AWS Optimizer Targets Unused NAT Gateways for Cost Savings
AWS Compute Optimizer now helps identify unused NAT Gateways to boost cost savings by analyzing traffic activity and route table associations.
Updates and recent posts about Gemini 3..
News FAUN.dev() Team
@devopslinks shared an update, 5 months, 3 weeks ago
FAUN.dev()
GitLab Uncovers Massive npm Attack - Developers on High Alert
GitLab's team discovers a large-scale npm supply chain attack with malware that spreads through npm packages, threatening data destruction if disrupted.
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@devopslinks added a new tool GitHub , 5 months, 3 weeks ago.
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@laura_garcia shared a post, 5 months, 3 weeks ago
Software Developer, RELIANOID
𝘐𝘯 𝘤𝘢𝘴𝘦 𝘺𝘰𝘶 𝘮𝘪𝘴𝘴𝘦𝘥 𝘪𝘵: Europe’s skies disrupted
Cyberattack on Collins Aerospace’s MUSE platform We shared this analysis a few months ago, but given the relevance of the topic and the growing impact of cyberattacks on critical infrastructure, it’s definitely worth resurfacing. The incident forced major airports like Heathrow, Brussels, and Berlin..
News FAUN.dev() Team
@kala shared an update, 5 months, 3 weeks ago
FAUN.dev()
DeepSeekMath-V2 Launches with 685B Parameters - Dominates Math Contests
DeepSeekMath-V2, an AI model with 685 billion parameters, excels in mathematical reasoning and achieves top scores in major competitions, now available open source for research and commercial use.
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@anjali shared a link, 5 months, 3 weeks ago
Customer Marketing Manager, Last9
9 Monitoring Tools That Deliver AI-Native Anomaly Detection
A technical guide comparing nine observability platforms built to detect anomalies and support modern AI-driven workflows.
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@kala added a new tool DeepSeekMath-V2 , 5 months, 3 weeks ago.
News FAUN.dev() Team
@kala shared an update, 5 months, 3 weeks ago
FAUN.dev()
A New Challenger: INTELLECT-3's 100B Parameters Punch Above Their Weight
INTELLECT-3, a 100B+ parameter model, sets new benchmarks in AI, with open-sourced training components to foster research in reinforcement learning.
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@kala added a new tool INTELLECT-3 , 5 months, 3 weeks ago.
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.
