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by Black Forest Labs
Black Forest Labs FLUX.2 [klein] 4B is a lightweight, fast image generation model optimized for speed and efficiency. With 4 billion parameters, it delivers quick image generation while maintaining good quality. Perfect for rapid prototyping, bulk generation, and applications requiring low latency. Supports both text-to-image and image-to-image generation with excellent cost-efficiency.
Black Forest Labs FLUX.1 [dev] is a cutting-edge 12 billion parameter rectified flow transformer for text-to-image generation. Second only to FLUX.1 [pro] with strong prompt following matching closed-source alternatives. Features guidance distillation for efficient inference, high-resolution generation (1024x1024), accurate text rendering, and detailed composition. Supports both text-to-image and image-to-image generation. Open weights enable scientific research and innovative workflows.
by MiniMax
MiniMax M2 is a powerful MoE model with 200B total / 10B active parameters. Optimized for reasoning and coding tasks with excellent performance in multilingual scenarios. Features 128K context window and efficient inference.
Black Forest Labs FLUX.2 [klein] 9B is a balanced image generation model offering excellent quality-to-speed ratio. With 9 billion parameters, it provides better detail and composition than the 4B variant while remaining faster than full-size models. Ideal for production workloads requiring a balance between quality, speed, and cost. Supports both text-to-image and image-to-image generation.
by intfloat
intfloat Multilingual E5 Large is a powerful multilingual dense retrieval embedding model supporting 100+ languages with strong cross-lingual capabilities. Features 1024-dimensional embeddings optimized for semantic search, document retrieval, and text similarity across diverse language families. Pre-trained on large-scale multilingual data with contrastive learning for robust cross-lingual transfer. Ideal for international search systems, multilingual document retrieval, and global content recommendation platforms requiring high-quality semantic understanding.
MiniMax M2.5 is a state-of-the-art reasoning MoE model with 229B total / 10B active parameters. Extensively trained with reinforcement learning across 200,000+ real-world environments, achieving SOTA performance in coding (80.2% SWE-Bench Verified), agentic tool use, search, and office productivity tasks. Features 197K context window, efficient MoE inference, and strong multilingual support.
intfloat Multilingual E5 Large Instruct is an instruction-tuned multilingual embedding model combining strong cross-lingual capabilities with instruction-following for guided retrieval. Supports 100+ languages with natural language instructions to customize embedding behavior. Features enhanced zero-shot retrieval performance through instruction-based query understanding. Ideal for complex multilingual search scenarios, domain-specific retrieval tasks, and applications requiring adaptive semantic understanding across languages.
by Mistral
Mistral's 12B parameter vision-language model. Capable of understanding and reasoning about images alongside text.
Black Forest Labs FLUX.1 [dev] with LoRA adapter support. This variant enables fine-tuned generation with custom trained LoRA weights for specialized styles, characters, or concepts. Based on the full 12B parameter FLUX.1 [dev] model with all its capabilities including high-resolution generation, accurate text rendering, and detailed composition. Perfect for custom workflows and specialized image generation tasks.
Black Forest Labs FLUX.2 [dev] is the latest generation text-to-image model with significant improvements over FLUX.1. Features enhanced prompt following, superior image quality, and faster generation. Built on the proven rectified flow transformer architecture with optimizations for better detail, composition, and text rendering. Excellent for creative workflows, concept art, and high-quality image generation with both text-to-image and image-to-image capabilities.
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Models
by Anonymous
A helpful, accurate, and thoughtful AI assistant. Built with EU values: privacy-first, transparent, and accessible.
Blueprints
Execute Python 3.12 code in a sandboxed environment. Generate documents (PDF, Excel, Word, PowerPoint), create visualizations, process data, and run analysis. Pre-installed with pandas, numpy, matplotlib, scikit-learn, and 40+ libraries.
Generate images from text descriptions. Supports text-to-image and image-to-image editing with uploaded reference images. Multiple sizes and reproducible seeds.
Extract and read content from web pages. Returns clean markdown text. Supports standard HTML pages, JavaScript-rendered sites, and YouTube video transcripts.
Search the web for real-time information. Returns results with titles, URLs, and content snippets. Supports time-range filtering and language selection.
Tools
Create product backlog items in Why-What-Acceptance format — independent, valuable, testable items with strategic context. Use when writing structured backlog items, breaking features into work items, or using the WWA format.
Design a detailed value proposition using a 6-part JTBD template — Who, Why, What before, How, What after, Alternatives. Use when creating a value proposition, analyzing customer value delivery, or articulating why customers should choose your product.
Generate value proposition statements for marketing, sales, and onboarding from existing value propositions. Use when writing marketing copy, creating sales messaging, or crafting onboarding messages.
Create user stories following the 3 C's (Card, Conversation, Confirmation) and INVEST criteria with descriptions, design links, and acceptance criteria. Use when writing user stories, breaking down features into backlog items, or defining acceptance criteria.
Segment users from feedback data based on behavior, JTBD, and needs. Identifies at least 3 distinct user segments. Use when segmenting a user base, analyzing diverse user feedback, or building a segmentation model.
Create refined user personas from research data — 3 personas with JTBD, pains, gains, and unexpected insights. Use when building personas from survey data, creating user profiles from research, or segmenting users for product decisions.
Create comprehensive test scenarios from user stories with test objectives, starting conditions, user roles, step-by-step actions, and expected outcomes. Use when writing QA test cases, creating test plans, defining acceptance tests, or preparing for feature validation.
Perform a detailed SWOT analysis — strengths, weaknesses, opportunities, and threats with actionable recommendations. Use when doing strategic assessment, competitive analysis, or evaluating a product or business position.
Summarize a meeting transcript into structured notes with date, participants, topic, key decisions, summary points, and action items. Use when processing meeting recordings, creating meeting notes, writing meeting minutes, or recapping discussions.
Summarize a customer interview transcript into a structured template with JTBD, satisfaction signals, and action items. Use when processing interview recordings or transcripts, synthesizing discovery interviews, or creating interview summaries.
Skills
