AI Tutorials

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.

Run Meta’s Muse Glimmer local agentic model using llama.cpp, dynamic quantization, DFlash speculative decoding, vision support, and OpenCode to run a fast, private, and low-cost AI coding agent.

No, it didn't solve the Riemann hypothesis. But it did stumble into a genuine, Lean-verified improvement on a 50-year-old bound.

Build and deploy a live financial research app on FastAPI Cloud using GPT-5.6 Luna, Olostep web search, tool calling, REST endpoints, and OpenAI Agents SDK tracing. 

Learn how to run fast, accurate, multilingual speech-to-text locally on a CPU using Microsoft VibeASR.cpp, with file transcription, real-time streaming, and a Gradio web interface on Windows, Linux, and macOS.

Learn how to use Grok Voice Think Fast 2.0 to build a real-time voice agent that handles spoken conversations, calls tools, manages interruptions, and resumes disconnected sessions.

This tutorial introduces Oracle AI Vector Search, explains its core features, and walks through a practical local project. It also covers setup requirements, semantic search, vector indexing, and common troubleshooting steps.

Learn how to use OpenAI’s gpt-live-transcribe API to stream microphone audio, generate multilingual live captions, improve domain-specific accuracy, and balance latency against transcription quality.

Learn to build a document QA system in Python with PageIndex, then benchmark on a real Federal Reserve financial report it against a vector RAG baseline using FAISS.

Learn how Claude Code sub-agents can explore codebases, plan implementations, and complete multi-step development tasks with minimal supervision.

Run the latest DeepSeek V4 Flash model on a multi-GPU setup with Unsloth Studio, connect it to OpenCode, and use a local AI coding agent to build an interactive stock analytics website.

Run Gemma 4 12B QAT locally with llama.cpp and see how Quantization-Aware Training improves 4-bit GGUF inference, reduces VRAM usage, boosts speed, and makes local AI more stable on consumer GPUs.