Search Results for "cloud mini project"

Private Mind is a mobile AI app built around fully offline, on-device intelligence. It is designed for users who want AI conversations without sending personal data to cloud servers. The app keeps conversations and data on the device, which makes it useful for privacy-sensitive workflows, personal notes, local assistance, and offline use. It supports customizable AI behavior, including choosing supported models, uploading models, and adjusting system prompts. The project also includes built-in benchmarking so users can compare performance, speed, and memory usage on their own hardware. ...

...Protenix provides a complete pipeline for turning protein sequences (with optional MSA / sequence alignment) or structural inputs (e.g. PDB/CIF) into full 3D atomic-level structure predictions. It supports both “full” models and lightweight variants such as “Protenix-Mini,” offering a trade-off between speed/compute cost and predictive accuracy — making structure prediction accessible even in resource-constrained environments. The project also includes support for constraints (e.g., specifying residue- or atom-level contact constraints, or pocket constraints) to guide predictions toward biologically or experimentally relevant conformations, which enhances its utility for tasks like modeling complexes, ligands, or antibody–antigen interactions.

The Omi project is an open-source AI wearable ecosystem developed by Based Hardware that combines hardware, software, and cloud infrastructure to create a persistent “second brain” for capturing and processing real-world interactions. It is designed as a system that continuously listens to conversations and monitors screen activity, converting this input into structured data such as transcripts, summaries, and actionable insights in real time.

qvac-fabric-llm.cpp is a cross-platform large language model inference and fine-tuning engine built as an advanced fork of llama.cpp, designed to run efficiently across desktops, mobile devices, and heterogeneous GPU environments. The project focuses on removing hardware limitations traditionally associated with LLM deployment by enabling support for a wide range of backends, including Vulkan, Metal, CUDA, and CPU, making it accessible on devices ranging from smartphones to enterprise servers. It introduces native LoRA fine-tuning capabilities that can be executed directly on consumer hardware, allowing developers to train and adapt models locally without relying on cloud infrastructure. ...

Klavis AI is a Y Combinator X25-backed open-source infrastructure platform that enables AI agents to reliably connect with external tools and services at scale through Model Context Protocol (MCP). Founded by ex-Google DeepMind and ex-Lyft engineers, Klavis provides 50+ production-ready MCP servers with enterprise OAuth support for GitHub, Slack, Gmail, Salesforce, Linear, Notion, and more. The flagship product Strata solves tool overload through progressive discovery, achieving +13% higher...

AppFlowy is an AI collaborative workspace where you can achieve more without losing control of your data. It is the best open source alternative to Notion, offering a 100% offline mode and self-hosting with a cloud service of your choice. Build a centralized workspace for your wiki, projects, and notes with AppFlowy. It allows you to organize and visualize your data in tables, Kanban boards, calendars, and more. You can filter and sort your data in any way you want. AppFlowy comes...

This is a free and open-source liveliness check and face recognition project. Essentially, it is an application that can be used as a standalone server or deployed in the cloud. You don’t need prior machine learning skills to set up and use. The application is customizable react based mobile friendly UI and Python based backend. The program is a real-time face detection application. It allows you to detect faces using your webcam and displays the video feed with oval drawn around the detected faces. ...

tts-server-android is an Android system TTS application that acts both as a powerful local text-to-speech engine and as a flexible TTS “server” for other apps via HTTP. It includes a built-in Microsoft TTS demo interface and lets users configure custom HTTP requests, making it possible to route TTS through various cloud providers or local servers. The app can import other local TTS engines, giving Android devices a unified interface to multiple voices and providers, and it features simple narration/dialogue detection based on Chinese quotation marks so it can read text with different styles for narration and dialogue. It is built with Kotlin and Jetpack Compose, and the project is structured into multiple libraries (lib-tts, lib-server, lib-compose, lib-database, etc.) to separate UI, server logic, and TTS handling. ...