World Wire

Pre-training large language models is expensive enough that even modest efficiency improvements can translate into meaningful cost and time savings. Nous Research is releasing Token Superposition Training (TST) , a method that substantially reduces pre-training wall-clock time at fixed compute without touching the model architecture, optimizer, tokenizer, parallelism strategy, or training data. At the 10B-A1B mixture-of-experts scale, TST reaches a lower final training loss than a matched-FLOPs baseline while consuming 4,768 B200-GPU-hours versus the baseline’s 12,311 — roughly a 2.5x reduction in total pre-training time. https://ift.tt/NhUGA36 The Problem TST is Solving Modern LLM pre-training is heavily data-driven. Recent training regimes routinely overtrain well beyond compute-optimal estimates, and raw text throughput. How much data a model can process per FLOP has become a key lever. Subword tokenizers like BPE already improve throughput by compressing seque...

In this tutorial, we build a realistic Zero-Trust network simulation by modeling a micro-segmented environment as a directed graph and forcing every request to earn access through continuous verification. We implement a dynamic policy engine that blends ABAC-style permissions with device posture, MFA, path reachability, zone sensitivity, and live risk signals such as anomaly and data-volume indicators. We then operationalize the model through a Flask API and run mixed traffic, including insider-lateral movement and exfiltration attempts, to show how trust scoring, adaptive controls, and automated quarantines block malicious flows in real time. Copy Code Copied Use a different Browser !pip -q install networkx flask import math import json import time import random import hashlib from dataclasses import dataclass, field from typing import Dict, Any, List, Tuple, Optional import networkx as nx from flask import Flask, request, jsonify import matplotlib.pyplot as plt def _si...

By the time a company’s legal team finishes drafting its generative AI acceptable use policy, a meaningful percentage of its engineers, analysts, and product managers have already moved past it. Not deliberately. Not maliciously. Just practically. This is the core dynamic of what the industry now calls shadow AI : the unauthorized, ungoverned use of AI tools across enterprise organizations, running parallel to — and often far ahead of — whatever governance frameworks IT and compliance teams have managed to put in place. It is not a niche problem affecting a handful of early adopters. It is the dominant operational reality of AI in 2026, and most enterprise AI governance programs are structured to solve a problem that has already fundamentally changed shape. The Scale is Not a Rounding Error The numbers are not ambiguous. Between 40 and 65 percent of enterprise employees report using AI tools not approved by their IT department, according to enterprise surveys document...