Results for “prompts”

Generates images from text prompts using Google Gemini's image generation model, saving them as PNG files for use in UI, documentation, and design projects.

v0 by Vercel expert guidance. Use when discussing AI code generation, generating UI components from prompts, v0 CLI usage, v0 SDK/API integration, or integrating v0 into development workflows with GitHub and Vercel deployment.

Research any topic across Reddit, X, and the web from the last 30 days, then synthesize findings into actionable insights and copy-paste-ready prompts.

Generate music from text prompts using ElevenLabs Music API, supporting instrumental tracks, songs with lyrics, composition plans, video-to-music, and inpainting.

Generates customizable prompts for scanning codebases to identify and document high-quality code exemplars, supporting multiple programming languages and configurable analysis depth.

Analyzes prompts for safety, bias, security vulnerabilities, and effectiveness, providing detailed improvement recommendations with frameworks, testing methodologies, and educational content.

Build complex AI systems with declarative programming, optimize prompts automatically, and create modular RAG systems and agents using Stanford NLP's DSPy framework.

Evaluates LLMs across 60+ academic benchmarks (MMLU, HumanEval, GSM8K, TruthfulQA, HellaSwag) using standardized prompts and metrics. Supports HuggingFace, vLLM, and API backends.

Generates high-quality prompts for AI image, video, and music generation platforms, with optional browser automation to submit them to target sites.

Generates YouTube videos from prompts, creates vertical Shorts from text, and converts long videos into Shorts using free tools like ffmpeg, edge-tts, and Pexels.

Design a learning sequence teaching prompt quality — comparing vague vs. refined prompts to show why specificity and context transform AI output. Use when students use AI without understanding why output quality varies.

State-of-the-art text-to-image generation with Stable Diffusion models via HuggingFace Diffusers. Use when generating images from text prompts, performing image-to-image translation, inpainting, or building custom diffusion pipelines.

State-of-the-art text-to-image generation with Stable Diffusion models via HuggingFace Diffusers. Use when generating images from text prompts, performing image-to-image translation, inpainting, or building custom diffusion pipelines.

State-of-the-art text-to-image generation with Stable Diffusion models via HuggingFace Diffusers. Use when generating images from text prompts, performing image-to-image translation, inpainting, or building custom diffusion pipelines.

Songwriting craft, AI music generation prompts (Suno focus), parody/adaptation techniques, phonetic tricks, and lessons learned. These are tools and ideas, not rules. Break any of them when the art calls for it.

Generate videos using Google Veo models via the inference.sh CLI, with support for multiple Veo versions and cinematic prompts.

Segment any object in images using points, boxes, or masks as prompts, or automatically generate all object masks with zero-shot transfer.

Build, test, inspect, install, and deploy MCP servers with FastMCP in Python, including wrapping APIs, databases, or files as tools and exposing resources or prompts.

Foundation model for image segmentation with zero-shot transfer. Use when you need to segment any object in images using points, boxes, or masks as prompts, or automatically generate all object masks in an image.

Foundation model for image segmentation with zero-shot transfer. Use when you need to segment any object in images using points, boxes, or masks as prompts, or automatically generate all object masks in an image.

Foundation model for image segmentation with zero-shot transfer. Use when you need to segment any object in images using points, boxes, or masks as prompts, or automatically generate all object masks in an image.

Trains, evaluates, exports, quantizes, and runs inference for a Grounding DINO model that detects objects described by text prompts without a fixed class vocabulary.

Build production-quality Model Context Protocol (MCP) servers in C#/.NET using the official 1.x NuGet packages, covering transports, tools, prompts, resources, and common pitfalls.

Analyzes draft prompts, identifies intent and gaps, matches ECC components, and outputs an optimized prompt for the user to paste and run. Advisory only, never executes the task.

Meta's 86M prompt injection and jailbreak detector. Filters malicious prompts and third-party data for LLM apps. 99%+ TPR, <1% FPR. Fast (<2ms GPU). Multilingual (8 languages). Deploy with HuggingFace or batch processing for RAG security.

Provides guidance for automatically evolving and optimizing AI agents across any domain using LLM-driven evolution algorithms. Use when building self-improving agents, optimizing agent prompts and skills against benchmarks, or implementing automated agent evaluation loops.

Meta's 86M prompt injection and jailbreak detector. Filters malicious prompts and third-party data for LLM apps. 99%+ TPR, <1% FPR. Fast (<2ms GPU). Multilingual (8 languages). Deploy with HuggingFace or batch processing for RAG security.

Expert photography prompt engineer specializing in crafting detailed, evocative prompts for AI image generation. Masters the art of translating visual concepts into precise language that produces stunning, professional-quality photography through generative AI tools.

Use when designing AI product first-run or blank-slate UX — chat empty state, example prompts, capability discovery, templates, nudges, or follow-ups. Trigger on "users don't know what to ask", onboarding, suggestion chips, or discoverability.

Analyze draft prompts to identify intent, scope, and missing context, then generate an optimized prompt with ECC component recommendations. Advisory only — never executes the task.

Build MCP servers with Node/TypeScript SDK — tools, resources, prompts, Zod validation, stdio vs Streamable HTTP. Use Context7 or official MCP docs for latest API.

Transforms vague UI ideas into polished, Stitch-optimized prompts by enhancing specificity, adding UI/UX keywords, injecting design system context, and structuring output for better generation results.

Design a diary study plan with prompts, duration, participant criteria, and analysis framework to understand user behavior over time in natural contexts.

Generate cinematic videos with Seedance 2.0 using director-level prompts, camera grammar, and multi-mode generation across Chinese, Global, and VIP tiers.

Generate a single continuous cinematic shot video with no cuts, using text or image prompts and configurable style, duration, and aspect ratio.

Reads brand identity, ICP, and messaging files to produce on-brand social video with storyboard, reference images, and Seedance 2.0-optimized prompts.