AI & Technology Updates

  • Apple Partners with Google for Siri’s AI Upgrade


    Google beats OpenAI to the punch: Apple signs exclusive Gemini deal for Siri, sidelining ChatGPT.Apple has reportedly signed an exclusive deal with Google to integrate its Gemini AI technology into the next generation of Siri, sidelining OpenAI's ChatGPT. This partnership suggests Apple is opting for Google's robust infrastructure and resources over OpenAI's offerings, potentially impacting OpenAI's position in the consumer AI market. The decision reflects Apple's strategy to align with an established partner, possibly prioritizing reliability and scalability. This matters because it indicates a significant shift in the competitive landscape of AI technology and partnerships among major tech companies.


  • Improving Document Extraction in Insurance


    So I've been losing my mind over document extraction in insurance for the past few years and I finally figured out what the right approach is.Document extraction in the insurance industry often faces significant challenges due to the inconsistent structure of documents across different states and providers. Many rely on large language models (LLMs) for extraction, but these models struggle in production environments due to their lack of understanding of document structure. A more effective approach involves first classifying the document type before routing it to a type-specific extraction process, which can significantly improve accuracy. Additionally, using vision-language models that account for document layout, fine-tuning models on industry-specific documents, and incorporating human corrections into training can further enhance performance and scalability. This matters because improving document extraction accuracy can significantly reduce manual validation efforts and increase efficiency in processing insurance documents.


  • Meta Delays Ray-Ban Display Global Launch


    Meta hits pause on Ray-Ban Display expansion plansMeta has decided to delay the international launch of its Ray-Ban Display smart glasses in countries like France, Italy, Canada, and the UK until after early 2026 due to overwhelming demand and limited inventory. Since their release last fall, the glasses have generated significant interest, leading to extended waitlists that now stretch well into 2026. Meta plans to prioritize fulfilling orders within the US while reassessing its strategy for international distribution. The delay is likely to be disappointing for international customers, especially given the positive reviews, such as The Verge's Victoria Song describing them as the best smart glasses she has tried. This matters because it highlights the challenges companies face in meeting global demand for innovative tech products.


  • Connect LLMs to Knowledge Sources with SurfSense


    Connect any LLM to all your knowledge sources and chat with itSurfSense is an open-source solution designed to connect any Large Language Model (LLM) to various internal knowledge sources, enabling real-time chat capabilities for teams. It serves as an alternative to platforms like NotebookLM and Perplexity, offering integration with over 15 connectors including Search Engines, Drive, Calendar, and Notion. Key features include deep agentic agent role-based access control (RBAC) for teams, support for over 100 LLMs, 6000+ embedding models, and compatibility with more than 50 file extensions. Additionally, SurfSense provides local text-to-speech and speech-to-text support, and a cross-browser extension for saving dynamic web pages. This matters because it enhances collaborative efficiency and accessibility to information across various platforms and tools.


  • Benchmarking 671B DeepSeek on RTX PRO 6000S


    Benchmark results for 671B DeepSeek in llama.cpp on 8 x RTX PRO 6000S (layer split mode)The benchmark results for the 671B DeepSeek model, tested on an 8 x RTX PRO 6000S setup in layer split mode, show significant performance metrics across various configurations. The tests, conducted on the modified DeepSeek V3.2 model, indicate that the model's performance remains consistent across different versions, including R1, V3, V3.1, and V3.2 with dense attention. The results highlight the model's efficiency in terms of throughput and latency, with specific configurations such as Q4_K_M and Q8_0 demonstrating varying levels of performance based on parameters like batch size and depth. These insights are crucial for optimizing AI model deployments on high-performance computing setups.