I'm a software developer since the 90s, basically before the internet, we had some C books for reference and that's it. I can tell you that I started last year to use copilot in vscode and some chatgpt on a web page, and it basically changed my world, and all my 50+ years old coworkers are amazed by what it can do really.
You are right it will not fade at all in software development.
Completely disagree. The difficult part of software development was never writing code OR speed of delivery. It was understanding requirements and problem solving. LLMs still can not do either of those things and there is no evidence they ever will be able to.
An example of how harmful LLMs actually are to development can succinctly be described with an issue I had a few weeks ago. I found an issue in an open source project, code was fine if a bit hard to understand. I came up with a PR to fix the problem.
In the time from me checking out the code to submitting the PR, a little less than 24 hours, the maintainer had completely rewritten the entire project with Claude. It was complete nonsense. Incredibly difficult to understand. Abstracting things that didn’t need abstracting. My PR was useless, because the entire project was new. The maintainer definitely didn’t understand the changes either. If a bug came up there’s no way AI would be able to solve it (the bug was still there even though the code was entirely new).
LLMs don’t understand the code. They just make things that look like they will work. And then a human has to maintain it (or keep paying billions of dollars for Claude to try to fix it).
It was understanding requirements and problem solving. LLMs still can not do either of those things and there is no evidence they ever will be able to.
I don't know... I just made a scheduling / timesheet creation app. Multi-user, overlapping clients and providers, multiple funding sources. Took 10 calendar days to make the initial app working part time, maybe 2-3 hours a day. Initially written in Python, decided at that point I'd rather have it in Go. Because the initial app had robust requirements and design docs, the translation to Go happened in less than 5 calendar days, with almost zero human involvement beyond telling the agent "continue" at each stopping point. After the Go translation was done (and debugged by the LLM to a flawless translation - only difference is that it runs faster), I was given a new timesheet to use for some of the workers, weekly instead of bi-weekly. Pay weeks start on Monday instead of Thursday. Various wrinkles about how the employees and clients and services are identified, weird sub-totals by service. All I told the LLM was: "Here's a new timesheet that we'll be using for some workers, design the necessary modifications and extensions to accomodate it." It did, independently. It highlighted three shortcuts it took and I told it not to take those shortcuts, it adjusted.
That's not quite rocket science, but it's still impressive: to dissect the given .pdf, determine what data goes in what fields, in what formats, with what calculations, based on just reading the page, then adapt the existing app to fill it out automatically.
In my professional work, I have watched AI code reviews catch 10x more dumb slop human errors than human reviews used to the same time a year earlier, consistently for about 8 months now.
I'm a software developer since the 90s, basically before the internet, we had some C books for reference and that's it. I can tell you that I started last year to use copilot in vscode and some chatgpt on a web page, and it basically changed my world, and all my 50+ years old coworkers are amazed by what it can do really.
You are right it will not fade at all in software development.
Completely disagree. The difficult part of software development was never writing code OR speed of delivery. It was understanding requirements and problem solving. LLMs still can not do either of those things and there is no evidence they ever will be able to.
An example of how harmful LLMs actually are to development can succinctly be described with an issue I had a few weeks ago. I found an issue in an open source project, code was fine if a bit hard to understand. I came up with a PR to fix the problem.
In the time from me checking out the code to submitting the PR, a little less than 24 hours, the maintainer had completely rewritten the entire project with Claude. It was complete nonsense. Incredibly difficult to understand. Abstracting things that didn’t need abstracting. My PR was useless, because the entire project was new. The maintainer definitely didn’t understand the changes either. If a bug came up there’s no way AI would be able to solve it (the bug was still there even though the code was entirely new).
LLMs don’t understand the code. They just make things that look like they will work. And then a human has to maintain it (or keep paying billions of dollars for Claude to try to fix it).
I don't know... I just made a scheduling / timesheet creation app. Multi-user, overlapping clients and providers, multiple funding sources. Took 10 calendar days to make the initial app working part time, maybe 2-3 hours a day. Initially written in Python, decided at that point I'd rather have it in Go. Because the initial app had robust requirements and design docs, the translation to Go happened in less than 5 calendar days, with almost zero human involvement beyond telling the agent "continue" at each stopping point. After the Go translation was done (and debugged by the LLM to a flawless translation - only difference is that it runs faster), I was given a new timesheet to use for some of the workers, weekly instead of bi-weekly. Pay weeks start on Monday instead of Thursday. Various wrinkles about how the employees and clients and services are identified, weird sub-totals by service. All I told the LLM was: "Here's a new timesheet that we'll be using for some workers, design the necessary modifications and extensions to accomodate it." It did, independently. It highlighted three shortcuts it took and I told it not to take those shortcuts, it adjusted.
That's not quite rocket science, but it's still impressive: to dissect the given .pdf, determine what data goes in what fields, in what formats, with what calculations, based on just reading the page, then adapt the existing app to fill it out automatically.
I truly don't want to try your app... I don't want all my data leaked or my PC, laptop or phone bricked just because a dumb AI-slop code error.
It's not for you, anyway.
In my professional work, I have watched AI code reviews catch 10x more dumb slop human errors than human reviews used to the same time a year earlier, consistently for about 8 months now.