Releases

Rust OS framework that facilitates the development of and innovation in OS kernels

uzor-desktop: winit-based desktop runtime for uzor apps. Implements AppBuilder::run() for native desktop targets.

uzor-render-hub: unified rendering backend hub — auto-detects GPU, instantiates the right backend (vello-gpu / vello-hybrid / wgpu-instanced / vello-cpu / tiny-skia), submits frames, collects metrics.

URX engine — cross-backend façade with per-region dirty tracking, needs_paint() cadence hook, RenderCadence policy. Consumes urx-core Scene + DirtyState; dispatches to urx-cpu / urx-wgpu / urx-hybrid.

URX Hybrid backend — CPU rasterises static/cacheable regions to pixmaps, uploads to wgpu textures, GPU composites everything in one pass. Marries urx-cpu (rasteriser) + urx-wgpu (compositor).

URX WGPU backend — consumes urx-core DrawCommand, dispatches to instanced-WGPU primitive pipelines (Quad SDF + Line capsule + Triangle + Text atlas). Wraps uzor-render-wgpu-instanced (the existing well-tested primitive crate) with the URX scene-consumer API + URX metrics.

Utility methods, traits, and types.

Standalone TUI framework for uzor — cell-buffer rendering, widgets, layout, crossterm backend

Desktop backend for uzor (winit integration)

Web/WASM backend for uzor

Mobile backend for uzor (iOS/Android)

High-performance instanced wgpu rendering backend

YAML-driven scenario engine for Rust RPA: branching, loops, retry, and sub-scenarios

Built-in node library for Rust RPA: image matching, clipboard, Excel, HTTP, scheduling, and more

Unified backend trait abstracting local, RDP, and VNC desktop automation targets

Cross-platform screen and window capture with HiDPI awareness for desktop automation

Hybrid CPU/GPU rendering backend using vello_hybrid

GPU rendering backend using vello + wgpu

CPU-only rendering backend using vello_cpu

Thin wrapper for tonlibjson

CPU rendering backend using tiny-skia

Web Canvas2D rendering backend for WASM

Native Model Context Protocol (MCP) implementation for systemprompt.io. Orchestration, per-server OAuth2, RBAC middleware, and tool-call governance — the core of the AI governance pipeline.

Provider-agnostic LLM integration for systemprompt.io AI governance — Anthropic, OpenAI, Gemini, and local models unified behind one governed pipeline with cost tracking and audit.

Marketplace bounded context for systemprompt.io. Resolves the active marketplace, loads the on-disk catalogue (plugins, skills, agents, hooks, managed MCP servers), scopes and per-user filters it, and assembles the canonical signed bridge manifest.

Markdown content management, sources, and event tracking for systemprompt.io AI governance dashboards. Governed publishing pipeline for the MCP governance platform.

File storage, metadata, and access control for systemprompt.io AI governance infrastructure. Governed file operations for the MCP governance pipeline.

OAuth 2.0 / OIDC with PKCE, token introspection, and audience/issuer validation for systemprompt.io AI governance infrastructure. WebAuthn and JWT auth for the MCP governance pipeline.

Local HTTP control plane for uzor apps. Read live LayoutManager state, inject input, and drive direct LM operations from external agents (LLMs, QA tools, scripts).

Security infrastructure for systemprompt.io AI governance: JWT, OAuth2 token extraction, scope enforcement, ChaCha20-Poly1305 secret encryption, the four-layer tool-call governance pipeline, and the unified authz decision plane (deny-overrides resolver + AuthzDecisionHook) shared by gateway and MCP enforcement.