This is such a lovely balanced thoughtful refreshingly hype-free post to read. 2025 really was the year when things shifted and many first-rate developers (often previously AI skeptics, as Mitchell was) found the tools had actually got good enough that they could incorporate AI agents into their workflows.
It's a shame that AI coding tools have become such a polarizing issue among developers. I understand the reasons, but I wish there had been a smoother path to this future. The early LLMs like GPT-3 could sort of code enough for it to look like there was a lot of potential, and so there was a lot of hype to drum up investment and a lot of promises made that weren't really viable with the tech as it was then. This created a large number of AI skeptics (of whom I was one, for a while) and a whole bunch of cynicism and suspicion and resistance amongst a large swathe of developers. But could it have been different? It seems a lot of transformative new tech is fated to evolve this way. Early aircraft were extremely unreliable and dangerous and not yet worthy of the promises being made about them, but eventually with enough evolution and lessons learned we got the Douglas DC-3, and then in the end the 747.
If you're a developer who still doesn't believe that AI tools are useful, I would recommend you go read Mitchell's post, and give Claude Code a trial run like he did. Try and forget about the annoying hype and the vibe-coding influencers and the noise and just treat it like any new tool you might put through its paces. There are many important conversations about AI to be had, it has plenty of downsides, but a proper discussion begins with close engagement with the tools.
> Break down sessions into separate clear, actionable tasks. Don't try to "draw the owl" in one mega session.
This is the key one I think. At one extreme you can tell an agent "write a for loop that iterates over the variable `numbers` and computes the sum" and they'll do this successfully, but the scope is so small there's not much point in using an LLM. On the other extreme you can tell an agent "make me an app that's Facebook for dogs" and it'll make so many assumptions about the architecture, code and product that there's no chance it produces anything useful beyond a cool prototype to show mom and dad.
A lot of successful LLM adoption for code is finding this sweet spot. Overly specific instructions don't make you feel productive, and overly broad instructions you end up redoing too much of the work.
This is actually an aspect of using AI tools I really enjoy: Forming an educated intuition about what the tool is good at, and tastefully framing and scoping the tasks I give it to get better results.
It cognitively feels very similar to other classic programming activities, like modularization at any level from architecture to code units/functions, thoughtfully choosing how to lay out and chunk things. It's always been one of the things that make programming pleasurable for me, and some of that feeling returns when slicing up tasks for agents.
I agree that framing and scoping tasks is becoming a real joy. The great thing about this strategy is there's a point at which you can scope something small enough that it's hard for the AI to get it wrong and it's easy enough for you as a human to comprehend what it's done and verify that it's correct.
I'm starting to think of projects now as a tree structure where the overall architecture of the system is the main trunk and from there you have the sub-modules, and eventually you get to implementations of functions and classes. The goal of the human in working with the coding agent is to have full editorial control of the main trunk and main sub-modules and delegate as much of the smaller branches as possible.
Sometimes you're still working out the higher-level architecture, too, and you can use the agent to prototype the smaller bits and pieces which will inform the decisions you make about how the higher-level stuff should operate.
> On the other extreme you can tell an agent "make me an app that's Facebook for dogs" and it'll make so many assumptions about the architecture, code and product that there's no chance it produces anything useful beyond a cool prototype to show mom and dad.
Amusingly, this was my experience in giving Lovable a shot. The onboarding process was literally just setting me up for failure by asking me to describe the detailed app I was attempting to build.
Taking it piece by piece in Claude Code has been significantly more successful.
I actually enjoy writing specifications. So much so that I made it a large part of my consulting work for a huge part of my career. SO it makes sense that working with Gen-AI that way is enjoyable for me.
The more detailed I am in breaking down chunks, the easier it is for me to verify and the more likely I am going to get output that isn't 30% wrong.
It's amusing how everyone seems to be going through the same journey.
I do run multiple models at once now. On different parts of the code base.
I focus solely on the less boring tasks for myself and outsource all of the slam dunk and then review. Often use another model to validate the previous models work while doing so myself.
I do git reset still quite often but I find more ways to not get to that point by knowing the tools better and better.
This matches my experience, especially "don’t draw the owl" and the harness-engineering idea.
The failure mode I kept hitting wasn’t just "it makes mistakes", it was drift: it can stay locally plausible while slowly walking away from the real constraints of the repo. The output still sounds confident, so you don’t notice until you run into reality (tests, runtime behaviour, perf, ops, UX).
What ended up working for me was treating chat as where I shape the plan (tradeoffs, invariants, failure modes) and treating the agent as something that does narrow, reviewable diffs against that plan. The human job stays very boring: run it, verify it, and decide what’s actually acceptable. That separation is what made it click for me.
Once I got that loop stable, it stopped being a toy and started being a lever. I’ve shipped real features this way across a few projects (a git like tool for heavy media projects, a ticketing/payment flow with real users, a local-first genealogy tool, and a small CMS/publishing pipeline). The common thread is the same: small diffs, fast verification, and continuously tightening the harness so the agent can’t drift unnoticed.
This is the most common answer from people that are rocking and rolling with AI tools but I cannot help but wonder how is this different from how we should have built software all along. I know I have been (after 10+ years…)
I think you are right, the secret is that there is no secret. The projects I have been involved with thats been most successful was using these techniques. I also think experience helps because you develop a sense that very quickly knows if the model wants to go in a wonky direction and how a good spec looks like.
With where the models are right now you still need a human in the loop to make sure you end up with code you (and your organisation) actually understands. The bottle neck has gone from writing code to reading code.
Finally, a step-by-step guide for even the skeptics to try to see what spot the LLM tools have in their workflows, without hype or magic like I vibe-coded an entire OS, and you can too!.
> I'm not [yet?] running multiple agents, and currently don't really want to
This is the main reason to use AI agents, though: multitasking. If I'm working on some Terraform changes and I fire off an agent loop, I know it's going to take a while for it to produce something working. In the meantime I'm waiting for it to come back and pretend it's finished (really I'll have to fix it), so I start another agent on something else. I flip back and forth between the finished runs as they notify me. At the end of the day I have 5 things finished rather than two.
The "agent" doesn't have to be anything special either. Anything you can run in a VM or container (vscode w/copilot chat, any cli tool, etc) so you can enable YOLO mode.
I recently also reflected on the evolution of my use of ai in programming. Same evolution, other path. If anyone is interested: https://www.asfaload.com/blog/ai_use/
I'd be interested to know what agents you're using. You mentioned Claude and GPT in passing, but don't actually talk about which you're using or for which tasks.
I find it interesting that this thread is full of pragmatic posts that seem to honestly reflect the real limits of current Gen-Ai.
Versus other threads (here on HN, and especially on places like LinkedIn) where it's "I set up a pipeline and some agents and now I type two sentences and amazing technology comes out in 5 minutes that would have taken 3 devs 6 months to do".
Good article! I especially liked the approach to replicate manual commits with the agent. I did not do that when learning but I suspect I'd have been much better off if I had.
You mentioned "harness engineering". How do you approach building "actual programmed tools" (like screenshot scripts) specifically for an LLM's consumption rather than a human's? Are there specific output formats or constraints you’ve found most effective?
I think this is something people ignore, and is significant. The only way to get good at coding with LLMs is actually trying to do it. Even if it's inefficient or slower at first. It's just another skill to develop [0].
And it's not really about using all the plugins and features available. In fact, many plugins and features are counter-productive. Just learn how to prompt and steer the LLM better.
There are so many stories about how people use agentic AI but they rarely post how much they spend. Before I can even consider it, I need to know how it will cost me per month. I'm currently using one pro subscription and it's already quite expensive for me. What are people doing, burning hundreds of dollars per month? Do they also evaluate how much value they get out of it?
I quickly run out of the JetBrains AI 35 monthly credits for $300/yr and spending an additional $5-10/day on top of that, mostly for Claude.
I just recently added in Codex, since it comes with my $20/mo subscription to GPT and that's lowering my Claude credit usage significantly... until I hit those limits at some point.
2012 + 300 + 5~200... so about $1500-$1600/year.
It is 100% worth it for what I'm building right now, but my fear is that I'll take a break from coding and then I'm paying for something I'm not using with the subscriptions.
I'd prefer to move to a model where I'm paying for compute time as I use it, instead of worrying about tokens/credits.
> babysitting my kind of stupid and yet mysteriously productive robot friend
LOL, been there, done that. It is much less frustrating and demoralizing than babysitting your kind of stupid colleague though. (Thankfully, I don't have any of those anymore. But at previous big companies? Oh man, if only their commits were ONLY as bad as a bad AI commit.)
For the AI skeptics reading this, there is an overwhelming probability that Mitchell is a better developer than you. If he gets value out of these tools you should think about why you can't.
> 1) We do NOT provide evidence that AI systems do not currently speed up many or most software developers. Clarification: We do not claim that our developers or repositories represent a majority or plurality of software development work.
> 2) We do NOT provide evidence that AI systems do not speed up individuals or groups in domains other than software development. Clarification: We only study software development.
> 3) We do NOT provide evidence that AI systems in the near future will not speed up developers in our exact setting. Clarification: Progress is difficult to predict, and there has been substantial AI progress over the past five years [3].
> 4) We do NOT provide evidence that there are not ways of using existing AI systems more effectively to achieve positive speedup in our exact setting. Clarification: Cursor does not sample many tokens from LLMs, it may not use optimal prompting/scaffolding, and domain/repository-specific training/finetuning/few-shot learning could yield positive speedup.
Perhaps that's the reason. Maybe I'm just not a good enough developer. But that's still not actionable. It's not like I never considered being a better developer.
Don't get it. What's the relation between Mitchell being a "better" developer than most of us (and better is always relative, but that's another story) and getting value out of AI? That's like saying Bezos is a way better businessman than you, so you should really hear his tips about becoming a billionaire. No sense (because what works for him probably doesn't work for you)
Tons of respect for Mitchell. I think you are doing him a disservice with these kinds of comments.
Maybe you disagree with it, but it seems like a pretty straightforward argument: A lot of us dismiss AI because "it can't be trusted to do as good a job as me". The OP is arguing that someone, who can do better than most of us, disagrees with this line of thinking. And if we have respect for his abilities, and recognize them as better than our own, we should perhaps re-assess our own rationale in dismissing the utility of AI assistance. If he can get value out of it, surely we can too if we don't argue ourselves out of giving it a fair shake. The flip side of that argument might be that you have to be a much better programmer than most of us are, to properly extract value out of the AI... maybe it's only useful in the hands of a real expert.
>A lot of us dismiss AI because "it can't be trusted to do as good a job as me"
Some of us enjoy learning how systems work, and derive satisfaction from the feeling of doing something hard, and feel that AI removes that satisfaction. If I wanted to have something else write the code, I would focus on becoming a product manager, or a technical lead. But as is, this is a craft, and I very much enjoy the autonomy that comes with being able to use this skill and grow it.
I consider myself a craftsman as well. AI gives me the ability to focus on the parts I both enjoy working on and that demand the most craftsmanship. A lot of what I use AI for and show in the blog isn’t coding at all, but a way to allow me to spend more time coding.
This reads like you maybe didn’t read the blog post, so I’ll mention there many examples there.
Nobody is trying to talk anyone out of their hobby or artisanal creativeness. A lot of people enjoy walking, even after the invention of the automobile. There's nothing wrong with that, there are even times when it's the much more efficient choice. But in the context of say transporting packages across the country... it's not really relevant how much you enjoy one or the other; only one of them can get the job done in a reasonable amount of time. And we can assume that's the context and spirit of the OP's argument.
>Nobody is trying to talk anyone out of their hobby or artisanal creativeness.
Well, yes, they are, some folks don't think "here's how I use AI" and "I'm a craftsman!" are consistent. Seems like maybe OP should consider whether "AI is a tool, why can't you use it right" isn't begging the question.
Is this going to be the new rhetorical trick, to say "oh hey surely we can all agree I have reasonable goals! And to the extent they're reasonable you are unreasonable for not adopting them"?
>But in the context of say transporting packages across the country... it's not really relevant how much you enjoy one or the other; only one of them can get the job done in a reasonable amount of time.
I think one of the more frustrating aspects of this whole debate is this idea that software development pre-AI was too "slow", despite the fact that no other kind of engineering has nearly the same turn around time as software engineering does (nor does they have the same return on investment!).
I just end up rolling my eyes when people use this argument. To me it feels like favoring productivity over everything else.
This is such a lovely balanced thoughtful refreshingly hype-free post to read. 2025 really was the year when things shifted and many first-rate developers (often previously AI skeptics, as Mitchell was) found the tools had actually got good enough that they could incorporate AI agents into their workflows.
It's a shame that AI coding tools have become such a polarizing issue among developers. I understand the reasons, but I wish there had been a smoother path to this future. The early LLMs like GPT-3 could sort of code enough for it to look like there was a lot of potential, and so there was a lot of hype to drum up investment and a lot of promises made that weren't really viable with the tech as it was then. This created a large number of AI skeptics (of whom I was one, for a while) and a whole bunch of cynicism and suspicion and resistance amongst a large swathe of developers. But could it have been different? It seems a lot of transformative new tech is fated to evolve this way. Early aircraft were extremely unreliable and dangerous and not yet worthy of the promises being made about them, but eventually with enough evolution and lessons learned we got the Douglas DC-3, and then in the end the 747.
If you're a developer who still doesn't believe that AI tools are useful, I would recommend you go read Mitchell's post, and give Claude Code a trial run like he did. Try and forget about the annoying hype and the vibe-coding influencers and the noise and just treat it like any new tool you might put through its paces. There are many important conversations about AI to be had, it has plenty of downsides, but a proper discussion begins with close engagement with the tools.
> Break down sessions into separate clear, actionable tasks. Don't try to "draw the owl" in one mega session.
This is the key one I think. At one extreme you can tell an agent "write a for loop that iterates over the variable `numbers` and computes the sum" and they'll do this successfully, but the scope is so small there's not much point in using an LLM. On the other extreme you can tell an agent "make me an app that's Facebook for dogs" and it'll make so many assumptions about the architecture, code and product that there's no chance it produces anything useful beyond a cool prototype to show mom and dad.
A lot of successful LLM adoption for code is finding this sweet spot. Overly specific instructions don't make you feel productive, and overly broad instructions you end up redoing too much of the work.
This is actually an aspect of using AI tools I really enjoy: Forming an educated intuition about what the tool is good at, and tastefully framing and scoping the tasks I give it to get better results.
It cognitively feels very similar to other classic programming activities, like modularization at any level from architecture to code units/functions, thoughtfully choosing how to lay out and chunk things. It's always been one of the things that make programming pleasurable for me, and some of that feeling returns when slicing up tasks for agents.
I agree that framing and scoping tasks is becoming a real joy. The great thing about this strategy is there's a point at which you can scope something small enough that it's hard for the AI to get it wrong and it's easy enough for you as a human to comprehend what it's done and verify that it's correct.
I'm starting to think of projects now as a tree structure where the overall architecture of the system is the main trunk and from there you have the sub-modules, and eventually you get to implementations of functions and classes. The goal of the human in working with the coding agent is to have full editorial control of the main trunk and main sub-modules and delegate as much of the smaller branches as possible.
Sometimes you're still working out the higher-level architecture, too, and you can use the agent to prototype the smaller bits and pieces which will inform the decisions you make about how the higher-level stuff should operate.
> On the other extreme you can tell an agent "make me an app that's Facebook for dogs" and it'll make so many assumptions about the architecture, code and product that there's no chance it produces anything useful beyond a cool prototype to show mom and dad.
Amusingly, this was my experience in giving Lovable a shot. The onboarding process was literally just setting me up for failure by asking me to describe the detailed app I was attempting to build.
Taking it piece by piece in Claude Code has been significantly more successful.
Exactly. The LLMs are quite good at "code inpainting", eg "give me the outline/constraints/rules and I'll fill-in the blanks"
But not so good at making (robust) new features out of the blue
so many times I catch myself asking a coding agent e.g “please print the output” and it will update the file with “print (output)”.
Maybe there’s something about not having to context switch between natural language and code just makes it _feel_ easier sometimes
I actually enjoy writing specifications. So much so that I made it a large part of my consulting work for a huge part of my career. SO it makes sense that working with Gen-AI that way is enjoyable for me.
The more detailed I am in breaking down chunks, the easier it is for me to verify and the more likely I am going to get output that isn't 30% wrong.
It's amusing how everyone seems to be going through the same journey.
I do run multiple models at once now. On different parts of the code base.
I focus solely on the less boring tasks for myself and outsource all of the slam dunk and then review. Often use another model to validate the previous models work while doing so myself.
I do git reset still quite often but I find more ways to not get to that point by knowing the tools better and better.
Autocompleting our brains! What a crazy time.
This matches my experience, especially "don’t draw the owl" and the harness-engineering idea.
The failure mode I kept hitting wasn’t just "it makes mistakes", it was drift: it can stay locally plausible while slowly walking away from the real constraints of the repo. The output still sounds confident, so you don’t notice until you run into reality (tests, runtime behaviour, perf, ops, UX).
What ended up working for me was treating chat as where I shape the plan (tradeoffs, invariants, failure modes) and treating the agent as something that does narrow, reviewable diffs against that plan. The human job stays very boring: run it, verify it, and decide what’s actually acceptable. That separation is what made it click for me.
Once I got that loop stable, it stopped being a toy and started being a lever. I’ve shipped real features this way across a few projects (a git like tool for heavy media projects, a ticketing/payment flow with real users, a local-first genealogy tool, and a small CMS/publishing pipeline). The common thread is the same: small diffs, fast verification, and continuously tightening the harness so the agent can’t drift unnoticed.
This is the most common answer from people that are rocking and rolling with AI tools but I cannot help but wonder how is this different from how we should have built software all along. I know I have been (after 10+ years…)
I think you are right, the secret is that there is no secret. The projects I have been involved with thats been most successful was using these techniques. I also think experience helps because you develop a sense that very quickly knows if the model wants to go in a wonky direction and how a good spec looks like.
With where the models are right now you still need a human in the loop to make sure you end up with code you (and your organisation) actually understands. The bottle neck has gone from writing code to reading code.
Much more pragmatic and less performative than other posts hitting frontpage. Good article.
Finally, a step-by-step guide for even the skeptics to try to see what spot the LLM tools have in their workflows, without hype or magic like I vibe-coded an entire OS, and you can too!.
> At a bare minimum, the agent must have the ability to: read files, execute programs, and make HTTP requests.
That's one very short step removed from Simon Willison's lethal trifecta.
I'm definitely not running that on my machine.
For those wondering how that looks in practice, here's one of OP's past blog posts describing a coding session to implement a non-trivial feature: https://mitchellh.com/writing/non-trivial-vibing (covered on HN here: https://news.ycombinator.com/item?id=45549434)
Nice writeup!
For those using Emacs, is there a Magit-like interface for interacting with agents? I'd be keen on experimenting with something like that.
This seems like a pretty reasonable approach that charts a course between skepticism and "it's a miracle".
I wonder how much all this costs on a monthly basis?
As long as we're on the same page that what he's describing is itself a miracle.
> I'm not [yet?] running multiple agents, and currently don't really want to
This is the main reason to use AI agents, though: multitasking. If I'm working on some Terraform changes and I fire off an agent loop, I know it's going to take a while for it to produce something working. In the meantime I'm waiting for it to come back and pretend it's finished (really I'll have to fix it), so I start another agent on something else. I flip back and forth between the finished runs as they notify me. At the end of the day I have 5 things finished rather than two.
The "agent" doesn't have to be anything special either. Anything you can run in a VM or container (vscode w/copilot chat, any cli tool, etc) so you can enable YOLO mode.
I recently also reflected on the evolution of my use of ai in programming. Same evolution, other path. If anyone is interested: https://www.asfaload.com/blog/ai_use/
I'd be interested to know what agents you're using. You mentioned Claude and GPT in passing, but don't actually talk about which you're using or for which tasks.
I find it interesting that this thread is full of pragmatic posts that seem to honestly reflect the real limits of current Gen-Ai.
Versus other threads (here on HN, and especially on places like LinkedIn) where it's "I set up a pipeline and some agents and now I type two sentences and amazing technology comes out in 5 minutes that would have taken 3 devs 6 months to do".
Good article! I especially liked the approach to replicate manual commits with the agent. I did not do that when learning but I suspect I'd have been much better off if I had.
Thanks for sharing your experiences :)
You mentioned "harness engineering". How do you approach building "actual programmed tools" (like screenshot scripts) specifically for an LLM's consumption rather than a human's? Are there specific output formats or constraints you’ve found most effective?
> a period of inefficiency
I think this is something people ignore, and is significant. The only way to get good at coding with LLMs is actually trying to do it. Even if it's inefficient or slower at first. It's just another skill to develop [0].
And it's not really about using all the plugins and features available. In fact, many plugins and features are counter-productive. Just learn how to prompt and steer the LLM better.
[0]: https://ricardoanderegg.com/posts/getting-better-coding-llms...
There are so many stories about how people use agentic AI but they rarely post how much they spend. Before I can even consider it, I need to know how it will cost me per month. I'm currently using one pro subscription and it's already quite expensive for me. What are people doing, burning hundreds of dollars per month? Do they also evaluate how much value they get out of it?
I quickly run out of the JetBrains AI 35 monthly credits for $300/yr and spending an additional $5-10/day on top of that, mostly for Claude.
I just recently added in Codex, since it comes with my $20/mo subscription to GPT and that's lowering my Claude credit usage significantly... until I hit those limits at some point.
2012 + 300 + 5~200... so about $1500-$1600/year.
It is 100% worth it for what I'm building right now, but my fear is that I'll take a break from coding and then I'm paying for something I'm not using with the subscriptions.
I'd prefer to move to a model where I'm paying for compute time as I use it, instead of worrying about tokens/credits.
Low hundreds ($190 for me) but yes.
> babysitting my kind of stupid and yet mysteriously productive robot friend
LOL, been there, done that. It is much less frustrating and demoralizing than babysitting your kind of stupid colleague though. (Thankfully, I don't have any of those anymore. But at previous big companies? Oh man, if only their commits were ONLY as bad as a bad AI commit.)
For the AI skeptics reading this, there is an overwhelming probability that Mitchell is a better developer than you. If he gets value out of these tools you should think about why you can't.
The AI skeptics instead stick to hard data, which so far shows a 19% reduction in productivity when using AI.
https://metr.org/blog/2025-07-10-early-2025-ai-experienced-o...
> 1) We do NOT provide evidence that AI systems do not currently speed up many or most software developers. Clarification: We do not claim that our developers or repositories represent a majority or plurality of software development work.
> 2) We do NOT provide evidence that AI systems do not speed up individuals or groups in domains other than software development. Clarification: We only study software development.
> 3) We do NOT provide evidence that AI systems in the near future will not speed up developers in our exact setting. Clarification: Progress is difficult to predict, and there has been substantial AI progress over the past five years [3].
> 4) We do NOT provide evidence that there are not ways of using existing AI systems more effectively to achieve positive speedup in our exact setting. Clarification: Cursor does not sample many tokens from LLMs, it may not use optimal prompting/scaffolding, and domain/repository-specific training/finetuning/few-shot learning could yield positive speedup.
Perhaps that's the reason. Maybe I'm just not a good enough developer. But that's still not actionable. It's not like I never considered being a better developer.
I'm not as good as Fabrice Bellard either but I don't let that bother me as I go about my day.
Don't get it. What's the relation between Mitchell being a "better" developer than most of us (and better is always relative, but that's another story) and getting value out of AI? That's like saying Bezos is a way better businessman than you, so you should really hear his tips about becoming a billionaire. No sense (because what works for him probably doesn't work for you)
Tons of respect for Mitchell. I think you are doing him a disservice with these kinds of comments.
Maybe you disagree with it, but it seems like a pretty straightforward argument: A lot of us dismiss AI because "it can't be trusted to do as good a job as me". The OP is arguing that someone, who can do better than most of us, disagrees with this line of thinking. And if we have respect for his abilities, and recognize them as better than our own, we should perhaps re-assess our own rationale in dismissing the utility of AI assistance. If he can get value out of it, surely we can too if we don't argue ourselves out of giving it a fair shake. The flip side of that argument might be that you have to be a much better programmer than most of us are, to properly extract value out of the AI... maybe it's only useful in the hands of a real expert.
>A lot of us dismiss AI because "it can't be trusted to do as good a job as me"
Some of us enjoy learning how systems work, and derive satisfaction from the feeling of doing something hard, and feel that AI removes that satisfaction. If I wanted to have something else write the code, I would focus on becoming a product manager, or a technical lead. But as is, this is a craft, and I very much enjoy the autonomy that comes with being able to use this skill and grow it.
There is no dichotomy of craft and AI.
I consider myself a craftsman as well. AI gives me the ability to focus on the parts I both enjoy working on and that demand the most craftsmanship. A lot of what I use AI for and show in the blog isn’t coding at all, but a way to allow me to spend more time coding.
This reads like you maybe didn’t read the blog post, so I’ll mention there many examples there.
I enjoy Japanese joinery, but for some reason the housing market doesn't.
Nobody is trying to talk anyone out of their hobby or artisanal creativeness. A lot of people enjoy walking, even after the invention of the automobile. There's nothing wrong with that, there are even times when it's the much more efficient choice. But in the context of say transporting packages across the country... it's not really relevant how much you enjoy one or the other; only one of them can get the job done in a reasonable amount of time. And we can assume that's the context and spirit of the OP's argument.
>Nobody is trying to talk anyone out of their hobby or artisanal creativeness.
Well, yes, they are, some folks don't think "here's how I use AI" and "I'm a craftsman!" are consistent. Seems like maybe OP should consider whether "AI is a tool, why can't you use it right" isn't begging the question.
Is this going to be the new rhetorical trick, to say "oh hey surely we can all agree I have reasonable goals! And to the extent they're reasonable you are unreasonable for not adopting them"?
>But in the context of say transporting packages across the country... it's not really relevant how much you enjoy one or the other; only one of them can get the job done in a reasonable amount of time.
I think one of the more frustrating aspects of this whole debate is this idea that software development pre-AI was too "slow", despite the fact that no other kind of engineering has nearly the same turn around time as software engineering does (nor does they have the same return on investment!).
I just end up rolling my eyes when people use this argument. To me it feels like favoring productivity over everything else.
"Why can't you be more like your brother Mitchell?"
[flagged]
"Please don't post shallow dismissals, especially of other people's work. A good critical comment teaches us something."
"Don't be snarky."
"Don't be curmudgeonly. Thoughtful criticism is fine, but please don't be rigidly or generically negative."
https://news.ycombinator.com/newsguidelines.html
[flagged]
Ok, but please don't post unsubstantive comments to Hacker News.
most AI adoption journeys are
>Underwhelming
Which is why I like this article. It's realistic in terms of describing the value-propositio of LLM-based coding assist tools (aka, AI agents).
The fact that it's underwhelming compared to the hype we see every day is a very, very good sign that it's practical.