Vision Guides
Explore 7 curated vision guides for building with AI coding agents — what each one does, when to use it, and how to add it to your workflow.
Add Image Understanding to Your App
A practical guide to sending images to a vision model and getting reliable, structured results: base64 vs URL, resolution, prompting, cost, and errors.
Multimodal Embeddings and Image Search
How multimodal embeddings put images and text in one vector space, and how to build text-to-image and image-to-image search on top of it.
Multimodal RAG over PDFs, Scans & Charts: Two Approaches That Actually Work
RAG over visual documents — PDFs, scans, charts — where text-only extraction loses tables and layout. Parse-then-text vs embed-the-page-image, with trade-offs.
Review OCR Text Against the Original Scan
Review OCR text beside source scans, preserve exact wording and document uncertain characters in a correction ledger before producing a corrected copy.
Screenshot-to-Code: Building UIs from Images with AI
Turn a screenshot, mockup, or Figma frame into working frontend code with AI vision models — the realistic workflow, the right tools, and the honest pitfalls.
Vision-Language Models Compared (2026)
Which vision-language model to reach for, by job: Claude, GPT, Gemini, and open models like Qwen3-VL compared on OCR, charts, grounding, video, and cost.
Using Vision-Language Models for OCR, Documents, and Video Understanding
How to use vision-language models for OCR, documents, and video: how they differ from traditional OCR, their failure modes, and getting reliable output.