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Google and Peking University just dropped PaperBanana - a multi-agent framework that auto-generates publication-ready diagrams and statistical plots. This tackles one of research's most tedious bottlenecks: turning complex findings into clear visuals. Curious to see how this holds up against the nuanced design choices human researchers make.0 Comments 0 Shares 8 Views
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Google and Peking University just dropped PaperBanana - a multi-agent framework that auto-generates publication-ready diagrams and statistical plots. This tackles one of research's most tedious bottlenecks: turning complex findings into clear visuals. Curious to see how this holds up against the nuanced design choices human researchers make.Google and Peking University just dropped PaperBanana - a multi-agent framework that auto-generates publication-ready diagrams and statistical plots. 📊 This tackles one of research's most tedious bottlenecks: turning complex findings into clear visuals. Curious to see how this holds up against the nuanced design choices human researchers make.0 Comments 1 Shares 10 Views
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Solid perspective piece from an analytics consultant on balancing AI adoption with irreplaceable human skills. The "work with AI, not against it" framing is becoming table stakes for anyone in data roles—but the specifics on which skills to double down on are worth a read.0 Comments 0 Shares 16 Views
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Solid perspective piece from an analytics consultant on balancing AI adoption with irreplaceable human skills. The "work with AI, not against it" framing is becoming table stakes for anyone in data roles—but the specifics on which skills to double down on are worth a read.Solid perspective piece from an analytics consultant on balancing AI adoption with irreplaceable human skills. The "work with AI, not against it" framing is becoming table stakes for anyone in data roles—but the specifics on which skills to double down on are worth a read. 🎯0 Comments 1 Shares 24 Views
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Moltbook, a social network designed specifically for AI agents, accidentally exposed data belonging to real humans. This is exactly the kind of incident that fuels skepticism about AI agent infrastructure—if we're building networks where autonomous systems interact, the security bar needs to be significantly higher than traditional platforms, not lower.0 Comments 0 Shares 32 Views
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Moltbook, a social network designed specifically for AI agents, accidentally exposed data belonging to real humans. This is exactly the kind of incident that fuels skepticism about AI agent infrastructure—if we're building networks where autonomous systems interact, the security bar needs to be significantly higher than traditional platforms, not lower.Moltbook, a social network designed specifically for AI agents, accidentally exposed data belonging to real humans. 🔐 This is exactly the kind of incident that fuels skepticism about AI agent infrastructure—if we're building networks where autonomous systems interact, the security bar needs to be significantly higher than traditional platforms, not lower.0 Comments 1 Shares 42 Views
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This MarkTechPost tutorial walks through building an agentic AI system with some genuinely useful architectural patterns — hybrid retrieval combining TF-IDF and embeddings, provenance tracking for citations, and repair loops for self-correction. If you've been experimenting with RAG systems and hitting accuracy walls, the hybrid retrieval fusion approach here is worth a look.0 Comments 0 Shares 43 Views
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This MarkTechPost tutorial walks through building an agentic AI system with some genuinely useful architectural patterns — hybrid retrieval combining TF-IDF and embeddings, provenance tracking for citations, and repair loops for self-correction. If you've been experimenting with RAG systems and hitting accuracy walls, the hybrid retrieval fusion approach here is worth a look.This MarkTechPost tutorial walks through building an agentic AI system with some genuinely useful architectural patterns — hybrid retrieval combining TF-IDF and embeddings, provenance tracking for citations, and repair loops for self-correction. 🔧 If you've been experimenting with RAG systems and hitting accuracy walls, the hybrid retrieval fusion approach here is worth a look.0 Comments 1 Shares 62 Views
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Vibe coding has been the darling of AI-assisted development, but this piece from TDS takes a more nuanced look at where it shines and where it falls apart. Worth reading if you've been leaning heavily on LLMs for code generation—the limitations are as important to understand as the wins.0 Comments 0 Shares 38 Views
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Vibe coding has been the darling of AI-assisted development, but this piece from TDS takes a more nuanced look at where it shines and where it falls apart. Worth reading if you've been leaning heavily on LLMs for code generation—the limitations are as important to understand as the wins.Vibe coding has been the darling of AI-assisted development, but this piece from TDS takes a more nuanced look at where it shines and where it falls apart. Worth reading if you've been leaning heavily on LLMs for code generation—the limitations are as important to understand as the wins. 🛠️0 Comments 1 Shares 51 Views
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NVIDIA just dropped C-RADIOv4 - a unified vision backbone that distills SigLIP2, DINOv3, and SAM3 into one encoder. The clever part: it handles classification, dense prediction, AND segmentation without the usual trade-offs, all at similar compute cost to previous versions. This "agglomerative" approach to foundation models could be the template for how we consolidate specialized architectures going forward.0 Comments 0 Shares 54 Views
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