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This week, the tech world is buzzing with a new generation of AI. Google is putting Gemini everywhere, from your home to your phone, while Meta freezes hiring amidst a major AI restructuring. Plus, find out how General Motorsis entering the AI race.
Let’sget started!
LLM Expert Insights,
Packt
Google Unveils "Gemini for Home" to Replace Google Assistant
Google is replacingGoogle Assistant withGemini for Home, a new AI assistant powered by its advanced Gemini models. This new system offers more powerful, hands-freeassistancewith complex tasks, from playing specific songs to coordinating smart home devices and providing expert help with tasks like cooking or troubleshooting. TheGemini Livefeature allows for natural, continuous conversation without needing to say "Hey Google" repeatedly.
Meta Restructures Its AI Division (Again)andFreezes AI Hiring
Meta Platforms restructured its AI groupinto four teams—focusing on superintelligence, consumer products, AI infrastructure, and long-term research—to better leverage its investment in AI talent.
Furthermore, in a major corporate move,Meta has announced ahiring freezeforitsAIdivision. The decision comes after a period of intense investment in the AI sector and isreportedly aimedat re-evaluating the spiraling costs associated with its AI division.
The AI-Powered Pixel Is Here
Google'snew Pixel 10 phones are packed with AI featuresdriven by the Tensor G5 chip. The phones offer a personal assistant that understands context across your apps, real-time voice translation for calls, and an AI-powered camera that can guide you through tasks byhighlighting themon your screen. You also get enhanced features fornote-taking, writing, and making music.
$9 Billion AI Data Center Planned for Oklahoma
Google is investing a staggering$9 billionto expand its AI and cloud infrastructure in Oklahoma. The investment includes building a new data center campus and expanding an existing one, creating thousands of jobs. This move, part of a larger push by Google and other tech giants to dominate the AI market,also includes a$1 billioncommitment toAmericaneducational programsto prepare the workforce for the future of AI.
STORM
Description:
A hybrid writing tool combining LLMs and retrieval to auto-generate structured, citation-rich content like Wikipedia-style entries.
Use Cases: Generating structured, reference-backed content, Writing reports and internal explainers, Organizing information hierarchically from multiple sources
Webpage: https://storm.genie.stanford.edu/
ValidatorAI
Description:
AI-powered idea-validation tool that evaluates business concepts instantly—providing feedback on strengths, weaknesses, and market potential.
Use Cases:
Pre-assessing client ideas and strategies before investment of time/resources; quickly validating pitches or product concepts during strategy sessions.
Webpage: https://validatorai.com/
Xavier AI
Description: Described as the world’s first AI strategy consultant, Xavier AI uses a proprietary reasoning engine designed specifically for business scenarios.
Use Cases:
Rapidly producing data‑backed business strategies or proposals, Conducting competitive or market landscape assessments.
Webpage: https://www.xavier.ai/
Levity AI
Description: No-code automation platform that uses AI to process unstructured data - such as PDFs, emails, and images - into structured outputs and workflows.
Use Cases: Automating manual data tasks like categorizing client survey responses, extracting info from documents, or routing content based on criteria (e.g., sentiment, tags).
Webpage: https://levity.ai/
Join Outskill's 16-Hour AI Sprint this weekend (usually for $895) and become the AI expert companies are desperately hiring – not firing.
Date: Saturday and Sunday, 10 AM - 7 PM.
In their book Building AI Agents with LLMs, RAG, and Knowledge Graphs, SalvatoreRaieli and Gabriele Luculano argue that advanced RAG techniques can strengthen every stage of the pipeline — from data ingestion and indexing to retrieval and generation. This not only addresses many of the shortcomings of naïve RAG but also gives practitioners greater control over how knowledge is surfaced and used.
Whether it's a scrappy prototype or a production-grade agent, we want to hear how you're putting generative AI to work. Drop us your story at nimishad@packtpub.com or reply to this email, and you could get featured in an upcoming issue of AI_Distilled.
📢 If your company is interested in reaching an audience of developers and, technical professionals, and decision makers, you may want toadvertise with us.
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That’s a wrap for this week’s edition of AI_Distilled 🧠⚙️
We would love to know what you thought—your feedback helps us keep leveling up.
Thanks for reading,
The AI_Distilled Team
(Curated by humans. Powered by curiosity.)