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Emdash is an open-source agentic development environment that allows developers to orchestrate multiple AI coding agents in parallel within a unified desktop application. It introduces a new paradigm where coding tasks are delegated to independent AI agents, each operating in its own isolated Git worktree, enabling concurrent development workflows without conflicts. The platform is provider-agnostic, supporting a wide range of CLI-based AI coding tools such as Claude Code, Codex, and others,...

ClawHost is an open-source, self-hostable cloud hosting platform designed to simplify the deployment of OpenClaw onto a dedicated VPS in minutes, giving users full control over their AI infrastructure without relying on shared or managed services. It automates server provisioning, DNS configuration, SSL certificates, and firewall setup, so developers can focus on running their AI workloads rather than configuring infrastructure manually. The platform includes a user-friendly web dashboard...

DBHub is a universal database gateway that implements the MCP server interface so assistants and IDEs can explore and query databases through typed tools. It supports multiple transports—stdio for desktop clients and HTTP for networked scenarios—making it flexible to embed or deploy. Configuration is environment-variable driven, with a DSN and per-engine settings covering Postgres, MySQL, MariaDB, SQL Server, and SQLite. Operational flags include read-only mode, row limits, and even SSH...

Void is an open-source, AI-powered code editor built as a fork of Visual Studio Code. Designed as a fully transparent and privacy-focused alternative to Cursor or GitHub Copilot, it lets you use AI models locally or via APIs (OpenAI, Claude, Gemini, Ollama, etc.)—without routing data through proprietary servers. Developed by YC-backed startup Glass Devtools, it supports traditional coding features inherited from VS Code, enhanced with in-editor LLM capabilities—autocomplete, inline quick...

OSMO is a developer-first orchestration platform designed to scale complex physical AI workflows across heterogeneous computing environments, including cloud GPUs, simulation clusters, and edge devices. It was originally built internally at NVIDIA to support robotics and embodied AI systems, where workflows span multiple stages such as data generation, training, simulation, and hardware testing. The platform addresses what NVIDIA refers to as the “three computer problem” by unifying these...