Updates and recent posts about Gemini 3..

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@faun shared a link, 11 months, 1 week ago

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AWS' custom chip strategy is showing results, and cutting into Nvidia's AI dominance

Graviton4just cranked up the juice to600 Gbps. In the grand race of public cloud champions, it's gunning straight for Nvidia's AI kingdom, powered by the formidableProject Rainier... read more

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@faun shared a link, 11 months, 1 week ago

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Announcing up to 45% price reduction for Amazon EC2 NVIDIA GPU-accelerated instances

AWS chops up to45%from Amazon EC2 NVIDIA GPU prices. Now your AI training costs less even as GPUs play hard to get... read more

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@faun shared a link, 11 months, 1 week ago

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Scaling Test Time Compute to Multi-Agent Civilizations

Turns out, Reasoning AIs use a single test compute unit to pack the punch of something 1,000 to 10,000 times its size—an acrobatics act impossible before the might of GPT-4.Noam Brown spilled the beans on Ilya's hush-hush 2021 GPT-Zero experiment, which flipped his views on how soon we'd see reasoni.. read more

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@faun shared a link, 11 months, 1 week ago

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End to End Argo-Workflow for CI/CD

Argo Workflowsisn't just another tool; it sings for Kubernetes-native CI/CD. It juggles complex workflows as DAGs, brings dynamic execution to life with CRDs and parameters. Got a weekly CI? Automate it withCronWorkflows. Secure those Docker pushes using Kubernetes secrets, and let shared volumes ha.. read more

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@faun shared a link, 11 months, 1 week ago

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GitOps for Kubernetes With Nixidy and ArgoCD

Nixidyturns Kubernetes YAMLs into sleek, declarative Nix setups. It offers a robust, repeatable config flow—even for those complex Helm charts. Spice up your deployment by pairingArgoCDwith encrypted secrets viasops-secrets-operator. Now you can wrangle sensitive data in Git with style—and security... read more

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@faun shared a link, 11 months, 1 week ago

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Kubernetes 1.33: Resizing Pods Without the Drama (Finally!)

Kubernetes 1.33brings in-place pod vertical scaling, allowing you to adjust CPU and memory without restarting pods, a game-changer for seamless resource management in production workloads. This feature simplifies vertical pod autoscaling especially for stateful workloads like databases... read more

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@faun shared a link, 11 months, 1 week ago

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The Ultimate Guide to Running Kubernetes in a Home Lab

K3sandMicroK8sshine in makeshift home labs with minimal hardware. Throw inLonghornfor storage andVelerofor backup bliss. Now that's a recipe for tech nirvana... read more

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@faun shared a link, 11 months, 1 week ago

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A Journey Through Kafkian SplitDNS in a Multitenant Kubernetes Offering

SCHIPfaced off with tenant demands for serverless Kafka. Their weapon of choice? A crafty DNS trick usingCoreDNSand a few clevernode-local DNSadjustments. They kept multitenancy alive and kicking without wearing out the ops team. Nice move... read more

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@faun shared a link, 11 months, 1 week ago

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Interesting Kubernetes application demos

Kubeappsis your backstage pass to deploying and controllingK8sapps with style. Dive into a treasure chest ofHelmcharts ready to roll. For those looking to jazz up a demo, unleashKubedoomorKubevaders. Obliteratepodsfor stress-testing, or just because you can. Craving some retro-futuristic fun? Check .. read more

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@faun shared a link, 11 months, 1 week ago

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Kernel-level container insights: Utilizing eBPF with Cilium, Tetragon, and SBOMs for security

eBPF, Cilium'sTetragon, andSBOMsare the dream team for exposing real-time kernel-level drama inside containers. When these powers combine, they hunt down surprise breaches likeLog4Shellwith a sleuth's precision. Bonus: they shave off20%fromCPU usagewhile they're at it... 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.