AI Agents & Automation

InterpretML is an open-source Python package designed to bring clarity to machine learning models. It provides a unified framework for state-of-the-art interpretability techniques, enabling users to both train inherently interpretable 'glassbox' models like the Explainable Boosting Machine (EBM) and explain complex 'blackbox' systems using methods like SHAP and LIME. This tool is crucial for tasks such as model debugging, feature engineering, detecting fairness issues, and ensuring regulatory compliance in high-risk applications. It supports various data types natively and offers functionalities for global model understanding as well as explanations for individual predictions, making it a comprehensive solution for data scientists and machine learning engineers.

Facetorch App is a Python library designed for comprehensive facial analysis, available as a Hugging Face Space. It allows users to upload photos or use a webcam to detect faces, generate 3D facial landmarks, and analyze various facial attributes. The app provides detailed reports on detected facial expressions, action units, and emotion scores. It also includes capabilities for extracting facial embeddings and performing face recognition. This tool is particularly useful for developers and researchers in computer vision who require advanced facial analysis functionalities for their projects.

Dynamax is a Python package designed for probabilistic state space modeling, leveraging the JAX library for efficient computation. It offers robust capabilities for both inference (state estimation) and learning (parameter estimation) across a range of state space models. These include Hidden Markov Models (HMMs), Linear Gaussian State Space Models (Linear Dynamical Systems), Nonlinear Gaussian State Space Models, and Generalized Gaussian State Space Models with non-Gaussian emission models. The library provides both low-level, functionally pure inference algorithms and a user-friendly, object-oriented interface through model classes. It is compatible with other JAX ecosystem libraries like Optax for stochastic gradient descent and Blackjax for Hamiltonian Monte Carlo or sequential Monte Carlo.

Inverse-Reinforcement-Learning is an open-source project providing implementations of various inverse reinforcement learning (IRL) algorithms. Developed as part of COMP3710, it was supervised by Dr Mayank Daswani and Dr Marcus Hutter. The project includes linear programming IRL (Ng & Russell, 2000), maximum entropy IRL (Ziebart et al., 2008), and deep maximum entropy IRL (Wulfmeier et al., 2015). Additionally, it features implementations of MDP domains like Gridworld (Sutton, 1998) and Objectworld (Levine et al., 2011). The repository also provides a final report detailing the implemented algorithms and offers module documentation for functions and classes.

testRigor is an AI-based test automation tool designed to simplify software testing by allowing users to build and maintain tests using plain English. It eliminates the need for complex coding, such as Selenium or Cucumber/Gherkin, by translating high-level instructions into specific steps. The platform supports comprehensive testing across web, mobile (iOS and Android), desktop, API, email, SMS, phone calls, 2FA, and mainframe applications. testRigor boasts ultra-stable tests not dependent on XPath, leading to significantly less maintenance compared to traditional methods. It integrates with popular tools like Gitlab, Github Actions, Jenkins, Jira, and Azure DevOps, and adheres to high security standards including ISO/IEC 27001:2022, SOC 2, HIPAA, and GDPR.

SiamTrackers is a comprehensive collection of PyTorch implementations for deep learning-based visual object tracking algorithms. It encompasses a wide range of models from 2020-2022, including SiamFC, SiamRPN, DaSiamRPN, UpdateNet, SiamDW, SiamRPN++, SiamMask, SiamFC++, SiamCAR, SiamBAN, Ocean, LightTrack, TrTr, and NanoTrack. A key highlight is NanoTrack, designed for lightweight and high-speed performance, suitable for deployment on embedded or mobile devices, capable of running at over 200FPS on Apple M1 CPU. The repository provides PyTorch code for training with lower GPU memory cost and includes Android and MacOS demos based on the ncnn inference framework. It also offers access to various datasets and toolkits for testing and training.

Worlder TEAM Pte. Ltd. specializes in providing AI-driven solutions to help small to medium-sized businesses (SMEs) digitalize their operations and achieve global growth. The company offers a suite of cutting-edge tools, including Worlder AI Solutions and Wolo Tools, designed to empower SMEs with modern AI capabilities. Their services also include cloud solutions and consultation to facilitate digital transformation. Worlder TEAM aims to bridge the gap for businesses looking to leverage AI for operational efficiency and market expansion, focusing on practical applications of AI to drive business success.

WEBSENSA is an AI services company specializing in rapid AI implementation, promising production-ready solutions within 30 days through a proven 3-step process: diagnosis, tailored offer, and go-live. They offer a suite of AI products including Voicebot AI for automating customer service, Knowledge Chat for transforming company documents into interactive knowledge bases, and Enterprise AI for growing organizations needing access to advanced AI tools and tailored models. WEBSENSA also provides AI Workshops for strategic skill development and proNote Research for analyzing research recordings. They serve various industries such as Manufacturing & Utilities, Banks & Financial Institutions, and Law & Legal Services, focusing on delivering immediate value and measurable business results.

artyom.js is a robust and constantly updated open-source JavaScript library that wraps the webkitSpeechRecognition and speechSynthesis APIs. It enables developers to integrate voice control, voice commands, speech recognition, and speech synthesis into their web applications. Key features include quick recognition of voice commands, easy addition of dynamic commands, smart commands with wildcards and regular expressions, and the ability to convert voice to text. The library supports synthesizing large blocks of text and works on both desktop browsers and mobile devices. It offers support for multiple languages and provides options for continuous listening, soundex algorithm for accuracy, and a remote command processor. Developers can create custom voice assistants similar to Siri, Google Now, or Cortana within their websites.

Counsel Stack offers an enterprise-grade legal citation verification API designed for legal professionals. It helps detect and correct over 40 categories of legal errors, including hallucinated citations, technical inaccuracies, fabricated holdings, and overturned cases. The platform is built to withstand legal scrutiny, ensuring attorneys can certify legal arguments under Rule 11 with confidence. Counsel Stack also provides a Research API to answer complex legal questions, outperforming generalist AI and lawyer baselines in independent benchmarks. It includes comprehensive federal law coverage, over 99% of precedential case law, and an expanding collection of state legal sources, accessible via API or local deployment. This tool is efficient and scalable, processing over 100 cite checks per minute at an average cost of $0.0085 per check.

AppointEze is a cloud-based appointment scheduling software designed to streamline booking processes for businesses of all sizes. It enables users to create online appointment schedulers, manage client appointments, staff availability, and calendars efficiently. The platform offers features like real-time availability display, automated scheduling, and reminders to reduce administrative overhead. AppointEze is particularly beneficial for service-based industries, allowing them to attract new clients and grow their business by simplifying the booking experience. It integrates with existing software tools and provides options for co-hosting calls with colleagues, ensuring a cohesive team scheduling environment. The system also supports screening clients before booking and reconfirming meetings, enhancing operational control.

temperature_scaling is an open-source Python module designed to calibrate neural networks by adjusting their confidence scores. Originally created as a demonstration for PyTorch 0.3, it implements temperature scaling, a post-processing technique that divides logits by a learned scalar parameter to minimize negative log-likelihood on a validation set. This helps address the common issue of neural networks outputting overconfident probabilities, ensuring that confidence scores better match true correctness likelihood. While the repository is unmaintained, it offers a clear example of how to integrate temperature scaling into a project for improved model calibration.

pysc2-examples offers a collection of Deep Reinforcement Learning examples specifically designed for StarCraft II. Built upon Deepmind's pysc2, OpenAI's baselines, and Blizzard's s2client-proto, it provides a robust framework for developers and researchers. The project leverages TensorFlow 1.3 and includes examples for tasks like 'CollectMineralShards' using Deep Q Networks and A2C algorithms. Users can quickly set up the environment, install necessary libraries like pysc2 and baselines, download StarCraft II maps, and then train and enjoy their AI agents. It supports various parameters for training, including algorithm choice (deepq, a2c), total timesteps, exploration fraction, and options for prioritized replay or dueling networks.

Maiven Energy is a platform designed to accelerate decarbonization and reduce energy costs for residents, utilities, and contractors. For utilities and energy program implementers, Maiven offers an all-in-one digital solution to streamline energy reduction, VPP, demand-side management, decarbonization, and electrification efforts, cutting costs and speeding up results. Homeowners and renters benefit from simplified access to energy technologies, rebates, and incentives, making clean energy upgrades more accessible and affordable. The platform helps residents lower energy costs and reduce their environmental impact. For trade professionals and aggregators, Maiven boosts electrification and weatherization revenue, expands reach, and cuts labor costs, while aggregators benefit from increased capacity and streamlined operations. Maiven aims to unify digital solutions for complex energy programs and enable mass electrification by making clean energy adoption easy.

MyIP is a comprehensive, open-source IP Toolbox designed for detailed network analysis and diagnostics. It enables users to easily view their local and public IP addresses, perform IP geolocation lookups, and conduct essential network tests such as DNS leak detection and WebRTC connection examination. The tool also includes speed tests, ping tests, and MTR tests to assess network performance and connectivity. Additionally, MyIP offers website availability checks, WHOIS searches for domain and IP information, MAC lookups, and browser fingerprint analysis. It supports multiple languages, dark mode, a minimalist mobile-optimized mode, and PWA installation, making it a versatile solution for network professionals and users concerned with their online privacy and connectivity.

HybrIK is an open-source project offering a hybrid analytical-neural inverse kinematics (IK) solution for 3D human pose and shape estimation. It provides the official code for the research papers "HybrIK: A Hybrid Analytical-Neural Inverse Kinematics Solution for 3D Human Pose and Shape Estimation" (CVPR 2021) and "HybrIK-X: Hybrid Analytical-Neural Inverse Kinematics for Whole-body Mesh Recovery" (TPAMI 2025). The tool allows users to convert accurate 3D keypoints into parametric body meshes. Key features include demo code for visualizing HybrIK on videos and images, support for both SMPL and SMPL-X models, and a Blender add-on for importing results as FBX files. It also supports multi-person demos with pose-tracking and provides pretrained models with various backbones.

Deep-tempest extends the original gr-tempest project, also known as Van Eck Phreaking, by integrating deep learning techniques to significantly enhance the quality of spied images. This tool focuses on recovering visual information from unintended electromagnetic emanations, particularly those originating from HDMI cables. By applying advanced deep learning architectures like DRUNet, deep-tempest can reduce the Character Error Rate from 90% in the unmodified gr-tempest to less than 30%, making the recovered text much more legible. The project includes open-sourced code and a comprehensive dataset of synthetic and real captured images for research, training, and evaluation, supporting both Python 3.10 and 3.12 environments with Conda or Pyenv + venv setups.

Own Tutor is an AI-powered educational platform designed to offer personalized learning experiences to students. The tool enables schools to develop custom AI tutors tailored to the specific needs of individual students, fostering a learning environment where students can progress at their own pace. By providing personalized guidance and support, Own Tutor aims to enhance understanding and improve academic success. The platform also supports the creation of virtual schools and offers features for managing lessons, making it a comprehensive solution for educational institutions looking to integrate AI into their teaching methodologies.

tf-gnn-samples is a GitHub repository offering TensorFlow implementations of various Graph Neural Network (GNN) architectures. It serves as the code release for an article introducing GNNs with feature-wise linear modulation (GNN-FiLM). The repository includes implementations for Gated Graph Neural Networks (GGNN), Relational Graph Convolutional Networks (RGCN), Relational Graph Attention Networks (RGAT), Relational Graph Isomorphism Networks (RGIN), GNN-Edge-MLP, and Relational Graph Dynamic Convolution Networks (RGDCN). It provides scripts for training and evaluating models on tasks such as citation networks (Cora, Pubmed, Citeseer), protein-protein interaction (PPI), quantum chemistry prediction (QM9), and variable misuse detection (VarMisuse). The code allows users to reproduce experimental results presented in the accompanying research paper, making it a valuable resource for researchers and developers working with GNNs.

Applying_EANNs is a 2D Unity simulation designed to showcase how cars can learn to navigate various courses. The cars are controlled by a feedforward neural network, whose weights are optimized using a modified genetic algorithm. This project provides a practical demonstration of evolutionary artificial neural networks in a simulated environment. Users can tinker with simulation parameters in the Unity Editor or run the built executable with default settings. The neural network architecture includes an input layer, two hidden layers, and an output layer, with its training managed by a customizable genetic algorithm. The user interface displays real-time data for the best performing car, including neural network output, evaluation value, and a generation counter, along with a visual representation of the neural network's weights.

n8n-docs serves as the official documentation repository for n8n, a fair-code licensed automation tool. It offers comprehensive resources for both the free community edition and powerful enterprise options, guiding users on how to effectively connect various applications and build automated workflows. The documentation specifically highlights how to integrate and build AI functionality into these workflows, making it a valuable resource for developers and technical users looking to leverage n8n's capabilities. It includes detailed guides on setting up local previews, troubleshooting common issues, and contributing to the documentation itself, ensuring a smooth experience for both new and experienced users.

OccWorld is an open-source 3D world model specifically designed for autonomous driving applications, presented at ECCV 2024. This tool allows for the joint modeling of 3D scene evolutions and ego movements, crucial for developing advanced autonomous systems. It can forecast the movements of surrounding agents and future map elements like drivable areas, demonstrating an understanding of the scene beyond mere memorization. OccWorld integrates with various 3D occupancy models such as SelfOcc, TPVFormer, and SurroundOcc, offering a scalable solution for large-scale training and paving the way for interpretable end-to-end large driving models. The project provides code for visualization, training logs, and a pretrained model, making it a valuable resource for researchers and developers in the autonomous driving domain.

A-dapt brings Emotion AI into LegalTech, providing lawyers with human-centered tools for scalable, privacy-first witness preparation and emotionally intelligent litigation training. Its TestMyWitness platform uses Emotional AI to prepare confident and credible witnesses by focusing on people, not paperwork. Key features include viewer emotion analysis, real-time emotional feedback during witness preparation, dynamic emotion labels, and "move the dot" coaching to improve composure. The platform also offers a transcript and annotation workspace with auto-generated Q&A, emotion tags, and sharable notes for follow-up coaching. It flags risk signals like hostility or low confidence, supporting legal teams in enhancing witness credibility before court or interviews. The system is designed for privacy, reduced bias, and eco-friendliness.

PiML-Toolbox (Python Interpretable Machine Learning) is a comprehensive Python toolbox designed for the development and diagnostics of interpretable machine learning models. It offers both low-code interfaces and high-code APIs, supporting a growing list of inherently interpretable ML models such as GLM, GAM, Tree, FIGS, XGB1, XGB2, EBM, GAMI-Net, and ReLU-DNN. The toolbox facilitates various outcome testing, including accuracy, explainability (PFI, PDP, ALE, LIME, SHAP), fairness, weak spot identification, overfitting detection, reliability assessment, robustness, and resilience evaluation. PiML-Toolbox aims to empower model developers and validators with tools for transparent, interpretable, and robust machine learning, particularly in high-stakes regulatory settings.