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…
LLMs have made impressive strides in generating code for various programming tasks. However, they mostly rely on recognizing patterns from static code examples rather than understanding how the code behaves during execution. This often leads to programs that look correct but fail when run. While recent methods introduce iterative refinement and self-debugging, they typically act…
The Growing Threat Landscape for LLMs LLMs are key targets for fast-evolving attacks, including prompt injection, jailbreaking, and sensitive data exfiltration. It is necessary to adapt defense mechanisms that move beyond static safeguards because of the fluid nature of these threats. Current LLM security techniques suffer due to their reliance on static, training-time interventions. Static…
Mirascope is a powerful and user-friendly library that provides a unified interface for working with a wide range of Large Language Model (LLM) providers, including OpenAI, Anthropic, Mistral, Google (Gemini and Vertex AI), Groq, Cohere, LiteLLM, Azure AI, and Amazon Bedrock. It simplifies everything from text generation and structured data extraction to building complex AI-powered…
Researchers from MetaStone-AI & USTC introduce a reflective generative model, MetaStone-S1, which attains OpenAI o3-mini’s performance through a new Reflective Generative Form. Key Innovations Reflective Generative Form Unified Policy and Reward Modeling: MetaStone-S1 integrates the policy model (for generating reasoning trajectories) and the step-level Process Reward Model (PRM) into a single architecture, using shared parameters. This…