Learner·Oct 23N-Gram Language Models: How Machines Predict the Next WordHello everyone 👋A response icon1A response icon1
Arun Jagota·Oct 17The Attention Mechanism In RNN-based Encoder-Decoder ModelThe story of its birth and what it is
Rayyan Merchant·Oct 17We built and deployed a GPT from scratch. The scores improved. The writing didn’t always follow.Fourteen experiments on Tiny Shakespeare, two combined models, and the gap between predicting text and writing it well.
Tamal Das·Sep 18Why Enterprises Still Use Large Language Models When Tokenisation Is ExpensiveAbstract
Zeromathai·Jun 24Attention and Language Modeling Basics — How Next-Token Prediction Makes LLMs WorkWhy Transformer-based LLMs generate text one token at a time, and how Attention, Context Vectors, and Query–Key–Value make that process…A response icon2A response icon2
Ali·May 4MoE: Why It WonBecause 80% of a transformer’s compute lives in one place, and once you see that, MoE becomes the only rational response.
Dong-Keon Kim·Mar 23Hyena Hierarchy: Do We Really Need Attention?Exploring an attention-free architecture that matches Transformers on long-context tasks
Ujjwal Sharma·Feb 24Neural Machine Translation: From Theory to ImplementationFor the past decade, Neural Machine Translation (NMT) has evolved from recurrent encoder–decoder models to fully attention-based…
Enrico De Santis·Feb 13Measuring the Structure of Modern Artificial IntelligenceComplex Systems, Human Language, and the Complex Geometry of Large Language Models
teamtthelp·Nov 27, 2025How to Build a Yiddish-Savvy AI Writing Engine with Python and LLMsDeveloping an AI that can write fluent, culturally aware Yiddish articles sounds exciting, but anyone who’s tried working with low-resource…
