Updates and recent posts about GPT-5.4..

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@kaptain shared a link, 4 months, 3 weeks ago

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AWS Backup now supports Amazon EKS

AWS Backup just added support forAmazon EKS. Now you can back up cluster state and persistent volumes, no agents, no third-party hacks. It handles scheduling, retention, and immutability out of the box. Restore full clusters or drill down to specific components, even across Regions and accounts... read more

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@kaptain shared a link, 4 months, 3 weeks ago

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ConfigHub: Why Your Internal Developer Platform Needs It

See why GitOps often feels like a sprawl of configs, discover how to manage Configuration as Data for your Kubernetes platform, and learn how ConfigHub can help... read more

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@kaptain shared a link, 4 months, 3 weeks ago

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Amazon Elastic Kubernetes Service gets independent affirmation of its zero operator access design

Amazon EKS just went full Fort Knox. It now runs on azero operator accessmodel - meaning even AWS can’t peek inside your Kubernetes control or data plane. The setup leans on theNitro System’s confidential compute,guarded APIs, andmulti-party approval pipelines. NCC Group also kicked the tires and ga.. read more

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@kaptain shared a link, 4 months, 3 weeks ago

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KServe becomes a CNCF incubating project

KServe is upgrading.The CNCF pulled it into incubation, backing it astheKubernetes-native way to serve both generative and predictive AI. Translation: it’s not a side project anymore - it’s core infra. Version 0.15 steps up with tighter integrations across the stack:vLLM,Envoy Gateway,llm-d,Knative,.. read more

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@kaptain shared a link, 4 months, 3 weeks ago

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Using Komodo to Run Docker Commands from a Web Interface

Komodo drops a slick browser-based UI for wrangling Docker - containers, images, networks, and Compose stacks - through a real-time visual dashboard. Think native Docker meets one-click redeploys, host curation via agents, and reusable container configs that don’t make you hate YAML... read more

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@kaptain shared a link, 4 months, 3 weeks ago

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Prepare for the Kubernetes Administrator Certification and Pass

A tight 2-hour YouTube course built for theCKA examgrind. It's all real-world tasks: cluster setup, upgrades, troubleshooting. No fluff, just shell commands and Kubernetes in action. It walks through the gritty bits:etcdbackup and restore, node affinity, tolerations, and how to set upIngresslike som.. read more

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@kaptain shared a link, 4 months, 3 weeks ago

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Streamline Complex AI Inference on Kubernetes with NVIDIA Grove

NVIDIA releasedGrove, a Kubernetes API baked intoDynamo, to wrangle the chaos of modern AI inference. It pulls apart your big, messy model into clean, discrete chunks - prefill, decode, routing - and runs them like a single, orchestrated act. The trick?Custom hierarchical resources. They let Grove h.. read more

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@kala shared a link, 4 months, 3 weeks ago

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The 1 Billion Token Challenge: Finding the Perfect Pre-training Mix

Researchers squeezed GPT-2-class performance out of a model trained on just1 billion tokens- 10× less data - by dialing in a sharp dataset mix:50% finePDFs, 30% DCLM-baseline, 20% FineWeb-Edu. Static mixing beat curriculum strategies. No catastrophic forgetting. No overfitting. And it hit90%+of GPT-.. read more

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@kala shared a link, 4 months, 3 weeks ago

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Jensen Huang's Stark Warning: China's 1 Million AI Workers vs America's 20,000

Nvidia CEO Jensen Huang, in some leaked comments, didn’t mince words: U.S. export bans aren’t hobbling China’s AI game - they’re fueling it. He pointed to Huawei’s 910C chip edging close to H100 territory, a forecast putting China ahead in AI compute by 2027, and a fast-growing local chip industry n.. read more

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@kala shared a link, 4 months, 3 weeks ago

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Context Management in Amp

Amp stretches the context window into something more useful. It pulls in system prompts, tool info, runtime metadata, even AGENTS.md files - fuel for agentic behavior. It gives devs serious control: edit messages, fork threads, drop in files with @mentions, hand off conversations, or link threads to.. read more

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.