Data warehouse migrations are often slowed down by the wrong assumptions. Focusing only on cost, treating it as SQL code conversion, or migrating all legacy objects can increase cost, extend timelines, and carry forward unnecessary technical debt. The reality is different.
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GPU Power Draw as True Utilization Metric in Data Centers
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Without getting all the way down to performance counters, GPU power from nvidia-smi is a better indicator of true utilization than job scheduling or “gpu busy”. I would love to see animated “heat maps” of the big data centers, with each pixel being an individual GPU’s power draw. I am confident that inference and frontier training at the big labs is highly efficient, but I wonder how many GPUs would be dark due to scheduling and inefficient research code. With a little calibration for base load and peak, just the power bill for the datacenter would be a pretty good first order indicator of utilization.
→ View original post on X — @id_aa_carmack, 2026-04-02 14:49 UTC
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Natural Disasters Amplify Income Inequalities
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Stanford scholars used Google Street View data to study the impact of extreme weather events and found income disparities are amplified after a disaster. hai.stanford.edu/news/how-na… [Translated from EN to English]
→ View original post on X — @stanfordhai, 2026-04-02 14:03 UTC
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Comparing Microsoft CSP Partners in Boston Guide
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Comparing Microsoft CSP partners in Boston: Which one is right for you? cloudcomputing-news.net/news… #Cloud #Automation #Data #Tech #DigitalTransformation #CloudComputing #RAG #CTO
→ View original post on X — @craigbrownphd, 2026-04-02 13:44 UTC
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Data curation bias undermines AI research integrity
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you curate the data to suit your conclusion
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Buckets: S3-like ML Data Storage Alternative to Git
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Hot take: Git was the wrong abstraction for 90% of ML data. Checkpoints, optimizer states, training logs, agent traces – none of this needs version control. It needs fast, cheap, mutable storage. So we built Buckets. S3-like storage on the @huggingface Hub with Xet dedup and
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Real-time Environmental Monitoring Transforms Sustainability Compliance
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Environmental data is moving from periodic reports to continuous streams across operations. IoT sensors connect air, water, and energy metrics to daily decisions, since real-time monitoring turns compliance and sustainability into managed processes. Microblog by @antgrasso
→ View original post on X — @antgrasso, 2026-04-02 13:08 UTC
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Next Enterprise Intelligence Era: Better Context Over Bigger Models
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Bottom line: the next era of enterprise intelligence won’t be “bigger models”—it’ll be better context. Watch the full video: Context Engineering—Defining the Next Era of Enterprise Intelligence. In partnership with Elastic. Check out the full article:
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From Data Collection to Actionable Insights: Executive Value
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Why this matters to execs: → From collection → comprehension: you stop hoarding and start explaining. → Accuracy scales because answers come from your ground truth. → You finally get explainability you can take to a board meeting.
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Context Engineering: Organizing Unstructured Data for AI Reasoning
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What is “Context Engineering”? It’s the discipline of stitching your unstructured mess—logs, chats, docs, images—into something an AI can actually reason over. → Vector DBs + hybrid search + embeddings to retrieve by meaning, not keywords. → Decisions anchored in your data,