Claude Desktop

In this segment, I’ll generate many candidate applications using my experimental framework, CodeAgents, choosing from a set of models: GPT-4.1, Claude 3.7, and GPT-4o. Then, I’ll compare and contrast the solutions. Along the way, I’ll present some ideas and tips on improving AI-generated code in ways that generally translate to other tools and frameworks. It isn’t easy to score how good an AI-coded solution is. Of the possible metrics, code complexity might not be as meaningful as long as the AI understands the code, as would “maintainability,” as that’s based on human limitations; the AI can refactor on the fly. Test coverage is a good metric as it measures how well the AI-generated test suite covers the code. ...

I’ve tried Cursor, Replit, Lovable, and Bolt with varying degrees of success and found recurring themes in the use of these tools that require “vibing” until you arrive at a finished, hopefully working, result. Whether the result is good can sometimes be in the eye of the beholder. I’ve also become fascinated by how these tools will change the way programmers think about code and its organization — how many rules will be thrown completely out the window and how, oddly, the new rules will harken back to the early days of programming before Google and the Internet. ...

The first time I fired up an MCP (Model Context Protocol) server plugin, “Agent,” I was excited to see it registered in Claude Desktop but immediately annoyed by the errors that popped up. I didn’t expect a smooth experience in my encounter with the future of Agentic AI, but I found many configuration tweaks, clunky debugging tools, and broken dependencies along the way. It was a stark reminder that we’re in the early days, and there’s a lot of ground to cover before Agents become seamless collaborators. ...