Projects

### Overview: Seedream 4 Prompts: 8 Tested Recipes With Outputs <!-- conv-kit:v1 --> <p align="center"> <img src="assets/badges/price.svg" alt="observed unit price"> <img src="assets/badges/billing.svg" alt="billing model"> <img src="assets/badges/compat.svg" alt="OpenAI-compatible endpoint"> </p> <p align="center"> <img src="assets/01-product-hero-perfume.jpg" width="820" alt="Sample render via the APIMart image API"> </p> > **from $0.0195 per image** on the APIMart route — un seul endpoint compatible OpenAI sur `https://api.apimart.ai/v1`, sans abonnement mensuel. *(relevé le 2026-10-12)* **[Get an API key](https://go.apimart.ai/k-64572e)** · **[Live pricing](https://go.apimart.ai/k-403b1d)** · **[Model page](https://go.apimart.ai/k-a2a580)** · [⚡ 60-second quickstart](#quickstart) **Pourquoi les équipes passent par APIMart pour Seedream 4 (`seedream-4-0`)** - **Une clé, tout le catalogue.** La même base URL `https://api.apim

### Overview: CuQwen is a high-performance CUDA inference engine designed specifically for exploring the theoretical performance limits of single-user autoregressive token generation using optimized custom CUDA kernels. It is written from scratch in C++/CUDA and is optimized for open-source Qwen models, aiming to run on local edge devices with Nvidia GPUs. ### Key features: - **Performance Optimization**: CuQwen achieves the highest average decode speed across various Qwen models, including the Qwen2.5 Instruct models, with an average throughput of 506 tokens/second on an RTX 3090. - **Precision Support**: It supports three weight-only precisions: **FP16** (W16A16), **INT8** (W8A16), and **INT4** (W4A16), achieving the best performance in all configurations that fit a 24 GB GPU. - **Model Support**: CuQwen natively supports the Qwen2.5 model family across multiple parameter scales, including **Qwen2.5-0.5B**, **Qwen2.5-1.5B**, **Qwen2.5-3B**, **Qwen2.5-7B**, and **Qwen2.5-14B**. ### Tech stack: C++/CUDA ### Why it matters: CuQwen is a significant contribution to the field of natural language processing by pushing the boundaries of performance for single-user autoregressive models. Its optimized CUDA implementation ensures high throughput and

### Overview: This repository contains Wan 2.7 prompts tested with example renders, aspect ratios, and the model ID. It offers insights into effective image prompt recipes and their associated costs. ### Key Features: - **Model ID:** `wan2.7` - **Aspect Ratios:** 16:9, 4:3, 1:1 - **Duration:** 5 seconds - **Cost:** $0.0664 per second ### Tech Stack: Python ### Why It Matters: Wan 2.7 prompts provide a practical way to craft effective image prompts, ensuring clear and specific instructions for optimal results. This repository offers a concise guide to crafting these prompts, along with detailed examples and a cheat sheet for structuring prompts effectively.

Overview: Seedream 4.5 API (`seedream-4-5`) is an OpenAI-compatible endpoint that allows developers to access Seedream 4.5's powerful text-to-image generation capabilities without the need for a subscription. The API is priced at $0.026 per image, making it an economical solution for creating detailed and high-resolution images. Key features: - Access to Seedream 4.5's comprehensive model id, pricing, and parameters. - Model id: `seedream-4-5` - Pricing: $0.026 per image - Parameters: - `model`: required - `prompt`: required - `size`: `auto` or `1:1` - `resolution`: `1K` - `n`: number of images (up to 1 per task) - `image_urls`: public HTTP(S) URLs or base64 data URLs for reference images - `nsfw_check`: optional boolean Tech stack: Python Why it matters: Seedream 4.5 API simplifies the process of generating high-quality images by providing a single API endpoint that integrates seamlessly with APIMart, eliminating the need for separate subscription plans. This makes it an ideal solution for developers looking to leverage Seedream 4.5's capabilities without the complexity of managing multiple services. Repository: ucy7hfyqm/seedream-4-5-api-ko Primary language: Python GitHub stars: 7 GitHub description: 시드림 — Seed

Overview: UNCASE is an open-source framework designed to generate privacy-preserving synthetic data for fine-tuning large language models (LLMs) in regulated industries. It ensures zero PII while leveraging blockchain verification and quality metrics to guarantee high-quality output. Key features: - **Privacy Interceptor + PromptShield**: Zero PII tolerance with advanced presidio NER, spaCy, and 9 hard-gated quality metrics. - **9 Hard-Gated Metrics**: ROUGE-L, Factual Fidelity, TTR, Coherence, Semantic Fidelity, Embedding Drift, Tool Validity, Privacy, Memorization. - **Blockchain Anchoring**: Every evaluation SHA-256 hashed → Merkle tree → Polygon PoS, ensuring verifiability via Polygonscan. Tech stack: Python 3.11+ with dependencies on PostgreSQL 16+, Transformers, Peft, TRL, and web3 for blockchain anchoring. Why it matters: UNCASE simplifies the process of fine-tuning LLMs in regulated industries by providing high-quality, privacy-preserving synthetic data, ensuring compliance and reducing the risk of legal issues.

### Overview: Imaginação 4.0 prompts: Testen Sie Ihre Bildvorlagen mit Beispielbildern, Ausrichtungen und Kosten für die API-Integration von APIMart. ### Key features: - **Model:** `imagen-4.0-apimart` - **Recipes:** 8 (mehr als 300 Modelle von Bild, Video und Sprache) - **Price:** $0.04 pro Bild – Kosten für die gesamte Menge: $0.32 ### Tech stack: Python ### Why it matters: **[Get an API key](https://go.apimart.ai/k-5696f0)** · **[Live pricing](https://go.apimart.ai/k-039e98)** · **[Model page](https://go.apimart.ai/k-4e29ed)** · [⚡ 60-second quickstart](#quickstart) **Por que Teams die Imagen 4.0 (`imagen-4.0-apimart`) via APIMart** - **Eine Schlüssel, das ganze Catalog.** Die gleiche Endpunkt `https://api.apimart.ai/v1` und den gleichen Header `Authorization` ermöglichen Imagen 4.0 (`imagen-4.0-apimart`) und über 300 Modelle von Bild, Video und Sprache – ändern Sie den `model`, nicht Ihren Kunden. - **Mindestpreis von $1, bet

Overview: This project provides a comprehensive API for generating and editing images using the FLUX.2 Flex model, accessible via the APIMart endpoint. It offers flexible pricing and billing options, including a per-image rate and a minimum cost of $1 for each task. Key features: - Supports multiple image, video, and language models for FLUX.2 Flex (`flux-2-flex`). - Provides detailed pricing information and a model-specific tier list. - Offers a per-image pricing model, with rates starting at $0.04 per image. - Supports both asynchronous and synchronous processing, with the latter providing real-time task status updates. Tech stack: Python Why it matters: This project simplifies the process of integrating FLUX.2 Flex into applications, offering flexible pricing, ease of use, and a straightforward API for generating and editing images. It's particularly useful for developers looking to integrate AI-powered image generation into their projects without the complexities of traditional APIs.

Overview: This project provides 8 tested Nano Banana Pro image prompt recipes, including examples, aspect ratios, and the associated cost on the `gemini-3-pro-image-preview` model. The cost is $0.03 per image, and there is no need for a monthly subscription. Key features: - 8 tested Nano Banana Pro image prompt recipes - Example renders, aspect ratios, and the model ID (`gemini-3-pro-image-preview`) - Cost per image: $0.03 Tech stack: Python Why it matters: The project offers a practical guide for developers to create effective image prompts using the Nano Banana Pro model, ensuring that the generated images are high-quality and relevant to the desired output.

Overview: Tabby is a self-hosted OSS AI coding assistant implemented in 20 lines of Python code, providing a simple and efficient backend for developers. Key features: - Self-hosted OSS AI coding assistant - Simple and efficient backend implementation - Supports various models like StableCode, StarCoder, and CodeLlama Tech stack: Python Why it matters: The project showcases the power of simplicity in software development, making it easy to understand, maintain, and customize.

### Overview: Basin is a serverless Postgres implementation that runs on your own S3 bucket, providing multi-tenant capabilities without the need for a managed instance. It offers native vector search and significantly lower RAM usage compared to traditional Postgres. ### Key Features: - **Serverless Postgres**: Runs on your own S3 bucket, eliminating the need for a managed instance. - **Multi-Tenant**: Supports multiple tenants within the same S3 prefix, ensuring isolation and security. - **Bucket-Native**: Uses the same S3 bucket for storage, eliminating the need for separate instances. - **Rust Implementation**: Built using Rust, offering high performance and low resource usage. ### Tech Stack: Basin is built using Rust, a language known for its efficiency and performance. ### Why It Matters: Basin is designed to provide a cost-effective, self-hosted Postgres solution, reducing RAM usage by up to 30x compared to traditional Postgres. It offers native vector search and is significantly more efficient in terms of RAM and storage usage, making it an attractive option for those looking to host Postgres without the overhead of a managed service.

Overview: This project is a production-ready AI image generation web app powered by Pollinations AI, deployed on Cloudflare Pages + Workers. It features secure backend API, deterministic seed-based generation, global usage cap, IP rate limiting, and a modern, responsive UI. Key features: - AI Image Generation: Turn text prompts into stunning visuals using the Flux model. - Quick Try Presets: Instant generation buttons for popular themes. - Secure Backend API: Worker-based gateway that protects your secret keys. - Unique Generations: Every image is unique due to random seed implementation. - Global Usage Cap: Built-in 1,000 images/week global limit via Cloudflare KV. - IP-Based Rate Limiting: Automatic protection against spam (1 req/60s per IP). - Responsive UI: Modern, dark-themed, and glassmorphic design optimized for all devices. - Privacy Focused: No sign-up required, no user data tracking. Tech stack: This project uses JavaScript, TypeScript, React, Vite, TailwindCSS, and Cloudflare Workers. Why it matters: This app is designed to be secure, efficient, and user-friendly, making it ideal for anyone looking to create stunning AI-generated images without the hassle of managing servers or dealing with complex APIs. GitHub stars: 13 GitHub description: Secure AI image generator using Pollinations API with a Cloudflare Workers backend, deterministic seed-based generation, and IP rate limiting.

Overview: dsh-mmx-bridge 是一个工具，它将 MiniMax 全部多模态能力集成到 DeepSeek Harness（DSH）的纯文本模型中，使得 DSH 可以看图、画图、生成视频、说话、唱歌、翻唱、搜索和查询额度。 Key features: - 图片识别与理解 - 文生图 - 生成视频 - 语音合成 - 音乐生成 - 音频翻唱 - 联网搜索 - 用量查询 Tech stack: JavaScript Why it matters: 通过 dsh-mmx-bridge，DSH 现在可以处理所有多模态任务，极大地扩展了其功能和应用范围，使得 DSH 成为一个更强大的工具。

Overview: This project implements 30+ variants of Approximate Nearest Neighbor (ANN) algorithms to efficiently find the K nearest neighbors in high-dimensional vector spaces. It serves as a convenient sandbox for developers to compare different algorithms' build/search speed and recall. Key features: - Multiple ANN algorithm implementations - Support for various datasets (SIFT, GLOVE, DEBUG) - Benchmarks against built-in baselines Tech stack: C++ Why it matters: This project provides a comprehensive comparison of ANN algorithms, making it a valuable resource for developers looking to optimize their nearest neighbor search implementations.

Overview: This project is a simple chat interface for local AI using llama-cpp-python and llama-cpp-agent. Key features: - GUI based on llama-cpp-python and llama-cpp-agent - Supports multiple model formats (MISTRAL, CHATML, VICUNA, LLAMA_2, SYNTHIA, NEURAL_CHAT, SOLAR, OPEN_CHAT, ALPACA, CODE_DS, B22, LLAMA_3, PHI_3, OPEN_INTERPRETER, AUTOCODER, GEMMA_2, DEEP_SEEK_CODER_2) - Optional system prompt - Adjustable context length and number of threads - Temperature adjustable - Runs on CPU Tech stack: Python Why it matters: This project provides a user-friendly interface for interacting with local AI using llama-cpp, making it accessible for developers and enthusiasts alike.

### Overview: This Go application demonstrates semantic product search and Retrieval-Augmented Generation (RAG) using OpenAI's GPT and embedding models, with Qdrant as the vector database. ### Key features: - Semantic product search - RAG-style question answering - Integration with Qdrant vector database ### Tech stack: Go ### Why it matters: This application simulates a product search system for an online catalog, showcasing the capabilities of semantic search and RAG in Go, and demonstrating how to integrate with Qdrant for efficient vector database storage and retrieval.

Overview: Imagen 4.0 API (`imagen-4.0-apimart`) is a model id, pricing, and parameters for the OpenAI-compatible APIMart endpoint, providing access to Google's image model at a cost per image. Key features: - Model id: `imagen-4.0-apimart` - Pricing: $0.04 per image - Parameters: `prompt`, `size`, `resolution`, `n`, `image_urls`, `nsfw_check` - Asynchronous processing Tech stack: Python Why it matters: It offers a cost-effective way to access Google's image model through APIMart, providing a wide range of image, video, and language models without the need for a monthly subscription. GitHub stars: 9

### Overview: Imagina 4.0 prompts: 8 tested recipes with example renders, aspect ratios, model ID (`imagen-4.0-apimart`), and cost per image. This repository provides a collection of 8 Imagen 4.0 prompts along with their example renders, aspect ratios, and the cost of each request. ### Key Features: - **Prompt Structure:** Each recipe includes a subject, light, framing, setting, and finish, providing a clear and structured approach. - **Aspect Ratios:** The aspect ratios are specified for each recipe, allowing for consistent and effective rendering. - **Cost Calculation:** The cost of each request is provided, helping users understand the financial impact of their Imagen 4.0 prompts. ### Tech Stack: - Python ### Why It Matters: - **Cost Efficiency:** The repository offers a cost-effective way to test Imagen 4.0 prompts, with a minimum cost of $1 per request. - **Versatility:** The prompts cover a wide range of subjects, including food photography, e-commerce, and more, making it a versatile tool for developers. - **Consistency:** The structured approach ensures that each recipe is tested and optimized, providing reliable results for developers. ### Repository: - **GitHub Stars:** 13 - **Primary Language:** Python - **GitHub Description:** Imagen 4.0 prompts: tested image prompt recipes with example renders, aspect ratios, the model ID (`imagen-4.0-apimart`) and the cost per image. *(observed the

Overview: RAG Cache is a high-performance caching layer designed to reduce costs and latency by leveraging Redis and Qdrant for semantic search, specifically optimized for use with LLM providers like OpenAI and Anthropic. Key features: - **💰 Cost Savings**: Avoids paying for duplicate queries, reducing API costs by 80%. - **⚡ Reduced Latency**: Queries are returned in milliseconds, significantly lowering latency from 8.5 seconds to 1 millisecond. - **🛡 Reliability**: Reduces dependency on external APIs, increasing application reliability. Tech stack: Python Why it matters: By caching LLM queries and utilizing semantic search, RAG Cache significantly improves application performance and reduces operational costs, making it an essential tool for developers looking to optimize their LLM-based applications. Repository: zakariaf/RAG-Cache GitHub stars: 13 GitHub description: High-performance LLM query cache with semantic search. Reduce API costs 80% and latency from 8.5s to 1ms using Redis + Qdrant vector DB. Multi-provider support (OpenAI, Anthropic).

### Overview: Seedance 1.0 Pro API (`seedance-1.0-pro`) is a fast, low-cost OpenAI-compatible API endpoint provided by ByteDance. It offers a single endpoint for generating high-quality video, image, and language models, with a price of $0.0184 per second based on resolution. The API supports various parameters such as model ID, prompt, duration, resolution, and image URLs, making it easy to integrate into applications. ### Key features: - **Single Endpoint**: Access to multiple models with a single API call. - **Low Cost**: Starts at $0.0184 per second, billed by resolution. - **Model ID and Routes**: Models are identified by `seedance-1.0-pro`, and the pricing is based on the resolution tier. - **Pricing Tier**: Prices are per generated second, with a live pricing page for the most up-to-date information. - **Model Page**: Detailed information on the model can be found on the model page. - **Request Parameters**: Includes fields such as `model`, `prompt`, `duration`, `resolution`, and `image_urls`. ### Tech stack: - Python ### Why it matters: - **Ease of Use**: A single API endpoint for multiple models, making it simple to integrate. - **Cost Efficiency**: Starts at $0.0184 per second, ideal for cost-sensitive applications. - **Model Flexibility**: Access to over 300 models, including video, image, and language models. - **Real

### Overview: Nano Banana Prompts: 8 Tested Recipes with Outputs ### Key Features: - **Model:** `gemini-2.5-flash-image-preview` - **Recipes:** 8 (machine-readable in `data/prompts.json`, table in `PROMPTS.md`) - **Effective Price:** $0.0125 per image ### Tech Stack: Python ### Why It Matters: Nano Banana Prompts provides a concise and effective way to generate high-quality image prompts, ensuring that each recipe is tailored to achieve the desired outcome without unnecessary complexity. The model is tested and optimized to provide the best possible results at a cost-effective rate, making it a valuable tool for developers and designers alike.