PixelRAG skips HTML parsing and grabs answers straight from page screenshots. Instead of converting web pages to text and embedding chunks, it renders each page into image tiles, embeds them with Qwen3-VL-Embedding (fine-tuned via LoRA on screenshots), and builds a FAISS index over those pixel embeddings. The team processed all of Wikipedia into 30 million+ screenshots, and on text-only question answering it outperforms the best text-based RAG system by 18.1 percent. That gain comes from preserving tables, charts and layout—elements that typical HTML-to-text parsers drop (often over 40 percent of page content) or flatten, which can swing QA accuracy by around 10 points if you swap parsers. The PixelRAG repo also ships a Claude Code plugin that gives Claude “eyes.” Point it at any URL, PDF or local site; it screenshots the rendered view and feeds pixels to the vision-language reader. You get a single setup script, no extra backend. On top of that, the article I wrote describes a retrieval approach that slashes corpus size by 40×, cuts tokens per query by 3× and boosts vector-search relevance by 2.3×. It boils down to smarter chunking, dynamic indexing and adaptive scoring models—so you store less, query faster and get more accurate hits without adding hardware.