Every article tagged Beginner, in one place.
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The easiest way to install PyTorch is inside a virtual environment, using the command generated by the selector on the official pytorch.org "Get Started" page. That page matches your operating system, package manager and compute platform (CPU or a specific CUD...
The agent framework space looks crowded, but the popular options fall into a few groups: graph and workflow libraries, lightweight agent SDKs from model providers, typed Python toolkits, role-based multi-agent libraries, data-centric frameworks and TypeScript...
MCP has three roles. The host is the AI application the user runs. The client is a connector inside the host that holds one connection to one server. The server is a program that exposes tools, resources and prompts. Getting these three terms straight removes...
A RAG pipeline has two separate flows. The ingestion flow runs ahead of time and builds a searchable index from your documents. The query flow runs for every user question: it finds relevant chunks and asks the model to answer from them. Keeping these two flow...
To call Claude from Python, install the anthropic package, put your API key in the ANTHROPIC_API_KEY environment variable, and call client.messages.create() with a model, a token limit and a list of messages. This post takes you from an empty folder to a worki...
An LLM API is a web service that accepts text (and sometimes images or files) and returns text generated by a large language model. You send an HTTPS request with your prompt, the provider runs the model on its own servers, and you receive the model's answer i...
Retrieval-augmented generation (RAG) is a pattern where your application looks up relevant information from your own data and puts it into the prompt before the language model answers. The model no longer has to rely only on what it learned during training. It...
Fine-tuning means continuing the training of an already pretrained model on your own examples so that its weights change and it behaves the way you want by default. Instead of describing the behaviour in a prompt every time, you show the model hundreds or thou...
Running an LLM locally means the model file sits on your own computer or server and the text generation happens on your own CPU or GPU. No prompt leaves your network, and you do not pay per token. You trade the convenience and top quality of hosted APIs for co...
The Model Context Protocol (MCP) is an open standard that lets AI applications connect to external tools, data and prompts through one common interface. Instead of writing a custom integration for every pairing of AI app and service, you write an MCP server on...
Context engineering is the practice of deciding exactly which information goes into a language model's context window on each call, and in what form. It covers the system prompt, the tool definitions, the conversation history, retrieved documents, tool results...
A multi-agent system is a program where two or more LLM-powered agents, each with its own instructions, tools and context, cooperate to finish a task that none of them handles alone. One agent might plan, another might search, a third might review. Your code,...
An agent framework is a library that handles the repetitive parts of building an LLM agent: running the tool-calling loop, tracking conversation state, validating tool arguments, streaming output and tracing what happened. You do not need one to build an agent...
An AI coding agent is a tool that takes a goal in plain language, such as "add a rate limit to the login endpoint", and then works towards it by reading your files, editing code and running commands until it believes the job is done. Autocomplete predicts the...
PyTorch is an open-source Python library for building and training neural networks. It gives you two core things: a fast n-dimensional array called a Tensor that can run on a CPU or a GPU, and an automatic differentiation engine called autograd that computes g...
