Element to LLM
We built a browser extension (Chrome + Firefox) that captures the runtime DOM and exports it as JSON. Not the pre-render source (HTML/CSS/JS, templates, bundles) and not a screenshot — but the live, post-render state the browser is actually displaying: - visibility/hidden, disabled/required - current input values and validation/validationMessage - dataset attributes - trimmed text - stable selector paths Why: LLMs often miss or guess UI state. Screenshots are too opaque, pre-render source is too noisy. A structured snapshot gives reproducible context for debugging…
Element to LLM launched on October 3, 2025 with 8 votes, #1,955 of 8,178 launches that month and more than 50% of that year's launches. The site was still online in September 2026, 1 year on.
What it does
1-click snapshots of an element’s runtime DOM into clean JSON (attributes, text, computed styles, visibility, hierarchy). Works in Chrome, Firefox & Arc. Local-only, zero telemetry. Ideal for LLM prompts, QA/UX reviews and debugging.
E2LLM captures live browser state as structured context for LLMs. SiFR is a proposed representation for runtime snapshots of supported standard DOM surfaces.
E2LLM turns a live browser page into structured, model-readable context. Instead of asking an LLM to infer a UI from screenshots, selectors, or static HTML, E2LLM captures runtime state from the supported standard DOM and represents it as SiFR : a proposed Structured Interface Representation. LLMs fail on web UIs when the perception layer is unstable or incomplete. E2LLM separates perception from reasoning: capture the browser state deterministically within supported scope, then let the model reason over a stable representation. Browser agents are usually discussed as if they are one category. They are not. E2LLM focuses on a specific layer: structured perception of the actual runtime…from insitu.im
In the maker’s words, at launch
We built a browser extension (Chrome + Firefox) that captures the runtime DOM and exports it as JSON. Not the pre-render source (HTML/CSS/JS, templates, bundles) and not a screenshot — but the live, post-render state the browser is actually displaying: - visibility/hidden, disabled/required…
Does a similar job
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Robust LLM extractor for websites in TypeScriptMar 2026 · github.com · ▲72We've been building data pipelines that scrape websites and extract structured data for a while now. If you've done this, you know the drill: you write CSS selectors, the site changes its layout, everything breaks at 2am, and you spend your morning rewriting parsers. LLMs seemed like the obvious fix — just throw the HTML at GPT and ask for JSON. Except in practice, it's more painful than that: - Raw HTML is full of nav bars, footers, and tracking junk that eats your token budget. A typical product page is 80% noise. - LLMs return malformed JSON more often than you'd expect, especially with…
I wrote an open-source browser alternative for Computer Use for any LLM2024 · github.com · ▲180Hey HN, I made Browser-Use, an open-source tool that lets (all Langchain supported) LLMs execute tasks directly in the browser just with function calling. It allows you to build agents that interact with web elements using natural language prompts. We created a layer that simplifies website interaction for LLMs by extracting xPaths and interactive elements like buttons and input fields (and other fancy things). This enables you to design custom web automation and scraping functions without manual inspection through DevTools. Hasn't this been done a lot of times? Good question, as a general…
Letting LLMs Run a Debugger2025 · github.com · ▲147Hey HN, I just built an experimental VSCode extension called LLM Debugger. It’s a proof-of-concept that lets a large language model take charge of debugging. Instead of only looking at the static code, the LLM also gets to see the live runtime state—actual variable values, function calls, branch decisions, and more. The idea is to give it enough context to help diagnose issues faster and even generate synthetic data from running programs. Here’s what it does: * Active Debugging: It integrates with Node.js debug sessions to gather runtime info (like variable states and stack traces). *…
Browser Harness – Gives LLM freedom to complete any browser taskApr 2026 · github.com · ▲134Hey HN, We got tired of browser frameworks restricting the LLM, so we removed the framework and gave the LLM maximum freedom to do whatever it's trained on. We gave the harness the ability to self correct and add new tools if the LLM wants (is pre-trained on) that. Our Browser Use library is tens of thousands of lines of deterministic heuristics wrapping Chrome (CDP websocket). Element extractors, click helpers, target managemenet (SUPER painful), watchdogs (crash handling, file downloads, alerts), cross origin iframes (if you want to click on an element you have to switch the target first,…
DOM snapshot for LLMs (demo + code)2025 · substack.com · ▲5A fun experimental Chrome extension that generates a Hybrid DOM Snapshot: readable like Markdown, structured like an accessibility tree. Demo + repo inside
Robust LLM Extractor for HTML/Markdown in TypeScript2025 · github.com · ▲14While working with LLMs for structured web data extraction, we saw issues with invalid JSON and broken links in the output. This led me to build a library focused on robust extraction and enrichment: - Clean HTML conversion: transforms HTML into LLM-friendly markdown with an option to extract just the main content - LLM structured output: Uses Gemini 2.5 flash or GPT-4o mini to balance accuracy and cost. Can also also use custom prompt - JSON sanitization: If the LLM structured output fails or doesn't fully match your schema, a sanitization process attempts to recover and fix the data,…
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