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📢 Nuklai Insights: Building Multi Datasets for Next Generation Insights Full Blog: We live in an era where #data is valuable, but only a few organizations mine ⛏️ insights from data. Idle data is now a cost, a risk, and an opportunity lost. $NAI

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Nuklai

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48,010 просмотров • 2 лет назад •via X (Twitter)

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AI needs data to grow. But accessing high-quality datasets is becoming harder than ever. · Data collection is increasingly centralized in the hands of a few large players. · Platforms are locking content behind expensive APIs and private data deals. · Specialized datasets (medical, legal, code) can cost 20–40× more than general web data. · And while 94% of companies are exploring Gen-AI, only 12% have reliable data channels. The result? A growing data bottleneck for AI development. The future of AI won’t just depend on better models. It will depend on who can access and activate quality data.
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AI needs data to grow. But accessing high-quality datasets is becoming harder than ever. · Data collection is increasingly centralized in the hands of a few large players. · Platforms are locking content behind expensive APIs and private data deals. · Specialized datasets (medical, legal, code) can cost 20–40× more than general web data. · And while 94% of companies are exploring Gen-AI, only 12% have reliable data channels. The result? A growing data bottleneck for AI development. The future of AI won’t just depend on better models. It will depend on who can access and activate quality data.

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New in Chrome DevTools! Debug an app's full performance trace with Gemini! After recording a trace, you can now chat with Gemini about the entire trace, related Performance insights, and even connected field data - all without needing to select specific context beforehand!
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New in Chrome DevTools! Debug an app's full performance trace with Gemini! After recording a trace, you can now chat with Gemini about the entire trace, related Performance insights, and even connected field data - all without needing to select specific context beforehand!

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Attention 🚨 #MSInspire is back, and registration is now LIVE! Join us on July 18-19, 2023, for an epic event filled with business-building strategies, empowering insights, and networking opportunities that will take your business to the next level:
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📢 NEW AI ADOPTION ALERT 📢 We’re excited to welcome @RingfenceAI to the Vanar ecosystem! 🚀 Ringfence is a fully agentic protocol redefining how data powers the AI economy. Leveraging Data Agents, Ringfence creates a decentralized infrastructure that ensures fairer data ownership, monetization and accessibility for the next generation of AI systems. Welcome to the future of decentralized data ownership! 🌌
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Exploring the Future of Legal Data Infrastructure Iagon, in partnership with Cloud Court, is pleased to announce that Ford Motor Company Motor Company will serve in an advisory capacity for this exploratory project, which seeks to evaluate the use of the Cardano blockchain and Iagon's decentralized cloud storage technology as a potential solution for the secure storage and management of legal documents and data. As a major corporation with sophisticated legal operations, Ford brings valuable perspective to this exploratory initiative based on their experience managing complex legal data infrastructures at scale. Ford is interested in exploring whether blockchain-based distributed storage could address persistent challenges in legal data infrastructure. In particular, Ford sees merit in exploring how blockchain technology might deliver economically efficient storage and audit solutions for legal data management. More insights 👉

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Something big is happening in robotics - and it’s hiding in plain sight. This post is not about dancing robots but in the data that powers them. Open robotics datasets have exploded this year, turning the field into a more scalable and collaborative ecosystem. In just two years, Hugging Face datasets grew from 11k to over 600k - and robotics is by far the fastest-growing segment. We went from 1k robotics datasets in 2024 to 27k in 2025! For comparison, text generation, the second-largest category, has only around 5k datasets in 2025. That gap is massive. Open datasets are important because robotics lives and dies by real-world robot data - video, actions, sensors, failures. By making this data easy to upload, reuse, and benchmark, researchers, startups, and large players are now releasing real-robot datasets that would have stayed locked inside labs just a few years ago. Major contributors include NVIDIA, LeRobot initiative, and a rapidly growing maker community. This surge is also enabled by cheaper video storage, better tooling, and an open-source AI culture now spilling into the physical world. And it really matters: open robotics data dramatically lowers entry barriers, accelerates learning-by-doing, and speeds up progress toward generalist and humanoid robots. Robotics won’t scale through hardware alone - but to a large extent through shared data. Viz below from AI World - link to the story and more viz/filters in comment.

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Conversational voice data exposes a subtle failure mode in many ASR pipelines. The metric says the data is bad, but human reviewers say the audio is clear. Often the issue isn’t transcription quality itself, but that the evaluation stack wasn’t built for turn-taking, silence, and multi-speaker structure. Learn how to solve this in our latest blog:

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