Projects

### Overview: This repository is a collection of papers and source code related to Deep Learning-based Image Colorization and Video Colorization. It includes automatic and user-guided colorization techniques, as well as video colorization methods. The project aims to provide a comprehensive overview of the latest advancements in the field, making it an invaluable resource for developers and researchers interested in image and video colorization. ### Key features: - **Automatic Image Colorization**: Includes software and demo projects for automatic colorization. - **User Guided Image Colorization**: Covers various methods including scribble-based, reference image-based, palette-based, language-based, and multimodal approaches. - **Video Colorization**: Features surveys, automatic methods, and techniques based on reference images, scribble, text, and palettes. ### Tech stack: - Python - TensorFlow - PyTorch - OpenCV - Other relevant libraries and frameworks ### Why it matters: This repository serves as a valuable resource for developers and researchers looking to implement and improve image and video colorization techniques. It provides a comprehensive overview of the latest advancements in the field, making it an essential tool for anyone interested in this area of computer vision.

Overview: Dr. Claw is a versatile AI research assistant designed to streamline the entire research workflow, from idea generation to publication preparation, ensuring researchers can focus on high-quality research without the clutter of multiple tools. Key features: - A full-stack research workspace - Supports full research lifecycle, from surveys to experiments and paper writing - Runs on your own machine with your own data and GPUs - Model-agnostic, compatible with multiple AI models via OpenRouter Tech stack: JavaScript Why it matters: Dr. Claw simplifies the complex process of AI research, making it accessible and efficient for researchers and builders alike. Repository: OpenLAIR/dr-claw GitHub stars: 1165 GitHub description: A Super AI Lab with massive AI Doctors as Assistants. Best IDE for Research via AI Power. Key features: - A full-stack research workspace - Supports full research lifecycle, from surveys to experiments and paper writing - Runs on your own machine with your own data and GPUs - Model-agnostic, compatible with multiple AI models via OpenRouter Tech stack: JavaScript Why it matters: Dr. Claw simplifies the complex process of AI research, making it accessible and efficient for researchers and builders alike.

Overview: ClawBio is a bioinformatics-native AI agent skill library built on OpenClaw, featuring local-first, privacy-focused, and reproducible capabilities. It includes 8,182 Galaxy tools, 5,040 tests, and a validation infrastructure. Key features: - Local-first approach - Reproducible results - Privacy-focused design - Built on OpenClaw (180k+ stars) Tech stack: Python Why it matters: ClawBio provides a robust, open-source toolset for bioinformatics tasks, ensuring reproducibility and reducing guesswork in research and development. Repository: ClawBio/ClawBio GitHub stars: 1,165 GitHub description: 🦖 ClawBio - The first bioinformatics-native AI agent skill library. Local-first. Reproducible. Open. Free.

Overview: MagicDrive is an official implementation of the paper "MagicDrive: Street View Generation with Diverse 3D Geometry Control," focusing on generating diverse 3D geometry for street views. Key features: - Implementation of MagicDrive algorithm - Support for various 3D geometry types - Versatility in generating realistic street views Tech stack: Python Why it matters: MagicDrive provides a practical solution for generating diverse 3D street views, offering significant improvements in realism and variety compared to existing methods.

Overview: This repository provides an in-depth look at the harness section of AI agent architectures, explaining how modern AI agents are built around large language models (LLMs). It covers the loop, tools, memory, permissions, context, tasks, and interfaces, making it easier to read and understand various AI agent implementations. Key features: - The harness section explains how AI agents are built around LLMs. - It covers the loop, tools, memory, permissions, context, tasks, and interfaces. - The repository focuses on the harness section, making it easier to learn and understand AI agent architectures. Tech stack: Python Why it matters: This repository is essential for developers looking to understand the inner workings of AI agent architectures, specifically focusing on the harness section. It provides a comprehensive guide to building and understanding AI agents, making it easier to read and implement various AI agent implementations. Repository: hardness1020/learn-agent-architecture GitHub stars: 1,152 GitHub description: Learn AI agents from scratch.

Overview: Tracely-ai is a self-hostable CI/CD tool designed for AI agents, providing trace-native CI/CD capabilities to grade, cluster, freeze, and block PRs that regress. It grades every agent trace as it lands, clusters failures into issues, freezes bad runs into hermetic regression tests, and blocks the pull request that would ship them again. Key features: - Trace-native CI/CD for AI agents - Grades every agent trace as it lands - Clusters failures into issues - Freezes bad runs into hermetic regression tests - Blocks pull requests that regress Tech stack: Python Why it matters: Tracely-ai provides a trace-native CI/CD solution for AI agents, ensuring that every run is treated as a test, and it automatically blocks regressions, making it an essential tool for continuous integration and continuous deployment in the AI development pipeline.

### Overview: DSH Vision Router is a powerful tool designed for text-only DeepSeek Harness agents. It provides free vision capabilities without requiring a key, making it easy to integrate into any existing DSH profile. The built-in vision chain and fourteen deep tools allow for pixel-level analysis and precise image manipulation, turning images into interactive vision-based tools. ### Key Features: - **Pixel-Level Vision Tools:** Enables precise image analysis and manipulation. - **Built-in Free Vision Chain:** Provides free vision capabilities without the need for a key. - **No Python Required:** Supports one-command installation and seamless integration. - **Grounded, Measurable, Repeatable:** Vision tools are grounded in context and can be measured and repeated. ### Tech Stack: JavaScript ### Why It Matters: DSH Vision Router simplifies the integration of vision capabilities into text-based agents, making it easier to analyze and understand images without the need for additional software or configuration. This tool is particularly useful for developers looking to enhance their chatbot or agent capabilities by adding visual analysis features. ### Repository: Repository: ysr666/dsh-vision-router GitHub Stars: 1165 GitHub Description: Eyes for text-only DeepSeek Harness agents: built-in free vision chain (no key) + pixel-level vision tools (Q&A, grounding, crop, pixel diff, colors, OCR, SVG trace, cutout, screenshots). One-command install, no Python, image turns work like ordinary tool-calling turns. ### Primary Language: JavaScript ### Data Flow and Strict Local-Only Use: DSH Vision

### Overview: Open Steps is a project designed to provide plain-language agent skills for developers, allowing them to track and report on their work in a straightforward manner. This tool aims to streamline communication and decision-making by keeping the session, decisions, and next steps open to the person executing the work. ### Key features: - **Plain-Language Report**: A one-screen report summarizing the session with a clear verdict, such as "Work is done" or "Anything needed from you?" - **Step-by-Step Instructions**: Non-technical users can follow numbered steps, with the agent asking only for what is necessary. - **Ask Simple**: Simple questions in plain language, with recommendations marked. - **What Could Go Wrong**: Assesses potential failures in a fresh agent to identify the root cause. ### Tech Stack: The project is built using Shell. ### Why it Matters: By providing a clear and straightforward way to track and report on development work, Open Steps helps ensure that developers can focus on their core responsibilities while still maintaining accountability and transparency. This approach is particularly valuable in environments where the team is not composed of engineers, allowing non-engineers to effectively contribute to and oversee the project.

Overview: VGGT-SLAM is a dense RGB SLAM algorithm optimized for the SL(4) manifold, enabling real-time dense feed-forward scene reconstruction. This project aims to provide a robust and efficient solution for dense SLAM applications, particularly in environments with complex structures and dynamic scenes. Key features: - Real-time dense feed-forward scene reconstruction - Optimized for the SL(4) manifold - Supports dense RGB SLAM - Enables real-time dense feed-forward scene reconstruction Tech stack: Python Why it matters: VGGT-SLAM offers a significant improvement in performance and efficiency compared to traditional SLAM algorithms, making it suitable for real-time applications in robotics, autonomous vehicles, and other fields requiring dense SLAM capabilities. Its ability to handle complex scenes and dynamic environments makes it a valuable tool for researchers and developers working on SLAM systems.

### Overview: LatentMAS is a multi-agent reasoning framework that moves agent collaboration from token space into the model’s latent space. Instead of producing long textual reasoning traces, agents communicate by passing latent thoughts through their own working memory. LatentMAS has the following key features: - **Efficient** multi-step reasoning with drastically fewer tokens - **Training-free** latent-space alignment for stable generation - **A general technique** compatible with **any HF model** and optionally **vLLM** backends Overall, LatentMAS achieves **superior performance**, **lower token usage**, and **major wall-clock speedups** of the multi-agent system. ### Key Features: - **Efficient Multi-Step Reasoning**: LatentMAS enables multi-step reasoning with significantly fewer tokens compared to traditional methods. - **Training-Free Latent-Space Alignment**: Agents can align their latent representations without the need for additional training steps. - **General Technique Compatibility**: LatentMAS can be used with any HF model and optionally with vLLM backends, making it versatile for various applications. ### Tech Stack: LatentMAS is built using Python, leveraging the power of the HF model and vLLM backends for enhanced performance and compatibility. ### Why It Matters: LatentMAS offers a more efficient and stable approach to multi-agent reasoning, reducing token usage and improving overall performance. Its compatibility with various models makes it a versatile tool for researchers and developers alike. ### Repository: The repository for LatentMAS is hosted on GitHub at [Gen-Verse/LatentMAS](https://github

### Overview: This repository is a comprehensive hub for learning Artificial Intelligence, Machine Learning, Deep Learning, and Data Science, covering books, roadmaps, and curated resources from beginner to advanced levels. ### Key Features: - **AI Roadmap**: A structured, end-to-end guide to mastering AI. - **Data Science Roadmap**: A detailed path for learning data science, including math, statistics, and visualization. - **Machine Learning Roadmap**: Focuses on machine learning concepts and techniques. - **Deep Learning Roadmap**: Covers advanced deep learning topics and applications. ### Tech Stack: All resources are selected from high-quality books, courses, playlists, research papers, tools, and learning roadmaps. ### Why It Matters: This repository provides a clear, practical, and industry-relevant approach to learning AI, ensuring you focus on what truly matters in modern AI. ### Repository: bishwaghimire/ai-learning-roadmaps ### GitHub Stars: 1156 ### GitHub Description: A complete, structured hub for learning Artificial Intelligence — covering AI, Machine Learning, Deep Learning, and Data Science with books, roadmaps, and curated resources from beginner to advanced.

Overview: Prompt Poet simplifies prompt design for developers and non-technical users, enhancing interaction efficiency and quality with a low-code approach. Key features: - YAML and Jinja2 template processing for flexible prompt creation - Truncation priority for optimal prompt display - Interpolating lists and parsing messages from chat history - Adaptation to user modality (audio or text) - Targeting specific queries for context-aware prompts Tech stack: Python Why it matters: Prompt Poet streamlines the prompt design process, making it accessible to both technical and non-technical users, thereby improving interaction efficiency and quality in AI model interactions.

Overview: Tiny-vllm is a smaller version of vLLM, designed specifically for developers to learn and implement a high-performance LLM inference engine in C++ and CUDA. Key features: - Load a real LLM model from Safetensors (Llama 3.2 1B Instruct) - Full LLM forward pass (prefill + decode) - All computation with CUDA kernels - KV cache - Static batching - Continuous batching - Online softmax, FlashAttention-like - Paged Attention - Paged KV cache Tech stack: C++ and CUDA Why it matters: This project is designed to help developers understand and implement a high-performance LLM inference engine, making it easier to learn and apply cutting-edge techniques in the field of large language models.

Overview: OpenSTL is a comprehensive benchmark suite for spatio-temporal predictive learning, covering a wide range of methods and tasks, including synthetic and real-world scenarios such as human motion, driving scenes, traffic flow, and weather forecasting. Key features: - Flexiable Code Design: Decomposes STL algorithms into methods, models, and modules, providing a unified API for experiment development. - Standard Benchmarks: Supports training and evaluation benchmarks for STL algorithms, aligning with open-source projects like MMDetection and USB. - Plans: Plans to provide benchmarks for various STL methods and MetaFormer architectures based on SimVP in diverse tasks, encouraging contributions. Tech stack: Python Why it matters: OpenSTL offers a modular and extensible framework that excels in user-friendliness, organization, and comprehensiveness, making it a valuable resource for researchers and developers in the field of spatio-temporal predictive learning.

### Overview: Ирина - русский голосовой ассистент, поддерживающий плагины, предназначен для работы в оффлайн-режиме. Поддерживает общение с ChatGPT, GPT-4, Claude 3 и получение справочной информации. ### Key features: - **Voice Assistant**: Ирина - русский голосовой ассистент, способный выполнять различные задачи и команды. - **Pluggable Skills**: Ирина поддерживает плагины, что позволяет расширять функциональность через дополнительные модули. - **Offline Support**: Ирина работает в оффлайн-режиме, обеспечивая доступность без интернета. - **ChatGPT Integration**: Ирина поддерживает общение с ChatGPT, GPT-4, Claude 3, предоставляя широкий диапазон возможностей. - **Internet Information**: Ирина способна получать справочную информацию из интернета, используя специальные модули Perplexity Online. ### Tech stack: Python 3.5+ ### Why it matters: Ирина представляет собой мощный русский голосовой ассистент, который обеспечивает высокую точность и удобство в работе в оффлайн-режиме. Его плагинная модель позволяет расширять возможности через дополнительные модули, что делает его универсальным помощником для различных

Overview: Lhotse is a Python library designed to simplify the handling of multimodal data in machine learning projects, including speech, audio, video, image, and text. It is a part of the Kaldi speech processing library and aims to make data preparation flexible and accessible to a broader community. Key features: - Support for dataset blending and efficient on-the-fly bucketing. - Data randomization for distributed multi-node training. - Standard data preparation recipes for commonly used corpora. - Flexible data preparation for model training with audio/video cuts. - Support for efficient sequential I/O data formats such as Lhotse Shar. Tech stack: Python. Why it matters: Lhotse provides a Python-centric design, making it easier to integrate with other Python projects and tools, and it offers state-of-the-art dataloading algorithms, including dataset blending and efficient on-the-fly bucketing, to handle complex multimodal data pipelines.

### Overview: Data-centric AI is an emerging field that focuses on improving AI systems by enhancing data quality and quantity. It emphasizes the systematic engineering of data rather than solely focusing on model advancements, addressing potential quality issues and flaws in data such as missing values, incorrect labels, and anomalies. ### Key Features: - **Data Engineering**: Focuses on the systematic engineering of data to improve AI systems. - **Model-Data Integration**: Integrates models with data to enhance AI performance. - **Quality Assurance**: Ensures data quality and quantity are optimized for better AI outcomes. ### Tech Stack: - **Python**: Used for data preprocessing, model training, and analysis. - **TensorFlow**: Framework for building and training neural networks. - **PyTorch**: Framework for building and training neural networks. - **SQL**: For database interactions and data management. - **Git**: For version control and collaboration. ### Why It Matters: Data-centric AI addresses the shortcomings of traditional AI models by focusing on the quality and quantity of the data, leading to more robust and reliable AI systems. It is crucial for industries looking to leverage AI for improved decision-making and innovation. ### Repository: daochenzha/data-centric-AI - **Stars**: 1158 - **Description**: A curated, but incomplete, list of data-centric AI resources. ### Contact: - **GitHub**: daochenzha/data-centric-AI - **Email**: daochen.zha@rice.edu ### Contributing: - **News**: Open-sourced [Large Time Series Model (LTSM)](

Overview: Glue Factory is a comprehensive library designed for training and evaluating deep neural networks to extract and match local visual features. It supports the training of state-of-the-art models for point and line matching, including LightGlue and GlueStick, and allows users to evaluate feature extractors or matchers on standard benchmarks like HPatches or MegaDepth-1500. Key features: - Training of state-of-the-art models for point and line matching - Ability to train models on multiple datasets using local features or lines - Evaluation of feature extractors or matchers on standard benchmarks Tech stack: Python with PyTorch Why it matters: Glue Factory simplifies the process of training and evaluating deep neural network models for feature detection and matching, making it accessible to developers and researchers in computer vision and related fields.

### Overview: This repository is a curated list of Blockchain projects that leverage AI and Machine Learning, exploring their applications in various fields such as finance, medicine, supply chains, and autonomous agents. ### Key features: - **Blockchain for AI Algorithms**: Projects that use blockchain to store and distribute AI algorithms. - **Blockchain for Data**: Solutions that utilize blockchain for secure data management and sharing. - **Blockchain for Computation**: Technologies that enable decentralized computation and AI models. - **Blockchain for AI in Finance**: Projects that integrate blockchain for financial transactions and AI-driven decision-making. - **Blockchain for AI in Medicine**: Innovations that leverage blockchain for secure and transparent medical data sharing. - **Blockchain for AI in Supply Chains**: Solutions that use blockchain for traceability and efficiency in supply chain management. - **Blockchain for Autonomous Agents**: Projects that develop AI-driven autonomous agents using blockchain for secure communication and decision-making. ### Tech stack: Node.js, Solidity, Python, JavaScript, and various blockchain platforms. ### Why it matters: This repository aims to provide a comprehensive overview of cutting-edge projects that combine blockchain technology with AI and Machine Learning, offering a fresh perspective on how these technologies can revolutionize various industries.

Overview: The vibecode-pro-max-kit is a powerful, self-improving context memory system designed for developers, product owners, CEOs, and real builders. It eliminates context rot, ships features efficiently, and ensures projects run smoothly without human intervention. Key features: - One-command install via `curl` for seamless integration into any project. - Works across various tech stacks and AI coding agents, including Claude Code, Codex, Cursor, Windsurf, and Copilot. - 7 gated phases (Research → Spec → Innovate → Plan → Validate → Execute → Update-Process) with self-healing check loops to prevent jumping to code. - Autopilot mode (quick / fast / full) for hands-free execution at any phase. - Run-until-done token (`/goal`) for continuous execution without stopping. - Reusable find-gaps → fix → repeat loop (`vc-autoresearch`) for identifying and fixing gaps. - Test-before-you-build checks (`feasibility probes`) to prevent bad designs. - Smart strategy picker to choose the cheapest and most efficient agent for each phase. - Mechanical correctness checks (`36 validators`) to ensure the kit's structure is correct. - Phase programs that adapt on the fly, splitting large projects into independent phases. Tech stack: JavaScript. Why it matters: This kit transforms the way developers approach projects by eliminating context rot, ensuring smooth execution, and automating the entire development process without human intervention, making it ideal for teams looking to streamline workflows and reduce errors.