SQL Essentials for Data Analysis: 50-Day Hands-on Challenge Book (to grow from Beginner to Pro) — http://
amzn.to/3LvAO52 by @RealBenjizo Benefits:
Strong command of SQL fundamentals & advanced features
Confidence to solve real-world data problems
Practical experience with
DATA
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SQL Essentials: 50-Day Hands-on Data Analysis Challenge
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Embeddings: The Unsung Hero Driving Model Accuracy Forward
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Embedding is an unsung hero in model accuracy and today is a big leap forward. It's at the heart of grounding; it's the layer that does the hard work of searching, retrieving, organizing, and connecting information across sources for a holistic response.
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Bing releases Harrier, new state-of-the-art embedding model
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Another SOTA model drop! This time from the @Bing team: meet Harrier, a new open-source embedding model with state-of-the-art performance and the #1 spot on the industry standard multilingual MTEB-v2 benchmark. Jordi Ribas (@JordiRib1) I’m pleased to share that our search team has open sourced an embedding model called Harrier that is currently ranking #1 on the multilingual MTEB-v2 benchmark leaderboard. Harrier delivers SOTA performance on retrieval quality, semantic matching, and contextual analysis across workloads, supporting more than 100 languages and handles long inputs up to 32K. It is built for the next generation semantic search for Bing and our web grounding (RAG) service for AI agents, which already powers nearly every major AI chatbot today. As you can see in the leadership board, our Harrier model is currently ahead of other excellent models based on Gemini, Gemma, Llama, Qwen, and more. I’m grateful for the hard work of our team to get to this top ranking, and I’m excited to see all the healthy competition in the space, which should ultimately lead to more innovations that will benefit everyone. Learn more: msft.it/6019QNB0b — https://nitter.net/JordiRib1/status/2041550352739164404#m
→ View original post on X — @clementdelangue, 2026-04-07 16:22 UTC
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Microsoft Open Sources Harrier: Top Multilingual Embedding Model
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I’m pleased to share that our search team has open sourced an embedding model called Harrier that is currently ranking #1 on the multilingual MTEB-v2 benchmark leaderboard.
— Jordi Ribas (@JordiRib1) 7 avril 2026
Harrier delivers SOTA performance on retrieval quality, semantic matching, and contextual analysis across… pic.twitter.com/asMVydbijhI’m pleased to share that our search team has open sourced an embedding model called Harrier that is currently ranking #1 on the multilingual MTEB-v2 benchmark leaderboard. Harrier delivers SOTA performance on retrieval quality, semantic matching, and contextual analysis across workloads, supporting more than 100 languages and handles long inputs up to 32K. It is built for the next generation semantic search for Bing and our web grounding (RAG) service for AI agents, which already powers nearly every major AI chatbot today. As you can see in the leadership board, our Harrier model is currently ahead of other excellent models based on Gemini, Gemma, Llama, Qwen, and more. I’m grateful for the hard work of our team to get to this top ranking, and I’m excited to see all the healthy competition in the space, which should ultimately lead to more innovations that will benefit everyone. Learn more: msft.it/6019QNB0b
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OpenMed dataset added to Hugging Face platform
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just added openmed data on @huggingface, what else
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Add Vibration Monitoring to Existing Modbus Infrastructure
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Plants already running Modbus for pressure and temperature can add vibration data to the same fieldbus. No new communication infrastructure needed.
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AI Tool Fragmentation in Large Organizations Creates Data and Context Chaos
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I keep seeing the same pattern inside large orgs: → Marketing uses one AI tool → Sales relies on CRM AI → Devs use something completely different → Data is everywhere → Context is nowhere So what happens? Decisions require stitching together 5 systems Insights get lost And sensitive data leaks into places it shouldn’t
→ View original post on X — @ronald_vanloon, 2026-04-07 15:00 UTC
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Bullshit Benchmark Data Viewer and GitHub Repository Released
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Data viewer: https://
petergpt.github.io/bullshit-bench
mark/viewer/index.v2.html
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20 Most Important AI Concepts Explained
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20 Most Important AI Concepts Explained! #BigData #Analytics #DataScience #AI #MachineLearning #NLProc #LLM #IoT #IIoT #PyTorch #Python #RStats #TensorFlow #Java #JavaScript #ReactJS #GoLang #CloudComputing #Serverless #DataScientist #Linux #Programming #Coding #Books #100DaysofCode geni.us/20-Concepts-Xplained
→ View original post on X — @gp_pulipaka, 2026-04-07 14:26 UTC
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Building a Self Improving Agentic RAG System
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Building a Self Improving Agentic RAG! #BigData #Analytics #DataScience #AI #MachineLearning #NLProc #LLM #IoT #IIoT #PyTorch #Python #RStats #TensorFlow #Java #JavaScript #ReactJS #GoLang #CloudComputing #Serverless #DataScientist #Linux #Programming #Coding #Books #100DaysofCode geni.us/Building-Improving-R…
→ View original post on X — @gp_pulipaka, 2026-04-07 14:26 UTC