How Eyesme Works: How AI Understands Your Screen (Plain English)
It looks like magic:
- you select an area on screen
- you ask “what’s the total?” or “what changed?”
- you get a usable answer
Under the hood, it’s a pipeline.
Step 1: Capture
Eyesme captures the smallest useful region (web content, screenshot region, or video transcript). Less noise = better results.
Step 2: Structure (OCR + layout)
For images/PDFs, Eyesme uses OCR and tries to preserve structure:
- headings, paragraphs, lists
- tables (rows/columns)
- code indentation (as much as possible)
Related:
- OCR: Extract Text from Images, Videos, and PDFs
- Extract Data from Tables in PDFs and Screenshots
- Extract Code from Screenshots (Developer OCR Guide)
Step 3: Reason (LLM)
Once the content is structured, the LLM can produce:
- TL;DR
- structured outlines
- action items
- risks/pitfalls
- follow-up answers in context
Step 4: Video pipeline (transcript + segmentation + timestamps)
For video, the key is:
transcript quality (ASR/subtitles)
semantic segmentation (topics)
timestamp alignment (jump to the right part)
Why multimodal context matters
Many tutorials hide the real value in the frame (code, charts, UI). Multimodal workflows combine video understanding with screenshot/OCR extraction.
Bottom line
Not magic—pipeline. Capture → structure → reason → output.