Building agents: When to fold subagents into your main agent loop

Anthropic's Diane Sarkis explains when teams building on Claude Platform should replace orchestrator-plus-subagent designs with a single agent loop that loads skills and tools on demand, how to migrate in stages while measuring with task-completion evals, and how several teams can keep contributing through MCP tools, skills and knowledge bases.

Tutorial · AI Agents · AI · Software Engineering

Do we really need bigger AI weather forecasting models to improve resolution?

Emma Scharfmann shows that instead of building a bigger AI weather model you can train a small downscaler on top of an existing global one: trained in about an hour, it sharpens AIFS-ENS temperature forecasts over France from ~28 km to 5.5 km and cuts station-measured error by 14% RMSE and 21% CRPS over the first two days.

Tutorial · Machine Learning · AI · Science · Open Source

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

You could have invented transformers from memory

Alex Wang builds intuition for linear maps, MLPs and self-attention as different kinds of memory stores, using the interference-to-signal ratio to estimate each one's capacity. With 64-dimensional keys, capacity grows from about 64 memories for a linear layer to about 2,700 for an MLP and over a million for self-attention.

Tutorial · AI · Machine Learning · Transformers · Mathematics

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

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 Is poppy.json? The /.well-known File That Tells AI Agents How to Work With Your Company

A walkthrough of poppy.json, the Personal Agent Protocol (PAP/Poppy) discovery file at /.well-known/poppy.json: required fields for organization, OAuth, agent/API/web interfaces and extensions, plus a fictional worked example of Draft 0.1.

Tutorial · AI Agents · Tutorials · Developer Tools · AI

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

Engineer-Led, AI-Assisted: A Practical Workflow for Building Software

Nathan Pickard describes an engineer-led, AI-assisted software workflow covering planning, development, testing, code review, and context management—arguing AI depends on engineering judgment rather than replacing it.

Tutorial · AI · Software Engineering · AI Agents · Developer Tools

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

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

Teaching a World Model to Play Pokémon

Training a JEPA-style LeWorldModel on Pokémon Red screenshots and button presses, then using CEM latent planning to select a starter—why latent collapse, SIGReg, and rollout fine-tuning mattered, and 52/100 plans succeeding after tuning.

Tutorial · AI · Machine Learning · Research · Programming

How to spot AI science slop

Chemist and science communicator Michelle Wong (Lab Muffin Beauty Science) explains why AI-generated science content is error-prone and lists practical tells for spotting it, from chatbot formatting and voice to hyperprolific output and inhuman errors.

Tutorial · AI · Science · Media literacy

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

V-JEPA: Learning Video Representations by Feature Prediction

A hands-on walkthrough of Meta’s V-JEPA: video tubelets, feature prediction, pretrained encoder features, temporal tests, and a small action-classification experiment.

Tutorial · Machine Learning · AI · Research · Tutorial

A deep dive into Ponytail

Flavio Copes explains Ponytail, a plugin that stops coding agents from over-engineering: how it works, how to install it in ChatGPT, Codex, and Claude Code, intensity settings, and a practical workflow for keeping agent-built features small.

Tutorial · AI · AI Agents · Programming · Productivity

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