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Read more in our X article: nitter.net/GoogleAIStudio/status/… Google AI Studio (@GoogleAIStudio) x.com/i/article/203683454608… — https://nitter.net/GoogleAIStudio/status/2036845857690313085#m
→ View original post on X — @officiallogank, 2026-03-25 16:52 UTC
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Introducing Lyria 3 Pro and Lyria 3 Clip, our full song and 30 second music models, available starting today in the Gemini API and our all new music experience in @GoogleAIStudio!! pic.twitter.com/AFvJRDSAIA
— Logan Kilpatrick (@OfficialLoganK) 25 mars 2026
Introducing Lyria 3 Pro and Lyria 3 Clip, our full song and 30 second music models, available starting today in the Gemini API and our all new music experience in @GoogleAIStudio!!
→ View original post on X — @officiallogank, 2026-03-25 16:50 UTC
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AI Hackathons, hosted by @Devpost Authorized to Act: Auth0 for AI Agents by Okta PRIZES: $10,000 in cash DEADLINE: Apr 7, 2026 Build an agentic AI application using Auth0 for AI Agents Token Vault JOIN THE HACKATHON: https://
bit.ly/auth026i ZerveHack by Zerve AI
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NVIDIA is the most innovative company in computing, according to Fast Company’s 2026 list of the World’s 50 Most Innovative Companies. NVIDIA also ranked No. 2 overall. This recognition reflects the work of NVIDIANs across the company who are creating AI breakthroughs that are shaping every industry. #NVIDIAlife
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Reshoring still requires decades of investment and knowledge transfer in cooperation with TSMC. Best option for Taiwan, America and the world is that China transitions away from CCP authoritarianism since dictators don’t live forever. It worked for Taiwan.
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Yep!
— Charly Wargnier (@DataChaz) 25 mars 2026
Similarity ≠ relevance.
If your AI is relying on flat vector search, it's just blindly pattern-matching.@Hydra_db's a stunning tool that:
→ builds an ontology-first graph
→ maps entities and relationships
… so your AI stops guessing 🙂pic.twitter.com/uR2I6d1yoZ https://t.co/dwIiCltynl
Yep! Similarity ≠ relevance. If your AI is relying on flat vector search, it's just blindly pattern-matching. @Hydra_db
's a stunning tool that: → builds an ontology-first graph
→ maps entities and relationships … so your AI stops guessing 🙂
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I’m incredibly proud of The AI Scientist team for this milestone publication in @Nature. We started this project to explore if foundation models could execute the entire research lifecycle. Seeing this work validated at this level is a special moment. I truly believe AI will forever change the landscape of how scientific discoveries and scientific progress are made. Sakana AI (@SakanaAILabs) The AI Scientist: Towards Fully Automated AI Research, Now Published in Nature Nature: nature.com/articles/s41586-0… Blog: sakana.ai/ai-scientist-natur… When we first introduced The AI Scientist, we shared an ambitious vision of an agent powered by foundation models capable of executing the entire machine learning research lifecycle. From inventing ideas and writing code to executing experiments and drafting the manuscript, the system demonstrated that end-to-end automation of the scientific process is possible. Soon after, we shared a historic update: the improved AI Scientist-v2 produced the first fully AI-generated paper to pass a rigorous human peer-review process. Today, we are happy to announce that “The AI Scientist: Towards Fully Automated AI Research,” our paper describing all of this work, along with fresh new insights, has been published in @Nature! This Nature publication consolidates these milestones and details the underlying foundation model orchestration. It also introduces our Automated Reviewer, which matches human review judgments and actually exceeds standard inter-human agreement. Crucially, by using this reviewer to grade papers generated by different foundation models, we discovered a clear scaling law of science. As the underlying foundation models improve, the quality of the generated scientific papers increases correspondingly. This implies that as compute costs decrease and model capabilities continue to exponentially increase, future versions of The AI Scientist will be substantially more capable. Building upon our previous open-source releases (github.com/SakanaAI/AI-Scien…), this open-access Nature publication comprehensively details our system's architecture, outlines several new scaling results, and discusses the promise and challenges of AI-generated science. This substantial milestone is the result of a close and fruitful collaboration between researchers at Sakana AI, the University of British Columbia (UBC) and the Vector Institute, and the University of Oxford. Congrats to the team! @_chris_lu_ @cong_ml @RobertTLange @_yutaroyamada @shengranhu @j_foerst @hardmaru @jeffclune — https://nitter.net/SakanaAILabs/status/2036840833690071450#m
→ View original post on X — @_yutaroyamada, 2026-03-25 16:24 UTC
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Foot-Tilt Controlled Cart Assistant from China
— Ronald van Loon (@Ronald_vanLoon) 25 mars 2026
by @RealXavier011
#Innovation #EmergingTech #Technology #Tech pic.twitter.com/gTSkVORGzZ
Foot-Tilt Controlled Cart Assistant from China
by @RealXavier011 #Innovation #EmergingTech #Technology #Tech
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At #MWC2026, Huawei and its customers released 115 industrial intelligence showcases, demonstrating how AI and digital infrastructure are being applied in real operational environments. My latest article explores key insights from the Industrial Digital and Intelligent
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The AI Scientist: Towards Fully Automated AI Research, Now Published in Nature Nature: nature.com/articles/s41586-0… Blog: sakana.ai/ai-scientist-natur… When we first introduced The AI Scientist, we shared an ambitious vision of an agent powered by foundation models capable of executing the entire machine learning research lifecycle. From inventing ideas and writing code to executing experiments and drafting the manuscript, the system demonstrated that end-to-end automation of the scientific process is possible. Soon after, we shared a historic update: the improved AI Scientist-v2 produced the first fully AI-generated paper to pass a rigorous human peer-review process. Today, we are happy to announce that “The AI Scientist: Towards Fully Automated AI Research,” our paper describing all of this work, along with fresh new insights, has been published in @Nature! This Nature publication consolidates these milestones and details the underlying foundation model orchestration. It also introduces our Automated Reviewer, which matches human review judgments and actually exceeds standard inter-human agreement. Crucially, by using this reviewer to grade papers generated by different foundation models, we discovered a clear scaling law of science. As the underlying foundation models improve, the quality of the generated scientific papers increases correspondingly. This implies that as compute costs decrease and model capabilities continue to exponentially increase, future versions of The AI Scientist will be substantially more capable. Building upon our previous open-source releases (github.com/SakanaAI/AI-Scien…), this open-access Nature publication comprehensively details our system's architecture, outlines several new scaling results, and discusses the promise and challenges of AI-generated science. This substantial milestone is the result of a close and fruitful collaboration between researchers at Sakana AI, the University of British Columbia (UBC) and the Vector Institute, and the University of Oxford. Congrats to the team! @_chris_lu_ @cong_ml @RobertTLange @_yutaroyamada @shengranhu @j_foerst @hardmaru @jeffclune
→ View original post on X — @sakanaailabs, 2026-03-25 16:21 UTC