Software-update - Home Assistant 2026.4.0
Versie 2026.4.0 van Home Assistant is uitgebracht. Home Assistant is een opensourceplatform voor domotica en is bedoeld om slimme apparaten in de gaten te houden en aan te sturen. Denk daarbij aan verlichting, schakelaars, sloten, camera's, audiovisuele apparatuur, witgoed, en sensoren voor aanwezigheid, temperatuur, vochtigheid, en zo meer. Voor meer informatie over Home Assistant verwijzen we naar deze pagina en ons eigen forum. De volledige releasenotes voor deze uitgave zijn hier te vinden. Dit is de aankondiging daaruit: 2026.4: Infrared never left the chat
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b8634
chat : add Granite 4.0 chat template with correct tool_call role mapping ( #20804 ) chat : add Granite 4.0 chat template with correct tool_call role mapping Introduce LLM_CHAT_TEMPLATE_GRANITE_4_0 alongside the existing Granite 3.x template (renamed LLM_CHAT_TEMPLATE_GRANITE_3_X ). The Granite 4.0 Jinja template uses XML tags and maps the assistant_tool_call role to assistant . Without a matching C++ handler, the fallback path emits the literal role assistant_tool_call which the model does not recognize, breaking tool calling when --jinja is not used. Changes: Rename LLM_CHAT_TEMPLATE_GRANITE to LLM_CHAT_TEMPLATE_GRANITE_3_X (preserves existing 3.x behavior unchanged) Add LLM_CHAT_TEMPLATE_GRANITE_4_0 enum, map entry, and handler Detection: + ( or ) → 4.0, otherwise → 3.x Add production Gr
Rusty Flying Robots: Learning a Full Robotics Stack with Real-Time Operation on an STM32 Microcontroller in a 9 ECTS MS Course
arXiv:2604.00032v1 Announce Type: cross Abstract: We describe a novel masters-level projects class that teaches robotics along the traditional robotics pipeline (dynamics, state estimation, controls, planning). One key motivational part is that students have to directly apply the algorithms they learn on a highly constrained compute platform, effectively making a robot fly. We teach nonlinear algorithms as deployed in state-of-the-art flight stacks such as PX4. Didactically, we rely on two core concepts: 1) avoidance of provided black-box software infrastructure, and 2) usage of the safe and efficient programming language Rust that is used on the PC (for simulation) and an STM32 microcontroller (for robot deployment). We discuss our methodology and the student feedback over two years with
Desktop Canary v2.1.48-canary.22
🐤 Canary Build — v2.1.48-canary.22 Automated canary build from canary branch. ⚠️ Important Notes This is an automated canary build and is NOT intended for production use. Canary builds are triggered by build / fix / style commits on the canary branch. May contain unstable or incomplete changes . Use at your own risk. It is strongly recommended to back up your data before using a canary build. 📦 Installation Download the appropriate installer for your platform from the assets below. Platform File macOS (Apple Silicon) .dmg (arm64) macOS (Intel) .dmg (x64) Windows .exe Linux .AppImage / .deb
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sync : ggml macOS/iOS: macOS Apple Silicon (arm64) macOS Intel (x64) iOS XCFramework Linux: Ubuntu x64 (CPU) Ubuntu arm64 (CPU) Ubuntu s390x (CPU) Ubuntu x64 (Vulkan) Ubuntu arm64 (Vulkan) Ubuntu x64 (ROCm 7.2) Ubuntu x64 (OpenVINO) Windows: Windows x64 (CPU) Windows arm64 (CPU) Windows x64 (CUDA 12) - CUDA 12.4 DLLs Windows x64 (CUDA 13) - CUDA 13.1 DLLs Windows x64 (Vulkan) Windows x64 (SYCL) Windows x64 (HIP) openEuler: openEuler x86 (310p) openEuler x86 (910b, ACL Graph) openEuler aarch64 (310p) openEuler aarch64 (910b, ACL Graph)
b8634
chat : add Granite 4.0 chat template with correct tool_call role mapping ( #20804 ) chat : add Granite 4.0 chat template with correct tool_call role mapping Introduce LLM_CHAT_TEMPLATE_GRANITE_4_0 alongside the existing Granite 3.x template (renamed LLM_CHAT_TEMPLATE_GRANITE_3_X ). The Granite 4.0 Jinja template uses XML tags and maps the assistant_tool_call role to assistant . Without a matching C++ handler, the fallback path emits the literal role assistant_tool_call which the model does not recognize, breaking tool calling when --jinja is not used. Changes: Rename LLM_CHAT_TEMPLATE_GRANITE to LLM_CHAT_TEMPLATE_GRANITE_3_X (preserves existing 3.x behavior unchanged) Add LLM_CHAT_TEMPLATE_GRANITE_4_0 enum, map entry, and handler Detection: + ( or ) → 4.0, otherwise → 3.x Add production Gr
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Relax prefill parser to allow space. ( #21240 ) Relax prefill parser to allow space. Move changes from prefix() to parser generation Only allow spaces if we're not having a pure content parser next macOS/iOS: macOS Apple Silicon (arm64) macOS Intel (x64) iOS XCFramework Linux: Ubuntu x64 (CPU) Ubuntu arm64 (CPU) Ubuntu s390x (CPU) Ubuntu x64 (Vulkan) Ubuntu arm64 (Vulkan) Ubuntu x64 (ROCm 7.2) Ubuntu x64 (OpenVINO) Windows: Windows x64 (CPU) Windows arm64 (CPU) Windows x64 (CUDA 12) - CUDA 12.4 DLLs Windows x64 (CUDA 13) - CUDA 13.1 DLLs Windows x64 (Vulkan) Windows x64 (SYCL) Windows x64 (HIP) openEuler: openEuler x86 (310p) openEuler x86 (910b, ACL Graph) openEuler aarch64 (310p) openEuler aarch64 (910b, ACL Graph)
Geometric Visual Servo Via Optimal Transport
arXiv:2506.02768v2 Announce Type: replace Abstract: When developing control laws for robotic systems, the principle factor when examining their performance is choosing inputs that allow smooth tracking to a reference input. In the context of robotic manipulation, this involves translating an object or end-effector from an initial pose to a target pose. Robotic manipulation control laws frequently use vision systems as an error generator to track features and produce control inputs. However, current control algorithms don't take into account the probabilistic features that are extracted and instead rely on hand-tuned feature extraction methods. Furthermore, the target features can exist in a static pose thus allowing a combined pose and feature error for control generation. We present a geo
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