PCWorld reports that Home Assistant has launched a major update to its default overview dashboard, replacing the old cluttered interface with a clean, organized design featuring three main sections. The redesigned dashboard includes Favorites for quick device access, Areas for room-specific control, and Summaries that aggregate devices by type with easy setup options.

We introduce Habitat 2.0 (H2.0), a simulation platform for training virtual robots in interactive 3D environments and complex physics-enabled scenarios. We make comprehensive contributions to all levels of the embodied AI stack - data, simulation, and benchmark tasks.

Here's how I cut down my family's reliance on Alexa. Until recently, my smart home setup was in chaos. After years of testing, buying, and upgrading to the latest smart home gadgets in an attempt to make my life easier, it became a bloated mess that was actually making it more complicated. My Alexa, Google Home, and Apple Home apps were awash with dead devices, duplicates, and automations that simply didn't work. My Hue Bridge, trying desperately to tie it all together, was creaking at the seams.

Maybe it was the sight of Sengled users literally left in the dark by their useless Wi-Fi bulbs, maybe it was another price hike, or just an overall sense that my smart devices weren't truly under my control. Whatever the reason, I'd developed a growing desire to build a smart home setup that wasn't a hostage to the cloud. Specifically, I'm talking about a locally hosted smart home setup, and I'm currently in the process of building one. And while I'm a smart home expert thanks to my six years' experience here at TechHive, I'm quickly realizing how much I still don't know as I tackle the steep learning curve of a DIY smart home. This isn't a step-by-step guide of how to build your own smart home system--that might come later--but more of a journal about where I am in my self-hosted smart home journey, where I started, and what I'm hoping to achieve.

Multimodal IoT systems coordinate diverse IoT devices to deliver human-centered services. The ability to incorporate new IoT devices under the management of a centralized platform is an essential requirement. However, it requires significant human expertise and effort to program the complex IoT integration code that enables the platform to understand and control the device functions. Therefore, we propose AutoBridge to automate IoT integration code generation. Specifically, AutoBridge adopts a divide-and-conquer strategy: it first generates device control logic by progressively retrieving device-specific knowledge, then synthesizes platformcompliant integration code using platform-specific knowledge. To ensure correctness, AutoBridge features a multi-stage debugging pipeline, including an automated debugger for virtual IoT device testing and an interactive hardware-in-the-loop debugger that requires only binary user feedback (yes and no) for real-device verification. We evaluate AutoBridge on a benchmark of 34 IoT devices across two open-source IoT platforms. The results demonstrate that AutoBridge can achieves an average success rate of 93.87% and an average function coverage of 94.87%, without any human involvement. With minimal binary yes and no feedback from users, the code is then revised to reach 100% function coverage. A user study with 15 participants further shows that AutoBridge outperforms expert programmers by 50% to 80% in code accuracy, even when the programmers are allowed to use commercial code LLMs.

Equipped with artificial intelligence (AI) and advanced sensing capabilities, social robots are gaining interest among consumers in the United States. These robots seem like a natural evolution of traditional smart home devices. However, their extensive data collection capabilities, anthropomorphic features, and capacity to interact with their environment make social robots a more significant security and privacy threat. Increased risks include data linkage, unauthorized data sharing, and the physical safety of users and their homes. It is critical to investigate U.S. users' security and privacy needs and concerns to guide the design of social robots while these devices are still in the early stages of commercialization in the U.S. market. Through 19 semi-structured interviews, we identified significant security and privacy concerns, highlighting the need for transparency, usability, and robust privacy controls to support adoption. For educational applications, participants worried most about misinformation, and in medical use cases, they worried about the reliability of these devices. Participants were also concerned with the data inference that social robots could enable. We found that participants expect tangible privacy controls, indicators of data collection, and context-appropriate functionality.

Reolink has become the first security camera manufacturer to obtain Works With Home Assistant certification for its Wi-Fi home security cameras. This means Reolink's cameras--not including its 4G models--can now process video feeds, AI alerts, and device controls entirely within users' home networks to enhance user privacy. Home Assistant is a free and open-source smart home software platform managed by the Open Home Foundation. It has been embraced by many DIY smart home enthusiasts, and it can run on lots of different hardware, ranging from Raspberry Pi and Arm processors to the 64-bit x86 architecture commonly found in Mini PCs. It can even operate as a virtual machine on a laptop or desktop running MacOS or Windows.

This paper investigates whether Large Language Models (LLMs), fine-tuned on synthetic but domain-representative data, can perform the twofold task of (i) slot and intent detection and (ii) natural language response generation for a smart home assistant, while running solely on resource-limited, CPU-only edge hardware. We fine-tune LLMs to produce both JSON action calls and text responses. Our experiments show that 16-bit and 8-bit quantized variants preserve high accuracy on slot and intent detection and maintain strong semantic coherence in generated text, while the 4-bit model, while retaining generative fluency, suffers a noticeable drop in device-service classification accuracy. Further evaluations on noisy human (non-synthetic) prompts and out-of-domain intents confirm the models' generalization ability, obtaining around 80--86\% accuracy. While the average inference time is 5--6 seconds per query -- acceptable for one-shot commands but suboptimal for multi-turn dialogue -- our results affirm that an on-device LLM can effectively unify command interpretation and flexible response generation for home automation without relying on specialized hardware.

As voice assistants cement their place in our technologically advanced society, there remains a need to cater to the diverse linguistic landscape, including colloquial forms of low-resource languages. Our study introduces the first-ever comprehensive dataset for intent detection and slot filling in formal Bangla, colloquial Bangla, and Sylheti languages, totaling 984 samples across 10 unique intents. Our analysis reveals the robustness of large language models for tackling downstream tasks with inadequate data. The GPT-3.5 model achieves an impressive F1 score of 0.94 in intent detection and 0.51 in slot filling for colloquial Bangla.

CyberGuy shows you how to create and customize events in the calendar app. When the pandemic hit and so many aspects of our lives went digital, older adults had to get accustomed to using more technology like Facetime, Zoom and more. Now, older adults have become a lot more tech-savvy and even have their favorite devices that they enjoy using. Here are five tech obsessions that older adults have adopted over the last few years. Perhaps the most popular devices among older adults are ones like Apple Watches, FitBits and other products that help people keep track of their health.