In 2025, health was the #1 topic for Copilot mobile users. In our latest paper, we analyze over half a million conversations to understand what questions they're asking, and how to help.
GENERATIVE AI
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AI Coding Tools Becoming Required for Software Developer Roles
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Out of 539 poll respondents here who had recently interviewed for software developer roles, 32% reported that experience with AI coding tools didn't come up at all, 25% said it came up as optional and 43% said that it came up as required
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Gemini AI Now Integrated Into Google Drive
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Finally: Gemini is now embedded in @GoogleDrive
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Gemini Google Sheets Achieves 70.48% Success Rate SpreadsheetBench
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While we don't have favorites, the evolution of Gemini in Google Sheets might be our most impressive yet. Gemini in Google Sheets has achieved a state-of-the-art benchmark, achieving a 70.48% success rate on the full SpreadsheetBench dataset. This performance not only exceeds
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Gemini Google Slides Custom Visual Creative Presentation Tool
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Most AI presentation tools rely on their own rigid templates and structured styles, but ask for something custom and they break. With Gemini in Google Slides, we use a custom translation between Gemini and Slides that fully leverages Gemini’s visual creative abilities while
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Gemini learns Google Docs native data model for enhanced formatting
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While most AI writing tools operate on plain text structured with Markdown, we’re teaching Gemini the underlying data model of @GoogleDocs
. By understanding Docs natively, Gemini generates and edits documents with full-fidelity formatting, fonts, and styles. That’s why when you -
Gemini AI Integration in Google Workspace Products
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Gemini in @GoogleWorkspace just got a lot more helpful. To do this, we had to tailor the model to each specific product and their user needs. Here's a little behind-the-scenes on how that came to life in @GoogleDocs
, Sheets, Slides, and @GoogleDrive
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New AI Results on Previously Unknown Technology Created
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I'm getting strong results for technology the absolutely cannot be in the training data because I just created it myself
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Structured-RAG: Solving Aggregative Queries in RAG Systems
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Standard RAG falls apart on aggregative queries. Things like "average ARR for companies with >1k employees" require reasoning across hundreds of docs, and vector retrieval just can't handle that reliably. We built Structured-RAG to fix this:
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Amazon Imposes Senior Review for AI-Assisted Code After Incidents
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Proceed with caution Lukasz Olejnik (@lukOlejnik) Amazon is holding a mandatory meeting about AI breaking its systems. The official framing is "part of normal business." The briefing note describes a trend of incidents with "high blast radius" caused by "Gen-AI assisted changes" for which "best practices and safeguards are not yet fully established." Translation to human language: we gave AI to engineers and things keep breaking? The response for now? Junior and mid-level engineers can no longer push AI-assisted code without a senior signing off. AWS spent 13 hours recovering after its own AI coding tool, asked to make some changes, decided instead to delete and recreate the environment (the software equivalent of fixing a leaky tap by knocking down the wall). Amazon called that an "extremely limited event" (the affected tool served customers in mainland China). — https://nitter.net/lukOlejnik/status/2031257644724342957#m [Translated from EN to English]
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