Android 17 introduces platform support for Encrypted Client Hello (ECH), a significant privacy enhancement for network communications. ECH is a TLS 1.3 extension that encrypts the Server Name Indication (SNI) during the initial TLS handshake. This encryption helps protect user privacy by making it more difficult for network intermediaries to identify the specific domain an app is connecting to.

The platform now includes the necessary APIs for networking libraries to implement ECH.

the official website.

News : Modular unlocks Mojo compiler internals with new open source release.

In the last week, Modular published the full source code of the Mojo compiler, completing the transition from a partially open language to a fully open development ecosystem. Until now, only the standard library and selected components were available. The newly released compiler code includes the entire parsing system, type inference engine, borrow-checking logic, multi-layer intermediate representation pipeline, optimization passes and hardware lowering mechanisms. This gives developers complete visibility into how Mojo transforms high-level code into optimized machine instructions.

The open source compiler exposes all internal stages used during code generation. Developers can inspect lexical and syntactic parsing, semantic analysis, SSA construction, control-flow optimization, loop transformations, vectorization, alias analysis and memory layout optimization. The release also includes the hardware-specific lowering logic that prepares IR for CPUs, GPUs and AI accelerators. This level of transparency allows engineers to study, modify or extend Mojo’s compilation pipeline for research, performance tuning or hardware integration.

Mojo is designed for high-performance workloads such as AI kernels, GPU compute pipelines, numerical simulation, parallel processing and accelerator-driven tasks. It provides Python-like syntax but compiles to low-level optimized code comparable to C++ or Rust. This combination enables deterministic memory behavior, fine-grained control over execution and high throughput on modern architectures. Mojo’s design makes it suitable for building inference operators, custom GPU kernels, scientific computing modules and high-performance data processing systems.

Mojo can be used to accelerate existing Python applications by replacing slow sections with Mojo modules. Developers can integrate Mojo into machine learning pipelines, create optimized kernels for inference engines, experiment with new compiler passes or extend the IR pipeline for specialized hardware. Because Mojo is built on top of LLVM, it can target multiple architectures without requiring a separate toolchain.

The official open source repository can be found on this GitHub repo.

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News : Darktable 5.6.1 released.

Darktable 5.6.1 was released on August 27, 2026, as a technical maintenance update focused on stability, memory optimization, and cross‑platform compatibility improvements across Linux, Windows, and Apple macOS systems.

The release enhances the RAW processing pipeline by improving ONNX model handling, adding automatic retry for interrupted downloads, and ensuring full metadata preservation for AI‑generated images, including EXIF, geotags, ratings, and color labels.

The AI RAW denoise module now explicitly rejects Fujifilm Super CCD sensors to prevent incorrect processing results caused by unsupported training data.

On Linux systems, Darktable 5.6.1 introduces native Wayland support in the official AppImage, while maintaining automatic fallback to X11 for environments with incomplete OpenCL or Vulkan implementations.

Fedora users benefit from major fixes related to AMD GPUs running Mesa Rusticl, eliminating pixel corruption and reducing OpenCL buffer allocation failures that previously caused crashes in modules such as denoise and sharpen.

The update resolves multiple stability issues in wavelet decomposition, path masks, and raster masks, which were more visible on Linux distributions using modern toolchains like GCC 14 and Clang 18.

Export reliability is improved through corrections in JPEG 2000 YCbCr 4:2:0 decoding and PNG 16‑bit color handling, addressing inconsistencies found across different libpng and openjpeg versions on Linux and macOS.

On Apple macOS, Darktable 5.6.1 provides separate builds for x86_64 and ARM64, ensuring optimized performance on Apple Silicon devices by improving integration with Metal and OpenCL layers.

The update reduces crash frequency on macOS when applying 3D LUTs or running AI modules, thanks to adjustments aligned with recent Apple Clang and SDK changes.

Windows users receive a fix for AMD OpenCL devices that were previously disabled, restoring full GPU acceleration for AI and pixelpipe operations.

Package distribution is updated across platforms, with Linux builds available through OBS, PPA, Flatpak, and community repositories, while macOS users receive synchronized .dmg installers for both Intel and Apple Silicon architectures.