A beginner's guide to the Model Context Protocol (MCP) for building scalable AI applications with tutorials in C#, Python, Java, and TypeScript.
A technical cheatsheet for using Google's Gemini AI models with the LangChain framework, covering setup, chat models, prompt templates, and image inputs.
A curated list of must-see developer sessions from Microsoft Build 2025, focusing on AI, cloud, and development tools.
Announcing EpicAI.pro, a new learning platform focused on building applications for the AI era, teaching foundational principles for AI-agent interaction.
Reflections on the first unit of the Hugging Face Agents course, focusing on the potential and risks of code agents and their evaluation.
Explores four main approaches to building and enhancing reasoning capabilities in Large Language Models (LLMs) for complex tasks.
A guide to building AI applications using the LangChain framework, covering core concepts, installation, and practical examples.
A tutorial on building an AI agent with a reasoning loop using Hugging Face's smolagents library and Azure OpenAI's GPT-4o model.
A summary of Chapter 6 from 'Prompt Engineering for LLMs', covering prompt structure, document templates, and strategies for effective context inclusion.
A curated list of key Microsoft Ignite 2024 sessions for developers in Australia and New Zealand, focusing on AI, DevOps, cloud, and development tools.
Analysis of Mozilla's public AI paper, highlighting the benefits of small, open-source language models for efficiency, privacy, and global access.
An overview of Azure AI Foundry, a unified platform for building and deploying AI solutions on Microsoft Azure, covering its features and benefits.
Explores the gap between generative AI's perceived quality in open-ended play and its practical effectiveness for specific, goal-oriented tasks.
A developer's monthly curated list of tech resources, covering databases, LLM performance, AI in development, and microservices.
A curated list of resources for learning Generative AI and Prompt Engineering, including guides, tutorials, and documentation from OpenAI, DeepLearning.AI, and Microsoft.
A comprehensive guide to Meta's LLaMA 2 open-source language model, covering resources, playgrounds, benchmarks, and technical details.
Explores the difference between rigorous prompt engineering and amateur 'blind prompting' for language models, advocating for a systematic, test-driven approach.
A satirical look at AI development and government funding, imagining a fictional 'Ministry of Silly Models' in the UK.
