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Citrus Search – Select a paper, explore related research

Hacker News

Citrus Search – Select a paper, explore related research

Citrus Search is a similarity-based search engine for scientific literature: select a paper, start the search and jump right to the heart of your research domain. Get an overview of important contributions from seminal papers to the state of the art. Searching for research with text queries can cumbersome: queries of just a couple of words are not a very high-fidelity signal, leading to lots of false positives in the search results. Also, you run the risk of missing papers which simply use different taxonomy than your query. This is where similarity-based search can be a great complement: select a paper you already know, and we take you right to closely related research. Citrus offers two ways to discover related work: - Citation Network: Finds papers with similar connections in the citation graph - Content: Finds papers with similar ideas in their abstract and title Behind the scenes, the similarity of papers is computed using graph and text-based machine-learning algorithms. We'd love to hear your thoughts and feedback!

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Product HuntOn track for Day 1 leaderboard · Strong signals: mac, using · Missing: agents, macos, agent
77%77% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
best fitHighest predicted score across all platforms for this description.
Hacker NewsStrong engagement from HN community · Strong signals: ide, io · Missing: https docs, excited, just released
62%62% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
60%60% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive
48%48% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: way · Missing: mobile apps, ios, personal
32%32% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue
15%15% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto
1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

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