Google Research has unveiled a groundbreaking method for fine-tuning large language models (LLMs) that slashes the amount of required training data by up to 10,000x, while maintaining or even improving model quality. This approach centers on active learning and focusing expert labeling efforts on the most informative examples—the “boundary cases” where model uncertainty peaks. The…
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…
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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…
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…
We build an advanced LangGraph multi-agent system that leverages Google’s free-tier Gemini model for end-to-end research workflows. In this tutorial, we start by installing the necessary libraries, LangGraph, LangChain-Google-GenAI, and LangChain-Core, then walk through defining a structured state, simulating research and analysis tools, and wiring up three specialized agents: Research, Analysis, and Report. Along the…
This article provides a technical comparison between two recently released Mixture-of-Experts (MoE) transformer models: Alibaba’s Qwen3 30B-A3B (released April 2025) and OpenAI’s GPT-OSS 20B (released August 2025). Both models represent distinct approaches to MoE architecture design, balancing computational efficiency with performance across different deployment scenarios. Model Overview Feature Qwen3 30B-A3B GPT-OSS 20B Total Parameters 30.5B…
The Model Context Protocol (MCP) has rapidly become a foundational standard for connecting large language models (LLMs) and other AI applications with the systems and data they need to be genuinely useful. In 2025, MCP is widely adopted, reshaping how enterprises, developers, and end-users experience AI-powered automation, knowledge retrieval, and real-time decision making. Below is…
In today’s data-driven world, valuable insights are often buried in unstructured text—be it clinical notes, lengthy legal contracts, or customer feedback threads. Extracting meaningful, traceable information from these documents is both a technical and practical challenge. Google AI’s new open-source Python library, LangExtract, is designed to address this gap directly, using LLMs like Gemini to deliver…
The tides have turned in the enterprise AI landscape. According to Menlo Ventures’ 2025 “Mid-Year LLM Market Update,” Anthropic’s Claude has overtaken OpenAI as the leading language model provider for enterprise, now capturing 32% of market share compared to OpenAI’s 25%—a dramatic reversal from OpenAI’s dominant 50% share just one year ago. This is more…
In this tutorial, we’ll explore a range of SHAP-IQ visualizations that provide insights into how a machine learning model arrives at its predictions. These visuals help break down complex model behavior into interpretable components—revealing both the individual and interactive contributions of features to a specific prediction. Check out the Full Codes here. Installing the dependencies Copy…