The Adaptive Learning Framework (ALF) is an innovative, open-source tool designed to enhance STEM education through a modular, bilingual, and JSON-driven approach. It operates on a simple adaptive learning loop—Diagnosis, Drill, Integration—to identify misconceptions, provide targeted practice, and confirm mastery. Educators can easily extend ALF by adding new topics through standalone JSON files, which define questions, correct answers, common errors, and drills. The framework's core is a Python-based adaptive learner that tracks progress through distinct phases, while a minimalistic Streamlit UI supports both English and Dutch. ALF is built to be transparent and accessible, encouraging collaboration and contribution from educators, developers, and researchers, with the aim of making adaptive learning more open and free from corporate constraints. This matters because it democratizes educational tools, allowing for broader access and innovation in learning methodologies.
![[D] Clean, self-contained PyTorch re-implementations of 50+ ML papers (GANs, diffusion, meta-learning, 3D)](https://www.tweakedgeek.com/wp-content/uploads/2026/01/featured-article-8490-300x171.png)
A repository offers clean and self-contained PyTorch implementations of over 50 machine learning papers, covering areas like GANs, VAEs, diffusion models, meta-learning, and 3D reconstruction. These implementations are designed to remain true to the original methods while minimizing unnecessary code, making them easy to run and inspect. The goal is to reproduce key results where feasible, providing a valuable resource for understanding and experimenting with advanced machine learning concepts. This matters because it facilitates learning and experimentation in machine learning by providing accessible and concise code examples.