Projects

Overview: Seedance 2.5 API is an OpenAI-compatible endpoint providing model id, pricing, and parameters for image, video, and language tasks. It offers a base URL and authorization header for easy integration. Key features: - Model id for image and video tasks - Pricing by resolution - Billing per generated second - Asynchronous processing by default - Supports 300+ models including Seedance 2.5 Tech stack: Python Why it matters: Seedance 2.5 API simplifies integration with OpenAI-compatible endpoints, providing a single API key for multiple models and avoiding the need for upfront billing. Repository: er2ig3b3114/doubao-seedance-2-5-api-ru Primary language: Python GitHub stars: 13 GitHub description: Seedance 2.5 API (`seedance-2.5`): model id, pricing, parameters and copy-paste examples for the OpenAI-compatible APIMart endpoint. README excerpt: Seedance 2.5 is ByteDance's video model with cinematic motion, audio synchronisation and per-second billing by resolution. ## Model id and routes | Route | `model` value | Billing | Notes | | --- | --- | --- | --- | | Text-to-video / image-to-video | `seedance-2.5` | per generated second, by resolution | see the [model page](https://go.apimart.ai/k-f528d3) for the exact tiers | Endpoint: `POST

### Overview: FOCUS is an optimal control framework designed to enhance the multi-subject fidelity of text-to-image models. By leveraging the principles of stochastic optimal control, FOCUS optimizes the sampling process to align with the desired multi-subject descriptions, thereby improving the quality of generated images. ### Key Features: - **Test-Time Controller**: A training-free approach that perturbs the base sampler to encourage disentanglement. - **Adjoint Matching**: A lightweight fine-tuning rule that regresses a control network to a backward adjoint signal while preserving base-model capabilities. - **Unified Heuristics**: Combines prior attention heuristics with a principled, optimizable objective. - **Model-Agnostic**: Works with various models like Stable Diffusion 3.5, FLUX, and Stable Diffusion XL. - **Plug-and-Play**: Minimal changes to official pipelines; FOCUS controller is passed into the sampler. - **Efficient**: Test-time control runs efficiently on commodity GPUs, with memory-saving modes available. ### Tech Stack: - **Jupyter Notebook**: Primary language used for development and experimentation. ### Why It Matters: FOCUS addresses the limitations of current text-to-image models by optimizing their sampling dynamics, leading to more accurate and diverse image generation. This framework not only improves the fidelity of multi-subject descriptions but also maintains the stylistic consistency of the base models, making it a valuable tool for researchers and practitioners in the field of image generation and text-to-image synthesis. ### GitHub Stars: 15 ### GitHub Description: [CVPR

Overview: Grok Imagine 2.0 API is a powerful image generation and region-edit model provided through the APIMart endpoint, serving images per image at a cost of $0.015 per image. Key features: - Supports OpenAI-compatible endpoint - Provides a single endpoint for multiple models - Offers a minimum price of $1 per image, with recharges for failed tasks - Assynchronous by design, allowing for easy task management and retries Tech stack: Python Why it matters: Grok Imagine 2.0 API offers a cost-effective and flexible way to generate images, making it ideal for developers and content creators looking to integrate image generation into their projects without the need for a dedicated API key. Repository: et3yo6leiak/grok-imagine-2-0-api-pt Primary language: Python GitHub stars: 13 GitHub description: Grok Imagine 2.0 API (`grok-imagine-2.0-ext`): model id, pricing, parameters and copy-paste examples for the OpenAI-compatible APIMart endpoint.

### Overview: Qwen-Image 3.0 prompts: tested image prompt recipes with example renders, aspect ratios, the model id (`qwen-image-3.0`) and the cost per image. ### Key features: - **Model ID:** `qwen-image-3.0` - **Aspect Ratios:** 1:1, 4:3, 8:5, 3:4, etc. - **Cost per Image:** $0.0206 ### Tech stack: Python ### Why it matters: - **Model Compatibility:** Qwen-Image 3.0 is compatible with OpenAI's API. - **Cost Efficiency:** The cost per image is estimated at $0.0206, making it an efficient option for image prompts. - **Ease of Use:** The API key can be obtained directly from the provided link, and the pricing is live and flexible. - **Batching and Retry Mechanism:** The system handles asynchronous tasks and retries, ensuring smooth functionality without the need for prepayment plans.

Overview: `@getpipher/vision` is a capability-aware vision extension for the pi coding agent, designed to optimize image analysis by delegating only when necessary, ensuring zero delegation calls, zero extra tokens, and zero extra latency. Key features: - Multimodal primary models pass image analysis through natively. - Text-only primary models delegate to a configured vision model. - Automatically detects and configures the active primary model. - Replaces existing extensions `pi-vision-tool` and `pi-paster`. - Configures the vision tool with a configured vision model. Tech stack: TypeScript Why it matters: `@getpipher/vision` optimizes image analysis by delegating only when necessary, ensuring zero delegation calls, zero extra tokens, and zero extra latency, making the workflow more efficient and reducing unnecessary resource usage. GitHub stars: 15 Repository: getpipher/vision

Overview: Awful Jade, or `aj`, is a Rust-based CLI tool designed for interacting with OpenAI-compatible APIs. It simplifies working with Large Language Models (LLMs) by providing a command-line interface. Key features: - **Ask the AI**: Run `aj ask "question"` to get answers from your configured model. - **Interactive Mode**: A REPL-style conversation with memory and vector search capabilities. - **RAG Support**: Loads documents for context-aware responses with the `--rag` flag. - **Pretty Printing**: Renders markdown with syntax highlighting for code blocks. - **Progress Indicators**: Provides real-time feedback with spinners for API calls, memory search, and model loading. - **Vector Store**: Uses HNSW + sentence embeddings to remember previous context. - **Brains with Limits**: Keeps only as many tokens as you allow. When full, it forgets the oldest stuff. - **Config & Templates**: YAML-driven configs and prompt templates for customization. - **Auto-embeddings Model**: Downloads the `all-MiniLM-L6-v2` BERT model automatically when needed. - **Pure Rust**: No Python dependencies, everything runs in pure Rust using Candle for ML inference. - **Thread-Safe**: Core types are `Send + Sync`, enabling concurrent workloads. Tech stack: Rust. Why it matters: Awful Jade simplifies the process of working with LLMs by providing a user-friendly command-line interface, making it accessible for developers and researchers alike. Repository: graves/awful_aj GitHub stars: 15

### Overview: Claude Image Generation is a developer-friendly plugin for generating and editing images using Google Gemini, OpenAI GPT Image, xAI Grok Image, and OpenRouter APIs. It provides a versatile workflow for text-to-image generation and image editing, with features like parallel generation, interactive selection, and inline image preview. ### Key features: - **Text-to-image generation** with Google Gemini, OpenAI GPT Image, xAI Grok Image, or any image model on OpenRouter. - **OpenRouter gateway** for reaching any OpenRouter image model through a single key. - **Image editing** with text instructions. - **Multi-image input** — repeatable `--input-image` for multi-image edits. - **Parallel generation** across all providers via `scripts/run-all.sh`. - **Interactive provider selection** via AskUserQuestion. - **Inline image preview** — generated images display directly in the terminal. - **Tmux pane display** — opens a split pane for image preview. - **Streaming display** — images appear progressively in a shared pane during parallel generation. - **Open in Finder/Preview** — press 'f' for Finder or 'p' for Preview. ### Tech stack: The plugin uses Shell scripting for command-line interface and automation. ### Why it matters: This plugin streamlines the process of generating and editing images, making it accessible for developers and artists alike. It leverages powerful APIs like Google Gemini, OpenAI GPT Image, and xAI Grok Image, offering high-quality image generation and editing capabilities. The

### Overview The LLM Glossary serves as a comprehensive reference guide for developers, researchers, and AI enthusiasts exploring the rapidly evolving landscape of Large Language Models and artificial intelligence. This glossary provides clear, concise definitions for key concepts, ensuring practitioners have the necessary knowledge to build, research, and understand generative language technologies. ### Key Features - **Definitions**: Clear and practical explanations of terms. - **Community-Maintained**: Updated to keep pace with the fast-moving AI field. - **Practical Focus**: Definitions written for practitioners, not just academics. - **Cross-Referenced**: Terms link to related concepts for deeper understanding. - **Resource-Rich**: Each entry includes links to papers, tutorials, and implementations. ### Tech Stack - **GitHub Repository**: holasoymalva/llm-glossary - **Stars**: 15 - **Description**: A glossary of key concepts in Large Language Models, Artificial Intelligence, Natural Language Processing, and Machine Learning, designed for developers, researchers, and enthusiasts. ### Why It Matters - **Always Current**: Community-maintained to keep pace with the fast-moving AI field. - **Practical Focus**: Definitions written for practitioners, not just academics. - **Cross-Referenced**: Terms link to related concepts for deeper understanding. - **Resource-Rich**: Each entry includes links to papers, tutorials, and implementations. ### Tech Stack - **GitHub Repository**: holasoymalva/llm-glossary - **Stars**: 15 - **Description**: Gloss

### Overview: The Strix Halo engine lab is a cutting-edge inference engine designed for AMD Strix Halo GPUs, specifically targeting the AMD Radeon 8060S `gfx1151` and 128 GB unified memory. It is written from scratch in C++ and HIP, with a focus on achieving exactness and performance. ### Key features: - **Production and Opt-In Models**: The engine supports both production-grade models and opt-in configurations, including Qwen3.8-Flash-Next, DeepSeek V4.1 Flash, and MiniMax H3. - **Exactness Guarantees**: The engine ensures token-identical inference, with speculative decoding and concurrency preserving the reference contract. - **CUDA and GPU Support**: It supports CUDA and GPU-based inference, with RTX PRO 6000 providing additional features like pipeline splits and CUDA prefill services. - **Server and Client**: The engine supports both server and client configurations, with server-side optimizations and client-side enhancements. ### Tech stack: The engine is built using C++ and HIP, with a focus on exact inference and performance. ### Why it matters: The Strix Halo engine lab is crucial for developers and researchers looking to optimize language models for high-performance GPUs. Its ability to achieve exact inference and support both production and opt-in configurations makes it a valuable tool for various applications, including natural language processing and AI research.

### Overview: Characterize Anything is an interactive tool that brings any object in a video or image to life, combining advanced video object tracking and segmentation technologies with large vision language models. This project aims to enhance human-object interactions and is under active development. ### Key Features: - Characterize Anything allows users to specify which object(s) they want to talk to by simple mouse clicks. - It leverages advanced video object tracking and segmentation technologies. - It integrates with large vision language models to enable meaningful conversations with objects. ### Tech Stack: Python ### Why it Matters: Characterize Anything offers a unique and exciting way to enhance human-object interactions, making it an inspiring tool for both education and entertainment.

### Overview: Qwen Image 2.0 API (`qwen-image-2.0`) provides powerful AI image generation capabilities. This model can generate high-quality images from text prompts or transform existing images. It supports various image and language models through a single API endpoint, making it easy to integrate into applications. ### Key Features: - **Model ID and Routes**: The API supports the `qwen-image-2.0` model, along with over 300 other models. The pricing is per delivered image, and the endpoint is `POST https://api.apimart.ai/v1/images/generations`. - **Pricing**: The effective price is $0.02 per image, with a list price of $0.025. Prices are updated by CI and reflect the exact cost in the response. - **Request Parameters**: The API accepts parameters such as `model`, `prompt`, `size`, `resolution`, `n`, and `image_urls`. The `nsfw_check` parameter is optional and runs `omni-moderation-latest` before submitting. ### Tech Stack: - **Python** - The primary language used for implementing the API. ### Why It Matters: - **Ease of Use**: A single API endpoint supports multiple models, making it simple to integrate into applications. - **Cost Efficiency**: The pricing is per image, making it suitable for both small and large projects. - **Flexibility**: The API supports various image and language models, allowing for versatile use cases. ### GitHub Repository: - **Repository**: `iqfu

### Overview: This repository contains tested image prompt recipes for the FLUX 2 Pro model, including 8 recipes with example renders, aspect ratios, and the cost per image. The recipes are designed to help developers and designers create high-quality images efficiently. ### Key Features: - **8 Recipes**: Detailed prompts for various subjects, including e-commerce products, food photography, and more. - **Aspect Ratios**: Specific ratios for different image formats (1:1, 4:3, etc.) to ensure optimal rendering. - **Cost Per Image**: Estimated cost of $0.024 per image, with a maximum of $0.192 for the entire set. ### Tech Stack: - Python ### Why It Matters: The FLUX 2 Pro model is known for its high-quality output, and this repository provides developers with tested recipes that can help them achieve similar results more efficiently. The detailed prompts and aspect ratios ensure that the images produced are both visually appealing and cost-effective.

Overview: Wan 2.7 API (`wan2.7`) is an OpenAI-compatible endpoint providing high-resolution image-to-video and text-to-video capabilities, with a $0.0664 per second price model and no need for a monthly subscription. Key features: - Supports Wan 2.7 (`wan2.7`) and 300+ other models through a single base URL and `Authorization` header. - Offers $1 minimum pricing with pay-as-you-go, no need for a subscription. - Reports the exact cost per call in the response, eliminating guesswork. - Batches and retries tasks in a queue, ensuring efficient processing. Tech stack: Python Why it matters: Wan 2.7 API on APIMart provides a powerful, high-quality AI video model with superior performance, making it ideal for various applications requiring professional-grade visual content generation.

Overview: This repository contains tested image prompt recipes for the Qwen Image 2.0 Pro model, including 8 recipes with examples, aspect ratios, the model ID (`qwen-image-2.0-pro`), and the cost per image. The recipes are designed to help developers create effective and efficient images. Key features: - 8 tested image prompt recipes - Example renders, aspect ratios, model ID, and cost per image - Effective price used: $0.05 per image - Estimated cost of the whole set: $0.4 Tech stack: Python Why it matters: The recipes and cost information provided can help developers create high-quality images using the Qwen Image 2.0 Pro model efficiently and economically.

Overview: 이마젠 — Imagen 4.0 API (`imagen-4.0-apimart`): 모델 ID, 가격, 파라미터 및 APIMart 엔드포인트에 대한 복사 붙여 넣기 예제입니다. Key features: - 모델 ID: Imagen 4.0(`imagen-4.0-apimart`) - 가격: 0.04원 per image - 파라미터: prompt, size, resolution, n, image_urls, nsfw_check - APIMart 엔드포인트: `POST https://api.apimart.ai/v1/images/generations`, `GET /v1/tasks/{task_id}` Tech stack: Python Why it matters: 이마젠은 Google에서 제공하는 Imagen 4.0 모델을 APIMart에서 사용할 수 있으며, 단일 `https://api.apimart.ai/v1` URL과 `Authorization` 헤더만 필요로 합니다. 이는 구독 없이도 사용할 수 있으며, 1부터 시작하는 최소 가격으로만 충전할 수 있습니다. 또한, 모든 작업의 청구 금액이 `cost` 필드에 보고되고, 실패한 작업은 환불됩니다. Repository: iupxojk1-f/imagen-4-0-api-ko Primary language: Python GitHub stars: 15 GitHub description: 이마젠 — Imagen

### Overview: 即梦图片 — Seedream 4.0 prompts: tested image prompt recipes with example renders, aspect ratios, the model id (`seedream-4-0`) and the cost per image. ### Key features: - **Prompt Recipes**: Eight tested recipes with examples, ratios, and costs. - **Model ID**: `seedream-4-0` for Seedream 4.0. - **Cost**: $0.0195 per image, $1 minimum charge. ### Tech stack: Python ### Why it matters: - **Single Base URL**: Access Seedream 4.0 and 300+ models with a single `Authorization` header. - **No Prepaid Plans**: No subscription fees or prepayment required. - **Real-Time Pricing**: Charges are reported per task, with failed tasks refunded automatically. - **Asynchronous Design**: Simple API calls and queue-based processing without custom integrations. ### Repository: j6qn3i24az-9a/seedream-4-0-prompts-cn ### Primary language: Python ### GitHub stars: 15

Overview: Seedream 5.0 Pro API is a comprehensive OpenAI-compatible API endpoint for generating high-quality images, including 1K and 2K layers, with precise text rendering and multi-reference editing capabilities. Key features: - Supports 1K and 2K image generation with layered pricing - Precise text rendering and multi-reference editing - Compatible with OpenAI's API endpoint - Asynchronous processing for efficient task management Tech stack: Python Why it matters: Seedream 5.0 Pro API simplifies the process of obtaining and using high-quality image models, offering a unified endpoint for various image and language tasks, making it ideal for developers and teams looking to integrate Seedream 5.0 Pro into their projects without the need for complex integrations or recurring payments. Repository: jef7is8ris/seedream-5-0-pro-api-fr Primary language: Python GitHub stars: 15 GitHub description: Seedream 5.0 Pro API (`seedream-5-0-pro`): model id, pricing, parameters and copy-paste examples for the OpenAI-compatible APIMart endpoint. README excerpt: Seedream 5.0 Pro is ByteDance's 1K/2K image model with layered pricing, precise text rendering and multi-reference editing. <!-- pricing:model:start --> | Output | List price | Effective price | | --- | --- | --- | | default | 0.045 | 0.036 | | 1K | 0.0365

### Overview: MagiCore is a powerful local-first C# library designed for building memory into AI agents, assistants, simulations, and robots. It combines semantic retrieval with historical audit, event-time recall, and cognitive consolidation, providing a robust infrastructure for long-term memory management. ### Key Features: - **Semantic Memory**: Dense and hybrid retrieval, including entities, relations, and multimodal memories. - **Temporal Memory**: Audit history, point-in-time reads, and event-time filtering. - **Spatial Memory**: Timestamped 3D observations, reconstructed object beliefs, and measured action episodes. ### Tech Stack: C# ### Why It Matters: MagiCore is essential for any application that requires a robust and scalable memory infrastructure. It ensures that your AI agents and systems can remember past events, recover crucial information, and recall it in the right context, making them more intelligent and reliable. ### GitHub Stars: 15 ### Repository: - **Repository**: jihadkhawaja/MAGICore - **Primary Language**: C# - **GitHub Stars**: 15 - **GitHub Description**: Long-term memory infrastructure for .NET AI agents with semantic, temporal, spatial, and robotics memory, hybrid retrieval, and pluggable providers.

### Overview: MagiCore is a C# library designed for building memory into AI agents, assistants, simulations, and robots. It provides semantic, temporal, and spatial memory capabilities, including hybrid retrieval, event-time recall, and pluggable providers. ### Key Features: - **Semantic Memory**: Dense and hybrid retrieval, reranking, entities, relations, and multimodal memories. - **Temporal Memory**: Audit history, point-in-time reads, rollback, and event-time filtering. - **Spatial Memory**: Timestamped 3D observations, reconstructed object beliefs, and measured action episodes. ### Tech Stack: C# ### Why It Matters: MagiCore is essential for developers looking to integrate semantic, temporal, and spatial memory into their .NET AI applications and agents. It provides a robust infrastructure for storing, retrieving, and recalling information in a context-aware manner, making it ideal for applications requiring historical context and accurate recall of events.

Overview: Imagen 4.0 API is a comprehensive OpenAI-compatible endpoint that offers access to 300+ models, including Imagen 4.0, for a flat rate of $0.04 per image. Key features: - Supports multiple models and resolutions - Pricing is per image, with a minimum of $1 for the first call - Asynchronous processing with task IDs for easy tracking - Provides detailed model information and pricing details Tech stack: Python Why it matters: This API simplifies access to advanced image generation capabilities without the need for monthly subscriptions, making it ideal for developers and creatives looking to integrate high-quality image generation into their projects. GitHub stars: 15 Repository: jlaj4ebu/imagen-4.0-api-de Primary language: Python Warum Teams Imagen 4.0 (`imagen-4.0-apimart`) über APIMart nutzen - **Ein Key, der gesamte Katalog.** Dieselbe Base-URL `https://api.apimart.ai/v1` und derselbe `Authorization`-Header erreichen Imagen 4.0 (`imagen-4.0-apimart`) und über 300 weitere Bild-, Video- und Sprachmodelle — Sie wechseln das `model`-Feld, nicht Ihren Client. - **$1 Minimum, Pay-as-you-go.** Kein Abo und kein Prepaid-Tarif, den Sie vorab dimensionieren müssen: ab $1 aufl