Category Added in a WPeMatico Campaign
«`html Too Much Thinking Can Break LLMs: Inverse Scaling in Test-Time Compute Recent advances in large language models (LLMs) have encouraged the idea that allowing models to “think longer” during inference typically improves their accuracy and robustness. Techniques such as chain-of-thought prompting, step-by-step explanations, and increasing “test-time compute” have become standard in the field. However,…
«`html Understanding the Target Audience for A Coding Guide to Build a Scalable Multi-Agent System with Google ADK The target audience for this tutorial primarily includes software developers, data scientists, and business analysts looking to leverage AI technologies for building scalable systems. These professionals typically work in enterprise settings and are interested in optimizing workflows…
Understanding the Target Audience for FastVLM The target audience for the introduction of FastVLM comprises primarily AI researchers, machine learning practitioners, and business leaders interested in the implementation and optimization of Vision Language Models (VLMs) in enterprise applications. This audience typically has a strong technical background and is engaged in fields such as AI development,…
«`html Is Vibe Coding Safe for Startups? A Technical Risk Audit Based on Real-World Use Cases Introduction: Why Startups Are Looking at Vibe Coding Startups are under pressure to build, iterate, and deploy faster than ever. With limited engineering resources, many are exploring AI-driven development environments—collectively referred to as “Vibe Coding”—as a shortcut to launch…
«`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.…