LLM

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

The NVIDIA DGX Spark (GB10 Grace Blackwell) is now available in Australia at major PC retailers with local stock. If you’ve been following the global DGX Spark pricing and availability, you’ll be interested to know that Australian pricing ranges from $6,249 to $7,999 AUD depending on storage configuration and retailer.

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.

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.

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.

Running LLMs locally is now practical for developers, startups, and even enterprise teams.
But choosing the right tool — Ollama, vLLM, LM Studio, LocalAI or others — depends on your goals:

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.

The democratization of AI is here. With open-source LLMs like Llama 3, Mixtral, and Qwen now rivaling proprietary models, teams can build powerful AI infrastructure using consumer hardware - slashing costs while maintaining complete control over data privacy and deployment.

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

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.

Configuring context sizes in Docker Model Runner is more complex than it should be.