ArxivSwipe
TL;DRs on every arxiv & Hugging Face paper
What it does
ArxivSwipe puts a 3-sentence AI TL;DR on every paper in your arxiv and Hugging Face Papers feed, so you can decide what's worth reading in second. Copy BibTeX in one click, jump to code and highlight any term for a plain-language explanation.
Does the same job
all alternatives →- AVArxiv Vanity – Read academic papers from Arxiv as responsive web pages2017 · arxiv-vanity.com · ▲721
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Hello HN, Talk2Arxiv is a small open-source RAG application I've been building for a few weeks. To use it just prepend any arxiv.org link with 'talk2' to load the paper into a responsive RAG chat application (e.g. www.arxiv.org/abs/1706.03762 -> www.talk2arxiv.org/abs/1706.03762). All implementation details are in the GitHub. Currently, because I've opted to extract text from the PDF of the paper rather than reading the LaTeX source code (since I wanted to build a more generic PDF RAG in the process), it struggles with symbolic text / mathematics, and sometimes fails…
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Hey HN! I've always found it hard to keep up with the latest AI research, so I built Arxiv Feed! https://arxiv-feed.vercel.app/ It's basically a feed of AI research papers + a one-liner explaining what problem its solving, etc. You can also click on any paper to get a TL;DR. Right now, I've only indexed a few hundred large language model papers, but will expand to indexing AI papers in other topics. Thinking of also adding a way for people to up-vote/down-vote papers. Would love to hear any thoughts/feedback! :D Thanks!
- IMI made a website to semantically search ArXiv papers2024 · papermatch.mitanshu.tech · ▲324
As a grad student (and an ADHDer), I had trouble doing literature review systematically. To combat this, I made a website that finds similar papers using the meaning of the thing I am looking for. I used MixedBread's [^1] embedding model to generate vectors from the abstracts. I store and search similar vectors using Milvus [^2] and finally use Gradio [^3] to serve the frontend. I update the vector database weekly by pulling the metadata dataset from Kaggle [^4]. To speed up the search process on my free oracle instance, I binarise the embeddings and use Hamming distance as a metric. I would…
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