A concise breakdown of VGGT — a feed-forward transformer that predicts camera poses, depth, and 3D point clouds from multi-view images in a single forward pass. Covers architecture, geometric grounding, and relevance to CT reconstruction.

A clear breakdown of Git and GitHub — core commands, .gitignore, undoing mistakes, branching, and stashing — everything you actually need.

Notes from a 2-hour podcast with Jiayi Weng — the engineer who built OpenAI's core RLHF post-training infrastructure behind GPT-3.5, GPT-4, and GPT-5.

A clear breakdown of Model Context Protocol — what it is, how it works, and why it's turning chatbots into real AI agents.

A concise breakdown of how high-performance computing clusters actually work — from node architecture and storage tiers to job schedulers, environment management, and scaling best practices.

Six battle-tested Claude prompts to accelerate learning — from building study plans to the Feynman Technique.

A practical guide based on real talks from NTU seniors and industry professionals — covering target companies, resume, networking, interviews, visa reality, and a year-by-year timeline.

A comprehensive guide to conversation starters for work — from casual chats and meetings to lunches, events, and parties.

A practical checklist for training better deep learning models — from diagnosing failures to regularization and 3D medical imaging tips.

A clear breakdown of three core AI terms — Foundation Models, LLMs, and Transformers — and how they nest together.