Six AI coding assistants tested: GitHub Copilot, Claude Code, Cursor, Windsurf, Kiro, and Gemini CLI. Through hands-on testing with Rust and React projects, I discovered each tool’s strengths and surprising limitations. Learn which AI coding agent matches your development workflow.
“I’ll admit it: I’m an acronym addict. My colleagues have even created their own acronym for me – AHtGLA (Always Have to Google Lothar’s Acronyms).
Discover how architectural choices can be a defense in securing agentic AI systems. This comprehensive guide explores OWASP’s latest “Securing Agentic Applications” paper, covering everything from Sequential and Hierarchical architectures to the 15 critical threat categories facing autonomous AI applications. Learn practical implementation strategies, universal security principles, and how to build trust into your agentic AI systems from the ground up.
I tested Kiro’s structured, spec-driven development approach against GitHub Copilot’s rapid iteration style by building the same React quiz app with both tools. While Kiro excelled at upfront planning and task breakdown, its rigid methodology failed to produce working code. Copilot’s pragmatic approach with real-time feedback ultimately delivered a functional prototype. The key insight? Understanding when to use structured planning versus rapid prototyping can make or break your AI-assisted development workflow.
Kiro IDE review: An agentic development environment featuring spec-driven workflows, EARS requirements syntax, and advanced security controls. Discover Kiro’s autopilot vs supervised modes, trusted command configurations, and real-time change tracking. Essential reading for developers evaluating structured alternatives to prompt-based coding assistants.
Dive into the discussion around integrating Kafka consumers within the Backend for Frontend (BFF) pattern. Learn when it’s a powerful tool for real-time UI updates and data projections, and critically, when business logic should remain separate for architectural health and scalability.
This analysis examines a multi-persona AI prompt technique applied to Rust development, testing its effectiveness on a protein translation exercise. While the approach successfully transformed imperative code into functional pipelines and improved error handling, it revealed critical limitations including AI hallucination risks and test compatibility issues. For security-conscious teams, the technique offers valuable code quality improvements when integrated with human oversight and comprehensive validation processes.
Introducing “flow coding”—a new multi-agent development approach where you orchestrate AI agents to collaborate on software projects. Unlike vibe coding’s human-AI partnership, flow coding enables AI-to-AI collaboration while you guide the creative process from above.
Curious about the speed of AI app creation? I put Google Gemini’s “Create with Canvas” and Claude Sonnet 4 to the test, generating a “weird animals quiz.” Explore my findings on their “fast vibe coding” capabilities, aesthetic preferences, and the critical role of speed in prototyping.
Hands-on review of Google’s new Gemini CLI. Covers sandbox mode, auto-code generation and configuration options.
Copyright © 2003 - 2024 | lotharschulz.info