World Wire

RouteLLM is a flexible framework for serving and evaluating LLM routers, designed to maximize performance while minimizing cost. Key features: Seamless integration — Acts as a drop-in replacement for the OpenAI client or runs as an OpenAI-compatible server , intelligently routing simpler queries to cheaper models. Pre-trained routers out of the box — Proven to cut costs by up to 85% while preserving 95% of GPT-4 performance on widely used benchmarks like MT-Bench. Cost-effective excellence — Matches the performance of leading commercial offerings while being over 40% cheaper. Extensible and customizable — Easily add new routers, fine-tune thresholds, and compare performance across multiple benchmarks. Source: https://github.com/lm-sys/RouteLLM/tree/main In this tutorial, we’ll walk through how to: Load and use a pre-trained router. Calibrate it for your own use case. Test routing behavior on different types of prompts. Check out the Full Codes here . Installing the d...

The year 2025 marks a defining moment in the evolution of artificial intelligence, ushering in an era where agentic systems—autonomous AI agents capable of complex reasoning and coordinated action—are transforming enterprise workflows, research, software development, and day-to-day user experiences. This articles focuses on five core AI agent trends for 2025: Agentic RAG, Voice Agents, AI Agent Protocols, DeepResearch Agents, Coding Agents, and Computer Using Agents (CUA). 1. Agentic RAG: Reasoning-Driven AI Workflows Agentic Retrieval-Augmented Generation (RAG) stands as the cornerstone use case in 2025 for real-world AI agents. Building on the standard RAG architecture, Agentic RAG introduces goal-driven autonomy, memory, and planning. Here’s how the agentic approach refines classical RAG: Memory & Context Retention: Agents track user queries across sessions, building short-term and long-term memory for seamless context management. Planning & Tool Use: Agents dynamica...

Table of contents TL;DR 1) What is an AI agent (2025 definition)? 2) What can agents do reliably today? 3) Do agents actually work on benchmarks? 4) What changed in 2025 vs. 2024? 5) Are companies seeing real impact? 6) How do you architect a production-grade agent? 7) Main failure modes and security risks 8) What regulations matter in 2025? 9) How should we evaluate agents beyond public benchmarks? 10) RAG vs. long context: which wins? 11) Sensible initial use cases 12) Build vs. buy vs. hybrid TL;DR Definition: An AI agent is an LLM-driven system that perceives, plans, uses tools, acts inside software environments, and maintains state to reach goals with minimal supervision. Maturity in 2025: Reliable on narrow, well-instrumented workflows; improving rapidly on computer use (desktop/web) and multi-step enterprise tasks. What works best: High-volume, schema-bound processes (dev tooling, data operations, customer self-service, internal reporting). How to ship:...

Table of contents Introduction What Is MCP- RL? ART: The Agent Reinforcement Trainer Code Walkthrough: Specializing LLMs with MCP- RL Explanation: Under the Hood: How MCP- RL Generalizes Real-World Impact and Benchmarks Architectural Overview Practical Integration Summary Introduction Empowering large language models (LLMs) to fluidly interact with dynamic, real-world environments is a new frontier for AI engineering. The Model Context Protocol (MCP) specification offers a standardized gateway through which LLMs can interface with arbitrary external systems—APIs, file systems, databases, applications, or tools—without needing custom glue code or brittle prompt hacks each time. Still, leveraging such toolsets programmatically, with robust reasoning across multi-step tasks, remains a formidable challenge. This is where the recent combination of MCP- RL (a reinforcement learning loop targeting MCP servers) and the open-source ART (Agent Reinforcement Trainer) library ...

Multimodal reasoning, where models integrate and interpret information from multiple sources such as text, images, and diagrams, is a frontier challenge in AI. VL-Cogito is a state-of-the-art Multimodal Large Language Model (MLLM) proposed by DAMO Academy (Alibaba Group) and partners, introducing a robust reinforcement learning pipeline that fundamentally upgrades the reasoning skills of large models across mathematics, science, logic, charts, and general understanding. Core Innovations VL-Cogito’s unique approach centers around the Progressive Curriculum Reinforcement Learning (PCuRL) framework, engineered to systematically overcome the instability and domain gaps endemic to multimodal reasoning. The framework includes two breakthrough innovations: Online Difficulty Soft Weighting (ODSW): This mechanism assigns dynamic weights to training samples according to their difficulty and the model’s evolving capabilities. Rather than rigidly filtering out “easy” or “hard” samples, ODSW...

Estimated reading time: 5 minutes Table of contents Introduction What Is a Proxy Server? Technical Architecture: Key Functions (2025): Types of Proxy Servers Descriptions: Key Use Cases in 2025 Emerging Trends in Proxy Servers (2025) Top Proxy Server Providers in 2025 Provider Notes: Choosing a Provider: Conclusion Introduction A proxy server is a vital intermediary between clients and destination servers, facilitating both security and speed in the modern internet. In 2025, with digital privacy, enterprise security, and data-driven automation to the forefront, proxy servers are indispensable for individuals and organizations. The global web proxy market is projected to reach $50 billion by 2026, propelled by ongoing privacy and compliance demands. What Is a Proxy Server? A proxy server is a dedicated system or software relay that takes requests from a client (e.g., a browser) and forwards them to a target server. The proxy then collects the server’s ...

NVIDIA has unveiled a major milestone in scalable machine learning: XGBoost 3.0, now able to train gradient-boosted decision tree (GBDT) models from gigabytes up to 1 terabyte (TB) on a single GH200 Grace Hopper Superchip. The breakthrough enables companies to process immense datasets for applications like fraud detection, credit risk modeling, and algorithmic trading, simplifying the once-complex process of scaling machine learning ML pipelines. Breaking Terabyte Barriers At the heart of this advancement is the new External-Memory Quantile DMatrix in XGBoost 3.0. Traditionally, GPU training was limited by the available GPU memory, capping achievable dataset size or forcing teams to adapt complex multi-node frameworks. The new release leverages the Grace Hopper Superchip’s coherent memory architecture and ultrafast 900GB/s NVLink-C2C bandwidth. This enables direct streaming of pre-binned, compressed data from host RAM into the GPU, overcoming bottlenecks and memory constraints th...