A unified library of SOTA model optimization techniques like quantization, distillation, pruning, neural architecture search, speculative decoding, etc. It compresses deep learning models for downstream deployment frameworks like TensorRT-LLM, TensorRT, vLLM, etc. to optimize inference speed.

• Model Optimizer @ GitHub.

A small, fast WebKit browser for macOS, by Office Commun.

Search is a browser with nothing in the way. A row of tabs — across the top or down the left, your choice — and the page. There is no toolbar, no start page, no sidebar of suggestions, no account to sign into, nothing that wants your attention. You type an address or a few words in one field and you are on the page.

It uses WebKit, the engine already inside every Mac (it is what Safari runs on). That is why the whole app is about 3 MB on disk and opens instantly: there is no second copy of Chromium to download, update and keep in memory.

Swift execution, long-horizon reliability, and high token efficiency.

Step Code runs in your terminal and handles the full task loop—reading code, making changes, and running tests. It works with the Step provider and discovers the available Step models after sign-in. MCP servers, Agent Skills, plugins, and multi-agent orchestration work out of the box; you can delegate long-running tasks to /goal and let Step Code drive them forward autonomously.