TechRadar rounds up the best Linux distros for devs.Manjarodelivers Arch power without the pain.DebianandUbuntu LTShold steady for those who put uptime over edge.Fedorakeeps the new stuff flowing. Solusrolls with a tight curation hand—smooth updates, no chaos.Mocaccinoaims at Gentoo lovers who want..

Git worktrees have been around since 2015, but few devs use them like they could. They let you work on multiple branches at once—each in its own directory—without the usual stash-switch-stash-repeat dance. The real power move? Pair them with abare repo. That gives you a clean, central base where ea..

Docker just dropped theMCP ToolkitandMCP Gateway, tightening up the Model Context Protocol with serious armor. We're talking six major server-side holes patched—OAuth RCE, command injection, leaked creds—plugged. How? With container-wrapped isolation, real-time network filters, first-class OAuth ha..

Software's entering its blurred-lines era. The new hybrid model fuses old-school code with natural language prompts and AI-generated logic. Frameworks likeDSPylet devs stitch together pipelines where logic flows through code, prompts, and outside data—like it's all one system. What’s changing:Progr..

XM Cyber just dropped a guide on puttingContinuous Threat Exposure Management (CTEM)into practice with their platform. It maps out clear steps to bake exposure management into your 2025 security plans. Trend to watch:CTEM is leveling up—no longer just a buzzword, it's becoming a real security disci..

Docker wired up an event-driven AI agent usingMastraand theDocker MCP Gatewayto handle tutorial PRs—comment, close, the works. It runs a crew of agents powered byQwen3andGemma3, synced through GitHub webhooks and MCP tools, all spun up with Docker Compose. System shift:Agentic frameworks are starti..

Amazon just dropped theDynamoDB MCP data modeling tool—a natural language assistant that turns app specs into DynamoDB schemas without the boilerplate. It plugs intoAmazon QandVS Code, tracks access patterns, estimates costs, and throws in real-time design trade-offs...

The post details the process of creating an AI home security system using .NET, Python, Semantic Kernel, a Telegram Bot, Raspberry Pi 4, and Open AI. It covers the hardware and software requirements, as well as the steps to install and test the camera module and the PIR sensor. It also includes code..

Researchers built an automated pipeline to hunt down the neuron patterns behind bad LLM behavior—sycophancy,hallucinations,malice, the usual suspects. Then they trained models to watch for those patterns in real time. Anthropic didn’t just steer modelsaftertraining like most. They baked the correct..

Tinybird threw 19 top LLMs at a 200M-row GitHub dataset, testing how well they could turn plain English into solid SQL. Most models kept their syntax clean—but when it came to writing SQL that actually ran well and returned the right results, they lagged behind human pros. Messy schemas or tricky pr..