LLM Tutorials

Learn how vision-language-action (VLA) models work, how they differ from VLMs, and how to choose between OpenVLA, pi0, and SmolVLA in 2026.

Discover what AI world models are, how they differ from large language models, and how they help AI predict the future. Explore the latest 2026 industry trends.

Learn how LMCache stores and reuses KV states across LLM requests, integrates with vLLM, and reduces repeated prefill computation for long-context inference.

Set up Muse Spark 1.3 in Muse Code and the Meta Model API, choose between the contributor and standard tiers, and see whether Meta's efficiency claims hold.

Learn how to use the Gemini 3.8 Flash API in Python: Interactions API setup, thinking_level tuning, PDF-to-JSON extraction, and function calling with code.

Use GPT-6 Astra via the OpenAI API to build a Python release check agent with async tools, reasoning controls, Structured Outputs, and cost tracking, then test computer use and steering.

Learn how to build a custom AI agent harness with Python, ReAct loops, and durable state.

Learn how to use Anthropic’s latest flagship model to build a Python agent that reads a Flask repository before planning a change. Add progress updates, read-only file tools, and cost controls.

Use Unsloth Desktop for local AI workflows, including LLM inference, fine-tuning, vision models, image and video generation, OpenCode coding agents, and local APIs from one desktop application.

Learn how quantization, KV-cache optimization, batching, speculative decoding, memory bandwidth, and hardware-aware tuning can improve speed, scalability, and cost efficiency when serving large language models.

Learn how to run Qwen3.8-Flash-Next GGUF locally with llama.cpp on an RTX PRO 6000, then connect it to OpenCode for a fully local agentic coding setup.

A hands-on look at Anthropic's Fable 5 model in Claude Code: how it handles a one-shot build, its safety refusals and Opus fallback, and when to use it.