Manus: Gen AI Agent! #BigData #Analytics #DataScience #AI #MachineLearning #NLProc #IoT #IIoT #PyTorch #Python #RStats #TensorFlow #Java #JavaScript #ReactJS #GoLang #CloudComputing #Serverless #DataScientist #Linux #Programming #Coding #100DaysofCode https://
geni.us/Manus-AI-Agent
GENERATIVE AI
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Manus: Gen AI Agent for Big Data Analytics and Machine Learning
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Emergence in early LLM work on unexpected ability gains
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(This is a big part of what was called emergence in earlier academic work on unexpected LLM ability gains)
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AI coding agents viable after model threshold crossed
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It is going to be like what happened in coding: as soon as models crossed a certain threshold (Opus 4.5, GPT-5.2, Gemini 3), suddenly Claude Code & Codex were viable. Before that, it was all about coding assistance, afterwards it was all about agents from relatively small gains.
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RiboGen: RNA Sequence and Structure Co-Generation Technology
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RiboGen: RNA Sequence and Structure Co-Generation! #BigData #Analytics #DataScience #AI #MachineLearning #IoT #IIoT #PyTorch #Python #RStats #TensorFlow #Java #JavaScript #ReactJS #GoLang #CloudComputing #Serverless #DataScientist #Linux #Mathematics #Programming #Coding #100DaysofCode #Genetics Media Lab. (n.d.). RiboGen: RNA sequence and structure co-generation. MIT Media Lab. Retrieved March 10, 2025, from media.mit.edu/projects/ribog… Nori, D., & Jin, W. (2024, May 1). RNAFlow: RNA structure & sequence co-design via inverse folding-based flow matching. In Proceedings of the GEM Workshop at ICLR 2024. Retrieved March 10, 2025, from openreview.net/pdf/31fa59255… Rubin, D., dos Santos Costa, A., Ponnapati, M., & Jacobson, J. (2025, March 5). RiboGen: RNA sequence and structure co-generation with equivariant MultiFlow. arXiv preprint arXiv:2503.02058v1. Retrieved March 10, 2025, from arxiv.org/html/2503.02058v1 Zhao, Q., Zhao, Z., Fan, X., & Yao, Y. (2020, August 15). RNA primary, secondary, and tertiary structures [Figure]. In Review of machine-learning methods for RNA secondary structure prediction. ResearchGate. Retrieved March 10, 2025, from researchgate.net/figure/RNA-…
→ View original post on X — @gp_pulipaka, 2026-04-14 03:42 UTC
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Anthropic’s Claude Mythos Enhances Cybersecurity for Tech Giants
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AI just quietly took a MASSIVE step into cybersecurity. Anthropic’s new Project Glasswing uses its Claude Mythos Preview “superhacker” model to help big tech and critical infrastructure hunt bugs before attackers do – backed by $100M in AI credits and 40+ security partners.
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AI Multiplies Engineer Productivity by 100
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RT @bindureddy: AI enables 100x engineer. Period! [Translated from EN to English]
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AI Missed Colon Cancer Diagnosis: Friend Dies at 40
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A great friend died at 40 because they missed his colon cancer. Thank you.
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MeanCache: Accelerating Generative AI Without Quality Loss
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Can we make generative AI models accelerate without sacrificing quality? Huanlin Gao and team from China Unicom & Nanjing University just unveiled MeanCache! This training-free caching framework tackles a key problem: traditional methods rely on instantaneous speed, leading to
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Building AI Agents: Rethinking the Foundational Approach
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Why "Build an #AIAgent" Is the Wrong Starting Point for #AI Systems
by Trillo AI @hackernoon Learn more: https://
bit.ly/4vmWouU #GenerativeAI #ArtificialIntelligence #MachineLearning -
AI Scientist Paper Published in Nature, Interview with Yutaro Yamada
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An interview article with Yutaro Yamada of Sakana AI, one of the lead authors of the AI Scientist paper recently published in @Nature magazine, has been published in Nikkei Digital Governance. nikkei.com/prime/digital-gov… In this article, Mr. Yamada provides a comprehensive explanation of the current state of AI-driven scientific research, covering both the achievements of AI Scientist and its challenges. As mentioned in the article, one of the important findings from this research is that we experimentally demonstrated that the quality of generated papers can improve as the performance of the underlying AI model improves. "Being published in a top-tier scientific journal means that Sakana AI's paper-creation system has been recognized as having 'passed' the scientific version of the Turing test." The article also accurately addresses the current limitations of AI Scientist. What matters to us is not just what today's models can do. The Nature paper's achievement is that we quantitatively tracked the relationship between model performance and research quality, thereby revealing what could be called "scaling laws for scientific research." We believe this provides valuable insights for broader discussions about the future of science. "In the paper published in Nature this time, we demonstrated that if the performance of the AI model underlying the system improves, there is potential to enhance the quality of generated papers, and future developments are expected. Yamada of Sakana AI states: 'I want to improve the quality of automated research and help human researchers achieve surprising results.'" The article also mentions that AI-generated papers are watermarked and that the experiments were conducted under the approval of ethics committees and academic societies. In an era when AI is significantly transforming the nature of science, Sakana AI will continue to explore the possibilities of AI in science through ongoing dialogue with the scientific community. Blog: sakana.ai/ai-scientist-natur… [Translated from EN to English]