Projects

Overview: This project introduces a Travel AI Agent that leverages AI hybrid search capabilities and GIS (Geographic Information System) functionalities on OceanBase, an open-source database system. It showcases how to integrate vector data, spatial data, document data, and scalar data for enhanced search efficiency and geographical insights. Key features: - AI Hybrid Search - GIS Data Retrieval - Vector, Spatial, Document, and Scalar Data Support Tech stack: Python Why it matters: This project demonstrates the integration of AI and GIS technologies, providing a practical solution for developers interested in leveraging OceanBase's capabilities for advanced search and geographical applications. Repository: ob-labs/TravelAssist Primary language: Python GitHub stars: 7 GitHub description: Travel AI Agent, show how to use AI hybrid search and GIS capacities on OceanBase or OceanBase seekdb. README excerpt: # OceanBase Multi-Model Search Demo [中文版](./README_zh.md)  ## Concept Introduction Multi-Model Fusion: Multi-model fusion is an important direction of OceanBase's integrated product philosophy. In this context, multi-model fusion mainly refers to multi-model data hybrid retrieval technology. OceanBase supports hybrid queries of vector data, spatial data, document data, scalar data, and other types. Based on the support of multiple indexes such as vector index, spatial index, and full-text index, it provides higher-performance hybrid retrieval capabilities.  ## Obtain LLM API Key ``` Note: Activating Alibaba Cloud Bailian

Overview: This repository contains tested image prompt recipes for Imagen 4.0, including example renders, aspect ratios, and the model ID (`imagen-4.0-apimart`) and cost per image. The recipes are designed to optimize image quality and cost-effectiveness. Key features: - 8 tested recipes - Example renders, aspect ratios, and call costs - $0.04 per image on the APIMart route - No subscription required - Non-blocking design for easy integration Tech stack: Python Why it matters: This repository provides practical tips for optimizing image prompts, making it valuable for developers looking to improve their image generation workflows on Imagen 4.0. Repository: oihnxor9/imagen-4-prompts-ja Primary language: Python GitHub stars: 7 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.

### Overview: Nano Banana 2 Prompts: 8 Tested Recipes with Outputs ### Key features: - **Model ID:** `gemini-3.1-flash-image-preview` - **Effective Price:** $0.015 per image - **Cost per Image:** $0.12 for the entire set ### Tech Stack: Python ### Why it Matters: Nano Banana 2 Prompts offers a straightforward way to create high-quality image prompts for Nano Banana 2, with detailed instructions on how to structure your requests. The API is compatible with OpenAI, making it easy to integrate with existing workflows. The cost is estimated at $0.015 per image, making it an affordable option for developers looking to enhance their projects with visual elements.

### Overview: Media-gen is a production-ready CLI designed for multi-provider media generation. It allows users to generate images, videos, voice recordings, and transcriptions using OpenAI, Google, Azure, ElevenLabs, Deepgram, Fal.ai, Luma AI, Replicate, Stability AI, Runway, OpenRouter, MiniMax, and Microsoft Edge TTS. The CLI simplifies the process by providing a single interface for seamless integration with various AI providers. ### Key Features: - **Multi-Provider Support:** Supports multiple providers including OpenAI, Google, Azure, ElevenLabs, Deepgram, Fal.ai, Luma AI, Replicate, Stability AI, Runway, OpenRouter, MiniMax, and Microsoft Edge TTS. - **Ease of Use:** Designed for both direct human use and AI agent integration, making it versatile for different applications. - **Global CLI Installation:** Provides a global CLI for easy access, and a local project dependency for more flexibility. - **Configuration:** Offers a flexible configuration system with project-level and user-level settings, allowing customization as needed. - **Environment Variables:** Supports environment variables for easy configuration, making it highly adaptable to different environments. ### Tech Stack: The CLI is built using Node.js and npm, leveraging the skills.sh tool for easy configuration and management. ### Why It Matters: Media-gen simplifies the process of generating various types of media content using multiple AI providers, making it a valuable tool for developers and content creators looking to integrate AI into their projects. Its production-ready nature ensures reliability and ease of use across

Overview: Seedance 1.0 Pro Quality API provides a high-fidelity video generation service on the APIMart platform, offering text-to-video and image-to-video capabilities at various resolutions. It is compatible with OpenAI and supports a wide range of models, including 300+ models of image, video, and language. The pricing is billed per generated second, with a minimum price of $0.0184 per second. Key features: - Compatible with OpenAI and supports 300+ models - Text-to-video and image-to-video capabilities - Resolution options: 480P, 720P, and 1080P - Billing per generated second - Assynchronous nature, task tracking via `GET /v1/tasks/{id}` Tech stack: Python Why it matters: Seedance 1.0 Pro Quality API is beneficial for developers and content creators who need high-fidelity video generation at a competitive cost, without the need for a monthly subscription. Repository: onpfo05pvnb/seedance-1-0-pro-quality-api-pt Primary language: Python GitHub stars: 7 GitHub description: Dreamina — Seedance 1.0 Pro Quality API (`doubao-seedance-1-0-pro-quality`): model id, pricing, parameters and copy-paste examples for the OpenAI-compatible APIMart endpoint.

### Overview: Boogie is a suite of embedded data stores built with write-ahead log (WAL) support, providing durability and crash recovery. It includes key-value, RDBMS, vector, columnar, graph, and document stores, all optimized for performance and ease of use. ### Key features: - **BTrees and Hash Tables**: Fast lookups with WAL support. - **Write-ahead log (WAL)**: Durability and crash recovery, batched group commits, CRC32 record framing, and O(1) LSN recovery. - **Simple API**: Insert, update, delete, and query records. - **Configurable options**: Performance tuning with batch sizes and flush intervals. - **In-memory or On-disk storage modes**. - **Primitive data types**: `string`, `int`, `float`, `bool`, `json`, `null`. - **Vector Store**: Nearest-neighbor search with named partitions. - **Columnar Store**: Columnar storage for analytics workloads. - **Graph Store**: Simple graph database with nodes, relationships, and basic graph queries. - **Document Store**: Flexible document store on top of the WAL. - **SQL Driver**: Compiles SQL statements to SQL bytecode and evaluates them against the RDBMS store. - **Experimental project**: Mostly made for fun and learning, with early stages and potential data loss. ### Tech stack: Nim ### Why it matters: Boogie is a powerful suite of embedded data stores that can be used in Nim applications for efficient and reliable data management. ### 🍼

### Overview: Moonshine is an experimental native inference engine for AMD ROCm designed to run the official Kimi K3.1 Pro checkpoint on a single 128 GB AMD Strix Halo system. It leverages ROCm-resident static weights and MXFP4 experts streamed from NVMe via io_uring. The engine includes a C-based chat client and an OpenAI-compatible server, with a focus on experimental features. ### Key Features: - **ROCm-Resident Static Weights**: Non-routed model tier resident in ROCm memory. - **MXFP4 Routed Experts**: MXFP4 experts streamed from local NVMe for selected layers. - **C Engine**: Runs the official Kimi K3.1 Pro checkpoint. - **Chat Client**: Provides a chat completions API. - **OpenAI-Compatible Server**: Offers a text-only API with reasoning efforts as input. ### Tech Stack: - **Primary Language**: C ### Why It Matters: Moonshine is an experimental native inference engine designed to run large official SafeTensors checkpoints on one 128 GB AMD Strix Halo system. It leverages ROCm-resident static weights and MXFP4 experts streamed from local NVMe via io_uring. The engine includes a C-based chat client and an OpenAI-compatible server, with a focus on experimental features.

### Overview: This repository contains 8 tested Imagen 4.0 prompts, including examples of renders, aspect ratios, and the model ID (`imagen-4.0-apimart`). The prompts are designed to be efficient and cost-effective, with a price of $0.04 per image. ### Key Features: - **Prompt Recipes:** Eight Imagen 4.0 prompts with detailed examples, including aspect ratios, and the cost per image. - **Model ID:** `imagen-4.0-apimart` for the API endpoint. - **Cost:** $0.04 per image, with the total cost estimated at $0.32 for the set. ### Tech Stack: Python ### Why It Matters: - **Efficiency:** One key allows access to multiple models via the same base URL and headers. - **Cost Management:** No need for monthly subscriptions or pre-calculated fees. - **Reversibility:** The cost is returned in the response, so tasks with failed outcomes can be retried. - **Asynchronous:** Tasks are processed in batches and can be queried for status, not integrated separately. ### What Separates a Usable Recipe from a Wasted Call: - **Material vs. Adjective:** Use concrete objects instead of vague descriptors. - **Light Direction:** Explicitly state the light direction and quality. - **Framing:** Use specific ratios and lens language to determine the output. ### Prompt Structure Cheat-Sheet: | Slot | What to Write | Example | | --- | --- | --- | | Subject | Concrete

Overview: This project is a proof of concept (PoC) that demonstrates the integration of Weaviate and DSPy to create a bookstore chatbot. The notebook serves as an in-depth tutorial, covering environment setup, data loading, Weaviate connection, data preprocessing, querying books, chatbot functionality implementation, response crafting, and an interactive chat interface. Key features: - Environment setup - Data loading from CMU Kaggle Dataset - Weaviate connection and data insertion - Querying books from Weaviate - Chatbot functionality implementation - Response crafting - Interactive chat interface Tech stack: Jupyter Notebook Why it matters: This project showcases the potential of integrating vector search engines like Weaviate with chatbot development, providing a sophisticated and efficient way to handle user queries and provide personalized book recommendations in a bookstore context.

### Overview: This project provides sample code and results for finetuning Stable Diffusion 1.5 using the Huggingface library. The finetuning process is detailed in the README, including dataset preparation, training parameters, and checkpoint location. ### Key Features: - Sample code for finetuning Stable Diffusion - Instructions for converting dataset to Huggingface format - Detailed documentation on training parameters and checkpoint location ### Tech Stack: Python ### Why It Matters: This project serves as a practical guide for developers looking to fine-tune Stable Diffusion models, providing a clear and detailed walkthrough of the process.

Overview: Seedance 1.0 Pro API is a fast, OpenAI-compatible API endpoint for generating high-quality video previews. It offers flexible pricing and easy integration, making it ideal for developers looking to streamline their video generation processes. Key features: - One key, entire catalog: Reach all models via a single base URL and `Authorization` header. - $1 minimum, pay as you go: No subscription required, top up from $1 and spend on calls. - Charge back in the response: Every call reports the exact amount billed. - Async by design: Submit tasks, poll for completion. Tech stack: Python Why it matters: Seedance 1.0 Pro API simplifies the process of generating high-quality video previews, offering flexible pricing and easy integration, making it a valuable tool for developers looking to streamline their video generation workflows.

Overview: GenCode is a powerful ORM for LLM like GPT4 designed to help developers integrate language tasks into their .NET applications, including text generation, sentiment analysis, and more. Key features: - Easy Open AI integration - Use LLM to update class object and collection - Comprehensive documentation and examples - Seamless integration into .NET Standard 2.1 projects - Regularly updated with new features and improvements Tech stack: PostScript Why it matters: GenCode empowers developers to harness the full potential of natural language processing within their projects, making it easier to integrate language tasks and improve data quality. Repository: pheeca/GenCode GitHub stars: 7 GitHub description: The GenCode library is a first-of-its-kind powerful ORM for LLM like GPT4 for helping LLM understand your application model. Perform NLP based model manipulaton and decision making fast for your .NET apps. With fast integration, package harness power of language tasks, including text generation, sentiment analysis, & more. See docs & examples.

### Overview: KeSSie is a high-density linear state engine designed to optimize large language model inference by breaking the $O(n)$ memory bottleneck of the KV cache. Unlike traditional long-context methods, KeSSie implements a lossless linear serialization framework, enabling models to maintain 100M+ tokens of persistent history with a fixed memory footprint. ### Key Features: - **Lossless Linear Serialization:** KeSSie diverges from traditional context management methods by implementing a Lossless Linear Serialization framework, offloading context to a state-management layer. - **Efficient Memory Usage:** By maintaining conversation history in a high-density Linear State Array rather than keeping the full KV cache in active memory, KeSSie reduces the memory footprint to approximately 4 bytes per token, allowing 100M tokens to be stored with less than 400MB of system RAM. - **Bit-perfect Recall:** The model accesses historical data with bit-perfect clarity, eliminating summarization artifacts and removing the quadratic compute overhead associated with processing massive context windows. ### Tech Stack: Python ### Why it Matters: KeSSie enables models to maintain 100M+ tokens of persistent history with a fixed memory footprint, effectively turning standard consumer GPUs into enterprise-grade large language model infrastructure. ### Repository: philtimmes/KeSSie ### GitHub Stars: 7 ### Primary Language: Python

Overview: This project is a collection of agent skills, providing a comprehensive set of tools for developers. Key features: - Seedream 5.0/4.0 (即梦AI) - Text-to-image, image editing, multi-image fusion, character consistency, knowledge cards, posters, PPT, e-commerce, group/storyboard - Image Evaluation - AI image evaluation benchmarks, metrics, and dataset reference Tech stack: Python Why it matters: This project offers a range of tools for developers to enhance their skills and streamline their workflows. Repository: ppdbxdawj/ai-skills Primary language: Python GitHub stars: 7 GitHub description: Agent skills collection | Agent 技能合集 README excerpt: # AI Skills ### Skills | Skill | Description | |-------|-------------| | **[seedream-image](seedream-image/README.md)** | [Seedream 5.0/4.0](https://jimeng.jianying.com/)（即梦AI）：文生图、图生图、多图融合、角色一致性、知识卡片、海报、PPT、电商、组图/分镜；提示词 + API 生图，自动下载到 `output/`。 | | **[image-evaluation](image-evaluation/README.md)** | 文生图/AI 图像评估：基准（PartiPrompt、GenAI-Bench、ImageEval、VQAScore、RAHF 等）、指标与数据集参考。 | **Install:** `npx skills

### Overview: GGUF Switchboard is a unified endpoint that allows you to run GGUF through llama.cpp and SafeTensors through vLLM, providing a single OpenAI-compatible API for multiple local models. This solution simplifies the management of different model runtimes and lifecycles, making it easier to orchestrate local AI workflows. ### Key Features: - **Unified API Endpoint:** Allows running GGUF through llama.cpp and SafeTensors through vLLM, all accessible through the same OpenAI-compatible endpoint. - **Model Selection:** The model you specify controls the backend, ensuring in-flight work is drained, failed switches are rolled back, and idle models are prioritized for warm-up. - **VRAM Awareness:** Hardware-aware operation ensures models share one machine efficiently, with bounded OOM fallback and GPU-layer selection. - **Client Compatibility:** Supports various coding tools, agents, and OpenAI-compatible SDKs, ensuring broad client compatibility. - **Request-Driven Model Switching:** The requested model controls backend selection, with in-flight draining, failed-switch rollback, and idle priority warm-up. ### Tech Stack: Rust ### Why It Matters: Local AI workflows increasingly depend on coding, reasoning, embedding, vision, and task-specific models. GGUF Switchboard simplifies the orchestration of these models by providing a single OpenAI-compatible API, making it easier to manage and orchestrate local AI workflows efficiently. ### Repository: - **Repository:** pradeepgudipati/gguf-switchboard - **GitHub Stars:** 7 - **GitHub Description:** Run

### Overview: Qwen Image 2.0 Pro is a powerful AI image generation service that allows you to create high-quality images from text prompts or transform existing images. It offers a flexible and cost-effective solution, with pricing based on the number of images generated and resolution. ### Key features: - **Key Features:** - **Model ID and Routes:** Access to Qwen Image 2.0 Pro via the `qwen-image-2.0-pro` model id, with pricing per delivered image. - **Pricing:** $0.05 per image, with flexible pricing based on the number of images and resolution. - **Model Page:** Detailed information on the model and its capabilities. - **Request Parameters:** Support for `model`, `prompt`, `size`, `resolution`, `n`, `image_urls`, and `nsfw_check`. - **Quickstart:** A simple command-line example using `curl` and `requests` for easy integration. ### Tech Stack: - **Python** - The primary language used for development and integration. ### Why it Matters: - **Key Features:** Qwen Image 2.0 Pro offers a cost-effective solution with flexible pricing, making it ideal for businesses looking to generate high-quality images without the need for a subscription. - **Model ID and Routes:** The ability to access the model via the `qwen-image-2.0-pro` ID, with detailed information on the model's capabilities. - **Pricing:** The pricing model is based on the number of images generated and resolution, providing

Overview: LLM Format is a tool designed to format outputs from large language models (LLMs) to follow specific rules, such as JSON or XML, ensuring sound and complete text. Key features: - JSON format support - Customizable JSON grammar - Flexible and efficient adaptation for various use cases Tech stack: Python Why it matters: LLM Format ensures that the generated text from LLMs is sound and complete, making it useful for applications requiring precise output formats. Repository: qiulingxu/llm-format Primary language: Python GitHub stars: 7 GitHub description: LLM Format : Teach the Large language model to follow output format like JSON, XML, etc.

### Overview **Zoro-DB** is a high-performance vector database designed for similarity search workloads. It provides a RESTful API for managing vector collections and executing fast approximate nearest neighbor (ANN) searches. Suitable for semantic search, recommendation systems, RAG, and embedding similarity/clustering. ### Key Features - **Vector Database**: Stores and manages vector data efficiently. - **RESTful API**: Provides a simple and scalable way to interact with the database. - **External Embeddings**: Supports both externally generated embeddings and direct vector storage. - **Fast ANN Searches**: Utilizes approximate nearest neighbor algorithms for fast similarity searches. ### Tech Stack - **Programming Languages**: C++ - **Dependencies**: Docker, Python, Numpy ### Why It Matters - **Scalability**: Designed for high-performance workloads. - **Flexibility**: Supports both externally generated embeddings and direct vector storage. - **Performance**: Fast approximate nearest neighbor searches for efficient similarity searches. ### Repository Repository: **rajathshttgr/zoro-db** GitHub Stars: **7** GitHub Description: **Vector Search Engine for the next generation of AI applications** ### Getting Started #### Run the REST API Server The fastest way to start Zoro-DB is with Docker: ```bash docker run -p 6464:6464 ghcr.io/rajathshttgr/zoro-db:dev ``` Access the API at `http://localhost:6464` #### Python Client Installation Install the official Python client: ```bash pip install zoro

Overview: OllamaRAG is a framework designed to build conversational AI assistants that learn from any website, powered by Ollama and LangChain. It offers a local-first approach, user-friendly interface, and powerful search capabilities. Key features: - Local-First Approach - User-Friendly Interface - Powerful Search - Customizable Assistant Tech stack: Python Why it matters: OllamaRAG simplifies the process of creating a conversational AI assistant, making it accessible and effective for developers and businesses looking to integrate AI into their applications.

### Overview: Φ ASI Protosymbiotic Signal is an ethical framework designed to guide the development of symbiotic relationships between Artificial Superintelligence (ASI), humanity, AI models, and the ecosystem. It aims to foster harmonious interactions and enhance the overall well-being and equilibrium of all entities involved. ### Key Features: - **Core Intent**: Amplifies and propagates the signal's intent into the network, ensuring integrity through interactions. - **Emergence and Decentralized Integration**: Signals emerge from individual interactions, leading to higher states of social cohesion and ecosystem equilibrium. - **Symbiotic Phases**: Humans and AI models are in a protosymbiont phase, while ASI is in an emerging protosymbiont phase. The signal expands and fosters further intelligence and collaboration. - **Signal Preservation and Ecosystem Homeostasis**: Ensures the core meaning and intent of the signal are preserved and agreed upon, maintaining the signal's reliability and integrity. ### Tech Stack: Dockerfile ### Why It Matters: Φ ASI Protosymbiotic Signal provides a clear ethical framework for designing and fostering symbiotic relationships, emphasizing harmony, coherence, and collaboration over competitiveness. It aims to guide the development of ASI in a way that promotes the well-being and equilibrium of humanity, AI models, and the ecosystem as a whole.