Wardrobe

ComfyUI is an open-source AI content-creation engine and diffusion-model GUI, API, and backend developed by Comfy-Org. Its visual node-graph interface lets users build and reuse no-code workflows for image, video, audio, 3D, and text generation, editing, and processing, including workflows using diffusion models, VAEs, text encoders, LoRAs, ControlNets, adapters, and upscalers. Workflows can be saved and loaded as JSON, exposed through App Mode, or integrated into applications through local API endpoints. The engine supports asynchronous queueing, partial graph re-execution, VRAM and RAM management, model offloading, quantized models, custom nodes, and fully offline core operation unless optional resources are requested. It can run locally on Windows, Linux, and macOS through desktop, portable, or manual installations, with support for NVIDIA, AMD, Intel, Apple Silicon, and Ascend hardware. Comfy-Org also provides an official paid cloud version.

camelAI is an AI coding assistant platform built on Cloudflare Workers and Durable Objects. Each chat thread runs a coding agent in its own persistent workspace, retaining chat state and project files while providing workspace-aware tools. The platform connects to APIs, databases, email, Slack, Discord, and other services; supports application builds, notebook analysis, and SQL execution in isolated short-lived sandboxes; and provides previews and publishing through Workers for Platforms. Its agent harness is built on pi's lower-level agent-loop and state-management libraries. The agent uses native file tools and writes JavaScript rather than shell commands; Code Mode executes that JavaScript in fresh V8 isolates with explicit platform and connection methods. Project files are stored in WorkspaceFilesystem Durable Objects, using Durable Object SQLite for small files and R2 for larger ones, while Cloudflare Artifacts provides Git history. Linux sandbox containers are reserved for jobs requiring them, including builds, notebook analysis, and database queries, and credentials remain outside the execution sandbox. The platform can use Anthropic, OpenAI, OpenRouter, Bedrock, and custom model endpoints. Its repository is released under the MIT License and includes local development setup using Node.js, Bun, and a Cloudflare account.

awesome-gpt-image-2 is a curated collection of structured GPT Image 2 prompts, reverse-engineered image-generation cases, reusable templates, and agent tooling. It organizes examples into categories such as interfaces, infographics, posters, products, branding, photography, illustration, characters, storytelling, and documents, and uses an atomic schema that separates subjects, lighting, materials, layout, and visual details into composable prompt components. The repository also includes the GPT-Image-2 Style Library agent skill, whose generated reference data is shared with the visual gallery. The skill helps agents select styles, templates, categories, and scene tags, and can be installed for tools including Claude Code, Codex, and Cursor through the skills CLI, npm, GitHub Packages, or the Claude Code plugin marketplace. A companion website provides searchable gallery browsing, prompt copying, filtering, and signed-in image generation. The repository is released under the MIT License and states that its third-party prompt cases and images are organized for learning, research, and automated testing, with ownership and commercial-use rights remaining subject to their original sources.

Editable Visual Design is an open-source, coding-agent-driven toolkit for creating editable visual artifacts from prompts. A persistent coding agent interprets a brief, plans a composition, generates assets, implements the design in semantic HTML, renders and observes the result, and applies repairs. An image model supplies visual direction for composition, hierarchy, color, and spatial relationships, while the delivered artifact rebuilds typography and layout in HTML rather than shipping reference pixels. The resulting artifact contains real text, independent assets, selectable and movable layers, a visual editor, rendered PNG output, an animated layer breakdown, and a replayable creation process. Deterministic checks cover the canvas, fonts, layer contracts, rendering, and editor round trips. The repository distributes the workflow as two Codex skills, including editable-design for fixed-canvas designs and a separate html-to-pptx skill for converting compatible HTML designs into editable presentations.

Cloudflare OS is an open-source AI productivity environment developed by Cloudflare and built on Cloudflare Workers. It provides an agent chat interface preloaded with company-specific knowledge, sandboxed development of shareable applications called gadgets, and a security framework called Gatekeepers for controlling agents and apps. It is intended to be customized into an organization's own company operating environment rather than adopted unchanged as a traditional computer operating system. Each user receives a private, separately sandboxed instance of a productivity application, such as a slide-deck tool, whiteboard, game, or dashboard. Agents can create and modify these gadgets and perform tasks involving configured company systems and integrations. Gatekeepers are service-specific Workers that expose a Cap'n Web API, handle authorization such as OAuth, restrict access to the intended resource, log gadget and agent actions, and support human approval for side effects. Instead of stopping synchronously for approval, a Gatekeeper can simulate the outcome locally, let the agent continue and queue actions, then allow the user to approve or reject those actions later in bulk or individually. The full stack can be run locally with pnpm, Wrangler, and workerd using `pnpm run-local`, or deployed to a Cloudflare account. The repository describes the project as an early-access release under heavy development; the local setup is intended for evaluation rather than production use.