URL to Markdown Converter

LLMs were trained on Markdown and plain text far more than they were trained on HTML. Every time you paste a web page into ChatGPT or Claude as raw HTML, you're burning 70–90% of your context window on <div>, <script>, inline styles, and tracking pixels — and confusing the model with structure it has to discard. This tool fetches any public URL, strips the noise, and returns clean Markdown that's 5–10x smaller, semantically equivalent, and exactly what LLMs want.

Why Markdown beats HTML for LLM context

Tokenisation is the issue. A typical web page is 80–200KB of HTML, which tokenises to 30,000–80,000 tokens — enough to blow past the context window of GPT-4o-mini, Claude Haiku, or any cheap model. The same page in Markdown is usually 5,000–15,000 tokens. You get the same content at 10–25% of the cost.

Beyond cost, LLMs reason more cleanly over Markdown. They don't have to decide whether a <span class="product-title-v2"> is meaningful structure or vestigial framework chrome. Markdown is unambiguous: # is a heading, - is a list item, [text](url) is a link. No tag soup to parse around.

How we strip the noise

Our converter removes <script>, <style>, <noscript>, <svg>, <iframe>, and HTML comments first. Then it walks the remaining DOM, converting headings, paragraphs, lists, links, code blocks, and images to their Markdown equivalents. Inline <strong>, <em>, <code> are preserved as **bold**, *italic*, ` code `. Everything else is stripped to plain text.

This isn't a full Readability-style content extractor (we don't guess which <div> is the article body and which is the sidebar). That's deliberate — for LLM consumption you usually want the full page text, and the LLM is better at filtering than any heuristic. If you need article-only extraction, run the Markdown output through a follow-up prompt: "Extract just the main article from this Markdown."

When to use this

• Feeding pages to LLMs: paste the Markdown into ChatGPT/Claude instead of HTML
• RAG pipelines: cleaner embeddings (no HTML tag noise to dilute semantic vectors)
• Documentation pipelines: turn old blog posts into Markdown for your docs site
• Training data prep: scrape + clean a corpus for fine-tuning
• Archive: save articles as Markdown — survives reformats, easy to grep

What it doesn't do (yet)

We don't execute JavaScript. For SPAs that render content client-side (most React/Vue blogs), you'll see the empty shell, not the rendered text. Use a headless-browser converter for those — or, better, ask the site for an RSS feed which already serves clean content. For static-rendered sites (most editorial, news, docs) this tool works perfectly.

Also: we don't respect paywalls or login gates. If the page returns 200 with "please subscribe" content, that's what you'll get. That's a feature of the underlying page, not a bug in this tool.