Table of contents Introduction: The Rising Need for AI Guardrails What Are AI Guardrails? Trustworthy AI: Principles and Pillars LLM Evaluation: Beyond Accuracy Architecting Guardrails into LLMs Challenges in LLM Safety and Evaluation Conclusion: Toward Responsible AI Deployment Introduction: The Rising Need for AI Guardrails As large language models (LLMs) grow in capability and deployment…
The Allure and The Hype Vibe coding—constructing applications through conversational AI rather than writing traditional code—has surged in popularity, with platforms like Replit promoting themselves as safe havens for this trend. The promise: democratized software creation, fast development cycles, and accessibility for those with little to no coding background. Stories abounded of users prototyping full…
Table of contents Introduction The Surge of Vibe Coding: Data and Adoption Trends How Vibe Coding Works: Workflow Innovations Key Drivers Behind the Trend Productivity Benefits and Statistical Impact Leading Vibe Coding Tools of 2025 How Different Audiences Benefit Emerging Features and Breakthroughs Risks and Limitations Best Practices for Vibe Coding in 2025 The Future:…
In this tutorial, we delve into the creation of an intelligent Python-to-R code converter that integrates Google’s free Gemini API for validation and improvement suggestions. We start by defining the conversion logic, mapping Python functions, libraries, and syntactic patterns to their closest R equivalents. Then, we leverage Gemini AI to assess the quality of our…
Table of contents 1. MCP Overview & Ecosystem 2. AWS: MCP at Cloud Scale 3. Microsoft Azure: MCP in Copilot & AI Foundry 4. Google Cloud: MCP Toolbox & Vertex AI 5. Cross-Cloud Best Practices 6. Security & Risk Management (2025 Threat Landscape) 7. Expanded Ecosystem: Beyond the “Big Three” 8. Example: AWS MSK MCP…
In this tutorial, we guide you through the design and functionality of AsyncConfig, a modern, async-first configuration management library for Python. We build it from the ground up to support powerful features, including type-safe dataclass-based configuration loading, multiple configuration sources (such as environment variables, files, and dictionaries), and hot reloading using watchdog. With a clean…
Over the past decade, deep learning has revolutionized artificial intelligence, driving breakthroughs in image recognition, language modeling, and game playing. Yet, persistent limitations have surfaced: data inefficiency, lack of robustness to distribution shifts, high energy demand, and a superficial grasp of physical laws. As AI adoption deepens into critical sectors—from climate forecasting to medicine—these constraints…
Personalized recommendations have become a vital component of many digital systems, aiming to surface content, products, or services that align with user preferences. The process relies on analyzing past behavior, interactions, and patterns to predict what users are likely to find relevant. Over time, techniques have shifted from basic filtering to advanced models powered by…
The development of large-scale language models (LLMs) has historically required centralized access to extensive datasets, many of which are sensitive, copyrighted, or governed by usage restrictions. This constraint severely limits the participation of data-rich organizations operating in regulated or proprietary environments. FlexOlmo—introduced by researchers at the Allen Institute for AI and collaborators—proposes a modular training…
In this tutorial, we’ll explore how to implement Chain-of-Thought (CoT) reasoning using the Mirascope library and Groq’s LLaMA 3 model. Rather than having the model jump straight to an answer, CoT reasoning encourages it to break the problem down into logical steps—much like how a human would solve it. This approach improves accuracy, transparency, and…