Distributed Network Monitoring: Guide to Getting Started & Troubleshooting
A practical guide to getting started with distributed network monitoring and solving common issues across modern, complex systems.
Updates and recent posts about Gemini 3.. Link
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@laura_garcia shared a post, 1 year, 1 month ago
Software Developer, RELIANOID
🌍💡 World Creativity and Innovation Day — April 21 💡🌍
At RELIANOID, creativity isn’t just a value — it’s the foundation of everything we do. In a world where technology evolves at lightning speed, standing still is not an option. That’s why our team constantly challenges the status quo, reimagining howApplication Delivery, Security, and High-Performanc..
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@anjali shared a link, 1 year, 1 month ago
Customer Marketing Manager, Last9
A Comprehensive Guide to Monitoring Disk I/O on Linux
Learn how to monitor and optimize disk I/O performance on Linux with this comprehensive guide to better manage system resources.
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Manage all your kubernetes port-forwards in one place with
Meet theRust-infused lifesaversweeping away Kubernetes port-forwarding mayhem. It tames connections by routing everything through one neat hub. TCP and UDP? Handled effortlessly. Picture a pod bridging UDP traffic over TCP with precision, serving up a swanky GUI or a no-nonsense terminal view. Add a.. read more
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@faun shared a link, 1 year, 1 month ago
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Creating a ClickHouse Cluster on Raspberry Pis
Craft a miniature powerhouse with threeRaspberry Pi 5s, each kitted out with NVMe drives. It's your ticket to an eye-opening, hands-on Kubernetes adventure. Start by installingK3s—the featherweight Kubernetes hero. Then, unleash theAltinity Operatorto deftly manage yourClickHousecluster. Say goodbye.. read more
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@faun shared a link, 1 year, 1 month ago
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Understanding new GKE inference capabilities
Google Cloud Nextswings open the curtains on GKE’s latest tricks for inference. Imagine serving costs dropping by 30%, tail latency by 60%, and a whopping 40% leap in throughput. Talk about upgrades with attitude!.. read more
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Kagent: Bringing Agentic AI to Cloud Native
Kagentrides on the back ofMicrosoft’s AutoGenlike a pro. This nifty tool empowers DevOps ninjas to unleash AI agents inKubernetes. Picture it automating all the drudgework: configuration hassles, network security fiddling—you name it. By syncing up with power players likePrometheusandArgo, it transf.. read more
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@faun shared a link, 1 year, 1 month ago
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VMware ups Tanzu's gen AI support, sheds Kubernetes dependence
VMware Tanzujust upped its game. It's infused with generative AI magic and has kicked Kubernetes to the curb. Now it taps into Anthropic'sModel Context Protocolfor a swift, almost cheeky, app creation rollercoaster. Ditch the config files—just throw code into the wild withSpring AI. Its data service.. read more
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I built a Pi‑powered Kubernetes cluster — was it worth it?
ARaspberry Pi 5nestled in a shoebox rack, spinning its wheels with "real" Kubernetes. It sips a dainty 10W but stumbles over hiccups like ARM64 chart voids and single-lane PCIe NVMe antics. Though NVMe drives rocket from 90MB/s to 350MB/s, reeling in those image pulls, thermal throttling and x86-exc.. read more
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@faun shared a link, 1 year, 1 month ago
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OpenTelemetry Observability in Crunchy Postgres for Kubernetes
OpenTelemetrybarges intoCrunchy Postgres for Kubernetesv5.8, tossing away those Prometheus sidecars in favor of OpenTelemetry collectors. It's a bold move: observability without chains. No more vendor handcuffs. Just pure, unfettered insights, delivered fast... read more
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.
