DeepTutor vs How LLMs Work

DeepTutor is an open-source, agent-native learning platform from Hong Kong University's Data Intelligence Lab that goes far beyond chatbot tutoring. Built on Python 3.11+ and Next.js 16, it provides five integrated learning modes in a single unified workspace: Chat with RAG and web search, Deep Solve for multi-agent step-by-step reasoning, Quiz Generation from your own knowledge bases, Deep Research across documents and academic papers, and a standout Math Animator that generates visual Manim animations of mathematical concepts. The platform's TutorBot feature lets users create fully autonomous AI tutors with persistent memory and custom personalities. Each bot maintains its own workspace, remembers user progress across sessions, and can connect to Telegram, Discord, Slack, WeChat, and other messaging channels. This means you can have a calculus tutor bot that lives in your Telegram and actually remembers where you got stuck last week. Released under Apache 2.0, DeepTutor surged past 1,400 GitHub stars shortly after launch. The combination of persistent memory, multi-channel bot deployment, and the Math Animator puts it in a different category from generic AI chat assistants. This is infrastructure-grade educational tooling built for serious learners.

"How LLMs Work" is a free, browser-based interactive guide that walks through the complete pipeline for building large language models — from raw web scraping to RLHF-trained conversational assistant. Created by Yash Narwal and based on Andrej Karpathy's technical deep-dive lecture, it's been getting significant traction on Hacker News (214+ points) for turning dense ML theory into something genuinely accessible. The site covers data collection and deduplication, Byte Pair Encoding tokenization with a live demo, pre-training and next-token prediction, inference with a probability sampling simulator, post-training with RLHF, and RAG. Each section uses animated visualizations, clickable pipeline diagrams, and canvas-based graphics — not static explainer images. The progressive narrative structure follows a single piece of text through every stage of the pipeline, making abstract concepts concrete. In an era where everyone uses LLMs but few understand how they work, this kind of high-quality educational resource matters for a different reason than tools: it raises the baseline competency of the entire developer ecosystem. Better-informed builders ask better questions, make better design decisions, and push vendors toward more transparency. This is the kind of project the HN community rewards — and deserves the signal boost.