Alternatives
Products that do what Kurate.org does
AI-powered scientific paper rankings
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2023 · bulletpapers.ai
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After waiting 8 months for a journal response or two months for co-author feedback that consisted of "looks good" and a single comma change, we built an AI-powered peer review system that helps researchers improve their manuscripts rapidly before submission. The system uses multiple specialized agents to analyze different aspects of scientific papers, from methodology to writing quality. Key features: 24 specialized agents analyzing sections, scientific rigor, and writing quality // Detailed feedback with actionable recommendations. // PDF report generation. //…
2025 · github.com
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Built an automated system to run a deep search of ArXiv and carefully find all the precise papers that exist on a complex topic. It's different from simple RAG because it searches, classifies, and adapts based on relevant papers it uncovers, and then continues until it finds every paper on a topic (trying to mimic the human research process). Benchmarked 10x higher accuracy and total retrieval compared to Google Scholar for a median search (whitepaper on website). Also knows when it is complete, and misses virtually nothing (< 3% or so, once it's converged). Website has a free trial and a…
2024 · app.undermind.ai
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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!
2023 · arxiv-feed.vercel.app
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We can now build drastically higher quality search because we can use LLMs in algorithms that mimic a human's systematic research process, instead of just roughly recommending results based on semantic embeddings or term frequency. We built a deep search LLM pipeline that takes a few minutes to carefully search all the scientific literature. You describe your complex goal, as you would to a colleague. Then, we carefully search 200M+ papers. We classify the preliminary results with GPT-4. We then adapt the search goals based on relevant/irrelevant papers uncovered and continue searching,…
2024 · undermind.ai
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Building a multi-agent system to analyze new AI research papers from 3 distinct perspectives: - Deep learning researcher agent: extract interesting deep learning methods that are related to paper - Theoretical mathematician agent: figure out theoretical mathematical concepts that are important in this paper and additional theoretical references that will be useful in understanding it - Skeptic agent: find unjustified assumptions that lack supporting evidence For this mvp, I used low-code agent platform StackAI (YC W23) and wrote about my process:…
2024 · stack-ai.com
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Autonomously analyzes data and writes human-verifiable scientific papers! Fun GUI for AI-human copiloting! Papers are "data-chained": each result can be click-traced all the way up to the raw code. Try it out: pip install data-to-paper
2024 · ai.nejm.org
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I needed a way to grab a random paper from arXiv, so I built one and wanted to share it with you. It 1) picks a random topic (of all the cs., econ., math.* etc. topics) 2) finds the maximum amount of papers in that topic, and 3) queries for a random paper in that topic. Note that this skews the distribution heavily in favor of topics that are less common, but it should get the job done. Suggestions for improvements are welcome.
2025 · jepedersen.dk
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AI-powered paper review platform with paper version control.
Jan 2026 · paperverse.io
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The project aims to organize computer science research in a logical, simple, and easy-to-follow way. It is designed to help us find papers worth reading first. I started building Trending Papers because following computer science research has become increasingly hard as the pace of innovation accelerates. The number of new articles on Arxiv has grown at 27% CAGR for the past 20 years. 240 new papers have been filed daily on average over the past 12 months. And the number is growing: last month, there were well over 300 new papers on average every single day. The system is based on some…
2023 · trendingpapers.com
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