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NVIDIA’s Nemotron 3: Pioneering Open, Efficient Models for the Agentic AI Era

NVIDIA has officially launched the Nemotron 3 family of open models, marking a major advancement in transparent, high-performance AI built for the agentic era. First announced in late 2025, this release includes model weights, extensive training datasets, and full reinforcement learning recipes—empowering developers to build specialized multi-agent systems with unprecedented visibility. Unlike closed-source alternatives, Nemotron…

Linux kernel 7.0 supports net namespace in AF_VSOCK.

Linux kernel 7.0 supports net namespace in AF_VSOCK.

The Linux kernel version 7.0 now supports network namespaces in AF_VSOCK. This fixes issues in how virtual sockets work in isolated environments like containers. It’s exciting for anyone working with virtualization!  What is AF_VSOCK? Let’s start simple. In normal networking, programs use sockets to talk over the internet or local networks. These use address families…

CopilotKit: The Open-Source Framework Revolutionizing In-App AI Copilots

CopilotKit: The Open-Source Framework Revolutionizing In-App AI Copilots

In 2026, AI agents are no longer just chatbots or external tools—they’re becoming deeply integrated parts of applications themselves. Developers increasingly want to build “agentic” experiences where AI can understand app context, control UI elements, execute actions, and collaborate with users in real time. Enter CopilotKit, an open-source framework that’s rapidly gaining traction for making…

Exploring Kine: The Etcd Shim Revolutionizing Kubernetes Storage

Exploring Kine: The Etcd Shim Revolutionizing Kubernetes Storage

In the world of container orchestration, Kubernetes is the de facto standard. However, its core component—the etcd key-value store—can be a heavyweight dependency for edge environments or small clusters. Enter K3s, Rancher’s minimalist distribution, and its secret weapon: Kine. What is Kine? Kine (pronounced “kine” like “bovine”) is an open-source etcd shim. It acts as…

Bring AI Vision to the Edge: Weaviate Releases CLIP Inference Support for NVIDIA Jetson

Bring AI Vision to the Edge: Weaviate Releases CLIP Inference Support for NVIDIA Jetson

The frontier of “Edge AI” just moved a significant step forward. In a recent announcement on X (formerly Twitter), vector database pioneer Weaviate revealed that its Multi2Vec CLIP inference container (v1.5.0) now officially supports NVIDIA Jetson devices. This update bridges the gap between powerful multimodal AI models and energy-efficient edge hardware, allowing developers to run…

Capturing Network Traffic Over Cilium VTEP Tunnels with eBPF: A Practical Guide

Capturing Network Traffic Over Cilium VTEP Tunnels with eBPF: A Practical Guide

In modern Kubernetes environments, connecting external systems—like virtual machines, legacy appliances, or third-party load balancers—to pods often requires overlay tunneling. Cilium’s VTEP (VXLAN Tunnel Endpoint) integration makes this seamless by allowing direct VXLAN communication between Cilium-managed pods and external VTEP devices. But how do you actually observe and debug the traffic flowing through these tunnels…

From Prompting to Autonomy: 10 GitHub Repos to Master AI Agents

From Prompting to Autonomy: 10 GitHub Repos to Master AI Agents

The transition from simple LLM chatting to building autonomous AI agents is the biggest shift in software engineering today. While the field moves fast, these 10 repositories represent the “Gold Standard” of learning resources, offering everything from foundational math to production-grade deployment patterns. Foundations and Frameworks 1. Hands-On Large Language Models The Deep Dive: This…

MadeWithML: Mastering the First Principles of Production AI

MadeWithML: Mastering the First Principles of Production AI

In the high-stakes world of machine learning, there is a notorious “valley of death” between a model that works on a data scientist’s laptop and one that actually delivers value in production. Most online courses focus on the former—teaching you how to optimize a loss function or pick the right architecture. But as any veteran…