Bing's web grounding already powers almost every major AI chatbot today. With Harrier, it just got a big upgrade for the agentic era. Better embeddings lead to better retrieval, often more accurate answers, and better multilingual performance in the 100+ languages Harrier
BIG TECH
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Microsoft Open-Sources Industry-Leading Embedding Model Harrier
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Big kudos to @JordiRib1 and the @bing team – awesome to see the speed and quality shipping across @MicrosoftAI
. More on Harrier in today's blog: https://
blogs.bing.com/search/April-2
026/Microsoft-Open-Sources-Industry-Leading-Embedding-Model
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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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Generative AI: 90% Accuracy, 10% Unacceptable Errors
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Imagine if your car randomly went out of control 10% of the time. That's commercial-grade generative AI web search. rat king 🐀 (@MikeIsaac) glass half full: 90 percent accuracy is an impressive accuracy rate glass half empty: 10 percent error rate for a company that does more than 5 Trillion search queries per year is still a gigantic number nytimes.com/2026/04/07/techn… — https://nitter.net/MikeIsaac/status/2041535422623609174#m [Translated from EN to English]
→ View original post on X — @garymarcus, 2026-04-07 16:16 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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Google’s 10% Error Rate: Shift from Pre-ChatGPT Standards
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10% error rate at any scale would never have been tolerated by Google pre-ChatGPT. The company has fundamentally changed rat king 🐀 (@MikeIsaac) glass half full: 90 percent accuracy is an impressive accuracy rate glass half empty: 10 percent error rate for a company that does more than 5 Trillion search queries per year is still a gigantic number nytimes.com/2026/04/07/techn… — https://nitter.net/MikeIsaac/status/2041535422623609174#m
→ View original post on X — @garymarcus, 2026-04-07 15:51 UTC
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Salesforce Uses Slackbot to Solve SaaS Crisis
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Salesforce is looking to Slackbot to help it solve the SaaSPocalypse puzzle go.theregister.com/feed/www.… [Translated from EN to English]
→ View original post on X — @craigbrownphd, 2026-04-07 15:49 UTC
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Character Assessment and Objective Standards in AI Leadership
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I agree that the control by a small group is not a good thing. But this wasn’t about that, more to do with Altman’s character. Who decides what character is “good”? You and I prob have different views on that. That’s why it requires a more objective lens in my view
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AI Public Backlash Turns Violent Amid Growing Opposition
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Absolutely wild. A councilor's home shot, with 'no data centres' sign left behind. AI is losing the public perception war, VERY BADLY, and it's already becoming violent. The numbers: 1/ AI is less popular than ICE (NBC Poll) 2/ $162 billion in AI projects blocked or
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Building Codex at OpenAI: Behind the Scenes with Live Demo
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Really enjoyed joining @petergyang with @embirico to talk about how we’ve been building Codex at OpenAI.
— Romain Huet (@romainhuet) 7 avril 2026
We show a live demo of the Codex app and go behind the scenes.
What’s been striking: role lines are blurring. Designers write code, engineers think product. https://t.co/tJZfFusYzIReally enjoyed joining @petergyang with @embirico to talk about how we’ve been building Codex at OpenAI. We show a live demo of the Codex app and go behind the scenes. What’s been striking: role lines are blurring. Designers write code, engineers think product.