Python

Open WebUI is a powerful, extensible, and feature-rich self-hosted web interface for interacting with large language models.

Melbourne’s tech community continues to thrive in 2026 with an impressive lineup of conferences, meetups, and workshops spanning software development, cloud computing, AI, cybersecurity, and emerging technologies.

vLLM is a high-throughput, memory-efficient inference and serving engine for Large Language Models (LLMs) developed by UC Berkeley’s Sky Computing Lab.

PDFMiner.six is a powerful Python library for extracting text, metadata, and layout information from PDF documents.

Playwright is a powerful, modern browser automation framework that revolutionizes web scraping and end-to-end testing.

The proliferation of AI-generated content has created a new challenge: distinguishing genuine human writing from “AI slop” - low-quality, mass-produced synthetic text.

Cognee is a Python framework for building knowledge graphs from documents using LLMs. But does it work with self-hosted models?

When working with Large Language Models in production, getting structured, type-safe outputs is critical. Two popular frameworks - BAML and Instructor - take different approaches to solving this problem.

Choosing the Best LLM for Cognee demands balancing graph-building quality, hallucination rates, and hardware constraints. Cognee excels with larger, low-hallucination models (32B+) via Ollama but mid-size options work for lighter setups.

Dependency injection (DI) is a fundamental design pattern that promotes clean, testable, and maintainable code in Python applications.

Jumpstart the Jupyter Notebook productivity with essential shortcuts, magic commands, and workflow tips that will transform your data science and development experience.

Ollama’s Python library now includes native OLlama web search capabilities. With just a few lines of code, you can augment your local LLMs with real-time information from the web, reducing hallucinations and improving accuracy.

Choosing the right vector store can make or break your RAG application’s performance, cost, and scalability. This comprehensive comparison covers the most popular options in 2024-2025.

Python linters are essential tools that analyze your code for errors, style issues, and potential bugs without executing it. They enforce coding standards, improve readability, and help teams maintain high-quality codebases.

As AI and ML workloads become increasingly complex, the need for robust orchestration systems has become greater. Go’s simplicity, performance, and concurrency makes it an ideal choice for building the orchestration layer of ML pipelines, even when the models themselves are written in Python.

Cross-modal embeddings represent a breakthrough in artificial intelligence, enabling understanding and reasoning across different data types within a unified representation space.