Agentic App Template is a fully configured Next.js project that serves as a foundation for developing AI-driven agentic applications. It incorporates a modular folder structure, environment variable management, and example agent workflows leveraging OpenAI’s GPT models and vector databases like Pinecone. The template demonstrates key patterns such as sequential multi-step chains, conversational Q&A agents, and text generation endpoints. Developers can easily customize chain logic, integrate additional services, and deploy to platforms like Vercel or Netlify. With TypeScript support and built-in error handling, the scaffold reduces initial setup time and provides clear documentation for further extension.

mini-AlphaStar demystifies the complex AlphaStar architecture by offering an accessible, open-source PyTorch framework for StarCraft II AI development. It features spatial feature encoders for screen and minimap inputs, non-spatial feature processing, LSTM memory modules, and separate policy and value networks for action selection and state evaluation. Using imitation learning to bootstrap and reinforcement learning with self-play for fine-tuning, it supports environment wrappers compatible with StarCraft II via pysc2, logging through TensorBoard, and configurable hyperparameters. Researchers and students can generate datasets from human gameplay, train models on custom scenarios, evaluate agent performance, and visualize learning curves. The modular codebase enables easy experimentation with network variants, training schedules, and multi-agent setups. Designed for education and prototyping rather than production deployment.