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Chaty is a private, local AI desktop application developed by Fangyuan Lin, currently at version 2.2.0 with ten versions released to date. The software is designed to run open large language models entirely on the user's own hardware, without requiring an account, cloud connectivity, or transmitting any telemetry. It supports any GGUF-format model through llama.cpp, with optional Vulkan GPU acceleration, enabling users to chat with open LLMs fully offline. Falling into the local AI and offline LLM category, Chaty targets users who prioritize privacy, data sovereignty, or work in environments where internet access is restricted or where sensitive information cannot leave the machine. Its feature set covers several common use cases: a local coding agent that can drive a real browser for development and automation tasks; a private knowledge base built on retrieval-augmented generation (RAG) that grounds model answers in the user's own documents; a Deep Research capability that generates cited reports from that material; and offline voice interaction for hands-free conversations. Together, these functions position Chaty as a single desktop environment for document analysis, research, coding assistance, and general question answering, all performed on-device. Because models and documents remain local throughout, the application suits professionals handling confidential data, researchers working with proprietary corpora, developers seeking an offline coding companion, and anyone preferring a self-contained alternative to cloud-hosted AI assistants. The combination of model flexibility through the GGUF ecosystem, hardware acceleration via Vulkan, retrieval over personal files, and voice input makes it applicable across desktop operating scenarios where offline operation and privacy are requirements rather than preferences. With ten published versions, the project reflects an ongoing release history, and version 2.2.0 represents the current state of the application as distributed by its publisher.
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