SeeAct is designed to empower vision-language agents with a two-stage pipeline: a planning module powered by large language models generates subgoals based on observed scenes, and an execution module translates subgoals into environment-specific actions. A perception backbone extracts object and scene features from images or simulations. The modular architecture allows easy replacement of planners or perception networks and supports evaluation on AI2-THOR, Habitat, and custom environments. SeeAct accelerates research on interactive embodied AI by providing end-to-end task decomposition, grounding, and execution.

Timetk provides a comprehensive suite of tools tailored for handling time series data. With its user-friendly interface, it simplifies tasks such as data visualization, feature engineering, and forecasting. Users can easily manipulate time-based indexes, making it particularly useful for data scientists and analysts engaged in predictive modeling. The package extends standard functionalities available in R, allowing for more seamless integration and functionality across various datasets. By offering these robust features, Timetk empowers users to extract insights and make informed predictions from complex time series data.

The Berkeley Pacman Projects repository offers a modular Python codebase where users build and test AI agents in a Pacman maze. It guides learners through uninformed and informed search (DFS, BFS, A*), adversarial multi-agent search (minimax, alpha-beta pruning), and reinforcement learning (Q-learning with feature extraction). Integrated graphical interfaces visualize agent behavior in real time, while built-in test cases and an autograder verify correctness. By iterating on algorithm implementations, users gain practical experience in state space exploration, heuristic design, adversarial reasoning, and reward-based learning within a unified game framework.