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How Kubernetes Learned to Resize Pods Without Restarting Them

Kubernetes v1.35 introduces in-place Pod resizing, allowing dynamic adjustments to CPU and memory limits without restarting containers. This feature addresses the operational gap of vertical scaling in Kubernetes by maintaining the same Pod UID and workload identity during resizing. With this breakt.. read more  

How Kubernetes Learned to Resize Pods Without Restarting Them
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Why Kubernetes is retiring Ingress NGINX

The Kubernetes Steering Committee is pulling the plug onIngress NGINX- official support ends March 2026. No more updates. No security patches. Gone. Why? It's been coasting on fumes. One or two part-time maintainers couldn't keep up. The tech debt piled up. Now it's a security liability. What's next.. read more  

Why Kubernetes is retiring Ingress NGINX
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How GKE Inference Gateway improved latency for Vertex AI

Vertex AI now plays nice withGKE Inference Gateway, hooking into the Kubernetes Gateway API to manage serious generative AI workloads. What’s new:load-awareandcontent-aware routing. It pulls from Prometheus metrics and leverages KV cache context to keep latency low and throughput high - exactly what.. read more  

How GKE Inference Gateway improved latency for Vertex AI
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CVE-2026-22039: Kyverno Authorization Bypass

Kyverno - a CNCF policy engine for Kubernetes - just dropped a critical one:CVE-2026-22039. It lets limited-access users jump namespaces by hijacking Kyverno'scluster-wide ServiceAccountthrough crafty use of policy context variable substitution. Think privilege escalation without breaking a sweat. I.. read more  

CVE-2026-22039: Kyverno Authorization Bypass
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Self-Optimizing Football Chatbot Guided by Domain Experts on

Generic LLM judges and static prompts fail to capture domain-specific nuance in football defensive analysis. The architecture for self-optimizing agents built on Databricks Agent Framework allows developers to continuously improve AI quality using MLflow and expert feedback. The agent, such as a DC .. read more  

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Nathan Lambert: Open Models Will Never Catch Up

Open models will be the engine for the next ten years of AI research, according to Nathan Lambert, a research scientist at AI2. He explains that while open models may not catch up with closed ones due to fewer resources, they are still crucial for innovation. Lambert emphasizes the importance of int.. read more  

Nathan Lambert: Open Models Will Never Catch Up
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My AI Adoption Journey

A dev walks through the shift from chatbot coding toagent-based AI workflows, think agents that read files, run code, and double-check their work. Things only clicked once they built outcustom tools and configsto help agents spot and fix their own screwups. That’s the real unlock... read more  

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Generative Pen-trained Transformer

MeetGPenT, an open-source, wall-mounted polargraph pen plotter with a flair for generative art. It blends custom hardware, Marlin firmware, a Flask web UI running on Raspberry Pi, and Gemini-generated drawing prompts. The stack? Machina + LLM. Prompts go in, JSON drawing commands come out. That driv.. read more  

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Towards self-driving codebases

OpenAI spun up a swarm of GPT-5.x agents - thousands of them. Over a week-long sprint, they cranked out runnable browser code and shipped it nonstop. The system hit 1,000 commits an hour across 10 million tool calls. The architecture? A planner-worker stack. Hierarchical. Recursive. Lean on agent ch.. read more  

Towards self-driving codebases
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Demystifying : Why You Shouldn’t Fear Observability in Traditional Environments

OpenTelemetry is friendly with the past. It now pipesreal-time observability into legacy systems- no code rewrite, no drama. Pull structured metrics straight from raw logs, Windows PDH counters, or SQL Server stats. It doesn’t stop there. Got MQTT-based IoT gear? OTLP export or lightweight adapters .. read more  

Demystifying : Why You Shouldn’t Fear Observability in Traditional Environments
Magecomp Magento 2 Multiple Wishlist Extension is a powerful tool designed to enhance the default wishlist functionality in Magento 2. Developed by Magecomp, a trusted provider of Magento extensions, this extension offers advanced features and flexibility for creating multiple wishlists on your Magento 2 store.

Wishlists are an essential feature for any e-commerce store as they allow customers to save products for future reference or purchase. However, the default Magento 2 wishlist functionality only allows customers to create a single wishlist. This limitation can be restrictive, especially for customers who want to organize their saved products into different categories or share specific wishlists with others.

Magecomp's Multiple Wishlist Extension addresses these limitations by enabling customers to create and manage multiple wishlists effortlessly. Here are some key features and benefits of this extension:

Multiple Wishlists: The extension allows customers to create multiple wishlists based on their preferences. Customers can organize their saved products into different wishlists, such as "Birthday Gifts," "Holiday Shopping," or "Favorite Items," providing them with better control and organization over their desired products.

Wishlist Sharing: With this extension, customers can easily share their wishlists with friends, family, or on social media platforms. The sharing functionality enhances customer engagement and encourages social interaction, allowing customers to get opinions or recommendations from others before making a purchase decision.

Privacy Control: Magecomp's Multiple Wishlist Extension provides customers with privacy control options. Customers can choose to keep their wishlists private or make them public, depending on their preferences. Public wishlists can be viewed and accessed by others, which can be useful for sharing gift ideas or creating collaborative wishlists.

Wishlist Search and Filtering: The extension enhances the usability of wishlists by adding search and filtering options. Customers can search for specific products within a wishlist or filter products based on various attributes such as price, brand, or category. This feature simplifies the process of finding desired products within a large wishlist, improving the overall user experience.

Bulk Actions: Customers can perform bulk actions on their wishlists, such as adding multiple products to the cart, removing selected products, or moving items between wishlists. These bulk actions save time and provide convenience to customers when managing their wishlists.

Customization Options: The Multiple Wishlist Extension allows store owners to customize various aspects of the wishlists, including layout, design, and display options. This customization capability ensures that the wishlists seamlessly integrate with the overall look and feel of the Magento 2 store.

Easy Integration: The extension is designed to seamlessly integrate with the Magento 2 platform. It is easy to install, configure, and use, without requiring extensive technical knowledge or coding skills.

In conclusion, Magecomp Magento 2 Multiple Wishlist Extension is a valuable addition to any Magento 2 store, empowering customers with the ability to create and manage multiple wishlists. With enhanced features like wishlist sharing, privacy control, search and filtering options, and customization capabilities, this extension improves the user experience and boosts customer engagement on your e-commerce website.