World Wire

The Transformer’s attention mechanism has barely changed since 2017. Most efficiency work has tried to replace softmax attention outright. A new paper takes a different route. It keeps softmax attention and bolts on a correction branch. A team of researchers from Northwestern University, Tilde Research, and University of Washington introduce a parameterized Local Linear Attention called ‘Parallax’ that scales to LLM pretraining and codesigns with Muon. Parallax does not chase efficiency by cutting compute. It adds compute deliberately, then makes that compute cheaper to run on modern GPUs. What is Parallax Parallax builds on Local Linear Attention (LLA) . LLA comes from the test-time regression framework. That framework reads attention as a regression solver over key-value pairs. In this view, keys are training data points. Values are labels. The query is the test point. Softmax attention is a nonparametric estimator called Nadaraya-Watson. It fits...

In this tutorial, we build a governed AI-agent workflow using Microsoft’s Agent Governance Toolkit as the reference point. We create a Colab-ready implementation where agents do not directly execute tools; instead, every action first passes through a governance layer that checks the agent’s identity, trust score, risk tier, requested tool, action type, sensitivity level, and policy rules. We define a YAML-based policy that controls destructive database operations, external email sending, shell execution, access to sensitive data, and financial transfers. We then wrap each tool with governance logic so that actions can be allowed, denied, sandboxed, or routed through an approval step before execution. We also generate tamper-evident audit records, run policy tests, activate a kill switch, summarize governance decisions, and visualize the relationships between agents, tools, rules, and outcomes as a graph. Copy Code Copied Use a different Browser import os import sys import json i...

In this tutorial, we implement a practical use case with Loguru , a powerful, flexible, and production-ready logging library for Python. We start by building a clean, idempotent logging setup that can be safely rerun without duplicating handlers or producing messy output. From there, we move step by step through structured logging, contextual logging, custom log levels, global patching, callable formatters, and in-memory sinks. We also handle real-world logging needs such as rich exception traces, JSON log files, custom rotation, compression, retention, async logging, threaded execution, multiprocessing-safe logging, and standard logging module interception. By keeping everything in a Colab-ready workflow, we make it easy to test, inspect, and understand how Loguru can support debugging, monitoring, and observability in serious Python applications. Copy Code Copied Use a different Browser !pip install -q loguru nest_asyncio import os, sys, time, json, glob, gzip, shutil, asyncio,...

In this tutorial, we implement a SkillNet use case as a practical framework for discovering, installing, inspecting, evaluating, and organizing reusable AI skills. We start by setting up a robust SkillNet client with SDK and REST fallback support, then compare keyword search with semantic search to understand how skills can be found for different task requirements. From there, we install curated skills from GitHub, inspect their metadata, apply a quality gate across key evaluation dimensions, and visualize relationships between skills as a graph. Finally, we build a skill-augmented agent planner that breaks a complex goal into subtasks, discovers relevant skills, filters them, and assembles an execution pipeline. Copy Code Copied Use a different Browser import sys, subprocess def _pip(*pkgs): subprocess.run([sys.executable, "-m", "pip", "install", "-q", *pkgs], check=False) print("Installing dependencies (skillnet-ai, networkx, mat...

Genesis AI released Genesis World 1.0 . The platform consists of four components: the Genesis World physics engine, Nyx (a real-time path-traced renderer), Quadrants (a Python-to-GPU compiler), and a simulation interface. It is designed to accelerate robotics foundation model development through simulation-based evaluation. Robotics model development has two bottlenecks: data and iteration speed. The field has focused heavily on data. Genesis AI argues the slower, less-discussed bottleneck is the model development cycle itself — specifically, how fast teams can evaluate candidate policies and compare model checkpoints. What Problem Does This Solve? A typical policy evaluation at Genesis spans hundreds of tasks with hundreds of episodes each. Running that in the real world requires more than 200 hours of continuous robot operation with one operator and one robot station — for a single evaluation pass. Statistically meaningful comparisons across checkpoints require many such...