Playtika Tech & AI

𝗔𝘂𝗴 𝟭𝟰𝘁𝗵, 𝗟𝗲𝗮𝗿𝗻 𝗮𝗯𝗼𝘂𝘁 𝗚𝗲𝗻 𝗔𝗜 𝗣𝗿𝗮𝗰𝘁𝗶𝗰𝗮𝗹 𝗜𝗺𝗽𝗹𝗲𝗺𝗲𝗻𝘁𝗮𝘁𝗶𝗼𝗻𝘀 𝗶𝗻 𝗠𝗼𝗯𝗶𝗹𝗲 𝗚𝗮𝗺𝗲𝘀 at GameDev TLV: Season 1, Episode 2. Leonid Raichman, Solutions Architect at Playtika, reveals how to: ⭐ 𝗤𝗔 𝗺𝗮𝗴𝗶𝗰: Let AI find the bugs ⭐ 𝗗𝗮𝘁𝗮 𝗹𝗮𝗯𝗲𝗹𝗶𝗻𝗴 𝗮𝘁 𝗹𝗶𝗴𝗵𝘁 𝘀𝗽𝗲𝗲𝗱: AI's got this ⭐ 𝗡𝗲𝘅𝘁-𝗹𝗲𝘃𝗲𝗹 𝗮𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀: See the future of player behavior ⭐ 𝗥𝗼𝗯𝗼-𝘁𝗲𝘀𝘁𝗲𝗿𝘀: Your tireless QA army ⭐ 𝗔𝘂𝘁𝗼-𝗱𝗼𝗰𝘀: Wave goodbye to manual documentation ⭐ 𝗦𝘂𝗽𝗲𝗿-𝘀𝗺𝗮𝗿𝘁 𝘀𝘂𝗽𝗽𝗼𝗿𝘁: AI-powered player happiness ⭐ 𝗜𝗻𝘁𝗲𝗹𝗹𝗶𝗴𝗲𝗻𝘁 𝗰𝗵𝗮𝘁: Conversations that understand context ⭐ 𝗘𝗻𝗱𝗹𝗲𝘀𝘀 𝗰𝗼𝗻𝘁𝗲𝗻𝘁 𝗴𝗲𝗻𝗲𝗿𝗮𝘁𝗶𝗼𝗻: Infinite possibilities This talk isn't just theory – it's about practical, implementable solutions that can transform your development process and player experiences. 𝗪𝗵𝗲𝘁𝗵𝗲𝗿 𝘆𝗼𝘂'𝗿𝗲 𝗮 𝘀𝗲𝗮𝘀𝗼𝗻𝗲𝗱 𝗱𝗲𝘃𝗲𝗹𝗼𝗽𝗲𝗿 𝗼𝗿 𝗷𝘂𝘀𝘁 𝘀𝘁𝗮𝗿𝘁𝗶𝗻𝗴 𝗶𝗻 𝘁𝗵𝗲 𝗶𝗻𝗱𝘂𝘀𝘁𝗿𝘆, 𝘁𝗵𝗶𝘀 𝗲𝘃𝗲𝗻𝘁 𝗽𝗿𝗼𝗺𝗶𝘀𝗲𝘀 𝘃𝗮𝗹𝘂𝗮𝗯𝗹𝗲 𝗶𝗻𝘀𝗶𝗴𝗵𝘁𝘀 𝗳𝗼𝗿 𝗲𝘃𝗲𝗿𝘆𝗼𝗻𝗲. 👉 Free entry! Reserve your spot: https://lnkd.in/dB2jZ-SY 🗓 Add to Calendar Wednesday, August 14, 2024 6:00 PM to 9:00 PM IDT 📍 Address: Playtika Hahoshlim St 8 · Herzliya Event Organizers: Miri Nudelman, Ilya Gelman 🔔𝗔𝗳𝗿𝗮𝗶𝗱 𝗼𝗳 𝗺𝗶𝘀𝘀𝗶𝗻𝗴 𝗼𝘂𝘁 𝗼𝗻 𝗼𝘂𝗿 𝗻𝗲𝘄𝘀? Worry not! Place the emoji 😀 in a comment below, and we'll make sure to tag you once we publish anything new. 👑 Here are our VIP alert subscribers: Omer Gerson, Shai Oliva Alon

𝗗𝗶𝗱 𝘆𝗼𝘂 𝗲𝘃𝗲𝗿 𝗱𝗿𝗲𝗮𝗺 𝗵𝗮𝘃𝗶𝗻𝗴 𝗮 𝘁𝘄𝗶𝗻? 𝗡𝗼𝘄 𝘆𝗼𝘂 𝗰𝗮𝗻! Meta has launched 𝗔𝗜 𝗦𝘁𝘂𝗱𝗶𝗼, a platform allowing users to create and share custom AI characters powered by Llama 3.1. Available in the US, AI Studio enables anyone to design AI personalities based on their interests or to extend their online presence. Users can create AIs for various purposes, from cooking assistance to meme generation, and share them across Meta's platforms including Instagram, Messenger, and WhatsApp. Instagram creators can leverage AI Studio to set up AI extensions of themselves, automating responses to common questions and story replies. This feature aims to help creators reach a broader audience while providing fans with quicker responses. 👉 Follow the link in the comments to know more 🐾 𝗢𝘂𝗿 𝗖𝗼𝗺𝗺𝗲𝗻𝘁 That solution can transform how developers communicate with players and how they conduct their marketing activities. It also opens up possibilities for creating AI-driven characters outside or within games, enhancing storytelling and player engagement. 🔔𝗔𝗳𝗿𝗮𝗶𝗱 𝗼𝗳 𝗺𝗶𝘀𝘀𝗶𝗻𝗴 𝗼𝘂𝘁 𝗼𝗻 𝗼𝘂𝗿 𝗻𝗲𝘄𝘀? Worry not! Place the emoji 😀 in a comment below, and we'll make sure to tag you once we publish anything new. 👑 Here are our VIP alert subscribers: Martyn Redstone, Maryanne Collins, Omer Gerson, Meta-Tech Stellar Systems, Marianna Inozemtseva

𝗪𝗮𝗻𝘁 𝘁𝗼 𝗲𝘅𝗰𝗲𝗹? 𝗟𝗼𝗼𝗸 𝗯𝗲𝘆𝗼𝗻𝗱 𝘆𝗼𝘂𝗿 𝗱𝗼𝗺𝗮𝗶𝗻. Sometimes you can find solutions in unexpected places. Researchers at the University of Hull have developed a method to 𝗱𝗲𝘁𝗲𝗰𝘁 𝗔𝗜-𝗴𝗲𝗻𝗲𝗿𝗮𝘁𝗲𝗱 𝗱𝗲𝗲𝗽𝗳𝗮𝗸𝗲 images by analyzing reflections in the subject's eyes. Adejumoke Owolabi, an MSc student, and Professor Kevin Pimbblet presented their findings at the Royal Astronomical Society's National Astronomy Meeting in Hull. They applied techniques used in astronomy to study galaxy images to examine eye reflections in real and AI-generated photos. The method involves analyzing the consistency of light reflections between the left and right eyes. Real images typically show matching reflections, while deepfakes often have inconsistent reflections. 𝗧𝗵𝗲 𝗿𝗲𝘀𝗲𝗮𝗿𝗰𝗵𝗲𝗿𝘀 𝘂𝘀𝗲𝗱 𝘁𝗵𝗲 𝗚𝗶𝗻𝗶 𝗰𝗼𝗲𝗳𝗳𝗶𝗰𝗶𝗲𝗻𝘁 𝗮𝗻𝗱 𝗖𝗔𝗦 (𝗰𝗼𝗻𝗰𝗲𝗻𝘁𝗿𝗮𝘁𝗶𝗼𝗻, 𝗮𝘀𝘆𝗺𝗺𝗲𝘁𝗿𝘆, 𝘀𝗺𝗼𝗼𝘁𝗵𝗻𝗲𝘀𝘀) 𝗽𝗮𝗿𝗮𝗺𝗲𝘁𝗲𝗿𝘀, 𝘁𝗼𝗼𝗹𝘀 𝗼𝗿𝗶𝗴𝗶𝗻𝗮𝗹𝗹𝘆 𝗱𝗲𝘃𝗲𝗹𝗼𝗽𝗲𝗱 𝗳𝗼𝗿 𝗴𝗮𝗹𝗮𝘅𝘆 𝗺𝗼𝗿𝗽𝗵𝗼𝗹𝗼𝗴𝘆 𝗮𝗻𝗮𝗹𝘆𝘀𝗶𝘀, 𝘁𝗼 𝗾𝘂𝗮𝗻𝘁𝗶𝗳𝘆 𝘁𝗵𝗲𝘀𝗲 𝗱𝗶𝗳𝗳𝗲𝗿𝗲𝗻𝗰𝗲𝘀. While not infallible, this technique provides a new approach in the ongoing challenge of detecting AI-generated fake images. Professor Pimbblet emphasized that while this method isn't perfect, it offers a valuable starting point in the "arms race" to identify deepfakes. 👉 The link in the comments to know more 🐾 𝗢𝘂𝗿 𝗖𝗼𝗺𝗺𝗲𝗻𝘁 Fascinating research! This perfectly illustrates how breakthroughs often come from unexpected places. We're excited to see whether AI generation or detection will pull ahead in this high-stakes technological sprint. 🔔𝗔𝗳𝗿𝗮𝗶𝗱 𝗼𝗳 𝗺𝗶𝘀𝘀𝗶𝗻𝗴 𝗼𝘂𝘁 𝗼𝗻 𝗼𝘂𝗿 𝗻𝗲𝘄𝘀? Worry not! Place the emoji 😀 in a comment below, and we'll make sure to tag you once we publish anything new. 👑 Here are our VIP alert subscribers: Martyn Redstone, Maryanne Collins, Omer Gerson, Meta-Tech Stellar Systems, Marianna Inozemtseva

𝗦𝗲𝗲𝗸 𝗗𝗲𝗲𝗽 𝗗𝗲𝗲𝗽𝗦𝗲𝗲𝗸-𝗖𝗼𝗱𝗲𝗿-𝗩𝟮 is an open-source 𝗠𝗶𝘅𝘁𝘂𝗿𝗲-𝗼𝗳-𝗘𝘅𝗽𝗲𝗿𝘁𝘀 (𝗠𝗼𝗘) code language model developed by DeepSeek. It claims to achieve performance comparable to GPT-4-Turbo in code-specific tasks. The model supports 338 programming languages and has a 128K context length. 𝗗𝗲𝗲𝗽𝗦𝗲𝗲𝗸-𝗖𝗼𝗱𝗲𝗿-𝗩𝟮 𝗶𝘀 𝗮𝘃𝗮𝗶𝗹𝗮𝗯𝗹𝗲 𝗶𝗻 𝘁𝘄𝗼 𝘃𝗲𝗿𝘀𝗶𝗼𝗻𝘀: a 16B parameter model (with 2.4B active parameters) and a 236B parameter model (with 21B active parameters). In benchmark evaluations, DeepSeek-Coder-V2 outperforms leading closed-source models (like GPT-4-Turbo, Claude 3 Opus, and Gemini 1.5 Pro) in coding and math benchmarks, achieving a 90.2% score on 𝗛𝘂𝗺𝗮𝗻𝗘𝘃𝗮𝗹 and a 76.2% score on 𝗠𝗕𝗣𝗣. The training process for DeepSeek-Coder-V2 involved a diverse dataset composed of 60% source code, 10% math corpus, and 30% natural language corpus. The source code was sourced from GitHub and CommonCrawl, while the math corpus was collected from CommonCrawl using a specialized pipeline. The model's architecture employs Next-Token-Prediction and Fill-In-Middle (FIM) training strategies to optimize its performance in code completion and other tasks. The model is released under a permissive license, allowing for both research and unrestricted commercial use. 👉 The link to DeepSeek is placed in comments 🐾 𝗢𝘂𝗿 𝗖𝗼𝗺𝗺𝗲𝗻𝘁 DeepSeek-Coder-V2 i could potentially streamline game development process, especially in areas like rapid prototyping, bug fixing, and optimizing game logic. The wide language support and extended context length are particularly appealing for handling complex game systems. However, it is recommended to carefully evaluate how well DeepSeek-Coder-V2 performs with game-specific code and frameworks before considering integration into the workflow. 🔔𝗔𝗳𝗿𝗮𝗶𝗱 𝗼𝗳 𝗺𝗶𝘀𝘀𝗶𝗻𝗴 𝗼𝘂𝘁 𝗼𝗻 𝗼𝘂𝗿 𝗻𝗲𝘄𝘀? Worry not! Place the emoji 😀 in a comment below, and we'll make sure to tag you once we publish anything new. 👑 Here are our VIP alert subscribers: Martyn Redstone, Maryanne Collins, Omer Gerson, Meta-Tech Stellar Systems, Marianna Inozemtseva

𝗖𝗵𝗼𝗶𝗰𝗲𝘀 𝗱𝗲𝗳𝗶𝗻𝗲𝗱 𝗶𝗻𝗻𝗼𝘃𝗮𝘁𝗶𝗼𝗻. Join Oded Nadel as he unveils the next chapter in building Playtika's ML platform! In this part, Oded explores the market research and technologies that shaped their journey. 𝗟𝗲𝗮𝗿𝗻 𝗵𝗼𝘄 𝗣𝗹𝗮𝘆𝘁𝗶𝗸𝗮'𝘀 𝗲𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝘀 𝗲𝘃𝗮𝗹𝘂𝗮𝘁𝗲𝗱 𝗱𝗶𝗳𝗳𝗲𝗿𝗲𝗻𝘁 𝗠𝗟 𝗽𝗹𝗮𝘁𝗳𝗼𝗿𝗺𝘀 𝗮𝗻𝗱 𝗰𝗼𝗻𝘀𝘂𝗹𝘁𝗲𝗱 𝗚𝗮𝗿𝘁𝗻𝗲𝗿 𝘀𝗽𝗲𝗰𝗶𝗮𝗹𝗶𝘀𝘁𝘀 𝘁𝗼 𝗳𝗶𝗻𝗱 𝘁𝗵𝗲 𝗯𝗲𝘀𝘁 𝘀𝗼𝗹𝘂𝘁𝗶𝗼𝗻𝘀 𝗳𝗼𝗿 𝘁𝗵𝗲𝗶𝗿 𝗔𝗜 𝗻𝗲𝗲𝗱𝘀. Discover the key decisions that drove innovation and led to AI success. 🐾 Follow the link Playtika Tech & AI Medium blog 👉 https://lnkd.in/eNDG_7jR 👈 🔔𝗔𝗳𝗿𝗮𝗶𝗱 𝗼𝗳 𝗺𝗶𝘀𝘀𝗶𝗻𝗴 𝗼𝘂𝘁 𝗼𝗻 𝗼𝘂𝗿 𝗻𝗲𝘄𝘀? Worry not! Place the emoji 😀 in a comment below, and we'll make sure to tag you once we publish anything new. 👑 Here are our VIP alert subscribers: Martyn Redstone, Maryanne Collins, Omer Gerson, Meta-Tech Stellar Systems, Marianna Inozemtseva

𝗔𝗿𝗲 𝘆𝗼𝘂 𝗡𝗲𝗼 𝗲𝗻𝗼𝘂𝗴𝗵 𝘁𝗼 𝗳𝗼𝗹𝗹𝗼𝘄 𝘁𝗵𝗲 𝘄𝗵𝗶𝘁𝗲 𝗿𝗮𝗯𝗯𝗶𝘁? CodeRabbit is an AI-driven platform designed to transform the code review process. It offers context-aware, line-by-line feedback on pull requests, enhancing code quality and significantly reducing the time required for manual reviews. Key features include auto-generated summaries, detailed walkthroughs, incremental reviews, and conversational capabilities that allow developers to interact with the AI for deeper insights and suggestions. Does that sound familiar? CodiumAI Let's compare! 𝗙𝗼𝗰𝘂𝘀: While 𝗖𝗼𝗱𝗲𝗥𝗮𝗯𝗯𝗶𝘁 focuses heavily on code review, providing summaries and line-by-line suggestions, 𝗖𝗼𝗱𝗶𝘂𝗺𝗔𝗜 emphasizes test generation and explanation, aiding in code behavior understanding and edge case identification. 𝗜𝗻𝘁𝗲𝗴𝗿𝗮𝘁𝗶𝗼𝗻 𝗮𝗻𝗱 𝗨𝘀𝗮𝗴𝗲: Both tools integrate well with popular IDEs, but 𝗖𝗼𝗱𝗲𝗥𝗮𝗯𝗯𝗶𝘁's continuous incremental review process can provide more immediate feedback during development. 𝗧𝗮𝗿𝗴𝗲𝘁 𝗔𝘂𝗱𝗶𝗲𝗻𝗰𝗲: 𝗖𝗼𝗱𝗶𝘂𝗺𝗔𝗜's free access for individual developers makes it accessible for personal projects, whereas 𝗖𝗼𝗱𝗲𝗥𝗮𝗯𝗯𝗶𝘁's initial trial period followed by a paid plan might be more suitable for teams and professional use. 👉 The link to CodeRabbit is placed in comments 🐾 𝗢𝘂𝗿 𝗖𝗼𝗺𝗺𝗲𝗻𝘁 We haven't explored 𝗖𝗼𝗱𝗲𝗥𝗮𝗯𝗯𝗶𝘁 yet, but the chance to focus more on creative aspects of game development is quite compelling. 🔔𝗔𝗳𝗿𝗮𝗶𝗱 𝗼𝗳 𝗺𝗶𝘀𝘀𝗶𝗻𝗴 𝗼𝘂𝘁 𝗼𝗻 𝗼𝘂𝗿 𝗻𝗲𝘄𝘀? Worry not! Place the emoji 😀 in a comment below, and we'll make sure to tag you once we publish anything new. 👑 Here are our VIP alert subscribers: Martyn Redstone, Maryanne Collins, Omer Gerson, Meta-Tech Stellar Systems, Marianna Inozemtseva

𝗞𝗔𝗡𝘀 𝘃𝘀 𝗠𝗟𝗣𝘀 We came across the compare study of 𝗞𝗼𝗹𝗺𝗼𝗴𝗼𝗿𝗼𝘃-𝗔𝗿𝗻𝗼𝗹𝗱 𝗡𝗲𝘁𝘄𝗼𝗿𝗸𝘀 (KANs) and 𝗠𝘂𝗹𝘁𝗶-𝗟𝗮𝘆𝗲𝗿 𝗣𝗲𝗿𝗰𝗲𝗽𝘁𝗿𝗼𝗻𝘀 (MLPs) across various tasks, controlling for parameters and FLOPs. 𝗦𝘂𝗺𝗺𝗮𝗿𝘆: ▫ MLPs generally outperform KANs in machine learning, computer vision, natural language processing, and audio processing tasks. ▫ KANs excel only in symbolic formula representation, primarily due to their B-spline activation function. ▫ When B-spline is applied to MLPs, their performance in symbolic formula representation improves significantly. ▫ KANs show more severe forgetting issues than MLPs in class-incremental continual learning settings. ▫ The main distinction between KANs and MLPs lies in their activation functions. MLPs use fixed activation functions like ReLU or GELU, which are uniform for all input elements and have no learnable parameters. In contrast, KANs use learnable, element-specific spline functions, which provide a different non-linear transformation for each input element 👉 The link to research paper is placed in comments 🐾 𝗢𝘂𝗿 𝗖𝗼𝗺𝗺𝗲𝗻𝘁 No doubt, selecting the right neural network architecture for specific tasks is crucial at the start of development. In mobile gaming, however, flexibility is the key parameter. Products designed for close cooperation with humans must offer diverse functionality—from AI-driven gameplay to real-time processing—requiring adaptable and efficient solutions. 🔔𝗔𝗳𝗿𝗮𝗶𝗱 𝗼𝗳 𝗺𝗶𝘀𝘀𝗶𝗻𝗴 𝗼𝘂𝘁 𝗼𝗻 𝗼𝘂𝗿 𝗻𝗲𝘄𝘀? Worry not! Place the emoji 😀 in a comment below, and we'll make sure to tag you once we publish anything new. 👑 Here are our VIP alert subscribers: Martyn Redstone, Maryanne Collins, Omer Gerson, Meta-Tech Stellar Systems, Marianna Inozemtseva

𝗝𝘂𝘀𝘁 𝘁𝗲𝗹𝗹 𝗶𝘁 𝘄𝗵𝗮𝘁 𝘆𝗼𝘂 𝘄𝗮𝗻𝘁 𝗪𝗲𝗯𝗦𝗶𝗺.𝗮𝗶 is an AI-powered platform designed to simulate various web technologies and applications with minimal effort. It utilizes AI models like 𝗖𝗹𝗮𝘂𝗱𝗲 𝟯.𝟱 𝗦𝗼𝗻𝗻𝗲𝘁 and 𝗚𝗣𝗧-𝟰𝗼 to generate functional web pages from simple text prompts. Describe your concept or input a URL to generate a simulation, including: ▫ Dynamic HTML structure ▫ Responsive CSS styling ▫ Interactive JavaScript functionality ▫ Realistic content and data WebSim AI's user-friendly interface allows for quick prototyping and experimentation, making web development more accessible to a broader audience. 👉 Follow the link in comments to know more. 🐾 𝗢𝘂𝗿 𝗖𝗼𝗺𝗺𝗲𝗻𝘁 This is an extremely fun tool to play with and see your concepts come to life. It works great for prototyping game mechanics and testing bold ideas. It's definitely a big boost for the development process. 🔔𝗔𝗳𝗿𝗮𝗶𝗱 𝗼𝗳 𝗺𝗶𝘀𝘀𝗶𝗻𝗴 𝗼𝘂𝘁 𝗼𝗻 𝗼𝘂𝗿 𝗻𝗲𝘄𝘀? Worry not! Place the emoji 😀 in a comment below, and we'll make sure to tag you once we publish anything new. 👑 Here are our VIP alert subscribers: Martyn Redstone, Maryanne Collins, Omer Gerson, Meta-Tech Stellar Systems, Marianna Inozemtseva

𝗛𝗲𝗹𝗹𝗼 𝗥𝗲𝗱𝗶𝘀! 𝗪𝗵𝗮𝘁 𝗱𝗼 𝘆𝗼𝘂 𝗿𝗲𝗰𝗼𝗺𝗺𝗲𝗻𝗱? Redis has introduced 𝗥𝗲𝗱𝗶𝘀 𝗩𝗲𝗰𝘁𝗼𝗿 𝗦𝗶𝗺𝗶𝗹𝗮𝗿𝗶𝘁𝘆 𝗦𝗲𝗮𝗿𝗰𝗵 (𝗩𝗦𝗦), a vector database solution designed to enhance generative AI applications. VSS has potential to solve common RAG challenges such as: 𝗛𝗮𝗻𝗱𝗹𝗶𝗻𝗴 𝗛𝗮𝗹𝗹𝘂𝗰𝗶𝗻𝗮𝘁𝗶𝗼𝗻𝘀 𝗮𝗻𝗱 𝗕𝗶𝗮𝘀𝗲𝘀 VSS leverages advanced filtering techniques to minimize AI hallucinations and biases, ensuring more accurate and reliable responses. 𝗢𝗽𝘁𝗶𝗺𝗶𝘇𝗮𝘁𝗶𝗼𝗻 𝗮𝗻𝗱 𝗘𝘃𝗮𝗹𝘂𝗮𝘁𝗶𝗼𝗻 The system's optimization is achieved through advanced indexing algorithms and multi-threaded query execution, evaluated through extensive benchmarking to ensure high performance. 𝗦𝗰𝗮𝗹𝗮𝗯𝗶𝗹𝗶𝘁𝘆 The scalable architecture of VSS supports seamless growth, maintaining performance even with increasing data volumes. The new architecture allows for concurrent query execution, maintaining sub-millisecond latency and averaging under 10 milliseconds for queries. 𝗥𝗲𝗱𝗶𝘀 𝗰𝗹𝗮𝗶𝗺𝘀 𝗮 𝟭𝟲-𝗳𝗼𝗹𝗱 𝗶𝗻𝗰𝗿𝗲𝗮𝘀𝗲 𝗶𝗻 𝗾𝘂𝗲𝗿𝘆 𝘁𝗵𝗿𝗼𝘂𝗴𝗵𝗽𝘂𝘁 𝗰𝗼𝗺𝗽𝗮𝗿𝗲𝗱 𝘁𝗼 𝗶𝘁𝘀 𝗽𝗿𝗲𝗱𝗲𝗰𝗲𝘀𝘀𝗼𝗿. VSS combines Redis's in-memory data structure capabilities with vector search functionality, enabling efficient storage and retrieval of high-dimensional data. It supports various distance metrics and indexing algorithms, making it suitable for a wide range of AI use cases, including recommendation systems and semantic search. 𝗧𝗵𝗲 𝘀𝗼𝗹𝘂𝘁𝗶𝗼𝗻 𝗶𝗻𝘁𝗲𝗴𝗿𝗮𝘁𝗲𝘀 𝘀𝗲𝗮𝗺𝗹𝗲𝘀𝘀𝗹𝘆 𝘄𝗶𝘁𝗵 𝗽𝗼𝗽𝘂𝗹𝗮𝗿 𝗺𝗮𝗰𝗵𝗶𝗻𝗲 𝗹𝗲𝗮𝗿𝗻𝗶𝗻𝗴 𝗳𝗿𝗮𝗺𝗲𝘄𝗼𝗿𝗸𝘀 𝗮𝗻𝗱 𝗰𝗮𝗻 𝗯𝗲 𝗱𝗲𝗽𝗹𝗼𝘆𝗲𝗱 𝗼𝗻-𝗽𝗿𝗲𝗺𝗶𝘀𝗲𝘀 𝗼𝗿 𝗶𝗻 𝘁𝗵𝗲 𝗰𝗹𝗼𝘂𝗱. One of the key advantages of Redis VSS is its ability to handle both structured and unstructured data, allowing for more comprehensive and context-aware AI applications. 👉 Follow the link in comments to know more 🐾 𝗢𝘂𝗿 𝗖𝗼𝗺𝗺𝗲𝗻𝘁 The ability to maintain low latency while scaling to handle massive datasets is crucial for creating immersive, responsive AI-driven gaming experiences. It can support personalized content delivery, player matchmaking systems, and in-game recommendation engines, potentially leading to increased player engagement and retention. 🔔𝗔𝗳𝗿𝗮𝗶𝗱 𝗼𝗳 𝗺𝗶𝘀𝘀𝗶𝗻𝗴 𝗼𝘂𝘁 𝗼𝗻 𝗼𝘂𝗿 𝗻𝗲𝘄𝘀? Worry not! Place the emoji 😀 in a comment below, and we'll make sure to tag you once we publish anything new. 👑 Here are our VIP alert subscribers: Martyn Redstone, Maryanne Collins, Omer Gerson, Meta-Tech Stellar Systems, Marianna Inozemtseva

𝗪𝗵𝗮𝘁'𝘀 𝗻𝗲𝘄, 𝗛𝘂𝗴𝗴𝗶𝗻𝗴 𝗙𝗮𝗰𝗲? Hugging Face has announced the availability of 𝗚𝗼𝗼𝗴𝗹𝗲 𝗖𝗹𝗼𝘂𝗱 𝗧𝗣𝗨𝘀 for users on their Inference Endpoints and Spaces platforms. This collaboration brings Google's custom-made AI hardware to AI builders, offering improved performance and cost-effectiveness for various AI workloads. 𝗛𝗲𝗿𝗲 𝗮𝗿𝗲 𝘀𝗼𝗺𝗲 𝗱𝗲𝘁𝗮𝗶𝗹𝘀: ⭐ Three TPU v5e configurations are available: 1-core, 4-core, and 8-core options. ⭐ Pricing ranges from $1.375 to $11.00 per hour. ⭐ Optimum TPU, an open-source library, facilitates easy deployment of Hugging Face models on Google TPUs. ⭐ Initially supported models include Gemma, Llama, and Mistral. ⭐ Users can upgrade their Hugging Face Spaces to run on TPUs through the Settings menu. 👉 Follow the link in comments to know more 🐾 𝗢𝘂𝗿 𝗖𝗼𝗺𝗺𝗲𝗻𝘁 This development is a significant step in making advanced AI hardware accessible to a broader range of developers and researchers, potentially accelerating innovation in the field of artificial intelligence. 🔔𝗔𝗳𝗿𝗮𝗶𝗱 𝗼𝗳 𝗺𝗶𝘀𝘀𝗶𝗻𝗴 𝗼𝘂𝘁 𝗼𝗻 𝗼𝘂𝗿 𝗻𝗲𝘄𝘀? Worry not! Place the emoji 😀 in a comment below, and we'll make sure to tag you once we publish anything new. 👑 Here are our VIP alert subscribers: Martyn Redstone, Maryanne Collins, Omer Gerson, Meta-Tech Stellar Systems, Marianna Inozemtseva