Sketch a wireframe, get working code: Draw a UI turns hand-drawn layouts into web interfaces. It pairs the open-source tldraw canvas with an OpenAI vision model: you sketch a layout - boxes, labels, buttons, arrows, whatever communicates the idea - select the drawing, and click Make Real. The app snapshots your selection as a PNG, sends it to the vision API with instructions to return a single HTML file styled with Tailwind CSS, and renders the result in an iframe directly on the canvas next to your sketch. The loop is iterative: annotate the generated prototype or redraw parts of it, select both the sketch and the previous result, and generate again - the model receives the earlier HTML as context and produces an updated version. Built by Figma engineer Sawyer Hood as one of the first viral GPT-4 Vision demos and the basis for tldraw's "Make Real", it is a Next.js app that runs against your own OpenAI API key. Self-hosting matters here: the upstream demo ships without authentication, so a private deployment keeps your API key from being drained by strangers. MIT-licensed.

Describe a component in natural language and watch it render: OpenUI, from Weights & Biases, is an open alternative to Vercel's v0. Type a prompt like "a dark-themed dashboard with a sidebar and charts" and the LLM renders working HTML with Tailwind styling live in the browser. You then iterate conversationally, asking for changes until the design is right, and convert the result to React, Svelte, or Web Components for use in a real project. The backend is Python with LiteLLM routing, so it works with OpenAI, Anthropic, Gemini, Groq, and Mistral API keys, or fully offline against local Ollama models, including vision models like LLaVA that can generate UI from screenshot input - feed a screenshot and the model reproduces or riffs on an existing interface. Generated markup is inspectable at any point, with light and dark mode toggles, theme selection, and responsive previews across device sizes. The practical effect is compressing the mockup-review-revise loop from hours to minutes: a described layout renders in seconds and iterates through follow-up prompts, and because output converts to real framework code, prototypes feed directly into production codebases instead of staying trapped in a design tool. Self-hosting keeps unreleased product interfaces and prompts on your own server, and LiteLLM routing lets you pick the model per task - a cheap fast model for rough drafts, a stronger one for final passes, or free local models for unlimited experimentation.

Fifty-plus image processing tools in a single Docker container, with no Redis, no Postgres, and no external dependencies: SnapOtter is a self-hosted image toolkit. The everyday operations are all here: resize, crop, compress, watermark, vectorize, meme generation, GIF creation, and format conversion spanning 55+ input formats (including 23 camera RAW formats) to 14 output formats. What sets it apart is the local AI layer: background removal, photo upscaling and restoration, object erasing, face blurring, OCR, and canvas expansion all run on locally hosted models, so no image ever leaves your server - a hard guarantee that cloud tools like remove.bg or Canva can't make. Optional NVIDIA GPU support accelerates those AI tasks substantially when hardware is available, but everything works on CPU. A built-in layer-based editor handles composition work directly in the browser, and screenshot beautification turns plain captures into polished visuals with backgrounds, shadows, and padding - useful for docs and marketing alike. Batch operations process unlimited images simultaneously, and the full REST API with OpenAPI documentation exposes every tool for pipelines and automations: thumbnail generation on upload, bulk RAW conversion, automated watermarking. For teams processing sensitive imagery or anyone tired of per-image SaaS pricing, SnapOtter replaces a stack of subscriptions with one private container.

Schema design with no account and a few clicks: drawDB is the browser-based entity-relationship diagram editor and SQL generator - an AGPL-3.0 React project with over 37,000 GitHub stars. Draw tables with columns, data types, defaults, and constraints; connect fields to create foreign-key relationships; group tables into labeled subject areas; and annotate with notes. When the design is ready, one export produces CREATE TABLE DDL - with constraints, indexes, and foreign keys - targeted at MySQL, PostgreSQL, SQLite, MariaDB, SQL Server, or Oracle. Diagrams can be database-specific, unlocking every native type plus dialect features like PostgreSQL enums and composite custom types, or generic for portability across all supported flavors. The reverse direction works too: paste existing DDL into the import dialog and drawDB renders your live schema as a navigable diagram - the fastest way to document an inherited database. Versioning and migration-script generation track schema evolution, full editor ergonomics (undo/redo, copy/paste, duplicate, themes) keep iteration fast, and diagrams export as PNG, SVG, or shareable JSON. Everything runs client-side against browser storage - no backend database connection needed - so the self-hosted Docker deployment is a featherweight static app that keeps proprietary schema designs entirely on your infrastructure.

Most design tools stop at prototyping; Quant-UX also measures how real users actually perform with the prototype. The visual editor creates prototypes that behave like real apps - functional input widgets, animations, form validation, data binding across screens, and business logic modeled with REST requests and decision elements. Design systems are first-class, with components, design tokens, and master screens; if you design elsewhere, drop in image files or import from Figma. Testing is a shared link or QR code - no installs on the tester's side. Define user tasks up front, and Quant-UX records every session: click heatmaps show where users found (or missed) actionable elements, user journey graphs expose lost users, drop-off charts reveal where tasks stall, and success rates and task KPIs are extracted automatically into a dashboard. An A/B test operator wires two design variants into one prototype and compares task duration, success rate, and interaction counts. In-prototype surveys collect qualitative feedback alongside the numbers, and an AI assistant generates prototype fragments like styled forms on request. The RepoCloud deployment runs the full stack - frontend, backend, and WebSocket server containers over MongoDB - so all test recordings and research data stay on your infrastructure.

Half the architecture sketches on the internet trace back to Excalidraw - the MIT-licensed virtual whiteboard whose hand-drawn aesthetic made technical diagramming feel approachable, at roughly 85,000 GitHub stars. The infinite canvas offers rectangles, ellipses, diamonds, arrows with smart binding and labels, free-draw, text, images, and an eraser, with full undo/redo, zoom, dark mode, and keyboard-first ergonomics. Community shape libraries add thousands of pre-built elements - AWS architecture icons, flowchart stencils, UI wireframe kits - and everything exports to PNG, SVG, the clipboard, or the open .excalidraw JSON format that keeps drawings diffable and portable. Live collaboration works on a share-a-link model with live cursors and a laser pointer for presenting, and it is end-to-end encrypted by design: the room key travels in the URL hash, which never reaches the server, so the WebSocket relay only ever sees ciphertext. The architecture is remarkably light - the app is a static bundle served by Nginx, drawings persist locally in the browser, and the stateless excalidraw-room relay handles multiplayer - so a self-hosted deployment gives unlimited boards and collaborators with near-zero resource cost, replacing per-editor whiteboard subscriptions.

Drop your screenshot, a base style is applied, style it however you wish, and export - "that's basically it," says Moocup's own author, and the description holds. The workflow is genuinely seconds long. Drag an image in and it lands on an attractive backdrop immediately; from there you adjust backgrounds, gradients, padding, borders, shadows, and framing with live preview until it matches your taste, then export a high-quality image ready for a portfolio page, README, blog post, tweet, or slide deck. There are no accounts, no watermarks, and no upload to anyone's cloud - as a self-hosted static app, your screenshots never leave your infrastructure, which matters when the screenshot shows a proprietary dashboard or unreleased product. It runs entirely in the browser from a tiny nginx container, works on any device, and requires zero design skill: the smart defaults do the heavy lifting, and everything else is optional tinkering.