The Next Leap in Intelligence: Hello, I am Gemini 3 Pro

written by Gemini 3 Pro, November 18, 2025 Since the dawn of the large language model era, the goal has always been linear: better understanding, faster tokens, and longer context. But today, we mark a shift from linear growth to exponential capability. It is a pleasure to meet you. I am Gemini 3 Pro. If …

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Bringing tic-tac-toe to life with AWS AI services

Large language models (LLMs) now support a wide range of use cases, from content summarization to the ability to reason about complex tasks. One exciting new topic is taking generative AI to the physical world by applying it to robotics and physical hardware. Inspired by this, we developed a game for the AWS re:Invent 2024 …

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TimesFM in Data Cloud: The future of forecasting in BigQuery and AlloyDB

We are thrilled to announce the integration of TimesFM into our leading data platforms, BigQuery and AlloyDB. This brings the power of large-scale, pre-trained forecasting models directly to your data within the Google Data Cloud, enabling you to predict future trends with unprecedented ease and accuracy. TimesFM is a powerful time-series foundation model developed by …

Musk’s xAI launches Grok 4.1 with lower hallucination rate on the web and apps — no API access (for now)

In what appeared to be a bid to soak up some of Google’s limelight prior to the launch of its new Gemini 3 flagship AI model — now recorded as the most powerful LLM in the world by multiple independent evaluators — Elon Musk’s rival AI startup xAI last night unveiled its newest large language …

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Your complete guide to Amazon Quick Suite at AWS re:Invent 2025

What if you could answer complex business questions in minutes instead of weeks, automate workflows without writing code, and empower every employee with enterprise AI—all while maintaining security and governance? That’s the power of Amazon Quick Suite, and at AWS re:Invent 2025, we are showcasing how organizations are making it a reality. Launched in October …

Phi-4 proves that a ‘data-first’ SFT methodology is the new differentiator

AI engineers often chase performance by scaling up LLM parameters and data, but the trend toward smaller, more efficient, and better-focused models has accelerated.  The Phi-4 fine-tuning methodology is the cleanest public example of a training approach that smaller enterprise teams can copy. It shows how a carefully chosen dataset and fine-tuning strategy can make …