AI

The Go ecosystem continues to thrive with innovative projects spanning AI tooling, self-hosted applications, and developer infrastructure. This overview analyzes the top trending Go repositories on GitHub this month.

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.

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.

Ollama’s Web Search API lets you augment local LLMs with real-time web information. This guide shows you how to implement web search capabilities in Go, from simple API calls to full-featured search agents.

Retrieval-Augmented Generation (RAG) has evolved far beyond simple vector similarity search. LongRAG, Self-RAG, and GraphRAG represent the cutting edge of these capabilities.

Linux has become the de facto operating system for data science professionals, offering unmatched flexibility, performance, and a rich ecosystem of tools.

FLUX.1-dev is a powerful text-to-image model that produces stunning results, but its 24GB+ memory requirement makes it challenging to run on many systems. GGUF quantization of FLUX.1-dev offers a solution, reducing memory usage by approximately 50% while maintaining excellent image quality.

Black Forest Labs has released FLUX.1-Kontext-dev, an advanced image-to-image AI model that augments existing images using text instructions.

Token optimization is the critical skill separating cost-effective LLM applications from budget-draining experiments.

The Model Context Protocol (MCP) is revolutionizing how AI assistants interact with external data sources and tools. In this guide, we’ll explore how to build MCP servers in Python, with examples focused on web search and scraping capabilities.

Here’s a side-by-side support comparison of structured output (getting reliable JSON back) across popular LLM providers, plus minimal Python examples

Large Language Models (LLMs) are powerful, but in production we rarely want free-form paragraphs. Instead, we want predictable data: attributes, facts, or structured objects you can feed into an app. That’s LLM Structured Output.

Nvidia is about to release NVIDIA DGX Spark - little AI supercomputer on blackwell architecture with 128+GB unified RAM and 1 PFLOPS AI performance. Nice device to run LLMs.