How to create Cilium Cluster Mesh between K3s and Azure Kubernetes Service
Ciliummasterfully knits clusters, weaving on-prem K3s with privateAKSusing Azure Virtual WAN. Efficient load-balancing? Piece of cake... read more
Updates and recent posts about GPT-5.4.. Link
Link
@faun shared a link, 1 year ago
FAUN.dev()
Azure Kubernetes Service (AKS) – eBPF-based networking & security + integration with Microsoft Sentinel
Banish premium woes.CiliumandTetragontake Kubernetes security and amp it up with instant insights and alerts inMicrosoft Sentinel—without costing you a dime. Forget kube-proxy. Harness eBPF magic for L7 inspection withEnvoy. Blend Cilium’s raw speed with Tetragon’s covert skills. Voilà—your cluster’.. read more
Link
@faun shared a link, 1 year ago
FAUN.dev()
Components of an Open Source AI Compute Tech Stack
AI stacks are zeroing in onKubernetes,Ray, andPyTorchto boost workload scaling, whilevLLMsteps up LLM processing. Yet, in research-heavy enclaves, the old warhorseSLURMstill has its spotlight... read more
Link
@faun shared a link, 1 year ago
FAUN.dev()
Your Barbershop Doesn't Need Kubernetes
A$50K enterprise AI solutionfor a small barbershop’s calendar woes? Get real. Instead, roll up your sleeves, shell out a modest used car budget, and letAIwrestle with the true hairballs: no-shows, last-minute swaps, and—bonus—gleaming, satisfied clients... read more
Link
@faun shared a link, 1 year ago
FAUN.dev()
A Year of Envoy Gateway GA: Building, Growing, and Innovating Together
Envoy Gatewayjust wrapped its rookie year, and it’s been anything but boring. Four major releases. Game-changing features. A community of builders that means business. Version 1.4? It roped in65 contributorsfrom54 companies, revamping the way cloud-native traffic flows. Meanwhile,Envoy Proxykeeps gr.. read more
Link
@faun shared a link, 1 year ago
FAUN.dev()
Dual-Stack: Cilium Complementary Features
Trade inRKE2 Nginxfor the nimbleCilium Gateway API. It cranks up your Layer 7 filtering, routing, and security magic—no BGP machine needed. And withCilium LB IPAM, IP addresses scatter across your local network like it’s confetti time... read more
Link
@faun shared a link, 1 year ago
FAUN.dev()
Run Kubernetes Clusters for Less with Amazon EC2 Spot and Karpenter
Karpenterbrings some much-needed swagger toAWS EKSclusters with its clever auto-scaling tricks. It grabsEC2 Spot Instancesand slashes costs by a dazzling90%for stateless, flexible workloads. Imagine dynamic nodes practically springing to life, optimized compute horsepower unleashed, and interruption.. read more
Link
@faun shared a link, 1 year ago
FAUN.dev()
Enhancing Kubernetes Event Management with Custom Aggregation
Kubernetes Eventshold the keys to your cluster's secrets, but when event torrents flood in, finding the gems takes effort. An avalanche of alerts tests your patience, bandwidth, and sanity. Enter custom event aggregation miracles: they slice troubleshooting tedium from weeks to minutes. By stitching.. read more
Link
@faun shared a link, 1 year ago
FAUN.dev()
Connecting Applications to Self-Service Datastores
Self-service datastore delivery just got easier with Kubernetes init containers and mutating admission webhooks automating secrets provision and rotation securely, simplifying developer workflows and enhancing data security... read more
Link
@faun shared a link, 1 year ago
FAUN.dev()
From Kafka to Ray: Deploying AI and Stateful Workloads on AKS with Confidence
Azure's new AKS guides slice through the fog around deployingKafka,Apache Airflow, andRay. Spotlights shine onJVM tuningmagic for Kafka and a peek atKubeRaywrangling distributed Ray... read more
One of the defining features of GPT-5.4 is its ability to operate as part of agent-style workflows. The model can interact with tools, APIs, and external systems to complete tasks that extend beyond simple text generation. It also introduces native computer-use capabilities, allowing AI agents to operate applications using keyboard and mouse commands, screenshots, and browser automation frameworks such as Playwright.
GPT-5.4 supports context windows of up to one million tokens, enabling it to process and reason over very large documents, long conversations, or complex project contexts. This makes it suitable for tasks such as analyzing codebases, generating technical documentation, working with large spreadsheets, or coordinating long-running workflows. The model also introduces a feature called tool search, which allows it to dynamically retrieve tool definitions only when needed. This reduces token usage and makes it more efficient to work with large ecosystems of tools, including environments with dozens of APIs or MCP servers.
In addition to improved reasoning and automation capabilities, GPT-5.4 focuses on real-world productivity tasks. It performs better at generating and editing spreadsheets, presentations, and documents, and it is designed to maintain stronger context across longer reasoning processes. The model also improves factual accuracy and reduces hallucinations compared with previous versions.
GPT-5.4 is available across OpenAI’s ecosystem, including ChatGPT, the OpenAI API, and Codex. A higher-performance variant, GPT-5.4 Pro, is also available for users and developers who require maximum performance for complex tasks such as advanced research, large-scale automation, and demanding engineering workflows. Together, these capabilities position GPT-5.4 as a model aimed not just at conversation, but at executing real work across software systems.
