Even if it's AI generated (or AI "improved"), I'll comment on the content:
> The learning loop got tighter. Ask a question. Inspect the answer. Run the code. Break it. Read the implementation. Correct the assumption. Try again.
This assumes that you have the resources (time and money/AI usage budget) and the interest to do that. If you do programming as a job rather than as a hobby, the time you can spend on things like this and the budget you can spare for it will be severely limited. Also, after you have finally coaxed the LLM into generating something that fits all the requirements without breaking in new and surprising ways, has code that's not ridiculously overengineered (that's probably the real reason behind vibe coding: as long as you don't look at the horror beneath the hood, you can tell yourself everything's fine), doesn't break some convention of the codebase and has the required test coverage and other metrics, you're not only out of time for the task at hand, but also more than ready to move on to something else.
> The part I find most exciting is not that the same ticket takes fewer hours. It is that entirely different projects now fit inside a human life.
This is my favorite part too. With my own project for example, I had the agent integrate libgit into it so I could add source control to my IDE. Yes, I could have done it myself, but it would have taken me months of trying to understand the API, testing various things, and finally implementing it all. Instead, the agent did almost all the work (I still designed the architecture and how it plugged in). It took about two weeks calendar time and most of that time I was doing something else while the agent worked.
The thing is, before AI agents, I wouldn't have even attempted the work. I wouldn't have been able to afford the time.
This seems to be a fairly common opinion, as does the opinion that this sits as a counterpoint to.
There has been a divergence that is quite stark. I have seen it argued that this is a reflection of ability, and that as a skill multiplier those lacking in skill are feeling left behind. I'm not certain that this is accurate. I definitely see people who have made things that impressed me in the past have been more likely to embrace AI. It may just be a measure of a type of personality.
I have noticed that people who have a strong sense of possessiveness over what they create are more resistant, but those who create in order to have the world contain the thing they are making are happy to have anything that will enable them to contribute to the world.
I think the biggest differentiator is simply that there are people for whom the act of typing the code into the editor was the part they enjoyed, and LLMs have basically killed that, by making it woefully inefficient by comparison.
I think the scary bit is down the road. We can all use AI well because we have the gist of how things work and a well-honed set of technical empathy for "how they probably wrote it."
I'm not sure how you build that sense as well if you're starting out today, though.
I think this is exactly it. And IMO - there's nothing wrong with enjoying that part of it. But it may not be a high paying career like it was previously.
Some people though society was paying them lots of money to type code into an editor. But that was never true - people and businesses were paying them to solve problems.
This guy can't even be bothered to type his own thoughts out, but expects us to read what claude dumped out? Typing your own thoughts is just not hard at all, I don't get it.
I think the style essay akin to a punchy, TED-talk style persuasive essay is dead.
Whether or not this author wrote it themselves (I strongly suspect at least an AI editor), the specific style that was, for a time, very attractive has become the hallmark of AI-written prose; and, while the content doesn’t seem wrong from a particular view, it also leaves a taste of waste to me - waste that I spent that time reading something they didn’t write, full of -isms that aren’t theirs, when the thesis could have padded out far less paper.
The word count feels unearned, even if the content is fine.
> The learning loop got tighter. Ask a question. Inspect the answer. Run the code. Break it. Read the implementation. Correct the assumption. Try again.
This assumes that you have the resources (time and money/AI usage budget) and the interest to do that. If you do programming as a job rather than as a hobby, the time you can spend on things like this and the budget you can spare for it will be severely limited. Also, after you have finally coaxed the LLM into generating something that fits all the requirements without breaking in new and surprising ways, has code that's not ridiculously overengineered (that's probably the real reason behind vibe coding: as long as you don't look at the horror beneath the hood, you can tell yourself everything's fine), doesn't break some convention of the codebase and has the required test coverage and other metrics, you're not only out of time for the task at hand, but also more than ready to move on to something else.
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