Updates and recent posts about Gemini 3..

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@faun shared a link, 1 year, 2 months ago

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Understanding Azure Functions for Microservice Architecture

Azure Functionsdemolish clunky monolithic apps with a serverless advantage. They scale like a dream and deploy with a breeze. Oh, and withAzure Blob Trigger, functions spring to life the moment new data hits. Instant updates ping straight to Slack without breaking a sweat... read more

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@faun shared a link, 1 year, 2 months ago

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Are AI agents the future of observability?

AI observability agentscould dial down on-call stress. These tireless workers slog through logs all day, every day, even fixing low-risk bugs so humans can catch some sleep. Sure, nagging worries about privacy andLLM costlurk, but early trials withXata Agentsuggest these costs barely scrape the surf.. read more

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@faun shared a link, 1 year, 2 months ago

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VMware sues Siemens for using unlicensed software

VMware slaps Siemens with a lawsuit over unlicensed software use, setting off a juicy legal drama. Rumors swirl about a 72-core licensing minimum. Small businesses? Less than thrilled. Meanwhile, VMware tightens its product download process. Unique tokens for everyone. Probably their way of dodging .. read more

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@faun shared a link, 1 year, 2 months ago

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15 GitOps Best Practices to Improve Your Workflows

GitOpsputs code in the driver's seat, steering apps and infrastructure straight from Git. Your deployments? They'll be twins with the repo's setup. Here are15 key factorsto consider when adopting GitOps in your teams... read more

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@faun shared a link, 1 year, 2 months ago

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Self-contained Python scripts with uv

Dive intouvand make Python scripting a breeze. Jam your dependencies right into the script header. Letuvmanage those virtual environments while you kick back. Debugging woes? A relic of the past. Execute your script—no circus act with virtual environments required. Feel the magic... read more

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@faun shared a link, 1 year, 2 months ago

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Handling billions of invocations – best practices from AWS Lambda

Amazon gets clever with AWS Lambda by usingshuffle-shardingandconsistent hashing. This isn't just tech jargon—it's magic for isolating asynchronous invocations. With these tools, they're boxing out noisy neighbors, smoothing out workloads across a jaw-dropping4500+ unique queue combos. Collisions? J.. read more

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@faun shared a link, 1 year, 2 months ago

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Microsoft unveils Microsoft Security Copilot agents and new protections for AI

MeetMicrosoft Security Copilot—the evolved AI superhero. It's diving into the chaos of phishing and identity management, juggling a staggering 84 trillion daily signals. In 2024, Microsoft flagged a mind-bending 30 billion phishing emails. That screams for AI intervention. EnterSecurity Copilotagent.. read more

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@faun shared a link, 1 year, 2 months ago

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Nvidia's roadmap shows just how deep Moores Law is buried

Nvidiajust dropped its 2028 GPU squad, honoring Richard Feynman. Enter the 600kW behemoth with576 GPUs. Moore's Law? Toast. YetAI'sappetite swells for more muscle, more density. Nvidia leads the pack, but watch out—AMDandIntelmight just dog-pile onto this trend and cook up their own dense chip wonde.. read more

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Avoid Cross-Shard Data Movement in Distributed Databases

Cross-shard joins? Total performance assassins. Instead, unleash strategies likereference table replicationorcollocationto outsmart needless data shuffle. Stay savvy on those trade-offs!.. read more

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@faun shared a link, 1 year, 2 months ago

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Industry experts react to World Backup Day

2025 is arming itself with a new title: The Year of Ransomware.Businesses swagger in delusional optimism. Meanwhile, reality sharpens its knives. Dr. Thomas King from DE-CIX has a word of caution: most businesses (82%, no less) downplay packet loss on SaaS. Spoiler alert—they're in for a rude awaken.. read more

Gemini 3 is Google’s third-generation large language model family, designed to power advanced reasoning, multimodal understanding, and long-running agent workflows across consumer and enterprise products. It represents a major step forward in factual reliability, long-context comprehension, and tool-driven autonomy.

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.