World Wire

In this tutorial, we explore the lambda/hermes-agent-reasoning-traces dataset to understand how agent-based models think, use tools, and generate responses across multi-turn conversations. We start by loading and inspecting the dataset, examining its structure, categories, and conversational format to get a clear idea of the available information. We then build simple parsers to extract key components such as reasoning traces, tool calls, and tool responses, allowing us to separate internal thinking from external actions. Also, we analyze patterns such as tool usage frequency, conversation length, and error rates to better understand agent behavior. We also create visualizations to highlight these trends and make the analysis more intuitive. Finally, we prepare the dataset for training by converting it into a model-friendly format, making it suitable for tasks like supervised fine-tuning. Copy Code Copied Use a different Browser !pip -q install -U datasets pandas matplotlib seab...

In this tutorial, we explore how we can decode linguistic features directly from brain signals using a modern neuroAI pipeline. We work with MEG data and build an end-to-end system that transforms raw neural activity into meaningful predictions, in this case, estimating word length from brain responses. We set up the environment, load and process neural events, design a custom feature extractor, and construct a structured data pipeline using NeuralSet. From there, we train a convolutional neural network to learn patterns in the temporal and spatial structure of MEG signals. Throughout the process, we focus on building a clean, modular workflow that mirrors real-world neuroAI research practices. Copy Code Copied Use a different Browser import subprocess, sys, importlib, pkgutil def pip_install(*pkgs): print(f"pip install {' '.join(pkgs)} ...") r = subprocess.run([sys.executable, "-m", "pip", "install", "-q", *pkgs], ...

The bottleneck in building better AI models has never been compute alone — it has always been data quality. Meta AI’s RAM (Reasoning, Alignment, and Memory) team is now addressing that bottleneck directly. Meta researchers have introduced Autodata , a framework that deploys AI agents in the role of an autonomous data scientist, tasked with iteratively building, evaluating, and refining training and evaluation datasets — without relying on costly human annotation at every step. And the results, tested on complex scientific reasoning problems, show that this approach doesn’t just match classical synthetic data generation methods — it significantly outperforms them. https://facebookresearch.github.io/RAM/blogs/autodata/ Why Synthetic Data Creation Has Always Been Hard To understand what Autodata is solving, you need to understand how AI training data is typically created today. Most modern AI systems started with human-written data. As models improved, rese...

In this tutorial, we walk through a complete, hands-on journey of post-training large language models using the powerful TRL (Transformer Reinforcement Learning) library ecosystem. We start from a lightweight base model and progressively apply four key techniques: Supervised Fine-Tuning (SFT), Reward Modeling (RM), Direct Preference Optimization (DPO), and Group Relative Policy Optimization (GRPO). Also, we leverage efficient methods like LoRA to make training feasible even on limited hardware, such as Google Colab’s T4 GPU. As we move step by step, we build intuition for how modern alignment pipelines work, from teaching models how to respond to shaping their behavior using preferences and verifiable rewards. Copy Code Copied Use a different Browser import subprocess, sys subprocess.check_call([sys.executable, "-m", "pip", "install", "-q", "-U", "torchao>=0.16", "trl>=0.20", "transformers...

Large language models are remarkably capable, yet frustratingly opaque. When a model misbehaves — generating responses in the wrong language, repeating itself endlessly, or refusing safe requests — AI devs have very few tools to diagnose why it happened at the level of internal computations. That’s the problem Qwen-Scope is built to solve. Qwen Team just released Qwen-Scope , an open-source suite of sparse autoencoders (SAEs) trained on the Qwen3 and Qwen3.5 model families. The release comprises 14 groups of SAE weights across 7 model variants — five dense models (Qwen3-1.7B, Qwen3-8B, Qwen3.5-2B, Qwen3.5-9B, and Qwen3.5-27B) and two mixture-of-experts (MoE) models (Qwen3-30B-A3B and Qwen3.5-35B-A3B). What is a Sparse Autoencoder, and Why Should You Care? Think of a sparse autoencoder as a translation layer between raw neural network activations and human-understandable concepts. When an LLM processes text, it produces high-dimensional hidden states — vectors with ...

In this tutorial, we build the entire Agentic UI stack from the ground up using plain Python, without relying on external frameworks to abstract away the core ideas. We implement the AG-UI event stream to make agent behavior observable in real time, and we bring in A2UI as a declarative layer that allows interfaces to be defined as structured JSON rather than executable code. As we progress, we enable an LLM to generate full user interfaces from natural language, synchronize agent and UI state through JSON Patch updates, and enforce human-in-the-loop safety for critical actions. Also, we gain a clear, end-to-end understanding of how agent reasoning transforms into interactive, protocol-compliant user interfaces. Copy Code Copied Use a different Browser import subprocess, sys for pkg in ["openai", "rich", "pydantic"]: subprocess.check_call([sys.executable, "-m", "pip", "install", "-q", pkg]) import os, getpa...