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Today you can build modern websites using only Python on both the frontend and the backend, and plug in AI for advanced experiences. Here's how to do it securely, using a children's story generator as the example.
Keep reading: A complete website in Python: frontend and backend with AI →
How to set up a remote MCP (Model Context Protocol) server over Streamable HTTP with secure Okta authentication and DPoP, protected by a reverse proxy in Python.
Keep reading: How to set up an MCP server with Okta and a reverse proxy in Python →
A Linux server running fail2ban, ClamAV, rkhunter, maldet and lynis, with their logs analysed by Azure OpenAI through LangChain: per-log reports, an aggregated report, email delivery and archiving.
Keep reading: How to build an AI security suite from open source tools →
Flowise is a solid open-source low-code option for building AI workflows fast — production-ready, multi-model and easy to integrate. How to install it with secure Entra ID authentication for Azure OpenAI and add observability with Phoenix.
Keep reading: Building AI applications with Flowise and Phoenix →
Run an AI process that reads bingo cards and checks whether the numbers make a bingo, using local LLMs with Ollama for inference and Nginx as a security layer for SSL and authentication.
Keep reading: Local AI with Ollama and Nginx: A Guide to Automated Validation →
In this tutorial we'll walk through a simple example of how to summarise news from different sources and languages using artificial intelligence.
How to build an AI chatbot using RAG, Qdrant as the vector database and an agent to manage memory, with a graphical user interface (GUI).
Keep reading: AI Chatbot with RAG, Memory and a User Interface →
How to use the AutoGen framework to build an agent that answers questions based on documents stored in a vector database, with Azure OpenAI.
How to turn a video into a written tutorial with artificial intelligence: extract frames, convert them to base64 and use Azure OpenAI's multimodal GPT-4o model to write the tutorial from them.
How to work with ChromaDB, a database for embedding vectors: running a server with authentication in Docker, then creating collections, adding documents and querying them from Python.
