Hax is a newly released coding agent developed in C, designed for terminal-native operation. It functions as a lightweight tool for developers to interact with large language models (LLMs) directly from their command line interface. The agent emphasizes minimalism and efficiency, aiming to provide a focused environment for AI-assisted coding.
The agent is built as a single native C binary with minimal dependencies, ensuring instant startup and low memory consumption, which frees up resources for local LLMs. Hax automatically discovers model and runtime capabilities, supporting various providers including OpenAI, Anthropic, and llama.cpp without extensive custom configuration. It also prioritizes terminal integration, offering streaming Markdown and live tool output that reflows for display, while preserving native scrollback and not interfering with terminal settings.
Hax includes an inspectable transcript view (Ctrl+T) to review model inputs and outputs, with an option for detailed wire protocol tracing. It adheres to Unix tool principles, utilizing XDG paths, providing clean stdout in one-shot mode, and managing configuration and session files in plain text. Composition is achieved via subprocesses rather than plugins, aligning with its minimalist design.
Hax is aimed at developers who primarily work in the terminal, utilize local models, audit their tools, package software for distributions, or operate agents in resource-constrained environments. It deliberately omits features like marketplaces, plugin runtimes, or IDE panels, distinguishing itself from more comprehensive AI development tools. The project's documentation explains these omissions and the alternative patterns it employs.
Hax is available for Linux and macOS, with Windows users able to run it under WSL. Installation can be done via Homebrew, by downloading prebuilt static binaries, or by building from source, which requires a C compiler and other standard build dependencies.
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Hax, a new coding agent written in C, has been released, offering a lightweight, terminal-native interface for interacting with large language models (LLMs). It is designed for developers who prioritize local model execution, resource efficiency, and a Unix-like tool philosophy, providing a minimalist alternative to more feature-rich AI development environments.