Welcome Muse Spark Open: open-weight frontier multimodal mixture-of-experts model from Meta

Hugging Face's guide to Meta's Muse Spark Open, an 816B-parameter (42B active) open-weight multimodal mixture-of-experts model with a 1M-token context: architecture, perception and audio encoders, and how to run it with Transformers, llama.cpp, SGLang, vLLM and MLX, deploy it, fine-tune it with TRL, plus demos.

Tutorial · AI · LLMs · Open Source · Machine Learning

How to be more ambitious: A tutorial for humans with models

Felix Rieseberg, who works on Claude at Anthropic, offers a practical tutorial for being more ambitious with AI models, grouping ambition into speed, altitude, depth and range, with worked examples around a quarterly business review and sample prompts, and closes with research on self-efficacy and why believing hard things are possible helps people achieve them.

Tutorial · AI · LLMs · Productivity

Running RAG with a (small) language model in the browser

Bart de Goede extends his static blog's in-browser semantic search into full retrieval-augmented generation by running small language models client-side with transformers.js on WebGPU or WASM, and evaluates how faithful and useful answers from Qwen3, Gemma and other small models are compared with larger ones.

Tutorial · LLMs · Web Development · Machine Learning · Tutorials

Build your own decision model

Nish Tahir explains "system one" decision models like TypeSafe AI's Jev, which answer in a single forward pass by masking the vocabulary to a fixed set of options, then walks through fine-tuning a small model on multiple-choice data and calibrating its confidence with temperature scaling, with a companion GitHub repo.

Tutorial · AI · Machine Learning · LLMs

What Makes LLM Tokenization Slow?Exploring the performance of byte-pair encoding by optimizing a GPT-2 tokenizer.

Andrew Healey dissects GPT-2’s reference BPE tokenizer, measures what makes tokenization slow, and shows concrete optimizations on the hot path of LLM products.

Tutorial · LLMs · Programming · Performance · AI

How to Build a Reliable AI Assistant with the Claude API

A freeCodeCamp tutorial building ShopHelper with the Claude API: conversation history, tools, multi-block responses, workflow patterns, and evaluating whether prompt changes actually help.

Tutorial · AI · Tutorials · LLMs · Programming

Build an LLM Tokenizer and Attention from Scratch in TypeScript

SitePoint tutorial that implements BPE tokenization, cosine similarity vector search, and scaled dot-product attention in TypeScript—exposing LLM primitives as ordinary readable code.

Tutorial · LLMs · Tutorials · Programming · Machine Learning

Automating eval design and hillclimbing with Claude

Lance Martin (claude.dev) explains principles for production-like evals with held-out sets, then shows how the claude-api skill’s build-eval and hillclimb commands automate design and overfitting-aware improvement—including cost and performance case studies.

Tutorial · AI · LLMs · Tutorials · Benchmarks

Turn GLM-5.3-Flash into a Jev-like System One model

Johannes Hötter walks through turning GLM-5.3-Flash into a fast Jev-like “System One” decision model—typed options with probabilities in a single forward pass, matching Jev’s accuracy and speed.

Tutorial · AI · LLMs · Machine Learning · Open Source

A Jev-like wrapper for LLMs, including vision models

Allan shows a small single-function Jev-style wrapper for LLMs that also handles vision models, with practical code for local and API backends.

Tutorial · LLMs · AI Agents · Tutorial · Programming

Mixture of Experts (MoE) for Backend Engineers

A detailed visual guide to token routing, expert batching, weighted combination, and the memory and communication tradeoffs of MoE serving. Suppose a model has dozens of feed-forward subnetworks, but each token uses only two of them. The arithmetic per token can stay modest while the total weight set grows. Now place that model on eight GPUs. If every GPU stores all experts, memory can become the limit; if experts are split across GPUs, token activations must travel to whichever GPU owns their…

Tutorial · AI · LLMs · Machine Learning · Engineering

Local AI on a 12 GB GPU: what survived testing, and how to set it up

Hands-on notes testing local AI models on a 12 GB RTX 3060: which stacks fit in VRAM, how context length decides spills to CPU, and a practical setup that survived the author’s trials.

Tutorial · AI · LLMs · Hardware · Tutorials

Why updatedInput in a PreToolUse Hook Doesn’t Rewrite the Command

A deep dive into Claude Code PreToolUse hooks: why returning updatedInput does not rewrite the shell command, and how permission decisions actually work.

Tutorial · AI Agents · Developer Tools · LLMs · Programming

How Forge Structures Requests for Prompt Cache Reuse

Mohit Ranka explains how Forge, an open-source terminal-first AI IDE, restructures its agent harness requests—stable prefixes, message ordering, history persistence and cache hints—to maximise provider prompt-cache reuse, and how request snapshots plus usage tracking make cache behaviour observable.

Tutorial · LLMs · AI Agents · Developer Tools · Performance

Getting the most out of Opus 5.5 in Claude and Claude Code

Addy Osmani shares practical prompting, long-run steering, and verification tips for Anthropic’s Opus 5.5 model in Claude apps and Claude Code.

Tutorial · ai · llms · tutorials · developer-tools

Heretic tutorial: automatic censorship removal for language models

A hands-on tutorial for Heretic, an open-source tool that automatically removes refusal/censorship behaviors from language models—setup, workflow, and what to watch for.

Tutorial · AI · Open Source · LLMs · Machine Learning

Transformer Explainer: LLM Transformer Model Visually Explained

Georgia Tech’s Polo Club walks through GPT-2’s Transformer stack—embeddings, multi-head attention, MLP, sampling—with an interactive in-browser model for learning how next-token prediction works.

Tutorial · AI · LLMs · Machine Learning · Education

Jev in 25 lines of Python

NobodyWho shows a local, 25-line Python sketch of Jev-style decision models: load a small GGUF, score labeled choices from logits, and print calibrated-looking probabilities without sending data to an API.

Tutorial · AI · LLMs · Programming · Open Source

aie_2.1: building it — a conversation engine where the LLM 'remembers'

Meraki builds a Python conversation engine that shows how LLMs fake memory: every turn resends the full history so the model appears to remember, with practical notes on context and design.

Tutorial · AI · LLMs · Programming · Education

Model Context Protocol with Spring AI, Building MCP Clients and Servers in Java

Ayush Shrivastava walks through building MCP clients and servers with Spring AI in Java: tool discovery, protocol basics, and wiring MCP into agentic Spring applications beyond a basic demo.

Tutorial · AI · AI Agents · Programming · Developer Tools