Generative coding assistants like Claude Code can produce surprisingly correct, compiler-validated architecture when given clear conversational guidance
AI tools also invent highly 'creative' bugs—unsafe globals, mutex bottlenecks, missing epsilon offsets—that require domain expertise to detect
For rote tasks (type definitions, data mapping, API clients, refactors) Claude saves hours, especially in TypeScript and Astro projects
Complex build or tooling changes (e.g., JavaScript module systems, Rust compiler internals) remain fragile; human review and testing are essential
Multi-modal workflows—hand-written drafts, observability data, shared context stores—hint at a future where AI augments the entire software lifecycle, not just code editing
AI 'YOLO Mode' can significantly accelerate website migrations, but requires constant human supervision to prevent security risks and unwanted changes
Combining different AI models (like Gemini 2.5 Pro for multimodal tasks and Claude Sonnet 3.7 for web searches) creates a more effective development workflow
Model Context Protocol (MCP) tools like Puppeteer and Sequential Thinking in Windsurf enable AI to interact with websites and execute multi-step processes
AI models struggle with large files (like CSS) and special formats (like Base64), requiring workarounds or alternative approaches
Long AI sessions face context window limitations; creating checkpoints and to-do lists helps maintain progress across multiple sessions
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