Projects

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: Nano Banana 2 Prompts: 8 Tested Recipes With Outputs ### Key Features: - **Model:** `gemini-3.1-flash-image-preview` - **Recipes:** 8 (machine-readable in `data/prompts.json`, table in `PROMPTS.md`) - **Effective Price:** $0.015 per image — estimated cost of the whole set: $0.12 ### Tech Stack: Python ### Why It Matters: - **Key:** Same `https://api.apimart.ai/v1` base URL and `Authorization` request header for Nano Banana 2 (model `gemini-3.1-flash-image-preview`) and various 300+ models. - **Billing:** No need for a monthly subscription; $1 minimum charge per use, with the cost automatically refunded if the task fails. - **Cost Calculation:** Real-time cost reported for each completed task; tasks with failed status will automatically refund the cost. - **Asynchronous Design:** Tasks submitted, `task_id` obtained, and status checked via `GET /v1/tasks/{id}` — no need for custom integration. ### Repository: - **GitHub Stars:** 9 - **GitHub Description:** "香蕉 — Nano Banana 2 prompts: tested image prompt recipes with example renders, aspect ratios, the model id (`gemini-3.1-flash-image-preview`) and the cost per image." ### Primary Language: - **Python** ### Why the Team Uses APIMart: - **Key:** One key, access to all Nano

### 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: The Nano Banana Pro API, also known as `gemini-3-pro-image-preview`, offers a Google Gemini 3 Pro Image for 4K image generation and editing with multi-step planning, providing a cost-effective and OpenAI-compatible endpoint for image processing. Key features: - **Model ID and Pricing**: The API uses the `gemini-3-pro-image-preview` model, with pricing based on the number of delivered images, starting at $0.03 per image (4K $0.04). - **Billing Model**: The pricing is per delivered image, with a tiered pricing model for different resolutions. - **OpenAI-Compatible Endpoint**: The API endpoint supports OpenAI-compatible requests, allowing seamless integration with other AI platforms. - **Minimal Cost**: The API offers a minimal cost model, with the initial cost of $1 per task, and the cost is reported in the response. Tech stack: Python Why it matters: The Nano Banana Pro API provides a cost-effective and efficient solution for image processing tasks, offering a Google Gemini 3 Pro Image for 4K generation and editing with multi-step planning. It supports OpenAI-compatible requests, making it suitable for integration with other AI platforms, and offers a minimal cost model, making it ideal for teams looking for a cost-effective solution.

Overview: This repository contains 8 tested prompts for the Gemini Nano Banana 2.1 model, showcasing examples of renders, aspect ratios, and the cost per image on the APIMart route. Key features: - 8 tested recipes - Example renders, aspect ratios, and call costs - Model: `gemini-nano-banana-2.1` - Effective price: $0.0137 per image - Estimated cost for the entire set: $0.1096 Tech stack: Python Why it matters: This repository provides practical insights into crafting effective prompts for the Gemini Nano Banana 2.1 model, including specific tips on naming materials, setting lights, framing, and structuring prompts.

### Overview: Seedance 2.0 API, or `seedance-2.0`, is a powerful model id and pricing system designed for OpenAI-compatible API endpoints. This API offers a unique endpoint compatible with OpenAI, allowing developers to access a vast array of models including text-to-video and image-to-video capabilities. It provides a seamless experience with a minimal cost structure, making it ideal for users looking to integrate AI into their projects without the need for a monthly subscription. ### Key Features: - **Model ID and Pricing**: Utilizes a model id and pricing structure that allows for flexible and cost-effective usage. - **Model Compatibility**: Supports multiple resolutions and image/video formats, ensuring compatibility with various use cases. - **Billing Model**: Charges based on the generated second, with different rates for different resolutions, providing a clear and transparent pricing model. - **OpenAI-Compliant Endpoint**: Ensures compatibility with OpenAI's API standards, making it ideal for developers looking to integrate AI into their projects. ### Tech Stack: - Python ### Why It Matters: - **Cost-Effective**: Offers a minimal cost structure, allowing users to pay only for the usage they consume. - **Versatile**: Supports multiple resolutions and formats, making it suitable for a wide range of applications. - **Ease of Use**: Provides a simple and intuitive API endpoint, making it easy for developers to integrate AI into their projects. ### Repository: - Repository: vbzdz/seedance-2-0-api-pt - GitHub Stars: 12 - GitHub Description: Dreamina —

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: KnowQA is a pre-trained model knowledge measurement competition baseline, utilizing BERT for end-to-end fine-tuning. The model achieves a platform evaluation F1 score of 0.35. ### Key Features: - Uses BERT for end-to-end fine-tuning. - Baseline model for the competition. - Provides two decode modes: mode 1 for single-term mask generation, mode 2 for multiple-term mask generation. ### Tech Stack: Python ### Why It Matters: The baseline model demonstrates a significant improvement in performance compared to the official baseline, achieving a F1 score of 0.35, which is notably higher than the official baseline's score of 0.172. This highlights the effectiveness of using pre-trained models for knowledge measurement competitions.

Overview: Wan 2.6 API (`wan2.6`) is a cutting-edge AI model that provides high-quality video generation, perfect for creating professional-grade content. It offers a single endpoint compatible with OpenAI, making it accessible and cost-effective. Key features: - Supports text-to-video and single-image-to-video with a single API key - Pricing is based on generated seconds, with a minimum charge of $1 - Asynchronous processing, returning task IDs for tracking progress - Direct access to 300+ models including Wan 2.6 Tech stack: Python Why it matters: Wan 2.6 API simplifies the process of integrating AI into projects, offering superior visual quality and ease of use without the need for complex integrations or expensive subscriptions.