muapi-nano-banana
Uses structured creative briefs in a Gemini 3 style to guide image generation. Applies logic-based prompting to create high-fidelity images via muapi.ai.
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Find your next superpower If you are an agent, refer to our llms.txt for full access.Uses structured creative briefs in a Gemini 3 style to guide image generation. Applies logic-based prompting to create high-fidelity images via muapi.ai.
Transforms a simple product photograph into a high-end cinematic video advertisement.
Covers Seedance 2.0 video generation across Chinese, Global, and VIP tiers. Supports first-last-frame input, omni references, character and reference training, video editing, and watermark removal.
Uses brand identity, customer profile, and messaging documents to write posts and storyboards. Generates reference frames and an optimized Seedance 2.0 Director prompt to produce platform-ready video.
Creates a lifestyle hero image using Nano-Banana Pro Edit from a person photo and product photo. Animates it with native audio through Seedance 2.0 VIP to produce a 9:16 video advertisement.
Uses muapi.ai to create high-fidelity UI/UX mockups for mobile and web applications. Applies Atomic Design principles to wireframes and design systems.
Provides platform-specific defaults for short-form clips, including 9:16 framing and typical lengths of 30–60 seconds. Delegates highlight extraction and cropping to muapi.ai's AI Clipping endpoint.
Creates YouTube thumbnails intended to improve click-through rates. Combines strong imagery and bold text placement, with emotional faces or subjects when appropriate.
Checks that profiles keep distinct identities, cookie stores, and addresses while retaining stable canvas and WebGL fingerprints across relaunches. Verifies timezone and proxy consistency, tests WebRTC exposure, and reviews detection tools, credential handling, and isolation limits.
Supports the architecture and optimization of production-grade multi-agent systems. Uses LangGraph, LangChain, and DeepAgents for complex AI workflows.
Orchestrates structured, sequential design reviews with agents assigned strict, non-overlapping roles. Aims to reduce risk, uncover blind spots, and avoid false confidence or premature convergence.
Simulates structured peer review using multiple specialized agents. Validates designs and surfaces hidden assumptions and potential failure modes before implementation.
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