Projects

### Overview: CoordiNode is a graph-native hybrid retrieval engine designed for AI applications and GraphRAG. It combines a graph database, a vector database, and a full-text search engine into a single, transactional engine with a single query language. ### Key Features: - **One Query**: Mixes graph traversal, vector similarity, and BM25 text relevance in a single pipeline. - **One Snapshot**: MVCC snapshot isolation across edges, embeddings, and the text index, ensuring a retrieval never reads partially applied writes. - **One Binary**: Runs as a single executable, eliminating the need for a JVM, C dependencies, or a separate router. ### Tech Stack: Rust - Built in Rust, zero garbage collection, single binary, openCypher-compatible, clustering included in the open-source edition. ### Why It Matters: - **Graph Database, Vector Store, and Search Engine**: All functionalities are integrated into a single engine, simplifying complex queries and reducing the need for multiple database setups. - **Single Query Language**: A unified query language simplifies development and reduces complexity. - **Zero GC**: Rust's zero garbage collection ensures efficient memory management. - **Single Executable**: The same binary runs standalone or as a Raft-replicated cluster, eliminating the need for a separate router or JVM. ### Repository: structured-world/coordinode - GitHub Stars: 7 - Primary Language: Rust - GitHub Description: The graph-native hybrid retrieval engine for AI and GraphRAG. - GitHub Releases: Latest release available via the GitHub repository. -

### Overview: This repository contains a collection of practical projects designed to deepen understanding of n8n's features and workflow automation capabilities. The projects are aimed at developers and include inventory management, order status tracking, RAG (Retrieval-Augmented Generation) with Pinecone and Supabase, customer support AI, and LinkedIn content creation workflows. ### Key Features: - **Inventory Agent**: Retrieves or updates inventory data from a Google Sheet. - **Order Status via Email**: Automatically sends email notifications based on order status from a Google Sheet. - **RAG - Uploading PDF to Pinecode Vector Database**: Implements a RAG pipeline for efficient retrieval from a Pinecone vector database. - **RAG - AI Agent**: Retrieves information from a Pinecone vector database to answer user questions. - **RAG with Supabase & Postgres**: Demonstrates RAG using Supabase and Postgres for efficient information retrieval. - **Customer Support AI Agent**: Automatically classifies emails and forwards them to an AI agent for response. - **LinkedIn Content Creator Agent**: Reads topics from a Google Sheet and generates concise summaries based on web searches. ### Tech Stack: n8n, Google Sheets, Pinecone, Supabase, OpenAI, Wikipedia, Gmail, Pinecone Vector Store, Supabase Vector Store, AI Agent, OpenAI Chat Model, Simple Memory, Recursive Character Text Splitter, Default Data Loader, Google Drive, Supabase, OpenAI Embeddings, Wikipedia, LinkedIn, Google Sheets, Pinecone, Supabase, Pinecone Vector Store, OpenAI Embed

### Overview LLAMA.CPP Manager is a modern GUI configuration tool designed for working with GGUF models. It provides a clean and intuitive interface to select and analyze models, configure inference parameters, and estimate VRAM usage. The application is lightweight, efficient, and cross-platform, making it ideal for developers and researchers. ### Key Features - **Ultra-lightweight**: <50 MB VRAM footprint - **Fast startup and single-binary deployment** - **Cross-platform**: Windows, macOS, Linux - **Clean Dear ImGui interface** - **Integrates with llama.cpp tools for accurate model analysis** - **Functional file browser for easy model selection** - **Export configurations to llama.cpp CLI commands** - **Optional native file dialog support** - **Recent models list with per-model settings persistence** - **Hybrid configuration storage for better organization** ### Tech Stack - **C++17 compatible compiler**: GCC 8+, Clang 7+, MSVC 2019+ - **CMake 3.15 or higher** - **SDL2** - **OpenGL 3.0+** ### Why It Matters - **Efficiency**: Streamlines the configuration process, reducing the need for manual adjustments. - **Accuracy**: Integrates with llama.cpp tools for precise model analysis. - **Cross-Platform**: Works seamlessly across different operating systems, ensuring compatibility and ease of use. - **Intuitive Interface**: Simplifies the selection and management of GGUF models, enhancing productivity. ### Installation - **Prerequisites**: Ensure you have a compatible C

Overview: TemRust-SMOL-v5-1.5B is a fine-tuned model of Qwen/Qwen2.5-Coder-1.5B-Instruct specialized for Rust code, trained on real GitHub PR fixes. It achieves a 67.6% pass rate on 37-task benchmark, surpassing the untrained base by 16.2 percentage points. Key features: - Fine-tuned model for Rust code fixes - 1.5B parameter count - 67.6% pass rate on 37-task benchmark - 83.8% pass rate in ensemble configuration - 70% pass rate for borrow-checker fixes - 77.8% pass rate for fix-this-bug tasks - 44.4% pass rate for test generation Tech stack: Python Why it matters: TemRust-SMOL-v5-1.5B provides a high-quality Rust code assistant, improving developer productivity and code quality through precise and effective fixes. Repository: temm1e-labs/temrust GitHub stars: 7

Overview: Bananen-KI's Nano-Banane API provides access to a comprehensive set of OpenAI-compatible models, including 300+ image, video, and speech models, all under a single endpoint. This API offers a cost-effective alternative to traditional AIs, with a flat rate of $0.0137 per image and no monthly subscription needed. Key features: - Access to 300+ models - Flat rate pricing of $0.0137 per image - OpenAI-compatible endpoint - Supports text-to-image and reference-image editing - Asynchronous processing Tech stack: Python Why it matters: This API is ideal for developers looking for an alternative to traditional AI solutions, offering a cost-effective and flexible way to integrate AI into their projects without the need for a monthly subscription. GitHub stars: 7 Repository: tn-gr0cr/nano-banana-2-1-api-de

Overview: This repository provides a public, machine-readable record of LLM inference prices across 100+ platforms, updated every 6 hours. Key features: - Public price history for LLM inference across 100+ platforms. - Updated every 6 hours. - Git is used as the time-series database. Tech stack: Python. Why it matters: This repo fixes the issue of only some providers publishing today's price, ensuring a consistent record of LLM inference costs.

Overview: `cuda-kernel-optimizer` is a project designed to optimize the performance of ChatGPT's GPU workload by leveraging evidence-driven CUDA, CUTLASS, Triton, and GPU workload optimization techniques. It provides a structured approach to identify bottlenecks, evaluate candidate solutions, and ensure the results are both reliable and verifiable. Key features: - GPU workload optimization - Evidence-driven approach - Integration with ChatGPT's environment - Support for various workload types - Detailed analysis of kernel and execution paths Tech stack: Python Why it matters: By leveraging evidence and ChatGPT's expertise, `cuda-kernel-optimizer` ensures that the optimization process is both effective and verifiable, providing a robust solution for improving ChatGPT's performance on GPU workloads. Repository: troycheng/cuda-kernel-optimizer GitHub stars: 7 GitHub description: Evidence-driven CUDA, CUTLASS, Triton and GPU workload optimization for ChatGPT · Using ChatGPT to optimize GPU workload performance.

Overview: tsquare is a text-based wireframe board system that is optimized for low-fidelity screens, featuring easy writing and fast rendering capabilities. Key features: - Supports multiple devices side by side - Allows notes to be added beside screens - Renders as SVG or PNG for easy integration with LLMs Tech stack: TypeScript Why it matters: tsquare is designed to be easily written by LLMs, making it ideal for use with large language models for creating wireframes. Repository: tsquare-js/tsquare GitHub stars: 7 GitHub description: Wireframe boards in plain text: easy for LLMs to write, fast to render as SVG or PNG.

### Overview: Seedream 4.5 prompts are tested image prompt recipes that showcase eight effective and efficient methods for generating high-quality images. The prompts are designed to be easily replicable, with clear instructions on the model ID (`seedream-4-5`), aspect ratios, and the cost per image. ### Key Features: - **Model ID:** `seedream-4-5` - **Prompt Recipes:** Eight recipes listed, each with an example render, ratio, and cost on this route. - **Effective Price:** $0.026 per image, estimated cost of the whole set: $0.208. ### Tech Stack: Python ### Why It Matters: - **Versatile Access:** Seedream 4.5 can be accessed through a single `https://api.apimart.ai/v1` base URL and `Authorization` header, allowing for easy integration with catalog management systems. - **Minimal Setup:** No need for prepayment plans or monthly subscriptions; users can start with a single payment and adjust as needed. - **Clear Pricing:** All costs are reflected in the response, and failed tasks are refunded. - **Efficient Design:** The prompt structure is designed to be simple and effective, ensuring that the final output is as close to the intended vision as possible. ### Other Names: Seedream ### Prompts Overview: - **Model:** `seedream-4-5` - **Recipes:** Eight recipes, each with a clear example render, ratio, and cost on this route. - **Effective Price

Overview: Nano Banana Pro is an OpenAI-compatible API endpoint that provides image generation and editing services, offering a unique and cost-effective alternative to traditional image generation methods. Key features: - Nano Banana Pro (`gemini-3-pro-image-preview`) supports 4K image generation and editing with multi-step planning. - The API endpoint is compatible with OpenAI, allowing for seamless integration with existing systems. - It offers a per-image pricing model, with a minimum cost of $0.03 per image for 4K resolution. - The API supports both default and 4K pricing tiers, with the latter being slightly more expensive. - Users can access multiple models via a single base URL and API key, eliminating the need for a separate subscription or plan. Tech stack: Python Why it matters: Nano Banana Pro provides a cost-effective and efficient solution for image generation and editing, making it ideal for developers who need to integrate image-based features into their applications without the need for a dedicated API key or plan. GitHub stars: 7

### Overview: 即梦视频 — Seedance 1.0 Pro Fast prompts: tested image prompt recipes with example renders, aspect ratios, the model id (`doubao-seedance-1-0-pro-fast`) and the cost per image. ### Key features: - **4 Tested Recipes**: Each recipe includes a concrete object or person, material, light direction, framing ratio, and setting. - **Effective Price**: $0.0079 per second, estimated cost of the set: $0.0316. - **Cost Per Image**: The cost is returned as part of the task result, and failed tasks are automatically refunded. ### Tech stack: Python ### Why it matters: - **Key, Entire Directory Access**: Same `https://api.apimart.ai/v1` base URL and `Authorization` header to access Seedance 1.0 Pro Fast (doubao-seedance-1-0-pro-fast) and 300+ models. - **Cost-Per-Second Pricing**: $0.0079 per second, no subscription or pre-paid plans required. - **Immediate Cost Reporting**: Each task reports the actual cost, and failed tasks are automatically refunded. - **Asynchronous Design**: Submit tasks, get `task_id`, and poll `GET /v1/tasks/{id}` for batch processing and retries without custom integration. ### Repository: v87tcvt6l/seedance-1-0-pro-fast-prompts-cn ### Primary Language: Python ### GitHub Stars: 7

### Overview: A multi-modal embedded database engine in pure Rust, designed for edge AI applications. It offers key-value, vector search, blob storage, TTL, merge operators, CDC, and flash storage backend all within a single ACID-transactional file. Key features include 95% recall@1 at 1,000+ QPS on SIFT1M, 1.6–3x faster writes than LMDB, and 2–3x faster reads than RocksDB. It is optimized for single-threaded, 8 GB RAM, and single-process, single-writer environments. ### Key features: - **Vector Search**: IVF-PQ with integer ADC, SIMD distance, and ACID-transactional index updates. - **Blob Storage**: Content-addressed deduplication, chunked writes, causal lineage graph. - **TTL Tables**: Per-key expiration for sessions. - **Merge Operators**: Built-in support for efficient data aggregation and joins. - **CDC (Change Data Capture)**: Built-in support for tracking changes in data. - **Flash Storage Backend**: Native vector search, blob store, TTL, merge operators, CDC, and flash storage backend. ### Tech stack: Pure Rust, no_std. ### Why it matters: - **Embedded Rust Applications**: Suitable for edge AI applications that require KV + vector search + blob storage in a single ACID-transactional process. - **Single-threaded**: Designed for single-threaded environments, making it efficient and lightweight. - **No C++ Dependency**: Avoids the

Overview: This project involves fine-tuning the Gemma-2b-it model specifically for generating code. Key features: - Fine-tuning Gemma-2b-it for generating code - Utilizing low-rank adapters (LoRA) with QLoRA parameters - Training on the TokenBender/code_instructions_122k_alpaca_style dataset - Preview of the model's performance before and after fine-tuning Tech stack: Jupyter Notebook Why it matters: This project showcases the potential of fine-tuning large language models for practical applications, such as generating code, and demonstrates the effectiveness of low-rank adapters in enhancing model performance.

### Overview: UltraMemory is a universal AI agent long-term memory engine that leverages hybrid vector retrieval, cross-encoder reranking, Weibull decay, LLM smart extraction, semantic deduplication, and L0/L1/L2 tiered content storage. It is designed for zero-ops LanceDB backend, making it highly efficient and scalable. ### Key Features: - **Hybrid Vector+BM25 Retrieval**: Combines vector-based and BM25 search for enhanced relevance. - **Cross-Encoder Reranking**: Improves recall by reranking documents based on cross-encoder scores. - **Weibull Decay**: Maintains memory freshness with a Weibull decay algorithm. - **LLM Smart Extraction**: Extracts meaningful information from large language models. - **Semantic Deduplication**: Ensures unique and accurate memory representation. - **Tiered Content Storage**: Stores content in L0, L1, and L2 layers for efficient recall. - **Zero-ops LanceDB Backend**: Efficiently stores and retrieves data without external dependencies. ### Tech Stack: TypeScript ### Why It Matters: UltraMemory is essential for agents that need to remember and recall information across sessions, agents, and time. Its advanced features make it highly competitive in the field of AI memory engines, ensuring efficient and effective memory management. ### Repository: win4r/UltraMemory ### GitHub Stars: 7 ### GitHub Description: Universal AI Agent Long-Term Memory Engine — Hybrid Vector+BM25, cross-encoder reranking, Weibull decay, L

Overview: DataFountain训练赛道赛题“天气以及时间分类”解决方案，测试成绩为0.94934795，第二赛段排行榜A榜排名第一。 Key features: - 使用迁移学习和多模型投票的方法进行天气和时间分类。 - 去除了训练集中未出现的三种类别，将剩余的“Dawn”、“Morning”、“Afternoon”和“Dusk”与“Cloudy”、“Sunny”和“Rainy”合并为12类进行分类。 - 所有经过预训练的模型均在ImageNet上完成预训练。 - 模型参数按网络结构分类并存放在voting_models文件夹下，用于投票过程。 Tech stack: Python Why it matters: 该解决方案在DataFountain训练赛道赛题中取得了优异的成绩，特别是在天气和时间分类方面，展示了强大的分类能力。

### Overview: eqx-zoo is a project that provides verified JAX/Equinox ports of Hugging Face language models and embedding models. These models are numerically matched against PyTorch, forward and backward, and saved in Hugging Face formats. The repository includes pretrained models as Equinox modules, making them compatible with the JAX ecosystem. ### Key features: - Pretrained models in Equinox format - Numerically verified against PyTorch - Saved in Hugging Face formats - Compatible with JAX ecosystem ### Tech stack: Equinox ### Why it matters: eqx-zoo provides a verified way to use Hugging Face models in JAX, ensuring compatibility and performance. It allows developers to leverage the latest advancements in JAX while maintaining the accuracy and reliability of their models.

Overview: ATLAS-TQ1.0 is a CPU-only ternary inference engine designed for 1.58-bit quantized LLMs. It packs the Falcon3-1B model at 10.1 tok/s on a DDR4 system. Key features: - TQ1.0 Packer (`pack_to_atlas.py`): Converts HuggingFace safetensors to TQ1.0 format. - 5 matmul modes: int8, int4, f32 bypass, ternary, TQ1-packed. - C API and C++ CLI. - Streaming SSE Server (`atlas_server.py`). - DeepSeek-V2 latent attention and sparse mixture-of-experts (v2.17.0). - Ported all 8 hot-path kernels to ARM64 NEON. - Binary Tokenizer v6 format, no transformers dependency. Tech stack: C++. Why it matters: ATLAS-TQ1.0 is the only production-grade format for ternary 1.58-bit quantization, providing a performance boost for 7B models on DDR4 systems. Repository: xxxn3m3s1sxxx/ATLAS-TQ1_0 GitHub stars: 7 GitHub description: TQ1.0 ternary inference engine for BitNet b1.58 on CPU. Pack + run Falcon3-1B/3B/7B/10B, no GPU needed. License: Apache 2.0.

### Overview: GeoGuessr Location Assistant is a project that focuses on developing a street-level image analysis pipeline for geographic location inference using YOLO object detection, Optical Character Recognition (OCR), and Overpass/Nominatim queries. The project aims to achieve high accuracy in predicting the correct city from street-level images, leveraging advanced machine learning techniques and geographic data sources. ### Key Features: - **YOLOv8 Detection**: Utilizes YOLOv8 for object detection. - **OCR Integration**: Integrates OCR to extract text from images. - **Overpass/Nominatim Queries**: Queries OpenStreetMap and Nominatim for geolocation data. - **Hybrid AI Pipeline**: Combines YOLO detections, OCR results, and Overpass/Nominatim queries to predict the correct city. ### Tech Stack: - **end_logic**: YOLOv8 detection, OCR, Overpass/Nominatim queries. - **backend**: FastAPI service exposing the YOLOv8 detection results. - **frontend**: ReactJS application for the chatbot interface. - **data_prep**: Scripts for preparing the dataset for model training. ### Why It Matters: This project is significant because it demonstrates the integration of various machine learning techniques to enhance the accuracy of geographic location inference from street-level images. The use of YOLOv8 for object detection, OCR for text extraction, and Overpass/Nominatim queries for geolocation data provides a robust solution for urban mapping and location inference. ### Repository: - **Repository**: yacine20

Overview: ComfyUI_RH_VoxCPM is a developer-friendly tool that integrates VoxCPM nodes into the ComfyUI workflow, enabling realistic speech synthesis and voice cloning directly within the ComfyUI environment. This plugin simplifies the process of processing 3D data and voxel models, making it easier to integrate advanced voice synthesis capabilities into existing projects. Key features: - Realistic speech synthesis - Voice cloning - Token-free TTS - Style guidance - LoRA training support Tech stack: Python Why it matters: This plugin streamlines the integration of advanced voice synthesis technology into the ComfyUI ecosystem, making it easier for developers to create more immersive and realistic voice-based applications.

### Overview: X-Forge Factory is a self-hosted operator console for Magnific, providing access to all capabilities on a single surface. It integrates with both the REST API and MCP servers, offering 48 image models, 52 video models, and a credit-safe job engine. ### Key Features: - **REST API and MCP Integration:** Connects to Magnific's REST API and MCP server via OAuth. - **Credit-Safe Job Engine:** Manages credits as if they were money, ensuring charges are accurate and credits are reserved before work starts. - **Full Catalogue Access:** Provides access to all 48 image models, 52 video models, and over 600 voices. - **Text-to-Speech and 3D Features:** Additional capabilities beyond the standard Magnific features. - **Dashboard Interface:** Simplifies the console's interface for easy navigation and management. ### Tech Stack: TypeScript ### Why It Matters: X-Forge Factory offers a unified interface for all Magnific capabilities, ensuring accurate cost management and access to a comprehensive catalog. The job engine's credit-safety feature ensures that credits are treated as valuable resources, enhancing the overall user experience. Repository: yuriiss/X-Forge-Factory GitHub Stars: 7 GitHub Description: Self-hosted operator console for Magnific / Freepik AI. REST + MCP, 48 image models, 52 video models, credit-safe job engine