Web3Gen0

@web3gen0

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Pendle’s PT-ATH lets you grab eATH at a fat discount and yes, eATH below market price. Don’t wait for the crowd to catch on and pump it. Get in early. Stake smart. Lock yield. 💸 Your future self will thank you. Aethir is the highly undervalued.

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OpenAI and Google fight for gold Experimental AI models from OpenAI and Google have both achieved gold-medal scores in the annual International Math Olympiad (IMO)—the world’s biggest, most challenging math competition for high school students. However, OpenAI didn’t officially enter the competition: They chose to complete the competition in their own time and publish the results before the IMO published the official scores. This sparked controversy and a public backlash from Google, who was invited to participate in the competition and waited for the IMO to publish its results, so they didn’t “steal the spotlight from the students.” Google and OpenAI both used “informal” AI systems, which translate, understand and answer questions, without human input to compete (Google scored silver last year, using a model that needed human input). Both models followed the same rules as the students. This included sitting two, 4.5-hour exams, over two days, to solve a total of six questions, with no access to the internet or any other tools. IMO officially verified that Google’s model answered 5 out of the 6 questions correctly (scoring higher than most IMO students) and OpenAI announced that their model also scored the same. Putting whether OpenAI did or didn’t formally enter the IMO competition aside, the gold-level results highlight a significant breakthrough for AI reasoning models and their ability to solve advanced math problems. It also shows how close Google and OpenAI are in terms of progress, which is something that will undoubtedly intensify the already fierce competition to hire the industry’s best AI talent. https://in.mashable.com/tech/97400/openai-claims-gold-medal-performance-at-prestigious-math-competition-drama-ensues

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🤖 How many questions must we ask to prove AGI? A thought-provoking piece by Dr. Lance Eliot explores whether 50…500…or even 4 billion questions might be needed to test true general intelligence in AI. Asking too few misses the depth of human cognition. Asking too many? Logistically wild. But the real question is: Are we even close to asking the right ones? 🧠 Link: https://www.forbes.com/sites/lanceeliot/2025/07/20/the-number-of-questions-that-agi-and-ai-superintelligence-need-to-answer-for-proof-of-intelligence/

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🚀 “The world needs 100× more compute than we thought last year.” said by Jensen Huang, CEO of @nvidia . That’s not a forecast. That’s a wake-up call. As NVIDIA’s market cap crosses $4T and the H20 chip returns to China, one thing is clear: GPUs are the new oil. 🌍 Enter Aethir Edge, a decentralized GPU infrastructure built for the AI era. While NVIDIA builds the most powerful chips, Aethir Edge powers a global network of 65K+ edge devices, offering scalable, community-driven GPU compute across AI, gaming, cloud, and more. We don’t just consume the future. We help build it.

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AI Didn't Kill Jobs... It Rewired Them. IBM made headlines by laying off 8,000 employees for AI-driven efficiency. But here’s the twist: they hired back just as many and this time in roles like engineering, sales, and marketing. What looked like a classic cost-cutting move became a blueprint for AI-enabled workforce reinvention. By letting AI handle 94% of repetitive HR tasks, IBM freed up $3.5B in efficiency and redirected talent toward high-impact, human-centered work. The message? AI isn't just replacing roles. It's redesigning the workforce. https://www.peoplematters.in/news/performance-management/ibm-fires-8000-for-ai-efficiencythen-quietly-rehires-to-fill-the-gaps-45659

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Aethir is redefining how we scale GenAI. While reading this excellent paper on scaling GenAI systems, one thing stood out is the shift from Scaling Up and Scaling Down to Scaling Out. That’s exactly where Aethir is making a massive difference. By providing decentralized GPU infrastructure at the lowest cost, Aethir enables AI to move beyond centralized, resource-heavy models into scalable, inclusive, and adaptive deployments. If you’re into GenAI infra trends, this paper is worth a read: https://arxiv.org/html/2502.01677v1

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Organizations are hitting walls.. memory walls, data bottlenecks, and power bills, trying to scale AI on outdated foundations. But a new stack is emerging: • ⚡ NVMe SSDs with built-in compression • 🧠 CXL modules to break memory limits • 🔐 Caliptra for hardware-level trust Together, they redefine what “scalable AI” really means. The future of AI isn’t just model-first… it’s infra-smart. https://www.genaitoday.ai/topics/genai-today/articles/462188-rising-ai-driven-infrastructure-costs-expose-critical-weaknesses.htm

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Help me boost my rank 🫩

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🚨 Gmail AI Users, Be Alert! 🚨 A new prompt injection threat is targeting Gmail's AI-powered features. Hidden within emails using invisible white-on-white text are malicious instructions designed to trick Google Gemini into generating fake security alerts that look real. 📌 If you see a Google warning in an AI-generated email summary, don’t trust it. It could be an attacker’s trap. This vulnerability, reported by 0din (Mozilla’s zero-day team), highlights how AI summaries can be hijacked—making every AI interaction a potential attack surface. As 0din puts it: “Prompt injections are the new email macros.” 🔐 Tip for users and teams: Never rely on Gemini summaries for security alerts. Avoid emails with hidden elements like white or zero-width text. Treat every AI-generated output with caution. AI is changing the game. So must your security awareness. 🧠🔒 https://www.forbes.com/sites/zakdoffman/2025/07/14/googles-gmail-warning-if-you-see-this-youre-being-hacked/?ctpv=searchpage

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Data Privacy Alert in Europe Germany’s data protection commissioner has officially reported Chinese AI app DeepSeek to Apple and Google, urging its removal from their app stores. The reason? Alleged illegal transfer of user data to China without proper EU safeguards. Meike Kamp, Berlin’s data protection chief, stated DeepSeek failed to prove it protects user data in line with GDPR. Chinese law’s broad access to personal data further raises red flags. Germany follows Italy, which banned the app earlier this year over similar privacy concerns. The question now: Will Apple and Google act? Privacy and compliance are no longer optional.

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Why the EU AI Act Is a Data Literacy Wake-up Call New EU regulations mandate workforce AI literacy as foundation for responsible implementation https://aibusiness.com/generative-ai

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Healthcare is increasingly embracing AI to improve workflow management, patient communication, and diagnostic and treatment support. It’s critical that these AI-based systems are not only high-performing, but also efficient and privacy-preserving. It’s with these considerations in mind that Google built and recently released Health AI Developer Foundations (HAI-DEF). HAI-DEF is a collection of lightweight open models designed to offer developers robust starting points for their own health research and application development. Because HAI-DEF models are open, developers retain full control over privacy, infrastructure and modifications to the models. In May of this year, Google expanded the HAI-DEF collection with MedGemma, a collection of generative models based on Gemma 3 that are designed to accelerate healthcare and lifesciences AI development. Today, Google announced two new models in this collection. The first is MedGemma 27B Multimodal, which complements the previously-released 4B Multimodal and 27B text-only models by adding support for complex multimodal and longitudinal electronic health record interpretation. The second new model is MedSigLIP, a lightweight image and text encoder for classification, search, and related tasks. MedSigLIP is based on the same image encoder that powers the 4B and 27B MedGemma models. https://research.google/blog/medgemma-our-most-capable-open-models-for-health-ai-development/

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Struggling to get ROI from GenAI? You’re not alone. Despite the hype, only 13% of orgs have seen real business value from GenAI. Why? ❌ Lack of C-suite backing ❌ Poor data readiness ❌ Skills & scaling gaps ✅ One big opportunity? Running AI workloads on-premises. A recent ESG study found it can be up to 75% more cost-effective than cloud-based solutions. Enter the Dell AI Factory with NVIDIA — designed to help you scale GenAI safely, efficiently, and cost-effectively. Learn more: https://www.forbes.com/sites/delltechnologies/2025/07/08/the-key-to-generating-roi-from-ai/

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OpenAI is locking things down. After DeepSeek’s rival model launch, OpenAI reportedly tightened security... offline dev machines, fingerprint access, and a strict “need-to-know” policy for sensitive projects like o1. Corporate espionage or just AI arms race heat? 👀 https://techcrunch.com/2025/07/07/openai-tightens-the-screws-on-security-to-keep-away-prying-eyes/

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MemOS introduces a memory operating system that enhances how Large Language Models manage and evolve memory—going beyond short-lived context windows and static parameters. By treating memory as a first-class system resource, MemOS enables: Cost-efficient storage & retrieval Continual learning Personalized modeling Core to this system is the MemCube—a unit that carries both memory and its metadata, enabling memory fusion, migration, and long-term knowledge evolution. This is a foundational step toward truly adaptive and memory-centric AGI systems. 🔗 Read more: https://arxiv.org/pdf/2507.03724

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🚨 Gartner’s Bold Prediction on Agentic AI 🚨 According to Gartner, over 40% of agentic AI projects will be canceled by 2027! Why? 💸 Escalating costs ❓ Unclear business value ⚠️ Inadequate risk controls 👉 Many of these projects are hype-driven experiments or misapplied proofs of concept. 👉 “Agent washing” is on the rise – vendors rebranding old tools as agentic AI without real capabilities. 🔍 Only ~130 vendors globally have credible agentic AI offerings, says Gartner.

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🔍 Why Do Multi-Agent LLM Systems Fail? A new study dives deep into this critical question. While multi-agent LLM systems (MAS) are gaining traction, their real-world performance often falls short of expectations. This paper introduces MAST (Multi-Agent System Failure Taxonomy), the first comprehensive framework to systematically categorize why MAS break down. Key insights: ✅ 14 failure modes across 3 categories: Specification issues Inter-agent misalignment Task verification gaps ✅ Analysis across 7 MAS frameworks and 200+ tasks ✅ A validated LLM-as-a-Judge pipeline for scalable failure analysis ✅ A practical roadmap for building more reliable multi-agent systems The team also open-sourced their dataset and LLM evaluation tools to push the field forward. 📄 Paper: Why Do Multi-Agent LLM Systems Fail? https://arxiv.org/abs/2503.13657

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WebSailor: Navigating Super-human Reasoning for Web Agents WebSailor introduces a powerful post-training method that pushes open-source LLMs to reason like top proprietary agents in complex web tasks. By combining structured sampling, information obfuscation, and an efficient RL algorithm (DUPO), WebSailor helps models tackle high-uncertainty scenarios and close the capability gap with systems like DeepResearch. https://arxiv.org/abs/2507.02592

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LangScene-X: A New Era for 3D Scene Understanding 🚀 Key highlights: 🎯 Goal: Recover 3D structures with open-vocabulary scene understanding from just sparse 2D views. ⚙️ Introduces LangScene-X, a generative framework for 3D language-embedded scenes. 🎥 Powered by TriMap Video Diffusion to generate appearance, geometry, and semantics consistently. 🧩 Uses Language Quantized Compressor (LQC) for cross-scene generalization without per-scene retraining. 🔍 Enables open-ended language queries on 3D surfaces. ✅ Outperforms state-of-the-art methods in both quality and generalizability. https://arxiv.org/abs/2507.02813

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