World Wire

Developers building production agents need higher token efficiency, lower latency, and more reliable performance. Today, Google has released three new Gemini models . The lineup is Gemini 3.6 Flash, Gemini 3.5 Flash-Lite, and Gemini 3.5 Flash Cyber. All three sit in the Flash tier, which Google tunes for speed, cost, and high-volume agentic work rather than maximum reasoning depth. Gemini 3.6 Flash: better quality, fewer tokens, lower price Gemini 3.6 Flash is the new default workhorse. It builds on 3.5 Flash and targets coding, knowledge work, and multimodal tasks. The main point is efficiency. On the Artificial Analysis Index , 3.6 Flash uses 17% fewer output tokens than 3.5 Flash. On the DeepSWE benchmark by Datacurve , Google reports up to a 65% reduction. The model also takes fewer reasoning steps and tool calls per multi-step workflow. Pricing moves down alongside efficiency. Gemini 3.6 Flash is priced at $1.50 per 1M input tokens and $7.50 per 1M output tokens. The ...

In this tutorial, we explore NVIDIA’s srt-slurm framework and learn how we use srtctl to convert declarative YAML configurations into reproducible SLURM benchmark workflows for distributed LLM serving. We set up the project in Google Colab, inspect its internal architecture, define a cluster configuration, dry-run built-in and custom recipes, and model a disaggregated prefill-and-decode deployment for DeepSeek-R1. We also generate parameter sweeps, interact with the typed Python API, validate expanded configurations, and analyze simulated benchmark results through a throughput-versus-latency Pareto frontier. Although Colab does not provide a real SLURM environment, we use it as a practical development workspace to understand, validate, and prepare production-grade benchmark recipes before we submit them to an actual GPU cluster. Copy Code Copied Use a different Browser import os, sys, subprocess, textwrap, json, shutil, importlib from pathlib import Path def run(cmd, check=True,...

Meta has released Astryx , an open source design system that is fully customizable and built to be operated by both people and the AI agents working alongside them. It is available now in Beta. Astryx is not a new experiment. It grew inside Meta over the last eight years, where the company says it became its most-used and largest design system, shaped by the engineers, designers, and product teams who depend on it daily. The system ships 150+ accessible components (the docs site now lists 160+), brand-level theming, dark mode, ready-to-ship templates, and a CLI as one cohesive package. It is built on React and StyleX . You import pre-built CSS and use typed React components. There is no build plugin to wire up and no styling library to adopt. The trade-off Astryx is trying to remove Most design systems force a choice. Adopt a large company’s system and your app tends to look like someone else’s product. Reach for copy-paste components instead and every project...