LLM

Strategic guide to hosting large language models locally with Ollama, llama.cpp, vLLM, or in the cloud. Compare tools, performance trade-offs, and cost considerations.

A performance engineering hub for running LLMs efficiently: runtime behavior, bottlenecks, benchmarks, and the real constraints that shape throughput and latency.

Self-hosting LLMs keeps data, models, and inference under your control-a practical path to AI sovereignty for teams, enterprises, nations.

Running large language models locally gives you privacy, offline capability, and zero API costs. This benchmark reveals exactly what one can expect from 14 popular LLMs on Ollama on an RTX 4080.

The Python ecosystem this month is dominated by Claude Skills and AI agent tooling. This overview analyzes the top trending Python repositories on GitHub.

The Rust ecosystem is exploding with innovative projects, particularly in AI coding tools and terminal applications. This overview analyzes the top trending Rust repositories on GitHub this month.

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.

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

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?

Related guides for persistent knowledge layers — agent memory plugins, graph tooling, and stack context — live under the AI Systems Memory hub.

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.