> 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 feel the same, but, also, within like three years this might look very different. Maybe you'll give the full end-to-end goal upfront and it just polls you when it needs clarification or wants to suggest alternatives, and it self-manages cleanly self-delegating.
Or maybe something quite different but where these early era agentic tooling strategies become either unneeded or even actively detrimental.
I think anyone who has worked on a serious software project would say, this means it would be polling you constantly.
Even if we posit that an LLM is equivalent to a human, humans constantly clarify requirements/architecture. IMO on both of those fronts the correct path often reveals itself over time, rather than being knowable from the start.
So in this scenario it seems like you'd be dealing with constant pings and need to really make sure you're understanding of the project is growing with the LLM's development efforts as well.
To me this seems like the best-case of the current technology, the models have been getting better and better at doing what you tell it in small chunks but you still need to be deciding what it should be doing. These chunks don't feel as though they're getting bigger unless you're willing to accept slop.
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.
[Edit: I may have been replying to another comment in my head as now I re-read it and I'm not sure I've said the same thing as you have. Oh well.]
I agree. This is how I see it too. It's more like a shortcut to an end result that's very similar (or much better) than I would've reached through typing it myself.
The other day I did realise that I'm using my experience to steer it away from bad decisions a lot more than I noticed. It feels like it does all the real work, but I have to remember it's my/our (decades of) experience writing code playing a part also.
I'm genuinely confused when people come in at this point and say that it's impossible to do this and produce good output and end results.
> 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.
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.
Your sentiment resonates with me a lot. I wonder what we’ll consider the inflection point 10 years from now. It seemed like the zeitgeist was screaming about scaling limits and running out of training data, then we got Claude code, sonnet 4.5, then Opus 4.5 and no ones looked back since.
but annoying hype is exactly the issue with AI in my eyes. I get it's a useful tool in moderation and all, but I also experience that management values speed and quantity of delivery above all else, and hype-driven as they are I fear they will run this industry to the ground and we as users and customers will have to deal with the world where software is permanently broken as a giant pile of unmaintainable vibe code and no experienced junior developers to boot.
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.
> The bottle neck has gone from writing code to reading code.
This has always been the bottleneck. Reviewing code is much harder and gets worse results than writing it, which is why reviewing AI code is not very efficient. The time required to understand code far outstrips the time to type it.
Most devs don’t do thorough reviews. Check the variable names seem ok, make sure there’s no obvious typos, ask for a comment and call it good. For a trusted teammate this is actually ok and why they’re so valuable! For an AI, it’s a slot machine and trusting it is equivalent to letting your coworkers/users do your job so you can personally move faster.
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!.
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.
It's so sad that we're the ones who have to tell the agent how to improve by extending agent.md or whatever. I constantly have to tell it what I don't like or what can be improved or need to request clarifications or alternative solutions.
This is what's so annoying about it. It's like a child that does the same errors again and again.
But couldn't it adjust itself with the goal of reducing the error bit by bit? Wouldn't this lead to the ultimate agent who can read your mind? That would be awesome.
> It's so sad that we're the ones who have to tell the agent how to improve by extending agent.md or whatever.
Your improvement is someone else's code smell. There's no absolute right or wrong way to write code, and that's coming from someone who definitely thinks there's a right way. But it's my right way.
Anyway, I don't know why you'd expect it to write code the way you like after it's been trained on the whole of the Internet & the the RLHF labelers' preferences and the reward model.
Putting some words in AGENTS.md hardly seems like the most annoying thing.
tip: Add a /fix command that tells it to fix $1 and then update AGENTS.md with the text that'd stop it from making that mistake in the future. Use your nearest LLM to tweak that prompt. It's a good timesaver.
Do you have any ideas on how to harness AI to only change specific parts of a system or workpiece? Like "I consider this part 80/100 done and only make 'meaningful' or 'new contributions' here" ...?
Just wanted to say that was a nice and very grounded write up; and as a result very informative. Thank you. More stuff like this is a breath of fresh air in a landscape that has veered into hyperbole territory both in the for and against ai sides
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.
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'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 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".
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.
> 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 feel the same, but, also, within like three years this might look very different. Maybe you'll give the full end-to-end goal upfront and it just polls you when it needs clarification or wants to suggest alternatives, and it self-manages cleanly self-delegating.
Or maybe something quite different but where these early era agentic tooling strategies become either unneeded or even actively detrimental.
> it just polls you when it needs clarification
I think anyone who has worked on a serious software project would say, this means it would be polling you constantly.
Even if we posit that an LLM is equivalent to a human, humans constantly clarify requirements/architecture. IMO on both of those fronts the correct path often reveals itself over time, rather than being knowable from the start.
So in this scenario it seems like you'd be dealing with constant pings and need to really make sure you're understanding of the project is growing with the LLM's development efforts as well.
To me this seems like the best-case of the current technology, the models have been getting better and better at doing what you tell it in small chunks but you still need to be deciding what it should be doing. These chunks don't feel as though they're getting bigger unless you're willing to accept slop.
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.
[Edit: I may have been replying to another comment in my head as now I re-read it and I'm not sure I've said the same thing as you have. Oh well.]
I agree. This is how I see it too. It's more like a shortcut to an end result that's very similar (or much better) than I would've reached through typing it myself.
The other day I did realise that I'm using my experience to steer it away from bad decisions a lot more than I noticed. It feels like it does all the real work, but I have to remember it's my/our (decades of) experience writing code playing a part also.
I'm genuinely confused when people come in at this point and say that it's impossible to do this and produce good output and end results.
> but the scope is so small there's not much point in using an LLM
Often it saves me a context switch to go find an example of `for` syntax. So much of my early LLM coding use was just this kind of thing.
> 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.
And lately, the sweet spot has been moving upwards every 6-8 weeks with the model release cycle.
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
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
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.
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.
Your sentiment resonates with me a lot. I wonder what we’ll consider the inflection point 10 years from now. It seemed like the zeitgeist was screaming about scaling limits and running out of training data, then we got Claude code, sonnet 4.5, then Opus 4.5 and no ones looked back since.
but annoying hype is exactly the issue with AI in my eyes. I get it's a useful tool in moderation and all, but I also experience that management values speed and quantity of delivery above all else, and hype-driven as they are I fear they will run this industry to the ground and we as users and customers will have to deal with the world where software is permanently broken as a giant pile of unmaintainable vibe code and no experienced junior developers to boot.
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.
> The bottle neck has gone from writing code to reading code.
This has always been the bottleneck. Reviewing code is much harder and gets worse results than writing it, which is why reviewing AI code is not very efficient. The time required to understand code far outstrips the time to type it.
Most devs don’t do thorough reviews. Check the variable names seem ok, make sure there’s no obvious typos, ask for a comment and call it good. For a trusted teammate this is actually ok and why they’re so valuable! For an AI, it’s a slot machine and trusting it is equivalent to letting your coworkers/users do your job so you can personally move faster.
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!.
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.
> 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.
The way this is generally implemented is that agents have the ability to request a tool use. Then you confirm "yes, you may run this grep".
It's so sad that we're the ones who have to tell the agent how to improve by extending agent.md or whatever. I constantly have to tell it what I don't like or what can be improved or need to request clarifications or alternative solutions.
This is what's so annoying about it. It's like a child that does the same errors again and again.
But couldn't it adjust itself with the goal of reducing the error bit by bit? Wouldn't this lead to the ultimate agent who can read your mind? That would be awesome.
> It's so sad that we're the ones who have to tell the agent how to improve by extending agent.md or whatever.
Your improvement is someone else's code smell. There's no absolute right or wrong way to write code, and that's coming from someone who definitely thinks there's a right way. But it's my right way.
Anyway, I don't know why you'd expect it to write code the way you like after it's been trained on the whole of the Internet & the the RLHF labelers' preferences and the reward model.
Putting some words in AGENTS.md hardly seems like the most annoying thing.
tip: Add a /fix command that tells it to fix $1 and then update AGENTS.md with the text that'd stop it from making that mistake in the future. Use your nearest LLM to tweak that prompt. It's a good timesaver.
It is not a mind reader. I enjoy giving it feedback because it shows I am in charge of the engineering.
I also love using it for research for upcoming features. Research + pick a solution + implement. It happens so fast.
Do you have any ideas on how to harness AI to only change specific parts of a system or workpiece? Like "I consider this part 80/100 done and only make 'meaningful' or 'new contributions' here" ...?
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)
Just wanted to say that was a nice and very grounded write up; and as a result very informative. Thank you. More stuff like this is a breath of fresh air in a landscape that has veered into hyperbole territory both in the for and against ai sides
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 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.
Refreshing to read a balanced opinion, from a person who has significant experience and grounding in the real world.
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?
> 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 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".
> 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?
Low hundreds ($190 for me) but yes.
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.)
[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
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.
There is no such hard data. It's just research done on 16 developers using Cursor and Sonnet 3.5.
I'm not as good as Fabrice Bellard either but I don't let that bother me as I go about my day.
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.
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]
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.