A deep-dive into how production RAG systems are actually built — covering infrastructure, multi-tenancy, ingestion pipelines, chunking, embedding versioning, blue-green indexing, hybrid search, reranking, orchestration, caching, model routing, faithfulness checks, observability, evaluation, cost control, and every failure mode in between.

Learn how to implement Random Forests from scratch in Python, understanding the underlying principles and algorithms.

Performance metrics are used to assess the performance of a model on a given dataset.

A practical introduction to RAG — why LLMs need it, how a basic pipeline is structured, and a working code example for question-answering over your own documents.

How I reduced Docker image size dramatically using Multi-Stage Builds and improved my deployment process.

A practical guide to real-time communication techniques on the web — how Polling, Long Polling, Server-Sent Events, and WebSockets differ, with server and client code for each.

Understanding the differences between React and Next.js, and choosing the right framework for your project.

Step-by-step guide to creating a Retrieval Augmented Generation (RAG) system using FastAPI and LangChain.

Essential system design concepts every developer should know, with real-world examples.

How to find your first open source project, make meaningful contributions, and grow as a developer.

A comparison of Python and JavaScript, their use cases, and how they complement each other.

A beginner's guide to what 42 School is, the unique learning model, and what to expect in your first months.