Projects with this topic
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Offline-first, CPU-first AI reader companion that narrates and analyzes stories locally using Ollama and structured feedback JSON.
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NyayaBloom is an AI-powered Judicial Intelligence Platform designed to democratize legal knowledge and improve judicial transparency across India. The platform enables citizens, lawyers, law students, researchers, journalists, and judicial professionals to access legal information, understand judicial processes, explore case precedents, and improve legal literacy through a multilingual AI-powered ecosystem. Instead of replacing judges, courts, or legal professionals, NyayaBloom serves as an intelligent legal knowledge assistant that simplifies legal information and promotes informed civic participation.
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StoryMirror CLI is a CPU-first command-line application that transforms unstructured stories into meaningful insights through local AI processing. It provides story analysis, multilingual text-to-speech (TTS), runtime performance metrics, and customizable terminal themes, all while running on a local FastAPI backend.
Designed for offline and resource-efficient environments, StoryMirror performs inference on the CPU without requiring a dedicated GPU, making it suitable for developers, students, and writers who need an accessible AI-powered storytelling tool.
Key Features📖 Analyze stories from text or files🎙️ Generate multilingual text-to-speech narration🎨 Customize the CLI with multiple themes📊 Monitor CPU usage, memory, and inference performance⚡ Run entirely on a local FastAPI backend💻 CPU-first design with no GPU dependency🌐 Supports offline or local deployment🚀 Simple command-line interface for quick workflowsUpdated -
Bhawesh Choudhary / AI_ARCH
CI/CD Catalog (unpublished)AI-powered multilingual software architecture generator supporting Gemini BYOK and Ollama(local setup) for startup planning and MVP design.
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AI-powered open-source repository onboarding assistant that analyzes GitHub repositories and provides onboarding guidance using Gemini and Ollama.
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The Image Captioning project is an AI-based system that automatically generates textual descriptions for images. It combines computer vision and natural language processing (NLP) techniques to understand the content of an image and produce a human-readable caption.
The system uses deep learning models, typically a Convolutional Neural Network (CNN) for extracting image features and a Recurrent Neural Network (RNN) or Transformer-based model for generating sentences. This enables applications such as assisting visually impaired users, enhancing image search engines, and automating social media content tagging.
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