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@kaptain shared a link, 3 months, 1 week 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, 3 months, 1 week 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  

Using Komodo to Run Docker Commands from a Web Interface
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@kaptain shared a link, 3 months, 1 week 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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@kaptain shared a link, 3 months, 1 week 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  

Prepare for the Kubernetes Administrator Certification and Pass
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@kala shared a link, 3 months, 1 week 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  

The 1 Billion Token Challenge: Finding the Perfect Pre-training Mix
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@kala shared a link, 3 months, 1 week 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  

Jensen Huang's Stark Warning: China's 1 Million AI Workers vs America's 20,000
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@kala shared a link, 3 months, 1 week 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  

Context Management in Amp
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@kala shared a link, 3 months, 1 week ago
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Inside Cursor - Sixty days with the AI coding decacorn

Cursor is shaking up recruiting by treating the hiring process as more about the person than the job, resulting in a fast-growing team of exceptional individuals drawn in by the company's compelling mission and focus on challenging technical problems. Women in product and engineering roles are a kno.. read more  

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@kala shared a link, 3 months, 1 week ago
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Google to release Nano Banana Pro next week

Google dropsGemini 3and the newNano Banana Pronext week. Big swing at image generation - now tied tight to Gemini 3 Pro. Early glimpses in Google Vids hint Nano Banana Pro is built for sharper visuals in creative tools. System shift:Google’s stacking its apps behind a single backbone: Gemini 3 Pro. .. read more  

Google to release Nano Banana Pro next week
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@kala shared a link, 3 months, 1 week ago
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Building a Healthcare Robot from Simulation to Deployment with NVIDIA Isaac

NVIDIA just droppedIsaac for Healthcare v0.4, and it’s a big one. Headliner: the newSO-ARM starter workflow- a full-stack sim2real pipeline built for surgical robotics. It covers the whole loop: spin up synthetic and real-world data capture, train withGR00t N1.5, and deploy straight to 6-DOF hardwar.. read more  

AIStor is an enterprise-grade, high-performance object storage platform built for modern data workloads such as AI, machine learning, analytics, and large-scale data lakes. It is designed to handle massive datasets with predictable performance, operational simplicity, and hyperscale efficiency, while remaining fully compatible with the Amazon S3 API. AIStor is offered under a commercial license as a subscription-based product.

At its core, AIStor is a software-defined, distributed object store that runs on commodity hardware or in containerized environments like Kubernetes. Rather than being limited to traditional file or block interfaces, it exposes object storage semantics that scale from petabytes to exabytes within a single namespace, enabling consistent, flat addressing of vast datasets. It is engineered to sustain very high throughput and concurrency, with examples of multi-TiB/s read performance on optimized clusters.

AIStor is optimized specifically for AI and data-intensive workloads, where throughput, low latency, and horizontal scalability are critical. It integrates broadly with modern AI and analytics tools, including frameworks such as TensorFlow, PyTorch, Spark, and Iceberg-style table engines, making it suitable as the foundational storage layer for pipelines that demand both performance and consistency.

Security and enterprise readiness are central to AIStor’s design. It includes capabilities like encryption, replication, erasure coding, identity and access controls, immutability, lifecycle management, and operational observability, which are important for mission-critical deployments that must meet compliance and data protection requirements.

AIStor is positioned as a platform that unifies diverse data workloads — from unstructured storage for application data to structured table storage for analytics, as well as AI training and inference datasets — within a consistent object-native architecture. It supports multi-tenant environments and can be deployed across on-premises, cloud, and hybrid infrastructure.