Microsoft Can Identify Users Through Unique Windows Device IDs, Case Reveals

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A recent hacker arrest has brought to light Microsoft's ability to track individuals using a unique identifier tied to their Windows device. The case revealed that Microsoft can link a device ID to a specific user, raising privacy concerns among security researchers and the public. This tracking capability was exposed during legal proceedings in which the device ID served as key evidence. The development highlights how deeply embedded telemetry and identification features are within the Windows operating system. It has reignited debate over user privacy and the extent of data collection by major technology companies.

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Flutter Core Widgets Guide Part 1: 50 Essential Widgets With Code Snippets

A developer on DEV Community has published a reference guide covering approximately 50 of the most commonly used Flutter widgets, organized by category. The categories include basic widgets, layout, scrolling and lists, buttons, input and forms, structural and material components, feedback and overlays, navigation, and more. Each widget entry provides a short description, a runnable code snippet, and a text-based description of how it renders on screen. The guide is designed for Flutter developers who want a quick, practical reference without needing to consult full documentation. Code snippets can be opened and tested directly in DartPad, though users are advised to allow 5 to 10 seconds for the environment to load.

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ProgrammingDEV Community ·

Azure Policy can block dangerously broad blob lifecycle rules in storage accounts

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ProgrammingDEV Community ·

Automated Restore Verification Turns Untested Backups Into Proven Safety Nets

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ProgrammingDEV Community ·

Pure-C TTS engine gains Metal and CUDA GPU backends with major speed gains

Developers have added two optional GPU backends — Apple Metal and NVIDIA CUDA — to qwen3-tts, a pure-C inference engine for the Qwen3-TTS model, while keeping the default CPU path unchanged. On Apple Silicon, the Metal backend reduced the 0.6B model's real-time factor from 1.5 to 0.60 on an M1 and 0.36 on an M2 Pro, with streaming first-audio latency of 314 milliseconds. The CUDA backend achieved an RTF of 0.44 on an RTX 4060-class GPU, scaling down to approximately 0.12 on an RTX 4090. Key performance gains came from keeping weights, KV cache, and activations resident on the device and encoding full decode steps into a single command buffer rather than offloading individual operations. Server-side request batching further improved throughput by roughly 2.8x on Metal and 3.35x on CUDA, allowing multiple concurrent users to be served in approximately the time of a single request.

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