Mastering Prompt Engineering for AI Assistants

📖 TUTORIALS & CASE STUDIES

Building Production-Ready AI Agents

Estimated read time: 15 min

This guide walks developers through creating robust AI agents, comparing LLM options like GPT-4, Claude, and Mistral. It covers essential infrastructure choices, framework selection, and security considerations, with practical tips for ensuring reliable agent behavior in production environments.

Free Course: AI Agents with RAG

Estimated read time: 15 min

This course introduces a free, practical approach for building production-grade AI agents. Developers will learn to create a philosophical gaming simulation using LangGraph, implement RAG systems, handle memory management, deploy real-time APIs, and master LLMOps practices including monitoring and evaluation.

HyperWrite's LLM Selection Using Stripe Data

Estimated read time: 25 min

This guide demonstrates how startups can use A/B testing and Stripe conversion data to evaluate LLM performance. HyperWrite's case study shows how they successfully switched to GPT-4.1, maintaining conversion rates while reducing costs, offering practical insights for developers building AI-powered applications.

Exa's RAG-Optimized Search Evaluation Framework

Estimated read time: 25 min

Exa's analysis reveals their approach to evaluating search engine performance for AI applications, particularly focusing on RAG systems. Their framework combines traditional metrics with LLM-based evaluation methods, offering insights for developers building retrieval-augmented applications. The analysis shows how different evaluation methodologies impact search quality and RAG effectiveness.

Mastering Prompt Engineering for AI Assistants

Estimated read time: 25 min

This guide teaches developers how to craft effective prompts for AI coding assistants, covering debugging, refactoring, and feature implementation. Learn systematic approaches to get more precise, relevant responses from tools like GitHub Copilot and other AI assistants, with practical examples and common pitfalls to avoid.

Model Context Protocol for LLM Integration

Estimated read time: 14 min

Dive into Model Context Protocol (MCP), an open standard that streamlines how LLMs interact with external tools and APIs. This guide explains MCP's client-host-server architecture, communication patterns, and lifecycle management, helping developers build more maintainable and scalable AI agent systems.

🧰 TOOLS

AWS Serverless MCP Server Launch

Estimated read time: 5 min

AWS has unveiled a new Serverless MCP Server that enables AI-driven coding agents to design, deploy, and troubleshoot serverless applications with minimal human intervention. The server provides agents with serverless architecture knowledge, templates, and best practices, while incorporating security features to protect sensitive operational data.

Google's AI Edge Stack for Mobile

Estimated read time: 8 min

Google's AI Edge platform introduces a complete stack for deploying AI models across mobile, web, and embedded applications. The platform includes MediaPipe for ready-made solutions, LiteRT for cross-platform model deployment, and tools for model visualization and optimization, enabling developers to build sophisticated AI features with reduced latency and offline capabilities.

Mistral Code Challenges GitHub Copilot

Estimated read time: 8 min

Mistral AI's new enterprise coding assistant combines advanced LLM capabilities with IDE integration, offering developers context-aware completions, autonomous coding features, and deep codebase understanding. The platform includes customizable models and enterprise controls, with deployment flexibility for maintaining code privacy and security.

Google's Enhanced Gemini 2.5 Pro Preview

Estimated read time: 4 min

Google has announced an upgraded preview of Gemini 2.5 Pro, featuring improved benchmark scores and enhanced coding capabilities. Available through Google AI Studio and Vertex AI, developers can now experiment with the model before its enterprise release, complete with new thinking budgets for better cost and latency control.

Claude Composer Streamlines AI Interactions

Estimated read time: 8 min

Claude Composer enhances Claude's development environment by automating permission dialogs, managing tool access, and providing configurable safety rules. This open-source utility helps developers streamline their AI assistant workflows through flexible rulesets, toolset management, and system notifications, reducing interruptions during coding sessions.

Container Use Manages Multiple AI Agents

Estimated read time: 8 min

Container Use introduces an open-source tool that enables developers to run multiple AI coding agents in isolated containerized environments. Each agent operates in its own git branch, allowing parallel development without conflicts while providing real-time visibility into agent actions. Compatible with Claude Code, Cursor, and other MCP-compatible agents.

📰 NEWS & EDITORIALS

Why LLM Skepticism is Misguided

Estimated read time: 25 min

A seasoned developer challenges common criticisms of LLMs in software development in this piece. The article addresses concerns about code quality, hallucination, and developer productivity, while highlighting how modern LLM agents are transforming software development through automated coding, testing, and debugging workflows.

Anthropic Cuts Windsurf's Claude Access

Estimated read time: 4 min

In a significant development for AI developers, Anthropic has terminated Windsurf's direct access to Claude models, citing OpenAI acquisition rumors and computing constraints. The company is pivoting towards agentic coding products and partnering with companies like Cursor, while expanding compute capacity through Amazon.

Cursor Hits $500M Revenue, Raises $900M

Estimated read time: 4 min

Anysphere's AI coding assistant Cursor has achieved remarkable growth, as detailed in this TechCrunch article. With revenue doubling every two months and new enterprise offerings, the company secured a $900M investment at a $9.9B valuation, highlighting the increasing demand for AI-powered development tools.