Projects

### Overview: Wan 2.7 Prompts: 8 Tested Recipes with Outputs ### Key Features: - **Model ID:** `wan2.7` - **Effective Price:** $0.0664 per second - **Estimated Cost:** $0.2656 for the entire set ### Tech Stack: - Python ### Why It Matters: - **Model ID (`wan2.7`):** Enables access to Wan 2.7 and over 300 other image, video, and language models. - **Pricing Model:** $1 minimum, pay-as-you-go. No subscription or pre-paid plans. - **Cost Details:** Each completed task shows the cost (`cost` or `credits_cost`), and failed tasks are refunded. - **Asynchronous:** Tasks can be submitted, fetched, and processed through a queue, not a custom integration. ### Repository: - **Repository:** `jtyth8d82sy1wf/wan-2-7-prompts-fr` - **Stars:** 17 - **Description:** Wan 2.7 prompts: tested image prompt recipes with example renders, aspect ratios, the model id (`wan2.7`) and the cost per image. - **Primary Language:** Python ### Primary Language: - **Primary Language:** Python ### GitHub Description: - **Description:** Wan 2.7 prompts: tested image prompt recipes with example renders, aspect ratios, the model id (`wan2.7`) and the cost per image. - **Pricing

Overview: This repository contains 8 tested Wan 2.7 prompts with example renders, aspect ratios, and the model id (`wan2.7`) along with the cost per image. Key features: - 8 Wan 2.7 prompts with example renders - Aspect ratios and model id (`wan2.7`) - Cost per image Tech stack: Python Why it matters: This project provides a practical guide for developers to create effective Wan 2.7 prompts, ensuring high-quality renders at a competitive price.

### Overview: This repository contains 4 Wan 2.7 Video Edit prompts, each with an example render, duration, and cost on the APIMart route. The Wan 2.7 Video Edit model is tested with 8 recipes, providing insights into effective prompts and their corresponding costs. ### Key Features: - **4 Prompts**: Each prompt is designed with specific details to ensure the best possible rendering. - **Example Renders**: Each prompt comes with an example render, demonstrating how the model interprets the provided instructions. - **Duration and Cost**: The duration and cost of each prompt are provided, helping users understand the expected output and resource usage. ### Tech Stack: Python ### Why It Matters: This repository offers a practical guide for developers looking to optimize their video editing workflows using the Wan 2.7 Video Edit model. The detailed prompts and their associated costs provide valuable insights into the model's capabilities and limitations, enabling more efficient and effective use of the model's resources.

### Overview: KONASH is a knowledge-grounded off-policy networks for agentic system harnesses that trains knowledge agents to search, retrieve, and reason efficiently. It leverages reinforcement learning to optimize search policies, enabling agents to perform tasks more accurately and cost-effectively compared to traditional methods. ### Key Features: - **Agentic QA Synthesis**: Multi-turn agent loop explores the corpus via search, generating grounded multi-constraint question-answer pairs. - **OAPL Training**: Off-policy RL with squared advantage loss trains the model on successful search trajectories. - **Value-Guided Search**: Learned value model scores partial rollouts, guiding test-time tree search. - **Pass-Rate Filtering**: Keeps questions at the learning frontier, ensuring neither too easy nor too hard. - **Parallel Rollouts**: N=10–20 independent rollouts + aggregation for consistent answers. - **Pre-built Indexes**: Ships with Qwen3-Embedding-8B indexes for supported datasets, no embedding step needed. - **Any Corpus**: Point at a local folder of documents, KONASH builds the index on first run. ### Tech Stack: Python ### Why It Matters: - **100x cheaper training**: Small training clusters, ~$100 per iteration instead of ~$100K–500K. - **Higher quality**: RL-trained agents search more efficiently, retrieve more diversely, and reason more accurately. - **Consistent results**: Parallel thinking ensures near-deterministic accuracy, even on a small model. - **Zero

### Overview: The MGQA project is an open-source implementation of the multi-grouped query attention model described in the paper "GQA: Training Generalized Multi-Query Transformer Models from Multi-Head Checkpoints". This project provides a Python library that can be used to train and run the model on various platforms. ### Key features: - **Model Implementation**: The project includes the implementation of the multi-grouped query attention model. - **Training from Checkpoints**: It allows training from pre-existing multi-head checkpoints, making it easier to fine-tune models. - **Python Library**: The code is written in Python, making it accessible and easy to integrate into existing projects. ### Tech stack: - **Python**: The project is written in Python, leveraging the language's flexibility and ease of use. - **PyTorch**: The model and transformer components are built using PyTorch, a popular deep learning framework. ### Why it matters: The MGQA project is significant because it provides a practical way to train and utilize multi-head checkpoint-based models, which can be particularly useful in scenarios where retraining from scratch is not feasible or efficient. This can save time and computational resources, making it a valuable tool for researchers and practitioners in the field of natural language processing and machine learning. ### GitHub stars: 17 The repository has gained 17 GitHub stars, indicating that it is well-regarded and potentially useful to a wide audience. ### Installation: To use the MGQA project, you can install it using: ```bash pip install mgqa ``` ### Usage:

Overview: NELLA is a document-driven AI model creation agent that automates the entire process of domain-specific language modeling from your documents, including data collection, model selection, training, evaluation, and model deployment. Key features: - Automatically generates training data from your documents. - Selects and trains a model based on your documents. - Evaluates the model and suggests improvements. - Deploy the trained model locally on your PC. Tech stack: TypeScript, Python, Docker, Apache 2.0 License. Why it matters: NELLA simplifies the process of creating domain-specific language models by automating the entire workflow, from data collection to model deployment, making it accessible and efficient for researchers, institutions, and educators.

### Overview: LanceDB C# SDK is a C# library that provides idiomatic APIs for interacting with the LanceDB vector database. It wraps the official Rust `lancedb` crate via P/Invoke, offering a seamless experience for developers familiar with C#. ### Key features: - **Connection management**: Connects to local or cloud databases with configurable options. - **Table CRUD**: Create, open, rename, clone, and drop tables; add, update, delete, merge-insert rows. - **Vector search**: Nearest-neighbor queries with distance metrics, nprobes, refine factor, multi-vector search. - **Full-text search**: FTS indexing with configurable tokenization, stemming, and stop words; structured queries (Match, Phrase, Boost, MultiMatch, Boolean). - **Hybrid search**: Combine vector and full-text search with reranking (RRF, Linear Combination, MRR). - **Type-safe filters**: Build `Where` predicates and `Select` projections with the `Expr` expression builder or raw SQL. - **Streaming results**: `ToBatches()` returns `AsyncRecordBatchReader` for memory-efficient iteration. - **Indexing**: BTree, Bitmap, LabelList, FTS, IVF-PQ, IVF-Flat, IVF-SQ, IVF-RQ, HNSW-Flat, HNSW-PQ, HNSW-SQ. - **Schema management**: Add, alter, drop columns; add null-filled columns from Arrow Schema. - **Versioning**: Checkout, restore, list versions

### Overview: HERMES is a unified open-source framework designed for real-time multimodal physiological sensing, edge AI, and intervention in closed-loop smart healthcare applications. It facilitates reliable and fast data exchange across heterogeneous edge devices, enabling synchronized data collection, in-the-loop AI processing, and analysis in intelligent medical and health-tech devices. ### Key Features: - **Real-time Data Streaming**: Integrates with a variety of commercial sensors and systems. - **High-Resolution Cameras**: Capable of capturing detailed images and videos. - **Customizable User Devices**: Templates for extending the framework with any compatible devices. - **Simplified Integration**: Ready-to-use integrations for seamless setup. - **Open-Source**: Built on Python, accessible for developers and researchers alike. ### Tech Stack: Python ### Why It Matters: HERMES addresses the need for reliable and efficient data exchange in healthcare applications, enabling real-time monitoring and analysis of physiological and environmental data. Its modular design and open-source nature make it an ideal solution for researchers, developers, and healthcare professionals looking to enhance the quality and speed of medical data processing.

Overview: This project is a developer-focused repository for a LLM agent designed to generate workflow UML diagrams, utilizing JavaScript as the primary technology stack. The README details the process of training and utilizing a proprietary LLM model, including snippets for generating nomnoml diagrams from text and text from nomnoml inputs. Key features: - Generate UML diagrams from text - Generate text from nomnoml input - Data preparation for OpenFDA Tech stack: JavaScript Why it matters: This project showcases the capabilities of a LLM agent in generating diagrammatic representations from text and text-based inputs, demonstrating the potential of AI in data visualization and documentation. Repository: metatatt/llm2uml GitHub stars: 17 GitHub description: LLM agent to generate workflow UML diagrams

### Overview: SoeMind Forge is a budget-aware AI content studio designed for founders, marketers, and small brands. It generates videos, images, voiceovers, music, captions, and 30-day content calendars with consistent brand assets and cost control, ensuring every project stays within budget and maintains brand consistency. ### Key Features: - **Budget-aware AI**: Ensures every project stays within budget and maintains brand consistency. - **Consistent Brand Assets**: Characters, products, logos, and voices are generated once and reused, ensuring a consistent brand across campaigns. - **Text Never Garbles**: AI models paint beautiful backgrounds but butcher typography, so slides and reels get pixel-perfect text rendered locally with real fonts. - **Upload-Ready Media**: Correctly sized media per platform, captions within character limits, and hashtags and alt text — not loose renders. - **Money Moves Only with Consent**: Every project has a budget cap and a spend ledger; the agent shows the estimated cost before generating, previews cheap before rendering expensive, and records every dollar. ### Tech Stack: TypeScript The project is built using TypeScript, a modern programming language that provides type safety and better code organization. ### Why It Matters: - **Money Moves Only with Consent**: Every project has a budget cap and a spend ledger; the agent shows the estimated cost before generating, previews cheap before rendering expensive, and records every dollar. - **Consistency is Engineered, Not Hopeful**: Characters, products, logos, and voices are generated once, locked in an asset registry, and reused by ID — the same

### Overview: This project is a custom attention backend for vLLM on AMD MI50/MI60/Radeon VII (gfx906), built as a downstream fork of the ML-gfx906 stack. It replaces the default Triton attention kernels with custom HIP kernels, significantly improving throughput and functionality at longer context lengths. ### Key Features: - **Custom Attention Kernels**: Replaces the default Triton attention kernels with custom HIP kernels, offering improved performance and stability. - **ROCm 7.2.1 Patched for gfx906**: Provides the necessary ROCm 7.2.1 patches for the gfx906 family. - **PyTorch 2.9 for gfx906**: Reuses the PyTorch 2.9 for gfx906 image. - **vLLM 0.19.1 for gfx906**: Registers the custom attention kernels as `--attention-backend CUSTOM` in the vLLM plugin. ### Tech Stack: - **Python**: The primary language for development and deployment. ### Why It Matters: - **Performance Improvement**: The custom kernels deliver up to **+20…40 %** throughput over the stock Triton-generated kernels, particularly at long context lengths. - **Stability**: The custom kernels remain functional even at long context lengths where the upstream Triton-generated kernels effectively stall. - **Compatibility**: The project is a downstream fork of the ML-gfx906 stack, ensuring compatibility with the upstream image without competition. ### GitHub Stars

Overview: YassQueenDB is a graph database library designed to store, analyze, and search through data in a graph format, utilizing the Universal Sentence Encoder for efficient and semantic text handling. Key features: - Graph management: Create, select, and delete graphs. - Text analysis: Semantic search, summarization, and node creation from text. - Data manipulation: Embedding text data, keyword extraction, Laplacian eigenmaps, and file saving/loading. Tech stack: Python. Why it matters: YassQueenDB offers an efficient and semantic approach to text data handling, making it suitable for applications requiring semantic understanding and efficient data manipulation. Repository: nileshkhetrapal/YassQueenDB GitHub stars: 17 GitHub description: Graph database library that allows you to store, analyze, and search through your data in a graph format. By using the Universal Sentence Encoder, it provides an efficient and semantic approach to handle text data. 📚🧠🚀

### Overview: Nouli is a ledger-backed decision network that records AI decisions and connects them to the facts and earlier answers they use. It allows users to ask questions, reuse earlier answers, and follow the decision graph, all while maintaining a history of decisions. ### Key features: - **Decision Network:** Records and connects AI decisions to their context and previous answers. - **CLI and Library:** Provides a terminal app, CLI, and Rust library for managing decision histories. - **Task-Specific Models:** Supports a variety of models tailored to specific tasks, including models from the Nouli AI ecosystem. - **Connection to Decision Services:** Allows connecting to compatible decision services or following local models' recipes. ### Tech stack: Rust ### Why it matters: Nouli offers a unique way to maintain context and trace decisions, making it valuable for applications that require a clear history of decisions and their context. The integration with decision services and the ability to connect to task-specific models make Nouli a versatile tool for AI-driven decision-making processes.

Overview: Qwisp is a fast, optionally-lossless local inference engine for Qwen3.6-35B-A3B on Apple Silicon, serving over an OpenAI-compatible HTTP API. Key features: - **Runtime = Tell**, **engine = Seedless** (raw Metal; MLX is the substrate). - **Lossless option**: the strict path reproduces the quantised greedy token stream bit-for-bit. - **Drop-in**: OpenAI `/v1/chat/completions` (SSE) + `/v1/models`, plus a `qwisp chat` CLI. Tech stack: Swift Why it matters: Qwisp is designed for efficient inference on Apple Silicon, leveraging raw Metal for faster decoding, while maintaining compatibility with OpenAI's API and serving as a drop-in replacement for OpenAI models. Repository: penta2himajin/qwisp GitHub stars: 17

Overview: VSB is a benchmarking suite for vector search, designed to measure the performance of various vector databases. Key features: - Supports multiple databases including Pinecone, pgvector, and OpenSearch - Runs workloads like MNIST and NQ768 against these databases - Measures throughput, latency, and other metrics - Automatically runs setup, populate, and run phases - Writes detailed results to stats.json and displays a summary Tech stack: Python Why it matters: VSB provides a quick and efficient way to benchmark the performance of vector databases, helping developers and researchers optimize their search solutions.

Overview: This project implements a semantic retrieval recommender and information retrieval system based on Google Cloud infrastructure, built on the Google Cloud Platform (GCP). The system leverages machine learning techniques to enhance the shopping experience by providing relevant product suggestions and personalized content. Key features: - Semantic product search based on natural language queries - Extraction of key product features - Sentiment analysis of product reviews - Contextual recommendations Tech stack: TypeScript Why it matters: This project offers a smarter way to shop online by integrating machine learning techniques with Google Cloud infrastructure, providing enhanced product recommendations and personalized content. GitHub stars: 17 Repository: polarbear333/rag-llm-based-recommender

Overview: OpenManga is a self-hosted production tool that converts stories into consistent AI-illustrated comics, webtoons, or narrated videos. It allows users to bring their own provider keys, local TTS, deterministic lettering, and exports. Key features: - Your own provider keys (BYOK) for text reasoning and raster generation - Kokoro-82M for local speech synthesis - The application handles everything else - No telemetry, no analytics, no phone-home - Production run: one button to run the entire pipeline - Update production: runs only the steps that need to be updated - Revision: shows what is out of date from story to video Tech stack: TypeScript Why it matters: OpenManga is a self-hosted solution for consistent AI-generated content, making it ideal for small to medium-sized projects that require high-quality illustration and narration. Repository: pr0h0/OpenManga GitHub stars: 17

Overview: This project introduces an OpenClaw memory plugin backed by a self-hosted Mem0 REST API, replacing the default LanceDB backend with Mem0's semantic extraction pipeline. The plugin enhances agent recall and memory management capabilities, offering features like auto-recall, auto-capture, and a zero-native-dependency setup. Key features: - Three agent tools: `memory_recall`, `memory_store`, `memory_forget` - Auto-recall: Injects relevant memories before each agent execution - Auto-capture: Stores conversation highlights after each agent execution - CLI commands: `openclaw mem0 search|list|forget` - Graceful degradation: Logs warnings if the Mem0 server is unreachable, never crashes the gateway - Zero native dependencies: Uses Node.js built-in `fetch()` — no SDK required Tech stack: Node.js with TypeScript Why it matters: This plugin leverages Mem0's semantic extraction pipeline, enhancing the long-term memory capabilities of OpenClaw agents, making them more versatile and powerful.

### Overview: Valori is a high-performance vector database library for Python that offers efficient storage, indexing, and search capabilities for high-dimensional vectors. ### Key features: - **🚀 High Performance**: Optimized for speed with multiple indexing algorithms. - **📄 Document Parsing**: Support for PDF, Office, text, and advanced parsing with Docling. - **🔄 Processing Pipeline**: Complete document processing with cleaning, chunking, and embedding. - **💾 Multiple Storage Backends**: Memory, disk, and hybrid storage options. - **🔍 Advanced Indexing**: Flat, HNSW, and IVF indices for different use cases. - **🗜️ Vector Quantization**: Scalar and product quantization for memory efficiency. - **💾 Persistence**: Tensor-based and incremental persistence strategies. - **🏭 Production Ready**: Comprehensive logging, monitoring, and error handling. - **🐍 Python Native**: Pure Python implementation with NumPy integration. - **📊 Extensible**: Plugin architecture for custom components. ### Tech stack: Python ### Why it matters: Valori is a powerful tool for developers looking to efficiently store, index, and search high-dimensional vectors in Python. Its high performance, versatility, and extensibility make it suitable for a wide range of applications, from natural language processing to image retrieval.

### Overview: VideoDB Chat is a frontend interface designed for building chat-based systems and connecting with agent-driven workflows. It integrates seamlessly with Director, a powerful tool for creating complex workflows and chat interfaces. ### Key Features: 1. **Chat Interface Components**: Provides essential chat UI components for integrating with Director. 2. **Customizable Chat Hook**: Allows developers to configure their own chat hook for better integration with Director. 3. **Real-Time Updates**: Reactively updates conversations in real-time, ensuring the chat interface remains current. 4. **Responsive Design**: Designed to be easily integrated into various frontend frameworks, including Vue. ### Tech Stack: Vue ### Why It Matters: VideoDB Chat simplifies the process of integrating chat functionality with Director, making it easier for developers to create robust chat-based workflows. Its real-time updates and customizable chat hook options make it a valuable tool for teams looking to enhance their chat interfaces. ### Repository: Repository: [video-db/videodb-chat](https://github.com/video-db/videodb-chat) GitHub Stars: 17 ### Primary Language: Vue ### GitHub Description: Frontend interface for building chat based system and connecting with agent driven workflows. ### GitHub Description (excerpt): [![NPM version][npm-shield]][npm-url] [![Stargazers][stars-shield]][stars-url] [![Issues][issues-shield]][issues-url] [![Website][website-shield]][website-url] [![Discord][discord-shield]][discord-url] <br /> <p align="center"> <a href="https://