videos, so the score reflects both current behaviour and historical patterns.

  • YouTube Shorts are excluded.
  • Videos longer than 90 minutes are excluded.
  • Visually-driven channels are excluded - transcript-based scoring isn’t a fair measure for them.

Why This Exists

Our feeds overstimulate us more than ever. Exaggerated titles, sensational previews, fake urgency - it all works amazingly well on our monkey brains. It’s just a natural consequence of capitalism and how algorithms have evolved in rewarding our dopamine circuits quickly.

But there should be a counterforce. A platform that’s trusted and keeps content creators accountable while rewarding the honest ones.

Current Limitations

  • Transcripts are the only content input - visuals, tone, editing, and pacing are not evaluated.
  • AI scoring can be inconsistent across runs.
  • 15 videos per channel is a small sample - outlier videos can skew a channel’s score.

Feedback

Found a bug? Disagree with a score? Have a feature suggestion?