Updates and recent posts about GPT-5.4..

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@faun shared a link, 9 months, 1 week ago

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Closing the gap: How KubeVirt, Kubernetes, and open ecosystems are reshaping virtualisation

KubeVirt spins up VMs inside Kubernetes clusters. It hooks intoPortworxfor stateful volumes. It tapsOpenShiftorRancherto match VMware’s arsenal. Declarative YAML meetsGitOpspipelines, unified schedulers and RBAC. Teams juggle VMs and containers on one toolchain. License bills shrink. Infra shift:Le.. read more

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@faun shared a link, 9 months, 1 week ago

FAUN.dev()

How To Deploy Fluent Bit in a Kubernetes-Native Way

Fluent Operator tapsCRDsto tameFluent Bitin Kubernetes. It channels inputs, filters, parsers, outputs into auto-generated configs. Then spins up the DaemonSet. TheFluent Bit Watcherwrapper hot-swaps configs on CRD tweaks. No pods restart... read more

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@faun shared a link, 9 months, 1 week ago

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The Evolution of AI Job Orchestration. Running AI jobs on GPU Neoclouds

Neocloudslike CoreWeave and Lambda Labs burst onto the scene, doling out affordableGPUpower and killer networking. They're tackling old-school cloud's weaknesses with style. Signal:The rise ofAI Neocloudsmarks a pivot in tech's landscape. They're carving out a niche with solutions crafted for AI's .. read more

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@faun shared a link, 9 months, 1 week ago

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Kong Gateway Operator and KIC, understanding the differences

Kong offers three different helm charts for Kubernetes ingress, leveraging the new Gateway API. Kong Gateway Operator simplifies deployment and management by using CRDs instead of custom helm charts. Using GatewayClass and Gateway resources are essential for the operator to spin up dataplanes and co.. read more

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@faun shared a link, 9 months, 1 week ago

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Flux CD: D1 Reference Architecture

ControlPlane Enterprise for Flux CD drops thed1 reference architectureandDesign 1 Reference Architecture Guide. It packs production-grade playbooks for sprawling multi-tenant, multi-cluster setups. The repo flexes real code:GitHub fine-grained Personal Access Tokens,Kubernetes RBAC, and auto-promoti.. read more

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@faun shared a link, 9 months, 1 week ago

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Deep dive into cluster networking for Amazon EKS Hybrid Nodes

EKS Hybrid Nodes corrals on-prem and edge servers as remote Kubernetes nodes over Direct Connect or VPN. It rides onCiliumorCalico, with BGP or static routes. For local load balancing, it spins upMetalLBat Layer 2/3. For NLB/ALB sync, it taps theAWS Load Balancer Controller. Workflows stay unified... read more

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@faun shared a link, 9 months, 1 week ago

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Enterprise Strategy Group Validates Tintri VMstore Kubernetes Data Services

ESG spots Tintri VMstore’sCSI driverpackingAuto-QoS,real-time I/O analyticsandpredictive tuningfor sub-ms container and VM workloads. That driver fires upinstant cloneandsnapshottest environments. It enforces policy-drivenRPO/RTOprotection. It unifies VM, container and database control. Infra shift.. read more

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@idjuric660 shared a post, 9 months, 1 week ago

Technical Content Writer, Mailtrap

Choosing the Best SMTP Providers – Top 5 SMTP Providers Compliance Comparison

#smtp pr... #smtp se... #compari... #smtp pr...

When you manage millions of transactional emails or orchestrate extensive marketing campaigns, the nuances of data protection, privacy, and regulatory adherence can make or break your operations. This is precisely why you need to hawk over compliance, and set a goal to find a provider that: - Safegu..

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@laura_garcia shared a post, 9 months, 1 week ago

Software Developer, RELIANOID

Enjoy your weekend and take it easy!

https://www.relianoid.com/about-us/contact-us/ #Relianoid#WeAreRelianoid#247Support#ExtremeSupport#AlwaysHereForYou#TechSupportExperts#DedicatedSupport#MissionCriticalCare#TakeItEasy#HappyFriday#FridaysDoneRight#RelaxWeGotThis#ITSupportTeam#BehindTheScenesHeroes#ReliableByNature..

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@anjali shared a link, 9 months, 1 week ago

Customer Marketing Manager, Last9

How sum_over_time Works in Prometheus

Understand how sum_over_time() aggregates metrics in Prometheus, handles gaps, and why step size and staleness can affect accuracy.

GPT-5.4 is OpenAI’s latest frontier AI model designed to perform complex professional and technical work more reliably. It combines advances in reasoning, coding, tool use, and long-context understanding into a single system capable of handling multi-step workflows across software environments. The model builds on earlier GPT-5 releases while integrating the strong coding capabilities previously introduced with GPT-5.3-Codex.

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.