Self-training, or pseudo-labeling (PL), algorithms have recently emerged as a powerful strategy for semi-supervised learning in speech recognition in the era of transformers and large scale data. In this talk, we will walk you from the first successful pseudo-labeling algorithms based on teacher-student training, that alternates between training a model and generating pseudo-labels (PLs) with it, to continuous pseudo-labeling algorithms, where PLs are generated in end-to-end manner as training proceeds, improving training speed and the accuracy of the final model. We will discuss different aspects of PL algorithms to make it simple and resource efficient and overall what are the key components of such huge success: what exactly the model learns, how training dynamics changes, how speaker diversity and amount of hours affect training, and how training depends on the language models. Finally, we will show how pseudo-labeling can be used to train a model on a source language with labeled data and to fine-tune it on a target language with only unlabeled data.