Grafana Tempo 2.9 Supercharges Distributed Tracing with LLM Integration
Grafana Tempo 2.9 debuts with MCP server support and TraceQL metrics sampling, enhancing data analysis and query efficiency.
Updates and recent posts about Gemini 3..
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@devopslinks added a new tool Grafana Tempo , 1 month, 3 weeks ago.
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LangChain Secures $125M and Launches LangChain & LangGraph 1.0
LangChain raised $125 million to enhance its agent engineering platform, introducing LangChain and LangGraph 1.0 with new tools like the Insights Agent and a no-code agent builder, aiming to transform LLM applications into reliable agents.
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@devopslinks added a new tool Snapper , 1 month, 3 weeks ago.
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SUSE Linux Enterprise 16 Launches with AI for Smarter Operations
SUSE releases Linux Enterprise Server 16, integrating AI to boost efficiency and cut costs, with features like secure AI integration and a 16-year support lifecycle.
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Ubuntu 25.10 Takes a Bold Leap — Targets Modern CPUs with New amd64v3 Architecture
Ubuntu 25.10 introduces support for amd64v3 architecture variants, enhancing performance by targeting specific silicon variants, with full support expected in the 26.04 LTS release.
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Google Introduces Quantum-Safe KEMs in Cloud KMS for Future Security
Google introduces quantum-safe KEMs in Cloud KMS to counter future quantum computing threats, urging organizations to transition to post-quantum cryptography.
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Red Hat Joins Forces with NVIDIA to Bring CUDA Everywhere
Red Hat and NVIDIA partner to distribute the NVIDIA CUDA Toolkit across Red Hat platforms, aiming to simplify AI adoption and enhance developer experience.
At its core, Gemini 3 emphasizes low hallucination rates, deep synthesis across large information spaces, and multi-step reasoning. Models in the Gemini 3 family are trained with scaled reinforcement learning for search and planning, enabling them to autonomously formulate queries, evaluate results, identify gaps, and iterate toward higher-quality outputs.
Gemini 3 powers advanced agents such as Gemini Deep Research, where it excels at producing well-structured, citation-rich reports by combining web data, uploaded documents, and proprietary sources. The model supports very large context windows, multimodal inputs (text, images, documents), and structured outputs like JSON, making it suitable for research, finance, science, and enterprise knowledge work.
Gemini 3 is available through Google’s AI platforms and APIs, including the Interactions API, and is being integrated across products such as Google Search, NotebookLM, Google Finance, and the Gemini app. It is positioned as Google’s most factual and research-capable model generation to date.
