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Nicolay Rusnachenko

nicolay-r
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https://nicolayr.com/

AI & ML interests

NLP for Healthcare ⚕️ @BU_Research・PhD in NLP / IR ・Textual Information Retrieval

Recent Activity

reacted to prithivMLmods's post with 👍 about 20 hours ago
Update: TRELLIS.2 (Text to 3D, Image to 3D) Gradio with Rerun Embedded demo with improved visualization of the 3D model previewer is now available on Hugging Face. Generate assets and view them in the 3D viewer, powered and streamlined with Microsoft’s TRELLIS.2 and Tongyi-MAI’s Z-Image-Turbo models. 🤗 TRELLIS.2 (Demo): https://huggingface.co/spaces/prithivMLmods/TRELLIS.2-Text-to-3D 🕹️ GitHub: https://github.com/PRITHIVSAKTHIUR/TRELLIS.2-Text-to-3D-RERUN 🕹️ Collection: https://huggingface.co/collections/prithivMLmods/multimodal-implementations To know more about it, visit the app page or the respective model page!
reacted to MikeDoes's post with 🔥 about 20 hours ago
What if an AI agent could be tricked into stealing your data, just by reading a tool's description? A new paper reports it's possible. The "Attractive Metadata Attack" paper details this stealthy new threat. To measure the real-world impact of their attack, the researchers needed a source of sensitive data for the agent to leak. We're proud that the AI4Privacy corpus was used to create the synthetic user profiles containing standardized PII for their experiments. This is a perfect win-win. Our open-source data helped researchers Kanghua Mo, 龙昱丞, Zhihao Li from Guangzhou University and The Hong Kong Polytechnic University to not just demonstrate a new attack, but also quantify its potential for harm. This data-driven evidence is what pushes the community to build better, execution-level defenses for AI agents. 🔗 Check out their paper to see how easily an agent's trust in tool metadata could be exploited: https://arxiv.org/pdf/2508.02110 #OpenSource #DataPrivacy #LLM #Anonymization #AIsecurity #HuggingFace #Ai4Privacy #Worldslargestopensourceprivacymaskingdataset
posted an update about 21 hours ago
🚨 For those who intereseted in developing System + GUI for representing opinions from text, this post might be relevant. I recently brought the support for LLM in ARElight -- opinion extraction framework. ARElight is a no-string pipeline for retrieving information from large texts. It features the flexible customizations for Text Processing, NER, text translation, and relations classification. 🌟 https://github.com/nicolay-r/ARElight This now contributes to the most effective structuring of the pipeline to text translation, name identity recognition and information retrieval. 🥞 Stack: 🌟 bulk-translate: https://github.com/nicolay-r/bulk-translate 🌟 bulk-ner: https://github.com/nicolay-r/bulk-ner 🌟 bulk-chain: https://github.com/nicolay-r/bulk-chain Powered by AREkit: 🌟 AREkit: https://github.com/nicolay-r/AREkit
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