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#cplusplus#ann_search#embedded_database#rag#vector_search#vectordb Zvec is a lightweight, open-source vector database built on Alibaba's Proxima engine that searches billions of vectors in milliseconds. You can install it instantly with a single command and start using it within seconds—no servers or complex configuration needed. It supports both dense and sparse vector embeddings, hybrid search combining semantic similarity with filters, and runs anywhere your code runs, from notebooks to edge devices. The key benefit is that you get production-grade, low-latency similarity search with minimal setup, making it ideal for AI applications like semantic search, recommendation systems, and retrieval-augmented generation without the overhead of traditional database infrastructure. https://github.com/alibaba/zvec

#Вакансия#RAG Всем привет! На связи команда Sprint Labs 🚀 Мы в поисках AI-инженера / Специалиста по RAG для проекта по созданию интеллектуального ассистента для психотерапевта на основе RAG-архитектуры. ✨Краткий обзор:✨ Мы ищем опытного AI-инженера для разработки прототипа интеллектуальной системы-ассистента, предназначенной для когнитивно-поведенческих терапевтов. Цель системы — автоматизировать процесс анализа данных из диагностических опросников и генерировать на их основе релевантные терапевтические гипотезы и рекомендации по применению конкретных техник. ⚡️Основная задача:⚡️ Разработать end-to-end RAG-пайплайн, который реализует следующий двухэтапный воркфлоу: - На вход система получает структурированные данные из заполненного пациентом диагностического бланка в формате JSON. - На выходе система генерирует структурированный отчет для терапевта, который включает: - Краткий диагностический анализ. - Список ключевых терапевтических мишеней. - Рекомендации по конкретным техникам с обоснованием их применимости. 🚀Ключевые технические и интеллектуальные вызовы:🚀 - Качество сегментации: От вас потребуется не просто техническая реализация, а продуманный подход к разбиению узкоспециализированных текстов на осмысленные единицы. - Логика цепочки вызовов: Необходимо грамотно спроектировать передачу данных между двумя RAG-шагами, где вывод одного является входом для поиска другого. - Точность и надежность: Система должна быть максимально точной и основываться исключительно на предоставленных источниках. Это не чат-бот общего назначения, а экспертная система. ⚡️Требуемые навыки и квалификация:⚡️ Обязательно:🔗 -Глубокое понимание и практический опыт построения RAG-систем. - Сильные навыки в Python и NLP. - Продвинутый промпт-инжиниринг. Крайне желательно:🔗 Опыт работы с фреймворками LangChain или LlamaIndex. Опыт построения многоэтапных (multi-step) или агентских LLM-воркфлоу. Будет плюсом:🔗 -Интерес к психологии или опыт работы с экспертными системами в других областях. ⚡️Условия⚡️ - Ставка в час от 1.500р - Проект с возможностью дальнейших совместных задач 🚀Ожидаемые результаты:🚀 - Рабочий прототип системы. - Документация с описанием архитектуры, выбранных моделей, стратегии сегментации и структуры промптов. - Исходный код с комментариями. Если тебя заинтересовала эта задача- отправляй резюме и пару слов о себе и своем опыте @NikaFromSL✅

🔖 Chunking Strategies for LLM Applications | Pinecone #pinboard#llm#rag Learn about effective chunking strategies for improved memory retention. https://www.pinecone.io/learn/chunking-strategies/

#cplusplus LiteRT-LM is Google's free, high-speed tool for running large language models like Gemma 4 on phones, computers, Raspberry Pi, and more, with GPU boosts, vision/audio support, and tool use for smart apps. It powers AI in Chrome, Pixel Watch, and Chromebook—try it fast via CLI command on Linux, macOS, Windows, or Pi without coding. You benefit by easily deploying fast, private on-device AI for apps, prototyping, or edge projects, saving time and cloud costs. https://github.com/google-ai-edge/LiteRT-LM

#cplusplus LiteRT is Google's free framework for running fast machine learning and generative AI on phones, computers, and web without cloud help. It uses GPU and NPU for up to 2x speed boosts, zero-copy data handling, and async execution on Android, iOS, Linux, and more, plus easy PyTorch model conversion. You benefit by building quick, private apps like real-time image editing or chatbots that work offline on everyday devices, saving battery and boosting performance. https://github.com/google-ai-edge/LiteRT

#cplusplus Godot RE Tools let you fully recover Godot projects from APK, PCK, or EXE files by extracting resources, decompiling GDScripts, recreating the project.godot file, and converting resources to original formats. It supports Godot 4.x, 3.x, and 2.x via easy GUI drag-and-drop or command line. This helps you restore lost projects quickly, edit games for modding, or regain work from exports/backups without starting over. https://github.com/GDRETools/gdsdecomp

#cplusplus Electrobun lets you build ultra-fast, tiny desktop apps in TypeScript for macOS, Windows, and Linux. Start with `npx electrobun init` for quick templates, get ~12-14MB bundles using system webviews and Bun runtime, and send tiny 14KB updates via bsdiff patches. It offers typed RPC for main-webview communication, fast startup under 50ms, and full tools for building, signing, and shipping. You benefit by coding once in familiar TypeScript, skipping Electron's bloat or Tauri's Rust, to ship performant apps in minutes with easy distribution and low user downloads. https://github.com/blackboardsh/electrobun

#cplusplus Pyrite64 is an open-source visual editor and engine for creating 3D games that run on real Nintendo 64 consoles or accurate emulators. It uses community libraries like Libdragon and tiny3d instead of proprietary Nintendo SDKs, avoiding legal complications. The tool features automatic toolchain installation, Blender model importing, HDR and bloom rendering, and a node-graph editor for scripting. You benefit by building authentic N64 games without wrestling with outdated 1990s development tools—the integrated environment handles compilers, dependencies, and asset management automatically, letting you focus on game creation rather than technical setup. https://github.com/HailToDodongo/pyrite64

#cplusplus FlashMLA is DeepSeek's optimized attention library that makes AI models run faster and use less memory. It works with advanced NVIDIA GPUs to speed up how language models process information, achieving up to 660 trillion floating-point operations per second. The library supports both dense and sparse attention modes, meaning it can focus on important tokens while skipping less relevant ones, reducing computational waste. For you, this means faster AI responses, lower costs for running large language models, and better performance on tasks like chatbots and code generation. The technology is open-source and integrates with popular AI frameworks like PyTorch and Hugging Face, making it accessible for developers building next-generation AI applications. https://github.com/deepseek-ai/FlashMLA

#cplusplus Tile Language (tile-lang) is a simple, Python-like programming language that helps you write fast GPU and CPU code for tasks like matrix multiplication and attention mechanisms. It uses a smart compiler based on TVM to optimize your code automatically, so you get high performance without dealing with complex low-level details. Tile-lang supports many devices including NVIDIA and AMD GPUs and offers examples and tools to help you write, test, and profile your kernels easily. Installing it is straightforward via pip or from source. This lets you develop efficient AI and math kernels faster and with less effort, improving productivity and performance on modern hardware. https://github.com/tile-ai/tilelang

#cplusplus Media Downloader is a user-friendly program that helps you download videos and playlists from many websites using a simple graphical interface. It supports multiple tools like yt-dlp and others through extensions, allowing you to download media in different formats and do many downloads at once or batch downloads from files. You can also manage playlist subscriptions and use it in many languages. It works on Windows, macOS, and Linux, with portable and installer versions available. This tool saves you time and effort by making media downloads easy, organized, and flexible, letting you watch offline without interruptions or internet issues. https://github.com/mhogomchungu/media-downloader

#cplusplus Monad is a fast, scalable Layer 1 blockchain fully compatible with Ethereum's EVM, allowing you to run Ethereum smart contracts without changes. It improves speed by separating consensus (agreement on transaction order) from execution (processing transactions), enabling parallel transaction execution and reaching 10,000 transactions per second with 1-second finality. Monad uses a custom EVM and a special database (MonadDb) optimized for parallel state access, reducing delays. This means you get much faster, cheaper transactions while keeping Ethereum compatibility, making it easier for developers and users to adopt and benefit from high performance and scalability. https://github.com/category-labs/monad