May 11, 2026 · 8 min read KBAI: Knowledge-Based AI Key knowledge-based AI concepts from Georgia Tech OMSCS KBAI (CS 7637): knowledge representation, reasoning strategies, learning, planning, and the ARC-AGI project. Knowledge-Based AI OMSCS AI +2
Apr 18, 2026 · 7 min read A Few Months Into Team-Wide AI: What I'm Still Figuring Out A few months into team-wide AI use, here are the patterns I'm seeing — and the question that keeps coming back about what should stay consistent across the team. AI Claude Code Software Engineering
Feb 14, 2026 · 4 min read How AI Changed My Development Workflow After Switching Jobs Moving from a Copilot-only environment to a team that actively uses Claude Code — what changed and what I'm still figuring out Software Engineering AI
Feb 14, 2026 · 13 min read Understanding How Claude Code Works Under the Hood A walkthrough of Claude Code's execution lifecycle — from the moment you type a prompt to the final response Software Engineering AI Claude Code
Feb 10, 2026 · 5 min read Building a Windows Installer for Offline Field Deployment How I introduced a WiX-based Windows installer for deploying field software to air-gapped environments, and the unexpected challenges along the way. Software Engineering .NET Best Practices
Dec 19, 2025 · 4 min read SDP: Software Development Process Key software engineering concepts from Georgia Tech OMSCS SDP (CS 6300): lifecycle models, UML, testing, Android development, and team collaboration. Software Engineering OMSCS UML +4
May 19, 2025 · 3 min read AI4R: Artificial Intelligence for Robotics Key robotics AI concepts from Georgia Tech OMSCS AI4R (CS 7638): localization, planning, perception, control, and SLAM. AI Robotics OMSCS +4
Dec 19, 2024 · 2 min read ML4T: Machine Learning for Trading Key ML-for-trading concepts from Georgia Tech OMSCS ML4T (CS 7646): portfolio theory, signals, risk, and evaluation. Machine Learning Portfolio Theory ML4T +2
Apr 9, 2024 · 6 min read Unsupervised Learning: K-means Clustering and Anomaly Detection Explore core concepts of unsupervised learning, including K-means clustering, optimization strategies, and how anomaly detection systems are designed and evaluated. Machine Learning Unsupervised Learning Clustering
Apr 3, 2024 · 8 min read Decision Trees and Ensembles: From Splits to Random Forests and XGBoost A complete guide to decision trees, covering entropy, information gain, one-hot encoding, regression trees, and ensemble methods like Random Forest and XGBoost. Machine Learning Decision Trees XGBoost
Apr 1, 2024 · 13 min read Practical Advice for Applying Machine Learning Learn how to make decisions, evaluate models, handle bias and variance, and manage real-world ML workflows with cross-validation, error analysis, and transfer learning. Machine Learning Model Evaluation Best Practices
Mar 28, 2024 · 11 min read Training Neural Networks: Activation Functions, Backpropagation, and TensorFlow Implementation Explore how neural networks are trained with gradient descent, softmax, and backpropagation using TensorFlow. Understand activation functions and multiclass classification techniques. Machine Learning Neural Networks TensorFlow
Mar 26, 2024 · 10 min read Understanding Neural Networks: From Biology to TensorFlow A comprehensive guide to neural networks, forward propagation, TensorFlow implementation, and efficient matrix computations. Machine Learning Neural Networks TensorFlow
Mar 24, 2024 · 5 min read Logistic Regression: From Sigmoid to Regularization A comprehensive breakdown of logistic regression, sigmoid function, loss functions, and regularization for classification tasks. Machine Learning Classification Logistic Regression
Mar 20, 2024 · 10 min read Regression with Multiple Input Variables Deep dive into multiple linear regression, vectorization, gradient descent, feature scaling, and polynomial regression. Machine Learning Regression Gradient Descent
Mar 20, 2024 · 10 min read Introduction to Machine Learning Overview of supervised and unsupervised learning, linear regression, and gradient descent Machine Learning Regression Gradient Descent