Alternatives
Products that do what ATLANIZE does
Sifting Research Papers Challenging Curiosity
- 1IM
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…
2024 · papermatch.mitanshu.tech
- 2
- 3NI
Understanding scientific articles can be tough, even in your own field. Trying to comprehend articles from others? Good luck. Enter, Now I Get It! I made this app for curious people. Simply upload an article and after a few minutes you'll have an interactive web page showcasing the highlights. Generated pages are stored in the cloud and can be viewed from a gallery. Now I Get It! uses the best LLMs out there, which means the app will improve as AI improves. Free for now - it's capped at 20 articles per day so I don't burn cash. A few things I (and maybe you will) find interesting: * This is…
Feb 2026 · nowigetit.us
- 4
- 5DS
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
- 6AV
2017 · arxiv-vanity.com
- 7
- 8
- 9
- 10
- 11
- 12SA
2024 · github.com
- 13
- 14
- 15
- 16
- 17
- 18
- 19
- 20
- 21
- 22PV
Not all improvements come from adding complexity — sometimes it's about removing it. PageIndex takes a different approach to RAG. Instead of relying on vector databases or artificial chunking, it builds a hierarchical tree structure from documents and uses reasoning-based tree search to locate the most relevant sections. This mirrors how humans approach reading: navigating through sections and context rather than matching embeddings. As a result, the retrieval feels transparent, structured, and explainable. It moves RAG away from approximate "semantic vibes" and toward explicit reasoning…
2025 · github.com
- 23UD
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
- 24TA
2023 · github.com
Ranked by how close each launch is in meaning, then by votes. Refine with a description →