Description
The 100% Offline AI Coding Workspace KAT is a powerful, privacy-first local coding environment built for developers who want the power of AI assistance without exposing their proprietary codebase to the cloud. By pairing with LM Studio as its core intelligence engine, the app keeps your entire development workflow completely local, secure, and lightning-fast.
Key Features
LM Studio Powered: Connects seamlessly to your local LM Studio instance to run any open-source LLM entirely on your own hardware.
Folder-Level RAG Memory: Instantly indexes your active project directory using local Retrieval-Augmented Generation (RAG) to provide hyper-contextual code suggestions.
Independent AI Planner: A dedicated, collapsible brainstorming and planning panel designed to map out complex architecture and step-by-step implementation goals.
Asynchronous Execution: The AI Planner works independently from your main editor canvas, allowing you to design new features while simultaneously writing code.
Hardware-Adaptive Performance: Intelligently scales its processing demand based on your machine’s CPU and GPU limits.
Zero Internet Required: Write, plan, compile, and prompt with absolute privacy—perfect for airplanes, secure labs, or off-grid development.
System Requirements & Setup
Minimal Requirements
16GB VRAM
32GB RAM DDR4
The initial LM Studio AI model requires 21GB of hard drive space. Additional models and coding space depend on the specific project you are coding and are at the discretion of the user.
Above specification will generate 4.5–12 tokens/second.
Recommended Requirements
24GB VRAM
32GB RAM DDR5
The initial LM Studio AI model requires 21GB of hard drive space. Additional models and coding space depend on the specific project you are coding and are at the discretion of the user.
Above specification will generate 35–70+ tokens/second.
Proven AI Model: QWEN Version 3.6, 35B, 83B (NOT TESTED ON LINUX OR iOS these platforms will be added in further stages of development)
Core Engine: Requires LM Studio running locally in the background with an active Local Server.
Hardware Dependability: Performance scales directly with your system’s hardware capability. Machines with dedicated GPUs (Nvidia RTX or Apple Silicon) will experience significantly faster planning and RAG indexing.
Reviews
There are no reviews yet.