This guide provides a comprehensive setup for using the Ghostty terminal emulator with Anthropic's Claude Code agentic coding tool. It highlights how Ghostty’s native splits, notification forwarding, and state restoration features minimize friction during long sessions where an AI agent is modifying files and running tests.
The article covers several key optimizations:
- Core configuration for typography (JetBrains Mono Nerd Font), themes, and window persistence to restore layouts between restarts.
- Implementation of the SAND keybinding pattern for intuitive split pane management.
- Three distinct workspace presets: Standard Split, Three-Pane Neovim Layout, and Multi-Project Tab Layout.
- Automation scripts for installing necessary fonts, configuring a compatible Starship prompt (to avoid issues with Powerlevel10k), and setting Ghostty as the default terminal handler.
- A specialized tmux script designed to handle remote sessions while ensuring Claude Code notifications and extended keys pass through correctly.
- Specific JSON settings to synchronize Claude Code's theme and system notification behavior with Ghostty’s environment.
The article covers several key optimizations:
- Core configuration for typography (JetBrains Mono Nerd Font), themes, and window persistence to restore layouts between restarts.
- Implementation of the SAND keybinding pattern for intuitive split pane management.
- Three distinct workspace presets: Standard Split, Three-Pane Neovim Layout, and Multi-Project Tab Layout.
- Automation scripts for installing necessary fonts, configuring a compatible Starship prompt (to avoid issues with Powerlevel10k), and setting Ghostty as the default terminal handler.
- A specialized tmux script designed to handle remote sessions while ensuring Claude Code notifications and extended keys pass through correctly.
- Specific JSON settings to synchronize Claude Code's theme and system notification behavior with Ghostty’s environment.
This repository provides the GGUF quantized weights for Qwen3.6-27B, a flagship-level coding model designed for stability and real-world utility. The model features significant upgrades in agentic coding capabilities, allowing it to handle frontend workflows and repository-level reasoning with high precision. It also introduces thinking preservation, which enables the model to retain reasoning context from historical messages to improve iterative development.
Key technical highlights:
* Native context length of 262,144 tokens, extensible up to 1,010,000 via RoPE scaling (YaRN).
* Enhanced tool-calling capabilities for complex agentic tasks.
* Support for multimodal inputs including images and video.
* Optimized for various inference frameworks like SGLang, vLLM, and KTransformers.
Key technical highlights:
* Native context length of 262,144 tokens, extensible up to 1,010,000 via RoPE scaling (YaRN).
* Enhanced tool-calling capabilities for complex agentic tasks.
* Support for multimodal inputs including images and video.
* Optimized for various inference frameworks like SGLang, vLLM, and KTransformers.
