{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"github-anil-matcha--open-generative-ai","slug":"anil-matcha--open-generative-ai","name":"Open-Generative-AI","type":"repo","url":"https://dev.muapi.ai/open-generative-ai","page_url":"https://unfragile.ai/anil-matcha--open-generative-ai","categories":["image-generation"],"tags":["ai-art-generator","ai-image-generation","ai-video-generation","creative-tools","flux-1","generative-ai","higgsfield","higgsfield-ai","higgsfield-alternative","image-to-video","javascript","kling-ai","midjourney-alternative","muapi","open-source","sora-alternative","text-to-video","uncensored","unrestricted","wan-video"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"github-anil-matcha--open-generative-ai__cap_0","uri":"capability://image.visual.multi.model.text.to.image.generation.with.dynamic.schema.driven.ui","name":"multi-model text-to-image generation with dynamic schema-driven ui","description":"Generates images from text prompts by routing requests through a unified MuapiClient that abstracts 50+ image generation models (Flux, DALL-E, Midjourney, Stable Diffusion variants). The ImageStudio component dynamically renders UI controls (resolution pickers, style selectors, guidance scales) based on each model's input schema defined in the models.js registry, eliminating hardcoded form logic and enabling new models to be added without frontend changes.","intents":["I want to generate images from text prompts using the latest open-source and commercial models without switching between platforms","I need to add a new image generation model to the platform and have the UI automatically adapt to its parameters","I want to compare outputs across multiple image models with the same prompt to find the best result"],"best_for":["creative professionals and designers building custom AI art workflows","developers integrating multi-model image generation into applications","teams migrating from proprietary platforms like Midjourney or Krea AI"],"limitations":["Generation latency depends on Muapi backend inference time (typically 30-120 seconds per image)","No built-in batch processing — each image requires a separate API call and polling cycle","Model availability and pricing determined by Muapi.ai service; no fallback if primary provider is down","UI schema generation assumes all model parameters map to standard control types; custom parameter types require manual schema extension"],"requires":["Node.js 18.0.0 or higher","Valid Muapi.ai API key (BYOK model — user provides their own key)","Modern browser with localStorage support for generation history persistence"],"input_types":["text (prompt string)","structured parameters (resolution, style, guidance scale, seed as defined per-model)"],"output_types":["image (PNG/JPEG URLs hosted on Muapi CDN)","generation metadata (model used, parameters, timestamp, job ID)"],"categories":["image-visual","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-anil-matcha--open-generative-ai__cap_1","uri":"capability://image.visual.text.to.video.and.image.to.video.generation.with.polling.based.job.tracking","name":"text-to-video and image-to-video generation with polling-based job tracking","description":"Generates videos from text prompts or image inputs by submitting requests to Muapi backend and polling for completion status via a job ID. The VideoStudio component manages the generation lifecycle: submission → polling loop (with configurable intervals) → result retrieval. Supports 30+ video models including Kling, Sora, Veo, and Runway, with model-specific parameter schemas (duration, aspect ratio, motion intensity) rendered dynamically. Pending jobs are persisted in localStorage and can be resumed across browser sessions.","intents":["I want to generate videos from text descriptions or image inputs without waiting for real-time completion","I need to track multiple video generation jobs in parallel and retrieve results when ready","I want to resume a video generation job that was interrupted or started in a previous session"],"best_for":["content creators producing AI-generated video assets for social media and marketing","developers building video generation pipelines that tolerate asynchronous processing","teams needing to generate videos at scale with job persistence and resumption"],"limitations":["Video generation is inherently slow (5-15 minutes typical); polling adds latency overhead (~1-5 seconds per poll cycle)","No webhook support — polling is the only mechanism for job status updates, requiring client-side polling loops","localStorage persistence is browser-specific; pending jobs cannot be shared across devices or browsers","Video quality and generation time vary significantly by model; no built-in model recommendation based on input characteristics"],"requires":["Node.js 18.0.0 or higher","Valid Muapi.ai API key with video generation quota","Browser with localStorage support for job persistence","Network connectivity maintained during polling cycles (interruptions may lose job tracking)"],"input_types":["text (prompt string for text-to-video)","image (file upload or URL for image-to-video)","structured parameters (duration, aspect ratio, motion intensity, seed)"],"output_types":["video (MP4/WebM URLs hosted on Muapi CDN)","job metadata (job ID, status, progress percentage, estimated completion time)"],"categories":["image-visual","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-anil-matcha--open-generative-ai__cap_10","uri":"capability://safety.moderation.uncensored.content.generation.without.safety.filters","name":"uncensored content generation without safety filters","description":"Provides unrestricted access to image and video generation models without applying content filters, safety checks, or moderation policies. The application does not implement NSFW detection, prompt filtering, or output validation; all generation requests are passed directly to Muapi backend models without modification. This design prioritizes user freedom and creative expression over content moderation, making it suitable for unrestricted artistic and experimental use cases.","intents":["I want to generate artistic content without platform-imposed content restrictions","I need to create experimental or adult-oriented content for legitimate creative purposes","I want full transparency about what content is being generated without hidden moderation"],"best_for":["artists and creators working with mature or experimental themes","researchers studying generative AI capabilities without restrictions","organizations that prefer user responsibility over platform moderation"],"limitations":["No built-in safeguards against generating illegal or harmful content; users are fully responsible for compliance with local laws","Muapi backend may still apply its own filters; Open Generative AI cannot guarantee unrestricted output if the underlying API enforces policies","Self-hosted deployments may violate terms of service of the hosting provider if used for restricted content","No audit trail or content logging; users cannot prove they did not generate specific content if disputes arise"],"requires":["Compliance with local laws regarding content generation and distribution","Understanding that Muapi backend may still enforce its own content policies","Responsible use of the platform; users are liable for content they generate"],"input_types":["any text prompt or image input (no filtering applied)"],"output_types":["any generated image or video (no content moderation applied)"],"categories":["safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-anil-matcha--open-generative-ai__cap_11","uri":"capability://tool.use.integration.muapiclient.abstraction.layer.with.unified.api.for.multi.provider.model.access","name":"muapiclient abstraction layer with unified api for multi-provider model access","description":"Provides a MuapiClient class that abstracts all communication with the Muapi backend, exposing unified methods for image generation (generateImage), video generation (generateVideo), lip-sync (generateLipSync), and job polling (pollJobStatus). The client handles request formatting, response parsing, error handling, and retry logic. It supports multiple model families (Flux, DALL-E, Midjourney, Kling, Sora, etc.) through a single interface, eliminating the need for model-specific API clients. All requests include the x-api-key header from localStorage for BYOK authentication.","intents":["I want a unified API client that works with all supported image and video models without switching between different SDKs","I need to handle API errors, retries, and timeouts consistently across all generation types","I want to extend the client with new generation methods without modifying existing code"],"best_for":["developers building applications that integrate multiple generative AI models","teams that want to abstract API complexity behind a clean interface","projects that need to switch between different model providers without code changes"],"limitations":["Client is tightly coupled to Muapi API; switching to a different backend requires rewriting the client","Error handling is generic; model-specific errors are not distinguished (e.g., rate limits vs. invalid parameters)","No built-in retry logic with exponential backoff; failed requests require manual retry handling","Polling mechanism is client-side only; no webhook support for asynchronous job completion notifications"],"requires":["Valid Muapi.ai API key (stored in localStorage)","Network connectivity to Muapi backend","JavaScript/TypeScript knowledge to extend the client"],"input_types":["generation parameters (prompt, model, resolution, etc.)","job ID (for polling)"],"output_types":["generation result (image/video URL, metadata)","job status (pending, completed, failed)"],"categories":["tool-use-integration","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-anil-matcha--open-generative-ai__cap_12","uri":"capability://automation.workflow.tailwind.css.styling.system.with.responsive.design.and.dark.mode.support","name":"tailwind css styling system with responsive design and dark mode support","description":"Uses Tailwind CSS utility classes for styling all UI components across web and desktop shells, providing a consistent design system with responsive breakpoints (mobile, tablet, desktop) and dark mode support. The styling system is defined in tailwind.config.js and applied via PostCSS (postcss.config.js). All studio components (ImageStudio, VideoStudio, etc.) use Tailwind classes for layout, spacing, colors, and typography, enabling rapid UI iteration and consistent theming across platforms.","intents":["I want a consistent, responsive UI that works on mobile, tablet, and desktop without writing custom CSS","I need to support dark mode and light mode themes without duplicating styles","I want to customize the design system (colors, spacing, typography) without modifying component code"],"best_for":["developers building responsive web and desktop applications","teams that want to maintain consistent design across multiple platforms","projects that need rapid UI iteration with minimal CSS overhead"],"limitations":["Tailwind CSS increases bundle size (~50KB gzipped); not ideal for bandwidth-constrained environments","Utility-first approach can result in verbose HTML with many class names; readability may suffer in complex components","Custom styling requires extending tailwind.config.js; complex designs may require custom CSS anyway","Dark mode is opt-in; requires explicit theme configuration and may not work correctly if CSS is not properly scoped"],"requires":["Tailwind CSS 3.0+ (installed via npm)","PostCSS for processing Tailwind directives","Understanding of Tailwind utility classes and responsive design patterns"],"input_types":["Tailwind CSS utility classes (applied to React components)"],"output_types":["styled HTML (responsive, dark-mode-aware)"],"categories":["automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-anil-matcha--open-generative-ai__cap_2","uri":"capability://image.visual.lip.sync.animation.generation.with.audio.to.video.alignment","name":"lip-sync animation generation with audio-to-video alignment","description":"Generates lip-synced video animations by accepting an audio file (MP3, WAV) and a reference video or image, then using Muapi's lip-sync models to align mouth movements with audio phonemes. The LipSyncStudio component handles audio upload, model selection (supporting multiple lip-sync architectures), and parameter tuning (sync intensity, mouth shape variation). Results are persisted in generation history with audio metadata for reproducibility.","intents":["I want to create lip-synced avatar videos from audio narration or dialogue without manual frame-by-frame editing","I need to generate multiple lip-sync variations with different intensity levels to find the most natural result","I want to preserve audio metadata and sync parameters for future regeneration of the same video"],"best_for":["video producers creating talking-head content and avatar videos","developers building conversational AI interfaces with animated avatars","teams producing multilingual video content with lip-sync localization"],"limitations":["Lip-sync quality depends heavily on audio clarity and reference video quality; poor audio inputs produce misaligned mouth movements","No real-time preview of sync results; users must wait for full generation to assess quality","Limited to audio files under 10MB; longer audio requires splitting into segments","Mouth shape variation is model-dependent; some models produce more natural results than others but this is not predictable without testing"],"requires":["Node.js 18.0.0 or higher","Valid Muapi.ai API key with lip-sync model access","Audio file in MP3, WAV, or OGG format (under 10MB)","Reference video or image file (PNG, JPG, MP4)"],"input_types":["audio (MP3, WAV, OGG file upload)","image or video (reference for mouth region)","structured parameters (sync intensity 0-1, mouth variation level, phoneme sensitivity)"],"output_types":["video (MP4 with lip-synced animation)","sync metadata (audio duration, detected phonemes, confidence scores per frame)"],"categories":["image-visual","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-anil-matcha--open-generative-ai__cap_3","uri":"capability://image.visual.cinematic.shot.generation.with.prompt.engineering.and.asset.library","name":"cinematic shot generation with prompt engineering and asset library","description":"Generates cinematic video sequences by combining a prompt builder (CinemaPromptBuilder) that structures narrative, camera movement, lighting, and composition into optimized prompts, with an asset library (CinemaAssetLibrary) containing pre-built cinematography templates (Dutch angle, tracking shot, crane shot, etc.). The Cinema Studio routes these structured prompts to video models optimized for cinematic output, with support for multi-shot sequences and scene composition. Prompts are engineered to maximize model understanding of camera techniques and visual storytelling.","intents":["I want to generate cinematic sequences with specific camera movements and lighting without writing complex technical prompts","I need to compose multi-shot scenes with consistent visual style and narrative flow","I want to reuse cinematography templates and asset combinations across multiple projects"],"best_for":["filmmakers and video directors prototyping shots before live production","content creators producing cinematic AI-generated video content","developers building narrative-driven video generation applications"],"limitations":["Cinematic quality is highly dependent on prompt engineering; poor prompt structure results in generic or incoherent output","Multi-shot sequences require multiple generation calls; no built-in shot sequencing or automatic transitions","Asset library is static and curated; custom cinematography techniques require manual prompt engineering","Camera movement consistency across shots is not guaranteed; users may need to regenerate multiple times for narrative coherence"],"requires":["Node.js 18.0.0 or higher","Valid Muapi.ai API key with access to cinematic video models","Understanding of cinematography terminology (Dutch angle, tracking shot, etc.) or reliance on asset library templates"],"input_types":["narrative text (scene description, dialogue, emotional tone)","cinematography parameters (camera movement type, lens type, lighting style, composition rule)","asset library selections (pre-built shot templates)"],"output_types":["video (cinematic sequence MP4)","prompt metadata (structured prompt components, asset references, parameter values)"],"categories":["image-visual","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-anil-matcha--open-generative-ai__cap_4","uri":"capability://tool.use.integration.bring.your.own.key.byok.api.authentication.with.localstorage.persistence","name":"bring-your-own-key (byok) api authentication with localstorage persistence","description":"Implements a BYOK authentication model where users provide their own Muapi.ai API key via an AuthModal component, which is then stored in localStorage and used in the x-api-key header for all subsequent API requests. No user accounts, billing, or backend authentication are managed by the application; the API key is the sole credential. Key is persisted across browser sessions and can be cleared via settings. This design eliminates backend infrastructure requirements and gives users full control over API usage and billing.","intents":["I want to use Open Generative AI without creating an account or sharing my data with a third-party service","I need to control my API spending by using my own Muapi.ai account and monitoring usage directly","I want to deploy this application without managing user authentication or billing infrastructure"],"best_for":["individual creators and developers who want privacy and control over API usage","teams deploying Open Generative AI internally without user management overhead","organizations that prefer BYOK models to avoid vendor lock-in"],"limitations":["No multi-user support; each browser instance requires its own API key, making shared deployments impractical","API key is stored in plain text in localStorage; vulnerable to XSS attacks if the application is compromised","No usage tracking or quotas enforced by the application; users must monitor Muapi.ai dashboard directly for spending","Key rotation requires manual intervention; no automatic key refresh or expiration handling"],"requires":["Valid Muapi.ai account with API key","Browser with localStorage support and no privacy mode (which clears localStorage on close)","HTTPS deployment recommended to prevent key interception (though not enforced)"],"input_types":["API key string (pasted into AuthModal)"],"output_types":["authenticated session (key stored in localStorage, used in all API requests)"],"categories":["tool-use-integration","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-anil-matcha--open-generative-ai__cap_5","uri":"capability://memory.knowledge.generation.history.persistence.and.resumption.with.localstorage.indexing","name":"generation history persistence and resumption with localstorage indexing","description":"Persists all generation results (images, videos, metadata) in localStorage under the muapi_history key, creating a local index of past creations that survives browser restarts. Each generation record includes job ID, model used, input parameters, output URL, and timestamp. Users can browse history, filter by model or date, and reuse parameters from past generations without re-entering them. Pending jobs are tracked separately and can be resumed by polling their job IDs.","intents":["I want to view all my past AI-generated images and videos without losing them when I close the browser","I need to quickly regenerate similar content by reusing parameters from a previous generation","I want to track which models and parameters produced my best results for future reference"],"best_for":["individual creators maintaining a personal portfolio of AI-generated work","designers iterating on variations and needing to compare past attempts","developers building applications that require generation history for audit or reproducibility"],"limitations":["localStorage is browser-specific and device-specific; history does not sync across browsers or devices","localStorage has a typical limit of 5-10MB per domain; large numbers of high-resolution images may exceed quota","No built-in export functionality; users must manually copy history data or use browser dev tools","History is lost if user clears browser cache or uses private/incognito mode","No server-side backup; if localStorage is corrupted, all history is permanently lost"],"requires":["Browser with localStorage support (all modern browsers)","Sufficient localStorage quota (typically 5-10MB available)","Muapi CDN URLs must remain accessible for image/video playback (URLs expire after 30 days by default)"],"input_types":["generation results (image/video URLs, metadata from API responses)"],"output_types":["history index (JSON array of generation records with timestamps, parameters, URLs)","parameter templates (reusable parameter sets for new generations)"],"categories":["memory-knowledge","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-anil-matcha--open-generative-ai__cap_6","uri":"capability://automation.workflow.multi.shell.deployment.architecture.with.shared.react.component.library","name":"multi-shell deployment architecture with shared react component library","description":"Implements a monorepo structure where core generation logic and UI components (ImageStudio, VideoStudio, LipSyncStudio, CinemaStudio) are packaged as a reusable React component library in packages/studio, which is then consumed by three independent frontend shells: a Next.js web application (app/), a Vite/Vanilla-JS development environment (src/), and an Electron desktop application (electron/). Each shell uses the same MuapiClient and studio components but provides different distribution and deployment models (cloud-hosted, local development, cross-platform desktop).","intents":["I want to deploy Open Generative AI as a web application, desktop app, and local development environment without duplicating UI code","I need to maintain a single codebase for generation logic while supporting multiple distribution channels","I want to extend the application with custom features without forking the entire project"],"best_for":["developers building multi-platform creative tools","teams maintaining applications across web and desktop with shared logic","open-source projects that need to support multiple deployment models"],"limitations":["Monorepo complexity increases setup and build time; npm workspace management requires careful dependency versioning","Shared component library must remain platform-agnostic; platform-specific features (file system access, native menus) require abstraction layers","Build pipeline is complex; changes to shared library require rebuilding all three shells","Testing must cover all three shells; bugs may manifest differently across web, Vite, and Electron environments"],"requires":["Node.js 18.0.0 or higher","npm 7 or higher (for workspace support)","Understanding of React, Next.js, Vite, and Electron architectures","Separate build and deployment pipelines for each shell"],"input_types":["React component definitions (studio modules)","MuapiClient API methods (generation, polling, history)"],"output_types":["Next.js web application (deployed to cloud)","Vite development environment (local dev server)","Electron desktop application (cross-platform binary)"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-anil-matcha--open-generative-ai__cap_7","uri":"capability://data.processing.analysis.model.registry.with.dynamic.parameter.schema.and.ui.generation","name":"model registry with dynamic parameter schema and ui generation","description":"Maintains a centralized models.js registry that defines all available image, video, lip-sync, and cinematic models with their input schemas (resolution options, style categories, guidance scales, etc.). Each model entry includes metadata (name, provider, capabilities) and a JSON schema describing required and optional parameters. The studio components (ImageStudio, VideoStudio, etc.) parse these schemas at runtime and dynamically generate UI controls (dropdowns, sliders, text inputs) without hardcoding model-specific logic. Adding a new model requires only updating models.js; the UI adapts automatically.","intents":["I want to add a new image or video model to the platform without modifying component code","I need to understand which parameters each model supports and how they affect generation","I want to build a model comparison tool that works with any model in the registry"],"best_for":["developers extending Open Generative AI with new models","teams building model-agnostic generation interfaces","researchers comparing outputs across multiple models with consistent parameter handling"],"limitations":["Schema generation assumes standard parameter types (enum, number, string); custom parameter types require manual UI component creation","No schema validation at runtime; invalid parameters are caught by the API, not the UI","Model metadata is static; no dynamic model discovery from Muapi API (models.js must be manually updated)","Parameter constraints (e.g., resolution must be divisible by 8) are not enforced by the schema; validation happens server-side"],"requires":["JavaScript/JSON knowledge to edit models.js","Understanding of JSON schema format","Familiarity with the model's API documentation to define correct parameter schemas"],"input_types":["model metadata (name, provider, capabilities)","parameter schema (JSON schema defining input types, constraints, defaults)"],"output_types":["dynamic UI controls (React components generated from schema)","parameter validation (client-side type checking before API submission)"],"categories":["data-processing-analysis","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-anil-matcha--open-generative-ai__cap_8","uri":"capability://data.processing.analysis.upload.picker.with.local.caching.and.redundant.upload.prevention","name":"upload picker with local caching and redundant upload prevention","description":"Provides an UploadPicker component that manages image and video uploads for image-to-video and reference-based generation tasks. Uploaded files are cached locally in localStorage (with file hash-based deduplication) to prevent redundant uploads of the same file. The component tracks upload history, displays previews, and manages file size validation before submission. Cached uploads are reused across multiple generation requests, reducing bandwidth and API overhead.","intents":["I want to upload reference images for image-to-video without uploading the same file multiple times","I need to quickly select from previously uploaded images without re-uploading them","I want to validate file size and format before uploading to avoid API errors"],"best_for":["users generating image-to-video content with multiple reference images","workflows that reuse the same reference images across multiple generations","teams with limited bandwidth or API upload quotas"],"limitations":["localStorage cache is limited to 5-10MB; large images or many uploads may exceed quota","File hash deduplication is based on client-side computation; identical files with different names are treated as duplicates","Cached uploads are device-specific; upload history does not sync across browsers or devices","No server-side cache validation; if a cached file is deleted from Muapi CDN, the local cache reference becomes stale"],"requires":["Browser with localStorage support","File size under 50MB (typical API limit)","Supported image formats (PNG, JPG, WebP) or video formats (MP4, WebM)"],"input_types":["image or video files (drag-and-drop or file picker)"],"output_types":["upload metadata (file hash, URL, timestamp, preview)","cached file reference (reusable across generations)"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-anil-matcha--open-generative-ai__cap_9","uri":"capability://automation.workflow.cross.platform.desktop.application.with.electron.and.native.os.integration","name":"cross-platform desktop application with electron and native os integration","description":"Packages the Open Generative AI application as a cross-platform Electron desktop application (electron/ directory) that runs on macOS, Windows, and Linux. The Electron main process handles window management, file system access for saving generated images/videos, and OS-level integrations (native menus, drag-and-drop, system tray). The Vite/Vanilla-JS frontend (src/) is bundled with Electron and communicates with the Muapi backend via the same MuapiClient used by web shells. Desktop app provides offline-capable UI and direct file system access without browser sandbox restrictions.","intents":["I want to use Open Generative AI as a native desktop application without opening a browser","I need to save generated images and videos directly to my file system without manual downloads","I want to integrate Open Generative AI with my OS-level workflows (drag-and-drop, system menus, file associations)"],"best_for":["creative professionals who prefer native desktop applications over web interfaces","teams deploying Open Generative AI internally with file system integration requirements","developers building Electron-based creative tools"],"limitations":["Electron adds significant bundle size (~150MB); slower installation and updates than web applications","Desktop app requires separate build and distribution pipeline; updates must be manually installed or managed via auto-update service","File system access requires Electron permissions; sandboxing is less strict than browsers, creating potential security risks if code is compromised","Linux deployment requires AppArmor configuration (Ubuntu 24.04+ has specific requirements); not all Linux distributions are equally supported"],"requires":["Node.js 18.0.0 or higher (for building)","macOS 10.13+, Windows 7+, or Linux (Ubuntu 24.04+ with AppArmor configuration)","Electron build tools and packaging dependencies"],"input_types":["React components from packages/studio","MuapiClient API methods"],"output_types":["Electron application binary (macOS .app, Windows .exe, Linux .AppImage)","Generated images/videos saved to local file system"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":51,"verified":false,"data_access_risk":"high","permissions":["Node.js 18.0.0 or higher","Valid Muapi.ai API key (BYOK model — user provides their own key)","Modern browser with localStorage support for generation history persistence","Valid Muapi.ai API key with video generation quota","Browser with localStorage support for job persistence","Network connectivity maintained during polling cycles (interruptions may lose job tracking)","Compliance with local laws regarding content generation and distribution","Understanding that Muapi backend may still enforce its own content policies","Responsible use of the platform; users are liable for content they generate","Valid Muapi.ai API key (stored in localStorage)"],"failure_modes":["Generation latency depends on Muapi backend inference time (typically 30-120 seconds per image)","No built-in batch processing — each image requires a separate API call and polling cycle","Model availability and pricing determined by Muapi.ai service; no fallback if primary provider is down","UI schema generation assumes all model parameters map to standard control types; custom parameter types require manual schema extension","Video generation is inherently slow (5-15 minutes typical); polling adds latency overhead (~1-5 seconds per poll cycle)","No webhook support — polling is the only mechanism for job status updates, requiring client-side polling loops","localStorage persistence is browser-specific; pending jobs cannot be shared across devices or browsers","Video quality and generation time vary significantly by model; no built-in model recommendation based on input characteristics","No built-in safeguards against generating illegal or harmful content; users are fully responsible for compliance with local laws","Muapi backend may still apply its own filters; Open Generative AI cannot guarantee unrestricted output if the underlying API enforces policies","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.6889673069001108,"quality":0.5,"ecosystem":0.6000000000000001,"match_graph":0.25,"freshness":0.75,"weights":{"adoption":0.3,"quality":0.2,"ecosystem":0.15,"match_graph":0.3,"freshness":0.05}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-05-24T12:16:21.549Z","last_scraped_at":"2026-05-03T13:59:47.980Z","last_commit":"2026-05-01T10:28:05Z"},"community":{"stars":10984,"forks":1935,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=anil-matcha--open-generative-ai","compare_url":"https://unfragile.ai/compare?artifact=anil-matcha--open-generative-ai"}},"signature":"4KgOGzJBPjzRP7syzFXZvkJKFIhD9j0rW3H9YGb3sF2srS1drV/CCIsWsllQljh0fUYqHwNJddr1q0iQsah3CQ==","signedAt":"2026-06-20T12:16:14.489Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/anil-matcha--open-generative-ai","artifact":"https://unfragile.ai/anil-matcha--open-generative-ai","verify":"https://unfragile.ai/api/v1/verify?slug=anil-matcha--open-generative-ai","publicKey":"https://unfragile.ai/api/v1/trust-passport-public-key","spec":"https://unfragile.ai/trust","schema":"https://unfragile.ai/schema.json","docs":"https://unfragile.ai/docs"}}