Researcher builds in-browser tool to explain research papers with AI

A machine-learning researcher posted on r/MachineLearning about a tool they built to streamline a frustrating workflow. While reading interpretation papers, they found themselves repeatedly copy-pasting passages to Claude to parse complex sections. Rather than continuing this manual process, they developed paper-reader.dev — a browser-based annotation tool that keeps explanations in-place.

The tool works by letting users select any passage, formula, or figure within a research paper displayed in the browser. Once selected, the tool sends that selection to an AI model (Claude, based on the creator's mention of API usage) along with the full paper as context, and returns an explanation inline. Beyond explaining text, the tool also handles citations: users can click on a cited paper to get a brief summary without navigating away from the current document. This is designed to preserve reading flow and reduce the number of browser tabs or document switches needed during research.

The implementation uses a lightweight stack: the code is hosted at github.com/tumanian/paper-reader and is built on Vercel (for hosting) and Supabase (for backend data storage). The creator notes that the codebase is "mostly Claude, some Cursor, some me" — indicating heavy use of AI coding assistants in the development itself. The tool is free to use and available at paper-reader.dev.

However, there is an important caveat: the tool runs on the creator's personal API key with a modest rate limit. The creator explicitly asks users to "be gentle" and avoid overuse, since high traffic could exhaust the quota. This is a practical constraint for an alpha-stage project that prioritizes keeping the service free over infrastructure spending. Finally, the creator is actively seeking feedback, particularly on cases where explanations are incorrect or unhelpful — an acknowledgment that AI-generated explanations can fail and that user reports are crucial for identifying which types of papers or concepts the tool struggles with.

The tool addresses a real friction point in academic research: the cognitive load of context-switching between a technical paper and an external AI assistant. Reading interpretation papers — which require deep understanding of mathematical formulas, methodological choices, and cited work — often forces researchers to copy-paste passages into ChatGPT or Claude, breaking focus and requiring manual document navigation. By embedding explanations directly in the paper interface, the tool reduces this switching cost and lets readers stay immersed in the document. The inclusion of citation lookup is particularly useful, since following referenced work is a natural part of reading but normally requires opening a new search or document.

The creator's request for "gentle" use and explicit mention of the API cap signals both the tool's alpha status and a pragmatic constraint: it runs on a personal API key, not a paid commercial service. This design choice keeps the tool free but means scaling depends on the creator's willingness to absorb cost. The emphasis on feedback regarding explanation quality is telling — the creator recognizes that AI explanations can be wrong or unhelpful and is seeking user input to identify failure modes. This open invitation for criticism suggests the tool is positioned as a learning/improvement project rather than a polished product.

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