I committed to the fully agentic approach for a while. No code by hand!
Setting aside the "capability" of the tools, I knew it was the wrong direction after a couple months or so in. I specifically remember during a large feature implementation running out of tokens for my 5 hour window or whatever, and I just couldn't continue on my own. Mostly due to laziness (I'll just wait till tomorrow when my limit resets!), but I also picked up on my first big whiff of skill rot / atrophy brewing, and that made feel uncomfortable.
I just don't see this fully agentic approach going well long term. Talk about the ultimate dependency!! If the lights are "turned off" for whatever reason - outages, cost increases, or the agentic velocity finally reaches a complexity tipping point and you've lost control and understanding of your system to the point the agents are making things worse, whatever it is - do you really expect to be able to turn back the clock and step in to code at the productivity level and output you used to when you actually... wrote code?
There's definitely skill rot that happens -- but there are also new skills to learn. Dependency on tools is not a new problem, though, is it?
Agents definitely run amok on code bases, create bloat, needless complexity, etc. However, they enable many people to do so much more than they could without them, at least in the moment, that they're not going to go away.
Yeah, I am also feeling the rot and do not like it one bit. However, the world does not revolve around my feelings, so I have adapted and fully embraced it. That being said, I think AI took away 90% of the enjoyment I got from the job of Software Engineer. It isn't just the code writing, it was the space to go deep and immerse myself fully to solve a problem. I think there is still a need to problem solve at different levels, but with AI the velocity has increased so much - the space to focus on one thing for any amount of time has also been drastically reduced (this could be because I work for a company and may be company specific. I also do freelance dev, and find much more enjoyment there, but still not the same as pre AI).
The problem I'm seeing is that most of that additional output is just increasing pressure on the real bottlenecks such as design, QA, operations, support, etc. Which is why we're seeing this dual phenomenon of programmer productivity increases at the same time as the productivity of the overall business crashes out.
At my last employer we were contending with real customer fatigue and attrition. The application was changing so rapidly that it was overwhelming both our ability to uphold quality standards, and customers' ability to keep up with the feature churn.
Or, to put it more succinctly: nobody actually wants all this slop.
> The bigger cost was the typing. Every time I wanted to build something, I could see the code in my head. I just couldn’t type it out fast enough.
Facts. For me the bigger the gap between what i saw in my head and the speed with which my fingers could physically make it a reality the more stress i would feel and some marathon coding sessions would end w my back all messed up just from the tension
Nice to hear others are feeling this too. I started using typing websites to improve my speed a few months ago. More recently I've noticed that I'm starting to make lots of typos and use sloppy language in general because AI still understands me. That can't be healthy in the long term.
I start with the first word, then move on to the second and so forth, letting the overall idea of what I am reading in my mind grow as I get further and further
I feel a really odd dissonance with all these accounts. I have access to top models via Cursor at work. Every time I think to myself, here’s a tedious but well defined task, with clear success criteria, where you can achieve a lot with persistent iterations. I’ll give it to Claude to do.
First, it takes me ages to describe the task properly. All these clear success criteria, well, instead of writing code from a clear spec in my head, I’m writing tons of prose, and trying to make it unambiguous. I’m programming in English++.
But then, one time in 5, it produces something that kinda works. Maybe not perfect but good enough. Two times out of 5 it kind of sort of looks alright, but actually ignores 80% of the spec, or pays it minimal lip service in the comments.
And sometimes, maybe a bit less than 2/5, it just completely diverges into total shit. Starts writing scripts that load the ast of the main script and pickle it then serialise to base64 for no good reason. Encode some stuff in strings then check ord(string[i]) repeatedly for string comparison. Eventually runs out of context and develops the LLM equivalent of severe dementia.
I truly cannot reconcile my experience with people who seem to say “hey computer write this” and it’s a good use of their time.
I've found a more productive workflow is to ask claude how it would do the task, go back and forth with it a bit until you both agree on the steps, then have it work through them. i also emphasise the need to have every step in its own commit, then go back and read through the stack suggesting code improvements and refactoring, letting the AI do the tedious work of fixing conflicts and restacking. I'm usually pretty happy with the process and the end result.
This is similar to my experience when cursor first came out. But now it's even trickier because LLM actually writes working code that pass the test. The issue is when i dig in deeper, there's often small security or design issues that will cause problem down the road when the app gets more traffic.
These issues are usually in really tedious places that I didn't bother to describe what to do to the agent
I’m m just starting to get results on a local/cloud agent pipeline I’ve been building. I don’t think I will ever code again. Plan, review, read, architect, test, yes. Code though simply doesn’t make sense when I’m getting these results at this velocity.
Every few months it feels like I have moved further up the chain. Now I don’t even look at a PR until it’s been coded and gone through at least 2 AI reviews (always different families). Code is truly cheap. I can have the pipeline produce an entire feature as a thought experiment just to see if the shape is the right fit for the codebase. People like to throw the slop term around a lot. It feels like the opposite of slop through. I can have things written and rewritten as many times as it takes until I’m happy with them.
The weakness I see in this is there at the end. The agents never want to throw things away. They just want to keep adding and adding. I think this is my real value in the chain. That intuition built up over years that says this isn’t right yet or that little tingle in your brain that you’ve laid a landline that you are going to step on in 6 months.
It's a mindset shift from producing code to producing a product. You may look at the code and see slop but look at the product and see value.
This should be extremely intuitive. We know this already. There has always been a divergence between product value and code quality. How many codebases that are widely used are kind of a mess inside? Pretty much all of them to some degree. This is a basic reality of software engineering.
Some slop accusations are fair. Others are people essentially expressing that they dont like shifting to a product mindset.
I dont have your whole pipeline but I recently took a step back and thought about how amazing the AI is. I recently gave Fable a nice, simple open source TUI app with some Claude Design artifacts for a UI/UX facelift + some new complex feature requests and it just one shots the entire thing in 20 minutes. It works great and looks amazing. It's significantly better in terms of UX and functionality than the original. I did this on the side with about 15 minutes of active attention while I was working.
It really isn't that hard. The LLM needs a few iterations of self review, and you need to ask it for an architecture up front, but from there, it's mostly manual testing to make sure the LLM didn't fuck up.
I don't think it'd take more than a few weeks for a high schooler to learn how to prompt well.
Your hypothetical high schooler would have to learn what architecture is, and then what self review is, and iteration, and manual testing... and what "code" and "software" is first.
This is something devs don’t give enough credit. You may think you’re just promoting, but ask someone outside the field to prompt their way to a feature and you will see very clearly what your experience is bringing to the table.
If it takes them more than a few weeks to figure out "Review your own output with a clean context. Check it for bugs, performance, and places where it doesn't do what the user wants", I don't know what to say. And where are you finding high schoolers that don't know what software is?
Note, people don't typically need to understand the architecture documents that the LLMs generate, and will certainly be filling in the rest of the gaps as new models come out. The function of design docs is to make the LLM ask clarifying questions and fill in the constraints.
You can get more confidence by asking the LLM about when the architecture would be appropriate than by trying to intuit it yourself. The LLM is trained on so many more architectures and stories of how they panned out than you are.
The skill is really in manually testing before handoff.
I've been a software dev for 25 years. In the past year I've hand written maybe 10 lines of code. For the first 2-3 months i would read it, then I figured out how to really use AI/code gen and the amount created was more than I could read. I can work on 2-3 projects at once, or 2-3 parts of a larger project at once. More than that I can't keep task switching. The speed and complexity of what I'm able to create is 10x doing it by hand. In the hands of someone that understands system design it's a great tool, but still need fundamentals. I've seen junior's build stuff that would never deploy and would crash with a handful of users.
I think you will always need a button pusher. whether it is the CEO, the product manager or the developer themselves. You will need some sort of overseer who will take accountability of the system that is generated by the AI.
Okay. Is that job going to provide a competitive salary, with good benefits? More importantly, is it going to be personally fulfilling?
Software developers who have adopted agentic coding are worse than class traders. They are actively sabotaging the entire careers future viability, and have made me ashamed to think anyone in this industry was worthy of the title "engineer".
> AI agents are just software, after all. I wasn’t going to understand them by reading prompt guides. I had to use them for real work, see where they failed, change the prompts, tools, or environment, and try again.
AI agents are just software, so the only way to understand them is trial and error?
I think that's similarly in line with software; you don't really know the limitations of a library until you use it, and see that it fails for X reason at 10Hz, or 1000Hz.
I have stopped reading blog posts for 6 months now! Since Fable-1337-B4242 agents became so good at reading that I delegated this load-bearing task. Once I became weak and read a paragraph, but I was reminded that reading is too laborious.
After reading this blog post for me, Fable-1337-B4242 asked me if I also wanted to create a load-bearing plain text website like exe.dev using agentic forces.
I honestly didn't read the whole article, but I think that this kind of experiment leads to a misleading way of seeing things.
I met a few architects/team leaders who wouldn't write almost any code and just technically oversaw things before LLMs got popular, obviously they were very experiencied.
What you have to ask yourself is whether you could oversee a swarm of bots(or people) if you didn't have any coding experience in the first place.
A lot of HN users seem to be, in my opinion, too dismissive about the complexity of creating software. It's true that we don't have to develop in assembly anymore to be a good developer and with time the general level of the entry abstractions that we deal with rises, but, in my opinion once again, it will never change the fact that you need a high number of hours of exposure to something in order to be truly competent to a professional level.
Unfortunate to see an otherwise-good article padded by AI-isms ("But here’s the twist: that search I described earlier was never sent to botd. All the history was in SQLite, so I pointed another agent at it and got the analysis anyway. The tool died; the data didn’t.", amongst other examples).
Maisem Ali writes about how they have not stopped engineering despite handing production of code over to AI. I think that's an interesting perspective for a few reasons, but most of all, I'm not sure I agree.
First, we see a prime example of how that can blow up. "botd died this month. It crumbled under its own weight." It's hard to imagine how an agent harness (or is it an overlay on top of other harnesses?) could crumble under its own weight from sheer technical complexity. What makes a tool like this "crumble"? If properly architected, it seems that any outside change could be adaptable. Failing tests could be fixed.
This AI coding evolution has given us the ability to spin up prototypes we don't understand the inner workings of, but I believe for anything of value (and, I would argue, botd appeared to have value to Maisem) we should at the very least understand and influence the architecture and engineering of what we're building. If Maisem had done that for botd, it would not and _could not_ have "crumbled under its own weight". Whatever outside influence required change within botd would have been manageable.
Second, the off-loading of the production of this article indicates to me a lack of respect (with no offence to Maisem, because this is what much of the industry is doing today) for the value in thinking through the writing process. There's a lot that I've learned personally about software I worked on that only came to me through trying to write explainers. Taking a step back from the software we build and looking at it from a birds-eye view, to share how it is constructed or how to use it, helps develop our understanding.
Maisem's article is already very good, and I hope that is demonstrative of their own writing! I would read more! But I want to read words written by a person for other people. AI can help edit, it can help us build ideas, but in code and in our written word, AI should not be _the author_.
>AI agents are just software, after all. I wasn’t going to understand them by reading prompt guides. I had to use them for real work, see where they failed, change the prompts, tools, or environment, and try again.
It seems that all this knowledge would be useless with a model update...Good for the AI companies though. More tokens as users like this come upto speed with the new models, which they now are totally dependent on...
Because, if you didn't like writing code, and is now used to agents writing code, good luck going back to writing code by hand...
It’s more AI copypasta as it goes further along where it gets really into the how and not the why.
“But here’s the twist: that search I described earlier was never sent to botd. All the history was in SQLite, so I pointed another agent at it and got the analysis anyway. The tool died; the data didn’t.”
If I had more time, I would have written a shorter letter…
I don't understand people that say "I no longer code" as a good thing. I think it's safe to assume that if you studied computer science or similar you like to code. If not, why did you even study that?
To me it just feels like you want to be a manager not an engineer
This is like if a plumber said. I no longer do any construction, I just tell people what to do. Then you probably aren't a builder anymore, you are a foreman or a project manager.
I think your job as an engineer is to know the codebase and how it was engineered
Some of us like the details. Bricklayers are not subhuman! You architect if you want, or manage which is what agentic seems like to me, I'm sticking to coding until I can't any more (or get properly sick of it all, which is likely to come first given everything is getting more and more remote and drowned in AI) then I'm doing something completely different because I don't want to manage (people or agents).
No, but architects can’t just make the plans and never look at the output, there needs to be someone that checks that the work is done properly and fit to spec.
I like to make computers do the things I want. Since I was a kid, the satisfaction from programming or any sort of "computer engineering" (using the word engineering VERY loosely here) is in making something specific happen.
Sometimes, that means understanding the correct data structure to use, picking the right algorithm, and other computer science-y things.
Other times, and more often (for me), it means death by a thousand papercuts in managing third party dependencies, boilerplate, and other tedium and minutia.
I'm not someone who is "proud" of barely coding anymore, and I'm not entirely sure how I feel about the "structural soundness" of using LLMs this way (most of my coding is personal/hobbyist work), but I can confidently say that I don't really code just because I love coding, at least necessarily. I like making stuff.
>"This is like if a plumber said. I no longer do any construction, I just tell people what to do."
I'm pretty sure that's the dream goal of any plumber. No one "plumbs for fun". No one wants to truly do the actual thing, they want the money that allows them to do whatever they want. It's just that doing the actual thing is the only way you can get the money at first. And being good at doing the actual thing and enjoying doing the actual thing makes doing the actual thing bareable or even fun while reaching your actual goal (the money). As soon as you can free yourself from that you will.
Some people like doing DIY plumbing and related tasks around their own homes. Work might not be "fun" in the traditional sense, but it can be satisfying to build and fix things.
This is just wrong. A lot of people take tremendous pride in their work. Particularly highly-skilled people who have been doing the work for a long time.
Joe Biden used to say this all the time - that a job is more than a paycheck, it's an identity and a sense of purpose, often a big part of our social lives as well. Or if you prefer the other side, Trump likes to say that coal miners don't want to learn computers, they want their coal mining jobs back.
I also haven't written more than a couple lines of code in months, but I definitely do read the code. You have to, or at least provide lots of Claude Rules to help steer it. AI has too many bad coding practices by default. But you don't need to modify lines of code by hand, you can just tell it what to fix.
I'm predicting a Docker for AI Coding. There really is a ton of stuff you need to set up to do AI coding/vibe coding in a way that's safe, has good results, and is fast. You don't just need a dev environment, you need a whole SDLC; codified good engineering decisions, test framework, credential management, an interactive UI and notifications, guardrails, a "waterfall" of progressive design/build/test, context management, and much more. It's too much for people to put together themselves without tons of time and experimentation. Same as containers, which is why Docker came about: to let an end user do complex things in an easy way.
AI coding is like a self-driving car. Making the car move is easy; making it get you to the destination you want safely is very hard.
> There is no one answer to agentic engineering. Everyone works differently; every model is good at different things. The key here is reps: do a lot, ask a lot, throw away a lot. You’ll learn what works for you.
That all reads to me like: The key here is: "Burn tokens as money in a bonfire, pray you typed in the right magic spell (prompt) and the ingredients (context) in the potion were right, toss away the failed elixir of life".
Instead of writing code and refactoring yourself and gaining an understanding, you're hoping that a slot machine will get luckily get the statistics right to put up code that you already need to read again.
> It’s the same loop that taught me to code, moved up a level: write, run, fail, fix. I used to iterate on code. Now I iterate on prompts, designs, whole features. A rewrite that used to cost a week costs a conversation.
And also costs you skills atrophy and however much tokens will be. Not to mention the negative externalities of using LLMs (massive increase of fraud, of disinformation, of rocketing prices in memory and in turn all electronics, massive use of energy that consumers have to also then pay extra for, environmental and noise cost of using on-site turbines, local destruction of water resources for the local community).
Setting aside the "capability" of the tools, I knew it was the wrong direction after a couple months or so in. I specifically remember during a large feature implementation running out of tokens for my 5 hour window or whatever, and I just couldn't continue on my own. Mostly due to laziness (I'll just wait till tomorrow when my limit resets!), but I also picked up on my first big whiff of skill rot / atrophy brewing, and that made feel uncomfortable.
I just don't see this fully agentic approach going well long term. Talk about the ultimate dependency!! If the lights are "turned off" for whatever reason - outages, cost increases, or the agentic velocity finally reaches a complexity tipping point and you've lost control and understanding of your system to the point the agents are making things worse, whatever it is - do you really expect to be able to turn back the clock and step in to code at the productivity level and output you used to when you actually... wrote code?
Agents definitely run amok on code bases, create bloat, needless complexity, etc. However, they enable many people to do so much more than they could without them, at least in the moment, that they're not going to go away.
At my last employer we were contending with real customer fatigue and attrition. The application was changing so rapidly that it was overwhelming both our ability to uphold quality standards, and customers' ability to keep up with the feature churn.
Or, to put it more succinctly: nobody actually wants all this slop.
Facts. For me the bigger the gap between what i saw in my head and the speed with which my fingers could physically make it a reality the more stress i would feel and some marathon coding sessions would end w my back all messed up just from the tension
1. https://github.com/matthiasn/talk-transcripts/blob/master/Hi...
First, it takes me ages to describe the task properly. All these clear success criteria, well, instead of writing code from a clear spec in my head, I’m writing tons of prose, and trying to make it unambiguous. I’m programming in English++.
But then, one time in 5, it produces something that kinda works. Maybe not perfect but good enough. Two times out of 5 it kind of sort of looks alright, but actually ignores 80% of the spec, or pays it minimal lip service in the comments.
And sometimes, maybe a bit less than 2/5, it just completely diverges into total shit. Starts writing scripts that load the ast of the main script and pickle it then serialise to base64 for no good reason. Encode some stuff in strings then check ord(string[i]) repeatedly for string comparison. Eventually runs out of context and develops the LLM equivalent of severe dementia.
I truly cannot reconcile my experience with people who seem to say “hey computer write this” and it’s a good use of their time.
These issues are usually in really tedious places that I didn't bother to describe what to do to the agent
Every few months it feels like I have moved further up the chain. Now I don’t even look at a PR until it’s been coded and gone through at least 2 AI reviews (always different families). Code is truly cheap. I can have the pipeline produce an entire feature as a thought experiment just to see if the shape is the right fit for the codebase. People like to throw the slop term around a lot. It feels like the opposite of slop through. I can have things written and rewritten as many times as it takes until I’m happy with them.
The weakness I see in this is there at the end. The agents never want to throw things away. They just want to keep adding and adding. I think this is my real value in the chain. That intuition built up over years that says this isn’t right yet or that little tingle in your brain that you’ve laid a landline that you are going to step on in 6 months.
This should be extremely intuitive. We know this already. There has always been a divergence between product value and code quality. How many codebases that are widely used are kind of a mess inside? Pretty much all of them to some degree. This is a basic reality of software engineering.
Some slop accusations are fair. Others are people essentially expressing that they dont like shifting to a product mindset.
I dont have your whole pipeline but I recently took a step back and thought about how amazing the AI is. I recently gave Fable a nice, simple open source TUI app with some Claude Design artifacts for a UI/UX facelift + some new complex feature requests and it just one shots the entire thing in 20 minutes. It works great and looks amazing. It's significantly better in terms of UX and functionality than the original. I did this on the side with about 15 minutes of active attention while I was working.
Software engineering is dead, long live software engineering!
I don't think it'd take more than a few weeks for a high schooler to learn how to prompt well.
Note, people don't typically need to understand the architecture documents that the LLMs generate, and will certainly be filling in the rest of the gaps as new models come out. The function of design docs is to make the LLM ask clarifying questions and fill in the constraints.
The skill is really in manually testing before handoff.
You serve no purpose here. The AI will soon be able to prompt itself, and it will be better at system design than you.
Good job at making yourself redundant.
Okay. Is that job going to provide a competitive salary, with good benefits? More importantly, is it going to be personally fulfilling?
Software developers who have adopted agentic coding are worse than class traders. They are actively sabotaging the entire careers future viability, and have made me ashamed to think anyone in this industry was worthy of the title "engineer".
AI agents are just software, so the only way to understand them is trial and error?
After reading this blog post for me, Fable-1337-B4242 asked me if I also wanted to create a load-bearing plain text website like exe.dev using agentic forces.
I met a few architects/team leaders who wouldn't write almost any code and just technically oversaw things before LLMs got popular, obviously they were very experiencied.
What you have to ask yourself is whether you could oversee a swarm of bots(or people) if you didn't have any coding experience in the first place.
A lot of HN users seem to be, in my opinion, too dismissive about the complexity of creating software. It's true that we don't have to develop in assembly anymore to be a good developer and with time the general level of the entry abstractions that we deal with rises, but, in my opinion once again, it will never change the fact that you need a high number of hours of exposure to something in order to be truly competent to a professional level.
Maisem Ali writes about how they have not stopped engineering despite handing production of code over to AI. I think that's an interesting perspective for a few reasons, but most of all, I'm not sure I agree.
First, we see a prime example of how that can blow up. "botd died this month. It crumbled under its own weight." It's hard to imagine how an agent harness (or is it an overlay on top of other harnesses?) could crumble under its own weight from sheer technical complexity. What makes a tool like this "crumble"? If properly architected, it seems that any outside change could be adaptable. Failing tests could be fixed.
This AI coding evolution has given us the ability to spin up prototypes we don't understand the inner workings of, but I believe for anything of value (and, I would argue, botd appeared to have value to Maisem) we should at the very least understand and influence the architecture and engineering of what we're building. If Maisem had done that for botd, it would not and _could not_ have "crumbled under its own weight". Whatever outside influence required change within botd would have been manageable.
Second, the off-loading of the production of this article indicates to me a lack of respect (with no offence to Maisem, because this is what much of the industry is doing today) for the value in thinking through the writing process. There's a lot that I've learned personally about software I worked on that only came to me through trying to write explainers. Taking a step back from the software we build and looking at it from a birds-eye view, to share how it is constructed or how to use it, helps develop our understanding.
Maisem's article is already very good, and I hope that is demonstrative of their own writing! I would read more! But I want to read words written by a person for other people. AI can help edit, it can help us build ideas, but in code and in our written word, AI should not be _the author_.
It seems that all this knowledge would be useless with a model update...Good for the AI companies though. More tokens as users like this come upto speed with the new models, which they now are totally dependent on...
Because, if you didn't like writing code, and is now used to agents writing code, good luck going back to writing code by hand...
“But here’s the twist: that search I described earlier was never sent to botd. All the history was in SQLite, so I pointed another agent at it and got the analysis anyway. The tool died; the data didn’t.”
If I had more time, I would have written a shorter letter…
To me it just feels like you want to be a manager not an engineer
This is like if a plumber said. I no longer do any construction, I just tell people what to do. Then you probably aren't a builder anymore, you are a foreman or a project manager.
I think your job as an engineer is to know the codebase and how it was engineered
I like to make computers do the things I want. Since I was a kid, the satisfaction from programming or any sort of "computer engineering" (using the word engineering VERY loosely here) is in making something specific happen.
Sometimes, that means understanding the correct data structure to use, picking the right algorithm, and other computer science-y things.
Other times, and more often (for me), it means death by a thousand papercuts in managing third party dependencies, boilerplate, and other tedium and minutia.
I'm not someone who is "proud" of barely coding anymore, and I'm not entirely sure how I feel about the "structural soundness" of using LLMs this way (most of my coding is personal/hobbyist work), but I can confidently say that I don't really code just because I love coding, at least necessarily. I like making stuff.
A manager has access to a larger, sloppier, and more powerful API than an engineer.
I'm pretty sure that's the dream goal of any plumber. No one "plumbs for fun". No one wants to truly do the actual thing, they want the money that allows them to do whatever they want. It's just that doing the actual thing is the only way you can get the money at first. And being good at doing the actual thing and enjoying doing the actual thing makes doing the actual thing bareable or even fun while reaching your actual goal (the money). As soon as you can free yourself from that you will.
Joe Biden used to say this all the time - that a job is more than a paycheck, it's an identity and a sense of purpose, often a big part of our social lives as well. Or if you prefer the other side, Trump likes to say that coal miners don't want to learn computers, they want their coal mining jobs back.
I'm predicting a Docker for AI Coding. There really is a ton of stuff you need to set up to do AI coding/vibe coding in a way that's safe, has good results, and is fast. You don't just need a dev environment, you need a whole SDLC; codified good engineering decisions, test framework, credential management, an interactive UI and notifications, guardrails, a "waterfall" of progressive design/build/test, context management, and much more. It's too much for people to put together themselves without tons of time and experimentation. Same as containers, which is why Docker came about: to let an end user do complex things in an easy way.
AI coding is like a self-driving car. Making the car move is easy; making it get you to the destination you want safely is very hard.
That all reads to me like: The key here is: "Burn tokens as money in a bonfire, pray you typed in the right magic spell (prompt) and the ingredients (context) in the potion were right, toss away the failed elixir of life".
Instead of writing code and refactoring yourself and gaining an understanding, you're hoping that a slot machine will get luckily get the statistics right to put up code that you already need to read again.
> It’s the same loop that taught me to code, moved up a level: write, run, fail, fix. I used to iterate on code. Now I iterate on prompts, designs, whole features. A rewrite that used to cost a week costs a conversation.
And also costs you skills atrophy and however much tokens will be. Not to mention the negative externalities of using LLMs (massive increase of fraud, of disinformation, of rocketing prices in memory and in turn all electronics, massive use of energy that consumers have to also then pay extra for, environmental and noise cost of using on-site turbines, local destruction of water resources for the local community).
Can we stop posting obviously slop articles? If you can't make the effort to write the article, I'm not going to make the effort to read it.