A comprehensive guide to chunking strategies for Retrieval-Augmented Generation, from basic splitting to advanced semantic and agentic approaches.

Speculative decoding speeds up autoregressive text generation by combining a small draft model with a larger verifier model. This two-step dance slashes latency while preserving quality, an essenti...

Mixture of Experts (MoE) lets you scale transformer models to billions of parameters without proportional compute costs. By selectively routing tokens through specialized experts, MoE achieves mass...

In this post, we explore how to simplify and optimize the output generation process in language models using guidance techniques. By pre-structuring inputs and restraining the output space, we can ...

The amount of visual data that we constantly ingest is massive, and our ability to function in an environment may greatly impove when we have access to this modality, thus being able to use it as a...

In this post we will explore how exploding and vanishing gradients may happen, and how normalization and a change of activation functions can help us deal with these issues.

Let's kick start your journey into reinforcement learning with a cool taxi-driving simulation! You'll get hands-on with Q-learning, starting from random exploration all the way to nailing it. Plus,...

A dissection of a system built for failure through the lens of an interview