Developing with GitHub Copilot Agent Mode and MCP

And again, the most convoluted setup for development with an example that fails to demonstrate why you should adopt such practice. It’s like doing a GDB demo with an hello world program. Or doing Linux From Scratch to show how you can browse the web.

The goal of software engineering is not to write code faster. Coding is itself a translation task (and a learning workflow, as you can’t keep everything in your head). What you want is the power of decision, and better decision can be made with better information. There’s nothing in the setup that helps with making decision.

There are roughly six steps in software engineering, done sequentially and iteratively. Requirements gathering to shape the problem, Analysis to understand it, Design to come up with a solution, Coding to implement it, Testing to verify the solution, and Maintenance to keep the solution working. We have methods and tooling that help with each, giving us relevant information based on important parameters that we need to decide upon.

LLMs are example generators. Give it a prompt and it will gives the answer that fits the conversation. It’s an echo chamber powered by a lossy version of the internet. Unlike my linting tool which will show me the error when there’s one and not when I tell it to.

ADDENDUM

It's like an ivory tower filled with yes-men and mirrors that always reply "you're the fairest of them all". My mind is already prone to lie to itself. What I need most is tooling that is not influenced by what I told it, or what others believe in. My browser is not influencing my note taking tool, telling it to note down the first two results it got from google. My editor is not telling the linter to sweep that error under a virtual rug. And QA does not care that I've implemented the most advanced abstraction if the software does not fit the specs.

I fear for the coming 2 to 3 generation of software engineers. Will they be able to handle problems if the AI is not available or is the source of the problem? Only time will tell.

> The goal of software engineering is not to write code faster

That just really depends on your situation. Here's a case I had just last week: we had artists in residency who suddenly showed up with a new, expensive camera that didn't have any easy to use driver but requires the use of their huge and bulky custom SDK.

Claude whipped a basic working c++ proprietary-camera-sdk-to-open-video-sharing-protocol in, what, 2 minutes? From the first go with a basic prompt? Without that it'd have been at least a couple days of development, likely a day just to go through the humongous docs -- except I had at most two hours to put on this. And I already have experience doing exactly this, having written software that involves realsenses, orbbec, leapmotion, Kinect, and all forms of weird cameras that require the use of their c++ SDK.

So the artists would just not be able to do their residency the way they wanted because they only have 3 days on-site to work too.

Or I'd have spent two days for some code that is very likely to only ever being used once, as part of this residency.

Thus in my line of work, being able to output code that works, faster than humans, is absolutely game changer - this situation I'm describing is not the exception, it's pretty much a weekly occurrence.

OP's comment also seems to be firmly stuck in 2023 when you'd prompt ChatGPT or whatever. The fact that LLMs today, when strapped into an agentic harness, can do or help with all of these things (ideation, architecture, use linters, validate code, evaluate outputs, and a million other things) seems to elude them.