Privacy
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Plano-Orchestrator: Fast Open Source LLMs for Multi-Agent Systems
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Plano-Orchestrator is a new family of open-source large language models (LLMs) designed for rapid multi-agent orchestration, developed by the Katanemo research team. These models prioritize privacy, speed, and performance, enabling them to efficiently determine which agents should handle user requests and in what order, acting as a supervisory agent in complex multi-agent systems. Suitable for various domains, including general chat, coding tasks, and extensive multi-turn conversations, Plano-Orchestrator is optimized for low-latency production environments. This innovation aims to enhance the real-world performance and efficiency of multi-agent systems, offering a valuable tool for developers focused on integrating diverse agent functionalities.
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Differential Privacy in Synthetic Photo Albums
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Differential privacy (DP) offers a robust method to protect individual data in datasets, ensuring privacy even during analysis. Traditional approaches to implementing DP can be complex and error-prone, but generative AI models like Gemini provide a more streamlined solution by creating a private synthetic version of the dataset. This synthetic data retains the general patterns of the original without exposing individual details, allowing for safe application of standard analytical techniques. A new method has been developed to generate synthetic photo albums, addressing the challenge of maintaining thematic coherence and character consistency across images, which is crucial for modeling complex, real-world systems. This approach effectively translates complex image data to text and back, preserving essential semantic information for analysis. This matters because it simplifies the process of ensuring data privacy while enabling the use of complex datasets in AI and machine learning applications.
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LLM in Browser for Infinite Dropdowns
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A new site demonstrates the capabilities of running a language model (LLM) locally in the browser, providing an innovative way to generate infinite dropdowns. This approach utilizes minimal code, with the entire functionality being implemented in under 50 lines of HTML, showcasing the efficiency and potential of local LLMs. The project is accessible for exploration and experimentation, with resources available on both a static site and a GitHub repository. This matters because it highlights the potential for more efficient and accessible AI applications directly in web browsers, reducing reliance on server-side processing.
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Local AI Image Upscaler for Android
Read Full Article: Local AI Image Upscaler for Android![[P] I built a fully local AI Image Upscaler for Android because I didn't want to rely on cloud servers.](https://www.tweakedgeek.com/wp-content/uploads/2025/12/featured-article-6253-300x171.png)
RendrFlow is an Android app developed to upscale low-resolution images using AI models directly on the device, eliminating the need for cloud servers and ensuring user privacy. The app offers upscaling options up to 16x resolution and includes features like hardware control for CPU and GPU usage, batch processing, and additional tools such as an AI background remover and magic eraser. The developer seeks user feedback on performance across different devices, particularly regarding the app's "Ultra" models and the thermal management of various phones in GPU Burst mode. This matters because it provides a privacy-focused solution for image enhancement without relying on external servers.
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Apple Pauses App Store Changes in Texas
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Apple has decided to pause its planned changes to the App Store in Texas following a federal judge's decision to block a new age-verification law, citing First Amendment concerns. The law, known as the App Store Accountability Act, would have required app stores to verify user ages and obtain parental consent for users under 18, while also sharing age data with developers. Apple had announced new requirements for apps in Texas to comply with the law, including the use of Family Sharing groups for minors and updated APIs for developers. Despite the legal setback for Texas lawmakers, Apple continues to offer developer tools for age assurance, emphasizing privacy concerns over the collection of sensitive information. This matters because it highlights the ongoing tension between tech companies and lawmakers over privacy and user data protection.
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Canvas Agent for Gemini: Image Generation Interface
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The Canvas Agent for Gemini is a frontend application designed to streamline the process of image generation through an organized, canvas-based interface. It features an infinite canvas that allows users to manage and generate images in batches efficiently. Additionally, the application enables users to reference existing images using u/mentions, enhancing the workflow by integrating previously created content seamlessly. As a pure frontend app, it operates entirely locally, ensuring user data remains private and secure. This development is significant as it provides a powerful tool for creators to manage complex image generation tasks without compromising on privacy.
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Google’s FunctionGemma: AI for Edge Function Calling
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Google has introduced FunctionGemma, a specialized version of the Gemma 3 270M model, designed specifically for function calling and optimized for edge workloads. FunctionGemma retains the Gemma 3 architecture but focuses on translating natural language into executable API actions rather than general chat. It uses a structured conversation format with control tokens to manage tool definitions and function calls, ensuring reliable tool use in production. The model, trained on 6 trillion tokens, supports a 256K vocabulary optimized for JSON and multilingual text, enhancing token efficiency. FunctionGemma's primary deployment target is edge devices like phones and laptops, benefiting from its compact size and quantization support for low-latency, low-memory inference. Demonstrations such as Mobile Actions and Tiny Garden showcase its ability to perform complex tasks on-device without server calls, achieving up to 85% accuracy after fine-tuning. This development signifies a step forward in creating efficient, localized AI solutions that can operate independently of cloud infrastructure, crucial for privacy and real-time applications.
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AI Police Cameras Tested in Canada
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AI-powered police body cameras are being tested in a Canadian city, where they are used to recognize faces from a 'watch list', raising concerns about privacy and surveillance. This technology, once considered controversial, is now being trialed as a tool to enhance law enforcement capabilities, but it also sparks debates about the ethical implications of facial recognition and AI in policing. While proponents argue that these cameras can improve public safety and efficiency, critics worry about potential misuse and the erosion of civil liberties. The integration of AI in law enforcement highlights the ongoing tension between technological advancement and the protection of individual rights. This matters because it reflects broader societal challenges in balancing security and privacy in the age of AI.