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«`html Understanding the Target Audience for MiroMind-M1 The MiroMind-M1 initiative targets a range of professionals involved in mathematics, AI, and machine learning. This includes researchers, data scientists, and AI developers who are seeking robust and transparent tools for mathematical reasoning. Their pain points often include a lack of transparency and reproducibility in proprietary models, as…
«`html Rubrics as Rewards (RaR): A Reinforcement Learning Framework for Training Language Models with Structured, Multi-Criteria Evaluation Signals Researchers have proposed the Rubrics as Rewards (RaR) framework, which utilizes checklist-style rubrics to enhance reinforcement learning in training language models (LLMs). This method focuses on guiding multi-criteria tasks, producing prompt-specific rubrics based on structured principles. Each…
Building a Comprehensive AI Agent Evaluation Framework with Metrics, Reports, and Visual Dashboards In this tutorial, we walk through the creation of an advanced AI evaluation framework designed to assess the performance, safety, and reliability of AI agents. We begin by implementing a comprehensive AdvancedAIEvaluator class that leverages multiple evaluation metrics, such as semantic similarity,…
Implementing Self-Refine Technique Using Large Language Models (LLMs) This tutorial demonstrates how to implement the Self-Refine technique using Large Language Models (LLMs) with Mirascope, a powerful framework for building structured prompt workflows. The Self-Refine technique is a prompt engineering strategy where the model evaluates its own output, generates feedback, and iteratively improves its response based…
«`html It’s Okay to Be “Just a Wrapper”: Why Solution-Driven AI Companies Win In today’s rapidly evolving AI landscape, many founders and observers find themselves preoccupied with the idea that successful startups must build foundational technology from scratch. This narrative is particularly prevalent among those launching so-called “LLM wrappers” — companies whose core offering builds…
Safeguarding Agentic AI Systems: NVIDIA’s Open-Source Safety Recipe Persona & Context Understanding The target audience for NVIDIA’s open-source safety recipe includes AI developers, data engineers, compliance officers, and business managers in enterprises adopting agentic AI systems. These professionals typically face challenges related to the risks and complexities of integrating autonomous AI solutions into existing workflows.…
9 Open Source Cursor Alternatives You Should Use in 2025 The demand for AI-powered coding tools has exploded, with open-source alternatives now rivaling commercial solutions like Cursor in features, flexibility, and privacy. If you’re seeking a powerful, cost-effective, and open-source code assistant, consider these top picks for 2025: Zed Zed is a high-performance, open-source code…
«`html Amazon Develops an AI Architecture that Cuts Inference Time 30% by Activating Only Relevant Neurons Amazon researchers have developed a new AI architecture that reduces inference time by 30% by activating only task-relevant neurons. This approach addresses a significant challenge in large AI models: the computational expense and latency associated with activating every neuron…
«`html Microsoft Edge Launches Copilot Mode to Redefine Web Browsing for the AI Era Microsoft has made a significant advancement in web browsing with the introduction of Copilot Mode in Edge, marking a crucial step toward an AI-native browser. This development not only transforms Edge but also reshapes our understanding of what a browser can…
Creating a Knowledge Graph Using an LLM In this tutorial, we’ll show how to create a Knowledge Graph from an unstructured document using a Large Language Model (LLM). Traditional Natural Language Processing (NLP) methods have been used for extracting entities and relationships, but LLMs like GPT-4o-mini enhance this process with improved accuracy and context awareness.…