Coding & Development

Stottler Henke Associates, Inc. has specialized in artificial intelligence since 1988, providing advanced software systems to tackle problems that traditional approaches cannot solve. Their expertise spans several key areas, including Education & Training, Planning & Scheduling, Decision Support, Knowledge Management & Discovery, and Autonomous Systems. The company offers a comprehensive suite of services, from technology consulting and feasibility studies to rapid prototyping, software development, implementation, and technology transfer, ensuring successful deployment of effective, long-term AI solutions. Notable products include Aurora for dynamic scheduling, InfoTracker for knowledge management, and DataMontage for data visualization. Stottler Henke is an employee-owned company, serving both corporations and government agencies like Boeing and NASA.

neurojs is an open-source JavaScript framework designed for deep learning and reinforcement learning applications within the browser environment. While it mainly focuses on reinforcement learning, it is versatile enough for various neural network-based tasks. The library includes practical examples and demos, such as a 2D self-driving car visualization, to showcase its capabilities. It supports advanced features like uniform and prioritized replay buffers, advantage-learning, and models such as deep-q-networks and actor-critic (via deep-deterministic-policy-gradients). neurojs also allows for binary import and export of network configurations, including weights, and is built for high performance. However, development on neurojs is no longer actively maintained, with the recommendation to use more general frameworks like TensorFlow-JS.

Norse is an open-source deep learning library designed for spiking neural networks (SNNs) within the PyTorch framework. It aims to leverage the advantages of bio-inspired neural components, which are inherently sparse and event-driven, offering a fundamental difference from traditional artificial neural networks. By expanding PyTorch with these specialized primitives, Norse provides a modern and robust infrastructure for researchers and developers. The library includes various neuron models, synapse dynamics, encoding and decoding algorithms, and dataset integrations, making it a comprehensive tool for modeling scalable experiments. Norse is actively used in research and is optimized for performance, scaling efficiently from single laptops to HPC clusters.

Siemba offers a full-stack Continuous Threat Exposure Management (CTEM) platform designed to continuously identify, prioritize, and remediate threats across an organization's attack surface. It leverages an AI-powered assistant to facilitate faster decision-making, smarter prioritization of risks, and automated threat response. Key features include External Attack Surface Management (EASM), AI-driven Vulnerability Assessments (GenVA), and AI-driven Dynamic Application Security Testing (GenPT) with PenTest as a Service (PTaaS). The platform aims to enhance developer efficiency, align security teams, reduce external attack surfaces, and simplify compliance with one-click reporting. Siemba is recognized by Gartner for its CTEM capabilities.

O-CNN is an open-source project from Microsoft that provides an implementation of Octree-based Convolutional Neural Networks (O-CNN) for various 3D shape analysis tasks. This framework is designed for researchers and developers working with 3D data, offering solutions for shape classification, retrieval, segmentation, autoencoding, and completion. It also supports unsupervised pretraining and Image2Shape functionalities. The repository includes code for different deep learning frameworks like PyTorch, TensorFlow, and Caffe, with recent updates focusing on PyTorch-based O-CNNs, including UNet and ResNet architectures. O-CNN has demonstrated strong performance on benchmarks like ScanNet and ModelNet40, often surpassing other state-of-the-art approaches.

CICube is an AI DevOps agent designed to optimize Continuous Integration (CI) processes, specifically for GitHub Actions. It offers deep analysis and clear insights to help teams reduce CI costs and improve efficiency. The platform features an AI DevOps Agent that detects anomalies, analyzes root causes, and suggests intelligent fixes for pipeline failures, saving significant debugging time. Users can converse with their CI data using an LLM to ask questions like "Why is my build so slow?" and receive immediate answers. CICube also provides AI-driven CI insights and alerting, helping to identify bottlenecks, generate conclusions, and reduce Mean Time To Resolution (MTTR). It aims to make CI pipelines transparent, addressing the common problem of CI being a "black box" by providing real-time intelligence and cost optimization features.

nlp_tasks is an open-source repository offering a curated collection of natural language processing tasks and selected references. It aims to provide a clear map of the NLP field, covering a wide array of tasks from Anaphora Resolution to Singing Voice Synthesis. The repository is continuously updated and encourages community collaboration through pull requests. It serves as an excellent starting point for researchers and practitioners looking to delve into specific NLP tasks, with references biased towards recent deep learning accomplishments. Each task entry includes relevant papers, projects, challenges, and datasets, making it a comprehensive resource for academic and practical exploration.

Truelink.ai is an AI and VPN application designed to offer comprehensive real-time protection against various online threats. It integrates advanced artificial intelligence capabilities with robust VPN functionality, creating a secure browsing environment for its users. The tool aims to safeguard individuals and businesses by providing a dual-layered defense system. While specific features beyond AI and VPN integration are not detailed, the core offering focuses on enhancing online security through intelligent threat detection and encrypted internet access. This combination positions Truelink.ai as a solution for those seeking a proactive approach to digital safety.

onnx-go offers Go developers the capability to integrate pre-trained neural networks into their applications. It acts as an interface to the Open Neural Network Exchange (ONNX) format, enabling the decoding of ONNX binary models into a computation backend. This tool is particularly useful for adding machine learning capabilities to Go code without requiring specialized data science skills or being tied to a specific framework. While the implementation of the ONNX spec is partial for import and non-existent for export, it supports various backends like Gorgonia. The project is actively maintained by Orama and provides utilities to run models from the ONNX model zoo, making it a valuable resource for Go-based AI development.

PromptHub is an open-source, local-first AI prompt and skill management tool designed to streamline prompt engineering workflows. It offers robust features for creating, editing, and organizing prompts with support for folders, tags, and automatic version control, including the ability to view, compare, and roll back changes. Users can leverage template variables for dynamic prompt generation and quickly access frequently used prompts. A standout feature is its skill management system, which includes a skill store with pre-built skills and one-click installation to over 15 mainstream AI coding tools like Claude Code and Cursor. PromptHub also provides AI testing capabilities for comparing various models and supports local data storage with WebDAV cloud synchronization, ensuring privacy and data security. It is available as a desktop application for macOS, Windows, and Linux, and also offers a self-hosted web version.

This GitHub repository, Tanu-N-Prabhu/Python, serves as a comprehensive Open Source resource for learning Python and Machine Learning. It caters to individuals ranging from novices to seasoned developers, offering a structured path to mastery. The repository includes materials on basic Python concepts, built-in functions, popular libraries like NumPy and Pandas, and various APIs such as Google Translate and Wikipedia. It also delves into Machine Learning foundations, supervised and unsupervised learning, neural networks, and MLOps. Additionally, it provides extensive Data Science materials, including EDA techniques and real-world data analysis questions with Python answers. The resource emphasizes practical application through hands-on exercises and real-world examples, making it ideal for those looking to enhance their coding journey.

The python-machine-learning-book-2nd-edition repository serves as the official code and information resource for the second edition of the "Python Machine Learning" book. It provides comprehensive code examples, including Jupyter notebooks and Python scripts, for various machine learning algorithms and applications. Users can explore topics such as classification, dimensionality reduction, model evaluation, ensemble learning, sentiment analysis, regression, clustering, and deep learning with TensorFlow. The resource is ideal for students and professionals looking to implement machine learning concepts using Python, offering a practical, hands-on approach to learning.

python-ml-course is an open-source educational resource designed to introduce individuals to Machine Learning using Python. The comprehensive course covers a wide range of topics, from basic Python installation and data preprocessing to advanced concepts like Deep Learning and Reinforcement Learning. It includes practical exercises, real-world datasets, and all source code on GitHub, making it suitable for hands-on learning. The course is taught by Juan Gabriel Gomila, a professional in Data Science, and aims to make complex mathematical theories and algorithms accessible. It caters to students, programmers, and data analysts looking to specialize or enhance their skills in the lucrative field of Data Science.

RemoteCLIP is the official repository for the paper "RemoteCLIP: A Vision Language Foundation Model for Remote Sensing." This tool addresses limitations in existing remote sensing models by learning robust visual features with rich semantics and aligned text embeddings, crucial for retrieval and zero-shot applications. It leverages data scaling and conversion of heterogeneous annotations, incorporating UAV imagery to create a significantly larger pre-training dataset. RemoteCLIP supports diverse downstream tasks including zero-shot image classification, linear probing, k-NN classification, few-shot classification, image-text retrieval, and object counting, consistently outperforming baseline foundation models across various scales and datasets.

Physics-Informed-Neural-Networks (PINNs) is a research repository dedicated to investigating and implementing PINNs for solving Partial Differential Equations (PDEs). It integrates the physics of the PDE and boundary conditions directly into the neural network's loss function, utilizing the Mean-Squared Error of the PDE and boundary residual measured on 'collocation points'. The repository currently offers implementations for Burgers' and Helmholtz PDEs in both TensorFlow 2 and PyTorch. It also explores various aspects of PINNs, including the effectiveness of the L-BFGS optimizer for stiff PDEs, bottom-up learning mechanisms, and the impact of transfer learning on solution error, providing valuable insights for researchers and practitioners in scientific computing.

Practical-Deep-Learning-for-Coders-2.0 offers a comprehensive collection of notebooks designed for the "A walk with fastai2" Study Group and Lecture Series. This resource is ideal for individuals looking to delve into practical deep learning, covering key areas such as computer vision, tabular neural networks, and natural language processing. The course, which includes live-streamed lectures and project work, provides a structured learning path for undergraduates and others interested in the fastai framework. While the notebooks are now hosted on a new GitHub repository, this original repository serves as a valuable archive of the course material, offering insights into various deep learning applications and techniques.

Practical Machine Learning with Python offers a structured and comprehensive three-tiered approach to learning machine learning and deep learning. This resource, based on a book, is packed with over 500 pages of useful information, helping readers master essential skills to recognize and solve complex problems with a data-driven mindset. It uses real-world case studies and leverages the popular Python Machine Learning ecosystem, including frameworks like scikit-learn, pandas, statsmodels, spaCy, nltk, gensim, tensorflow, and keras. The content covers machine learning concepts, the Python ecosystem, standard pipelines, and real-world case studies across diverse domains like retail, finance, and computer vision, making it ideal for practitioners.

PointMamba is an open-source state space model (SSM) specifically designed for point cloud analysis, leveraging the success of Mamba from natural language processing. Unlike traditional Transformers, PointMamba employs a linear complexity algorithm, enabling global modeling while substantially reducing computational costs and GPU memory usage. This tool utilizes space-filling curves for efficient point tokenization and features a simple, non-hierarchical Mamba encoder as its backbone. Comprehensive evaluations demonstrate its superior performance across various datasets, making it a valuable resource for researchers and developers in 3D vision. PointMamba underscores the potential of SSMs in 3D vision-related tasks and provides a robust baseline for future research.

Porcupine is a highly-accurate and lightweight wake word engine developed by Picovoice, designed to enable always-listening voice-enabled applications. It utilizes deep neural networks trained in real-world environments, making it compact and computationally-efficient, ideal for IoT devices. The engine boasts broad cross-platform compatibility, supporting Arm Cortex-M, STM32, Arduino, Raspberry Pi, Android, iOS, Chrome, Safari, Firefox, Edge, Linux, macOS, and Windows. A key feature is its scalability, allowing detection of multiple always-listening voice commands without increasing runtime footprint. Developers can also train custom wake word models using the Picovoice Console, offering self-service customization. Porcupine is suitable for detecting static voice commands, providing a robust solution for hands-free control and voice interface design.

rag-tutorial-v2 is an open-source tutorial designed to guide users through the process of building Retrieval Augmented Generation (RAG) systems. This improved version (v2) focuses on practical implementation, incorporating local LLMs for enhanced privacy and control, and demonstrating effective database update strategies. The tutorial also emphasizes robust testing methodologies to ensure the reliability and performance of the RAG system. It's a valuable resource for developers and researchers looking to understand and implement advanced RAG techniques, offering a hands-on approach to integrating LLMs with external knowledge bases.

Anthropic's Interactive Prompt Engineering Tutorial offers a comprehensive, step-by-step guide to mastering prompt engineering for Claude. This course is designed to help users understand the basic structure of effective prompts, recognize common failure modes, and apply '80/20' techniques to address them. It delves into Claude's strengths and weaknesses, enabling users to build robust prompts from scratch for various use cases. The tutorial is structured into 9 chapters with accompanying exercises, allowing for hands-on practice. Each lesson includes an "Example Playground" for experimentation and an answer key for self-assessment. While it uses Claude 3 Haiku, it acknowledges the existence of more intelligent models like Claude 3 Sonnet and Opus. A Google Sheets version with Anthropic's Claude for Sheets extension is also available for a more user-friendly experience.

PromptBench is a PyTorch-based Python package designed as a unified evaluation framework for large language models (LLMs). It offers user-friendly APIs for researchers and developers to conduct comprehensive evaluations of LLMs, including quick performance assessments, prompt engineering method testing (like Chain-of-Thought, Emotion Prompt, and Expert Prompting), and adversarial prompt robustness analysis. The framework integrates dynamic evaluation techniques such as DyVal to mitigate test data contamination and efficient multi-prompt evaluation with PromptEval. It supports a wide range of language and multi-modal datasets and models, both open-source and proprietary, making it a versatile tool for understanding and benchmarking LLM capabilities.

stable-diffusion-webui-images-browser is an extension designed for stable-diffusion-webui, providing comprehensive image browsing and management capabilities. Users can easily view previously generated pictures, inspect their generation information, and send prompts directly to txt2img or img2img for further use. The tool also allows for collecting favorite images into a dedicated folder and deleting unwanted ones. Furthermore, it offers the flexibility to browse images located in any folder on the user's computer, making it a versatile solution for organizing and interacting with Stable Diffusion outputs. Installation is straightforward via a git clone command within the stable-diffusion-webui extensions directory.

StableDiffusionReconstruction is a research-oriented tool designed for reconstructing visual experiences directly from human brain activity. Utilizing Stable Diffusion models, it allows for the generation of high-resolution images based on neural data. The project, stemming from research by Takagi and Nishimoto presented at CVPR 2023, also incorporates advanced decoding techniques. These include methods for decoding text prompts from brain activity, integrating GANs for improved image quality, and incorporating decoded depth information, significantly enhancing reconstruction accuracy. This repository provides the necessary code and instructions for reproducing these methods, making it a valuable resource for researchers in neuroscience and AI.