YTThumbFetcher
YouTube thumbnail workspace

AI Thumbnail Diagnosis

Inspect the live cover at recommendation size and get a visual-only design review.

Public videos No channel sign-in AI runs on demand
Original fictional cooking thumbnail used as a visual example
Original example canvas1280 × 720
Worked example

Reading a complete visual diagnosis

This fictional desk-makeover thumbnail shows what the report looks for and how its evidence can lead to a practical edit. The scores below were written for this example. They are not a saved model response or a performance forecast.

Fictional crowded desk reset thumbnail before revision
Before: the headline is readable, but the cable bundle, mug, boxes, notebook, lamp, plant, and laptop all compete for attention. Original fictional image created for YTThumbFetcher.
Example summary
The left message field reads quickly, but the desk has no clear first object. Small props and similar edge detail make the frame feel busier as it shrinks.

The report separates a finding from its evidence. "Busy" alone is an opinion. Naming the cable bundle, repeated small props, and equal edge detail tells the editor what can change.

Clarity profile1 to 5 heuristic scale
Focus 2Text 4Contrast 3Hierarchy 2Density 1Mobile 2
Evidence, not a composite score

Why each axis received its level

Focus 2 / 5
The laptop is the likely subject, yet the lamp, boxes, plant, and foreground mug have comparable size and sharpness.
Text readability 4 / 5
Two large words sit on a plain field. Their shape survives reduction and no object crosses the letters.
Contrast 3 / 5
White type separates from navy, but the desk objects share similar midtone values and begin to merge.
Hierarchy 2 / 5
The headline appears first. The image then offers several possible second subjects instead of one clear object.
Information density 1 / 5
Most props are individually relevant to a desk, but together they require too much sorting at thumbnail size.
Mobile legibility 2 / 5
The words remain legible at 168 pixels wide. The small props turn into an uneven band of texture.
Three priority edits

Turn the evidence into a shorter edit list

  1. Choose the laptop and clean work surface as the result. Keep the lamp as context, then remove the boxes, mug, and loose cable bundle.
  2. Quiet the background behind the desk edge. A softer wall and fewer small contours give the subject a stable outline.
  3. Keep the existing two-word message. It already works. Rewriting it would spend time without addressing the weak part of the frame.
Fictional desk reset thumbnail after clutter and hierarchy edits
After: the same message now leads to one recognizable result. The image is clearer by the rubric, but this still says nothing about likely clicks or views.

What this example can establish

The revision has fewer competing objects, a more deliberate second focal point, and less detail to decode on a small screen. Only audience testing and authorized channel analytics can show how a published video performs. The diagnosis does not see those data.

Review boundary

A design critique, not an outcome forecast

The model reviews visible composition. It does not receive view, like, comment, channel, or title fields from the YouTube Data API.

Scores describe how clearly the image meets the published rubric. They do not estimate click-through rate, rank, revenue, or future performance.

Read the complete methodology