Coding & Development

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Shopless Business Solutions is a digital agency with over 5 years of experience, specializing in a comprehensive range of digital services. These include web design, mobile development, branding services, social media marketing, and management. They focus on building bright brands, unique visual systems, and digital experiences, offering future-ready solutions from branding and customer experiences to e-commerce and emerging technologies. Shopless also provides AI-integrated products such as e-commerce platforms, e-learning platforms, ERP systems, healthcare systems, and virtual tour builders, designed to unlock efficiency, innovation, and growth. Their services aim to optimize workflows, enhance efficiency, and foster collaboration for businesses.

awesome-programming-books is a meticulously curated list of programming books, offering a wide array of topics essential for both aspiring and experienced developers. This resource encompasses fundamental areas such as Algorithms and Data Structures, Artificial Intelligence, Software Architecture, and Human–Computer Interaction. It also delves into specialized fields like Operating Systems, Database Systems, IT Security, Concurrency, Interpreters and Compilers, High-Performance Computing, Distributed Systems, Game Development, and Mathematical Optimization. Each category provides a selection of highly-regarded books, complete with ISBNs, making it an invaluable guide for students, educators, and professionals looking to deepen their knowledge or explore new domains within computer science and software engineering.

Bambot is an open-source project designed to make AI robotics accessible and easy to use. It provides a platform for individuals to experiment with and develop AI-powered robotic systems using low-cost components. The project aims to lower the barrier to entry for AI robotics, allowing users to build and interact with their own AI robots. It includes resources and code to facilitate the creation and control of these robots, making it an ideal tool for learning and prototyping in the field of AI and robotics.

awesome-6d-object is a valuable open-source repository dedicated to collecting and organizing significant works in the field of 6 DoF (Degrees of Freedom) object pose estimation. This resource is particularly useful for researchers and developers in computer vision and deep learning, offering a curated list of papers, projects, and other materials. It covers various aspects of object pose estimation, including methods for 3D object reconstruction from a single view and techniques for 3D hand-object pose estimation. The repository aims to provide a centralized hub for staying updated on advancements and finding relevant information in this specialized domain.

AI App Factory is a unique tool hosted on Hugging Face Spaces that allows users to generate web applications simply by describing their concept in a text box. It automates the initial stages of app creation, providing a foundation for development. Users need to provide a Hugging Face token for authentication, and the system will generate a web app under their name. While it offers a significant head start, the current description suggests that human intervention is often needed to complete and refine the generated applications. This tool is particularly useful for quickly prototyping ideas or generating boilerplate code for web projects.

AxonWave.store provides an intuitive online shopping store builder designed for small businesses, enabling them to establish and expand their e-commerce presence without requiring any coding knowledge. The platform focuses on ease of use, allowing users to quickly launch their digital storefronts. It offers tools for managing products, orders, and overall store operations, making it a comprehensive solution for online retail. While primarily based in the United Kingdom and Europe, AxonWave.store aims to support businesses in growing their e-commerce ventures efficiently. It positions itself as an alternative to platforms like Shopify, offering a straightforward path to online sales.

FLUX.2 Klein LoRA Studio is a Hugging Face Space that provides a demo collection of FLUX.2-Klein Model LoRAs. This tool enables users to upload one or two images, select a specific style from the available LoRAs (or a face-swap adapter), and then input a brief text prompt. The system processes these inputs to generate a new, edited image that adheres to the chosen style while preserving key elements from the original picture(s). It's designed for experimentation with image generation and style transfer using advanced AI models, offering a hands-on experience with LoRA technology.

basebox AI provides a secure AI stack designed for organizations handling critical data, offering deployment options for on-premises or private cloud environments. It ensures data sovereignty and control, making it suitable for regulated and classified workloads. The platform features ready-to-use AI apps, centralized governance for compliance, and the ability to build custom AI applications. Key differentiators include no server-side prompt logs, zero data retention for model training, and GDPR-compliant hosting in German/EU data centers for cloud deployments. It offers comprehensive protection for critical data with security as a core architectural principle, built-in controls for regulatory compliance, and monitoring of all system activities.

Opyrator is an open-source tool designed to transform Python functions into production-ready microservices rapidly. It automatically generates web APIs based on FastAPI and interactive web UIs using Streamlit, leveraging open standards like OpenAPI, JSON Schema, and Python type hints. This tool simplifies the productization and sharing of Python code, allowing users to deploy and access services via HTTP API or an interactive UI. Opyrator also supports exporting services into portable, shareable executable files or Docker images, making deployment and scaling for production usage seamless. It aims to cut out the complexities typically associated with deploying machine learning models and other Python-based applications.

Contextualized Topic Models (CTM) is a powerful Python package designed for advanced topic modeling. It integrates pre-trained language representations, such as BERT embeddings, with traditional topic models to produce highly coherent topics. The package offers two main models: CombinedTM, which merges contextual embeddings with bag-of-words for enhanced topic coherence, and ZeroShotTM, ideal for tasks with missing words in test data and cross-lingual topic modeling when trained with multilingual embeddings. CTM supports various languages through HuggingFace models and allows for the use of different embedding methods, ensuring adaptability to new advancements. It also includes 'Kitty,' a submodule for human-in-the-loop classification to quickly categorize documents and create named clusters. The tool is particularly effective when the bag-of-words size is restricted to around 2000 elements, and it provides a preprocessing pipeline to manage this. CTM uses SBERT for embedding creation, offering flexibility in choosing embedding models and handling multilingual data.

The 'deploying-machine-learning-models' repository offers comprehensive code and materials for an online course focused on the deployment of machine learning models. This open-source resource is designed to accompany the Udemy course "Deployment of Machine Learning Models," providing practical examples and guidance for students. It includes various sections covering research and development, production model packaging, model serving APIs, continuous integration, and deployment with containers. The repository is primarily written in Jupyter Notebook and Python, making it an invaluable tool for those looking to understand and implement machine learning model deployment strategies.

Introduction to Machine Learning with Python is a comprehensive open-source repository designed to accompany the book of the same name by Andreas Mueller and Sarah Guido. It provides all the notebooks and code examples used in the book, making it an invaluable resource for students and practitioners looking to learn machine learning with Python. The repository includes helper functions from the `mglearn` library for creating figures and datasets, and all necessary datasets are included, with the exception of `aclImdb`. Users can set up their environment using `conda` or `pip` to install required packages like `numpy`, `scipy`, `scikit-learn`, `matplotlib`, `pandas`, `pillow`, and `graphviz`. It also supports `nltk` and `spacy` for text processing chapters.

HyperLandmark is a free and open-source tool designed for real-time face landmark detection, primarily targeting mobile applications. It utilizes deep learning to accurately identify 106 facial landmark points, offering a detailed facial contour description. The tool is noted for its high accuracy, even in challenging lighting conditions, and its efficient, small model size (around 2MB for the tracking model), making it highly suitable for mobile integration. It also supports multi-face tracking and boasts fast processing speeds, with the Android version achieving 7ms per single face on a Qualcomm 820. The project provides both Android and Windows implementations, with the Android version based on deep learning and the Windows version on traditional SDM algorithms.

hyperparameter-optimization is an open-source project providing implementations of Bayesian hyperparameter optimization for machine learning algorithms. This tool allows users to explore different approaches to hyperparameter tuning, specifically focusing on gradient boosting machines. It includes Jupyter Notebooks demonstrating the application of Bayesian optimization with libraries like Hyperopt, and provides examples for plotting search results. The project is designed to help data scientists and machine learning engineers enhance the performance of their models by systematically finding optimal hyperparameters, making the optimization process more efficient and effective.

flutter_chat_box is an open-source Flutter application that enables users to chat with ChatGPT across various platforms. Developed using a Flutter scaffold, it supports macOS, Linux, Windows, Android, and iOS, providing broad accessibility. Key features include code coloring, the ability to copy code, and fast response times thanks to its use of a stream API. The app boasts a clean UI, mobile support, multi-language support, and robust global data management with flutter_bloc. It also offers theme switching, unified routing, global state management, multi-turn conversation prompt support, and a typewriter vibration effect. Additionally, it includes web search capabilities and one-click export for conversations, making it a comprehensive tool for interacting with ChatGPT.

MineRL is a Python package designed for sample-efficient reinforcement learning research, primarily within the Minecraft environment. It provides easy-to-use Gym environments and data access, making it suitable for training AI agents. The package has evolved through several versions, with v1.0 supporting OpenAI VPT models and the MineRL BASALT 2022 competition, featuring a new Minecraft version (1.12 -> 1.16.5), larger default resolution (64x64 -> 640x360), and a near-human action-space focused on GUI and mouse control. It requires Java JDK 8 for installation and can be integrated into projects much like any standard Gym environment for developing and testing AI models.

SeeleAgent is a multimodal AI creation agent powered by Seele AI's in-house foundation models (Seele01, eva01). It allows users to generate complete games, images, videos, 3D assets, and code from a single text prompt. Unlike other tools that only produce assets, SeeleAgent understands 3D space and gameplay mechanics, handling the full production pipeline from concept to playable build. It supports 2D and 3D games across various genres, with options to run in-browser or export to Unity 6 and Unreal Engine. The platform is designed for both non-technical creators and experienced developers, offering a built-in IDE for customization.

Whacka is an innovative mobile application development tool designed to empower users to build real, working apps without requiring any coding knowledge. By simply describing or speaking their needs, Whacka's AI-powered platform translates these inputs into functional applications. This tool streamlines the entire app development lifecycle, from initial concept and design to the final deployment, making it accessible for individuals, teams, or businesses. It aims to democratize app creation, allowing anyone to bring their app ideas to life quickly and efficiently, directly from their mobile device.

Machine-Learning-in-Action is an open-source GitHub repository offering practical code implementations for various machine learning algorithms, all based on the popular book "Machine Learning in Action." Developed in Python 3, this resource is designed to help users understand and apply machine learning concepts through hands-on examples. The repository includes code for algorithms such as K-Nearest Neighbors, Decision Trees, Naive Bayes, Logistic Regression, Support Vector Machines, AdaBoost, and different regression techniques. It also provides datasets to accompany the code, making it a comprehensive learning resource for students and developers looking to deepen their understanding of machine learning.

makeyourownneuralnetwork is an open-source code repository hosted on GitHub, designed to accompany the 'Make Your Own Neural Network' book. It offers practical examples and implementations of neural network concepts, making it an invaluable resource for individuals looking to learn and understand the fundamentals of neural networks through hands-on coding. The repository includes various Jupyter Notebooks covering topics such as MNIST dataset handling, neural network implementation, loading custom images, and backquerying. This resource is ideal for students and self-learners who want to dive deep into the mechanics of neural networks and build their own models from scratch.

Open-AutoGLM is an open-source framework designed to create intelligent phone agents capable of understanding and interacting with mobile device screens. Built upon the AutoGLM model, it leverages multimodal perception to interpret screen content and automate tasks through ADB (Android Debug Bridge) or HDC (HarmonyOS Debug Bridge). Users can issue natural language commands, such as "Open Meituan to search for hotpot restaurants," and the Phone Agent will parse the intent, understand the current interface, plan, and execute the necessary actions. The system includes sensitive operation confirmation mechanisms and supports manual intervention for login or verification code scenarios. It also offers remote ADB/HDC debugging capabilities via WiFi for flexible control and development. The framework supports both Android and HarmonyOS devices, with specific models optimized for Chinese and multilingual applications.

nn-from-scratch is an open-source project available on GitHub that provides a practical implementation of a neural network from scratch. This resource is designed for individuals looking to deepen their understanding of how neural networks function at a foundational level. The project includes Python code, an iPython notebook for interactive learning, and a related blog post that explains the concepts in detail. It covers the setup of a virtual environment and installation of necessary requirements, making it accessible for hands-on learning and experimentation with neural network architectures.

machine-learning-project-walkthrough is an open-source project hosted on GitHub, offering a comprehensive demonstration of a machine learning solution implemented in Python. It utilizes a real-world dataset to showcase how all the steps of a machine learning pipeline integrate to solve a specific problem. This project is designed to be a valuable resource for individuals looking to understand or teach machine learning concepts, providing a practical, end-to-end example from data processing to model deployment. It includes various files such as Jupyter Notebooks for different parts of the project, data definitions, and documentation.