I don't want to be that contrarian guy, but I find it energizing to go faster. For example, being able to blast through a list of niggling defects that need to be fixed is no longer a stultifying drag.
I recently used a coding agent on a project where I was using an unfamiliar language, framework, API, and protocol. It was a non-trivial project, and I had to be paying attention to what the agent was doing because it definitely would go off into the weeds fairly often. But not having to spend hours here and there getting up to speed on some mundane but unfamiliar aspect of the implementation really made everything about the experience better.
I even explored some aspects of LLM performance: I could tell that new and fast changing APIs easily flummox a coding agent, confirming the strong relationship of up-to-date and accurate training material to LLM performance. I've also seen this aspect of agent assisted coding improve and vary across AIs.
There's something exhilarating about pushing through to some "everything works like I think it should" point, and you can often get there without doing the conscientious, diligent, methodical "right" way of doing things, and it's only getting easier. At the point where everything works, if it's not just a toy or experiment, you definitely have to go back and understand everything. There will be a ton to fix, and it might take longer to do it like that than just by doing it right the first time.
I'm not a professional SWE, I just know enough
to understand what the right processes look like, and vibe coding is awesome but chaotic and messy.
> I don't want to be that contrarian guy, but I find it energizing to go faster. For example, being able to blast through a list of niggling defects that need to be fixed is no longer a stultifying drag.
It depends. No one is running their brain at full-throttle for more than a few hours on end.
If your "niggling" defects is mostly changes that don't require deep thought (refactor this variable name, function parameters/return type changes, classes, filename changes, etc), then I can see how it is energising - you're getting repeated dopamine hits for very little effort.
If, OTOH, you are doing deep review of the patterns and structures the LLM is producing, you aren't going to be doing that for more than a few hours without getting exhausted.
I find, myself, that repeatedly correcting stuff makes me tired faster than simply "LGTM, lets yolo it!" on a filename change, or class refactor, etc.
When the code I get is not what I wanted even though it passes the tests, it's more mental energy to correct the LLM than if I had simply done it myself from the first.
A good example of the exhausting tasks from today - my input has preprocessing directives embedded in it; there's only three now (new project), so the code generated by Claude did a number of `if-then-else-if` statements to process this input.
My expectation was that it would use a jump table of some type (possibly a dictionary holding function pointers, or a match/switch/case statement).
I think a good analogy is self-driving cars: if the SDC requires no human intervention, then sure it's safe. If the SDC requires the human to keep their hand on the wheel at all time because it might disengage with sub-second warnings, then I'm going to be more tired after a long drive than if I simply turned it off.
> I don't want to be that contrarian guy, but I find it energizing to go faster. For example, being able to blast through a list of niggling defects that need to be fixed is no longer a stultifying drag.
It's often that just getting started at all on a task is the hardest part. That's why writers often produce a "vomit draft" (https://thewritepractice.com/vomit-first-draft/) just to get into the right frame of mind to do real writing.
Using a coding agent to fix something trivial serves the same purpose.
> But not having to spend hours here and there getting up to speed on some mundane but unfamiliar aspect of the implementation
At some point you realize if you want people to trust you you have to do this. Otherwise you’re just gambling, which isn’t very trustworthy.
It’s also got the cumulative effect of making you a good developer if done consistently over the course of your career. But yes, it’s annoying and slow in the short term.
> I don't want to be that contrarian guy, but I find it energizing to go faster.
Is that contrarian though? Seems like pretty normal corparate setting bragging to me. (Note: I am not accusing you of it since your boss or collegues does not read this).
On the variant of "I am bad at not working too hard".
> I recently used a coding agent on a project where I was using an unfamiliar language, framework, API, and protocol.
You didn’t find that to be a little too much unfamiliarity? With the couple of projects that I’ve worked on that were developed using an “agent first” approach I found that if I added too many new things at once it would put me in a difficult space where I didn’t feel confident enough to evaluate what the agent was doing, and when it seemed to go off the rails I would have to do a bunch of research to figure out how to steer it.
Now, none of that was bad, because I learned a lot, and I think it is a great way to familiarize oneself with a new stack, but if I want to move really fast, I still pick mostly familiar stuff.
Can you share why it was non-trivial? I'm curious about how folks are evaluating the quality of their solutions when the project space is non trivial and unfamiliar
I think it's less "going fast" and more "going fast forever."
To your point, you can blow through damn-near anything pretty quickly now. Now I actually find myself problem-solving for nearly 8 hours every day. My brain feels fried at the end of the day way more than it used to.
Everyone in this conversation talks about different activities. One version of vibe coding happens with Netflix open and without ever opening a text editor, and another happens with thoroughly reviewing every change.
I think the counter-point to that is what I experience.
I agree it can be energizing because you can offload the bullshit work to a robot. For example, build me a CRUD app with a bootstrap frontend. Highly useful stuff especially if this isn't your professional forte.
The problems come afterwards:
1. The bigger the base codebase generation the less likely you're going to find time or energy to refactor LLM slop into something maintainable. I've spent a lot of time tailoring prompts for this type of generation and still can't get the code to be as precise as something an engineer would write.
2. Using an unfamiliar language means you're relying entirely on the LLM to determine what is safe. Suppose you wish to generate a project in C++. An LLM will happily do it. But will it be up to a standard that is maintainable and safe? Probably not. The devil is in the mundane details you don't understand.
In the case of (2) it's likely more instructive to have the LLM make you do the leg work, and then it can suggest simple verifiable changes. In the case of (1) I think it's just an extension of the complexity of any project professional or not. It's often better to write it correct the first time than write it fast and loose and then find the time to fix it later.
> But not having to spend hours here and there getting up to speed on some mundane but unfamiliar aspect of the implementation
Red flag. In other words you don’t understand the implementation well enough to know if the AI has done a good job. So the work you have committed may work or it may have subtle artefacts/bugs that you’re not aware of, because doing the job properly isn’t of interest to you.
This is ‘phoning it in’, not professional software engineering.
I 100% agree. It's been incredible for velocity and the capabilities and accuracy of the models I've been (mostly from Anthropic) have improved immensely over the last few months.
If you do this on your personal stuff, eh, I wouldn't do it, but you do you.
But we're seeing that this becomes OK in the workplace, and I don't believe it is.
If you propose these changes that would've normally taken you 2 weeks as your own in a PR, then I, as the reviewer, don't know where your knowledge ends and the AI's hallucinations begin.
Do you need to do all of these things? Or is it because the most commonly forked template of this piece of code has this in its boilerplate? I don't know. Do you?
How can you make sure the code works in all situations if you aren't even familiar with the language, let alone the framework / API and protocol?
* Do you know that in Java you have to do string.Equals instead of == for equality?
* Do you know in Python that you assigning a new value to a function default persists beyond the function?
* And in JavaScript it does not?
* Do you know that the C# && does not translate to VB.NET's And?
There probably needs to be some settled discussion on what constitutes "vibe coding." I interpret this term as "I input text into $AI_MODEL, I look at the app to see my change was implemented. I iterate via text prompts alone, rarely or never looking at the code generated."
vs. what this author is doing, which seems more like agent assisted coding than "vibe" coding.
With regard to the subject matter, it of course makes sense that managing more features than you used to be able to manage without $AI_MODEL would result in some mental fatigue. I also believe this gets worse the older you get. I've seen this within my own career, just from times of being understaffed and overworked, AI or not.
Yes, I'm getting increasingly confused as to why some people are broadening the use of "vibe" coding to just mean any AI coding, no matter how thorough/thoughtful.
Personally I think "vibe-coding" has semantically shifted to mean any AI-assisted coding and we should just run with it. For the original meaning of vibe-coding, I suggest YOLO-Coding.
> There probably needs to be some settled discussion on what constitutes "vibe coding." I interpret this term as "I input text into $AI_MODEL, I look at the app to see my change was implemented. I iterate via text prompts alone, rarely or never looking at the code generated."
Agreed. I've seen some folks say that it requires absolute ignorance of the code being generated to be considered "vibe coded". Though i don't agree with that.
For me it's more nuanced. I consider a lack of review to be "vibed" related to how little you looked at it. Considering LLMs can do some crazy things, even a few ignored LOC might end up with a pretty "vibe coded" feelings, despite being mostly reviewed outside of those ignored lines.
I don't see a distinction. Vibe coding is either agent assisted coding or using chatbots as interpreters for your design goals. They are the same thing.
My interpretation is that you can look at the code but vibe coding means ultimately you're not writing the code, you're just prompting. It would make sense to prompt "I'd like variable name 'bar' to be 'foo' instead." and that would still be vibe coding.
I think the difference between the two is shrinking by the day. At this point I almost never need to address anything with the LLM's solution and could easily just go straight to testing for most things.
The key difference is still the prompts and knowing what to reference/include in the context.
I've felt this too as a person with ADHD, specifically difficulty processing information. Caveat: I don't vibe code much, partially because of the mental fatigue symptoms.
I've found that if an LLM writes too much code, even if I specified what it should be doing, I still have to do a lot of validation myself that would have been done while writing the code by hand. This turns the process from "generative" (haha) to "processing", which I struggle a lot more with.
Unfortunately, the reason I have to do so much processing on vibe code or large generated chunks of code is simply because it doesn't work. There is almost always an issue that is either immediately obvious, like the code not working, or becomes obvious later, like poorly structured code that the LLM then jams into future code generation, creating a house of cards that easily falls apart.
Many people will tell me that I'm not using the right model or tools or whatever but it's clear to me that the problem is that AI doesn't have any vision of where your code will need to organically head towards. It's great for one shots and rewrites, but it always always always chokes on larger/complicated projects, ESPECIALLY ones that are not written in common languages (like JavaScript) or common packages/patterns eventually, and then I have to go spelunking to find why things aren't working or why it can't generate code to do something I know is possible. It's almost always because the input for new code is my ask AND the poorly structured code, so the LLM will rarely clean up it's own crap as it goes. If anything, it keeps writing shoddy wrapper around shoddy wrappers.
Anyways, still helpful for writing boilerplate and segments of code, but I like to know what is happening and have control over how my code is structured. I can't trust the LLMs right now.
Agreed. Some strategies that seem to help exist, though. Write extensive tests before writing the code. They serve as guidance. Commit tests separately from library code, so you can tell the AI didn't change the test. Specify the task with copious examples. Explain why yo so things, not just what to do.
I think it taxes your brain in two different ways - the mental model of the code is updated in the same way as a PR from a co-worker updates code, but in a minute instead of every now and then. So you need to recalibrate your understanding and think through edge cases to determine if the approach is what you want or if it will support future changes etc. And this happens after every prompt. The older/more experienced you are, the harder it is to NOT DO THIS thinking even if you are intending to "vibe" something, since it is baked into your programming flow.
The other tax is the intermittent downtime when you are waiting for the LLM to finish. In the olden days you might have productive downtime waiting for code to compile or a test suite to run. While this was happening you might review your assumptions or check your changes or realize you forgot an edge case and start working on a patch immediately.
When an LLM is running, you can't do this. Your changes are being done on your behalf. You don't know how long the LLM will take, or how you might rephrase your prompt if it does the wrong thing until you see and review the output. At best, you can context switch to some other problem but then 30 seconds later you come back into "review mode" and have to think architecturally about the changes made then "prompt mode" to determine how to proceed.
When you are doing basic stuff all of this is ok, but when you are trying to structure a large project or deal with multiple competing concerns you quickly overwhelm your ability to think clearly because you are thinking deeply about things while getting interrupted by completed LLM tasks or context switching.
My least favorite part is where it runs into some stupid problem and then tries to go around it.
Like when I'm asking it to run a bunch of tests against the UI using a browser tool, and something doesn't work. Then it goes and just writes code to update the database instead of using the user element.
My other thing that makes me insane is when I tell it what to do, and it says, "But wait, let me do something else instead."
> One reason developers are developers is the dopamine loop
> You write code, it doesn’t work, you fix it, it works, great! Dopamine rush. Several dozens or a hundred times a day.
This statement resonates with me. Vibe coding gets the job done quickly, but without the same joy. I used to think that it was the finished product that I liked to create, but maybe it's the creative process of building. It's like LEGO kits, the fun is putting them together, not looking at the finished model.
On the flip side, coding sessions where I bang my head against the wall trying to figure out some black box were never enjoyable. Nor was writing POCOs, boilerplate, etc.
I see people with no coding experience now generating PRs to a couple of repos I manage.
They ask a business question to the AI and it generates a bunch of code.
But honestly, coding isn't the part that slowed me down. Mapping the business requirements to code that doesn't fail is the hard part.
And the generated PRs are just answers to the narrow business questions. Now I need to spend time in walking it all back, and try to figure out what the actual business question is, and the overall impact. From experience I get very little answer to those questions.
And this is where Software Engineering experience becomes important. It's asking the right questions. Not just writing code.
Next to that I'm seeing developers drinking the cool-aid and submitting PRs where a whole bunch of changes are made, but they don't know why. Well, those changes DO have impact. Keeping it because the AI suggested it isn't the right answer. Keeping it because you agree with the AI's reasoning isn't the right answer either.
This morning I attended and paid attention to three separate meetings and at one point had three coding agents running in parallel solving some quite complex problems for me.
It's now 11:47am and I am mentally exhausted. I feel like my dog after she spends an hour at her sniff-training class (it wipes her out for the rest of the day.)
I've felt like that on days without the meetings too. Keeping up with AI tools requires a great deal of mental effort.
Going fast is awesome. If you have a bit of caffeine in the morning, turn on some tunes, and get into your workflow it's awesome. I get so sucked into my coding projects that when I'm done I'm disoriented. Nothing quite like being in the zone.
A guy at work did a demo of an agent work flow for some higher ups (we have chatbots but haven't adopted agents yet). He raved about how after writing a several hundred line spec, being extremely specific about the technology to use, and figuring out where to put all the guardrails, he was able to get Claude to generate weeks worth of code. When all was said and done it was like 20k lines of code between implementation, tests, and helper tools. Along the way he acknowledged you have to keep a close eye on it, or it will generate functions that pass tests but don't actually do their jobs, tests that pass but don't test anything, and a bunch of other issues.
To people with little to no practical software experience, I can see why that seems incredible. Think of the savings! But to anyone who's worked in a legacy code base, even well written ones, should know the pain. This is worse. That legacy code base was at least written with intention, and is hopefully battle tested to some degree by the time you look at it. This is 20k lines of code written by an intern that you are now responsible for going through line by line, which is going to take at least as long as it would have to write yourself.
There are obvious wins from AI, and agents, but this type of development is a bad idea. Iteration loops need to be kept much smaller, and you should still be testing as you go like you would when writing everything yourself. Otherwise it's going to turn into an absolute nightmare fast.
Even asking it to do little tests, Claude 4.5 Sonnet Thinking still ends up writing tests that do nothing or don't do what it says will do. And it's always fucking cheery about it: "you're code is now production-ready!" and "this is an excellent idea!" and "all errors are now fixed! your code is production-ready!" and "I fixed the compiler issue, we're now production ready!"
...almost as if it's too eager to make its first commit. Much like a junior engineer might be.
It's not eager enough to iterate. Moreover, when it does iterate, it often brings along the same wrong solutions it came up with before.
It's way easier to keep an eye on small changes while iterating with AI than it is with letting it run free in a green field.
This is what loom workers experienced after the introduction of the power loom, what factory workers experienced under Taylorism, what Amazon workers experience today in many cases. Just working at a pace that is unsustainable. This is why unions exist.
I find vibe coding similar to visiting a country where I don't know the local language very well.
Usually that requires saying something, seeing if the other person understands what I'm saying, and occasionally repeating myself in a different way.
It can be real tiring when I'm with friends who only speak the other language so we're both using translator tools and basically repeating that loop up to 2-3 hours.
I've found the same situation with vibe coding. Especially when the model misunderstands what I want or starts going off on a tangent. sometimes it's easier to edit the original query or an earlier step in the flow and re-write it for a better result.
Claude Code latency is at the unfortunate balance where the wait is long enough for me to go on twitter, but not long enough to do anything really valuable. Would be more productive if it took minutes or under 5-10 seconds.
Same for me.
I feel fatigue and I have also been reflecting about that as it reminded me the same type of mind exhaust I felt when I was a manager.
Choosing, analysing, verifying, talking to others rather than thinking in a sequential and organised way.
One of the reasons I moved away of that position was this constant fatigue by the end of the day.
I was happy on seeing things moving forward but it felt that it took more energy to do less
I find the increased speed to be an opportunity to slow down and focus on certain things, particularly the architectural changes of the feature I'm looking to introduce.
If you're able to blaze through feature tickets using GenAI on existing projects of any major complexity, there's almost certainly something which would produce better code that you're skipping.
I have plenty of info and agents for Claude Code to take into account when I use it to make features in our projects, but what it can't know is the cadence of what our business partners expect, the unknown unknowns, conversations that humans have about the projects, and the way end-users feel about the project. My job is to direct it with those factors in mind, and the work to account for those factors takes time.
If you're automating code generation but not automating verification, there will be a mismatch.
Maybe the fatigue comes from that mismatch?
The classical vibe coder style is to just ignore verification. That's not a good approach as well.
I think this space has not matured yet. We have old tools (test, lint) and some unreliable tools (agent assisted reviews), but nothing to match the speed of generation yet.
I do it by creating ad-hoc deterministic verifiers. Sometimes they'll last just a couple of PRs. It's cheap to do them now. But also, there must be a better way.
Downtime for the conscious brain is paramount in life, as that allows the sub-conscious to absorb and act on new information. I have no science to point to, but I believe wholeheartedly that the conscious and sub-conscious minds cannot access the same neurons at the same time, it's like single-ported RAM. More than one thing in my life has been improved by taking a conscious break and letting the subconscious churn
I did alot of AI research around this (memory/finetuning)
Coolest bit of research I cam across was what the brain does during sleep. It basically reduces connection during this. But it also makes you hallucinate (sleep). This was found in researching fish and also training LLMs there's great value in "forgetting" for generalization.
After studying it in LLMs for awhile I also came to your same conclusion about my own brain. Problems are often so complex you must let your brain forget in order to handle the complexity in the same sense I also believe this is the path to AGI.
The author confounds "vibe coding" and "AI-assisted programming".
According to the descriptions in the article you don't do vibe coding, you do AI-assisted programming.
While the productivity can be exciting. I personally observe the sa.e fatigue as described. I chock it up to the speed or frequency of context switching as a result of faster implementation.
There’s a lot of points I agree with but I think what’s important is fully conceptualizing the mental model of your project. Then, context switching doesn’t even induce much mental fatigue.
true in my case. when i get into the zone while coding, i can go on and on. while llm can help, there's a cognitive mismatch between coding directions to llm and reading the code when it comes to continuing coding. brain and the generated code aren't aligned. i prefer at the moment, after coding a feature, to see if it can be improved using llm. and it's of great help to write tests.
In my experience, it depends on the task. As a hobby, I develop personal projects (mostly web apps) where I'm not an expert in the relevant technology. In this case, LLM-assisted coding is empowering - and I don't think it's Dunning-Kruger, as I can understand the generated code, and it "feels" good enough given my 20+ years' experience in software engineering.
However, when it comes to my professional work on a mature, advanced project, I find it much easier to write the code myself than to provide a very precise specification without which the LLM wouldn't generate code of a sufficiently high quality.
Zigurd|2 months ago
I recently used a coding agent on a project where I was using an unfamiliar language, framework, API, and protocol. It was a non-trivial project, and I had to be paying attention to what the agent was doing because it definitely would go off into the weeds fairly often. But not having to spend hours here and there getting up to speed on some mundane but unfamiliar aspect of the implementation really made everything about the experience better.
I even explored some aspects of LLM performance: I could tell that new and fast changing APIs easily flummox a coding agent, confirming the strong relationship of up-to-date and accurate training material to LLM performance. I've also seen this aspect of agent assisted coding improve and vary across AIs.
observationist|2 months ago
I'm not a professional SWE, I just know enough to understand what the right processes look like, and vibe coding is awesome but chaotic and messy.
lelanthran|2 months ago
It depends. No one is running their brain at full-throttle for more than a few hours on end.
If your "niggling" defects is mostly changes that don't require deep thought (refactor this variable name, function parameters/return type changes, classes, filename changes, etc), then I can see how it is energising - you're getting repeated dopamine hits for very little effort.
If, OTOH, you are doing deep review of the patterns and structures the LLM is producing, you aren't going to be doing that for more than a few hours without getting exhausted.
I find, myself, that repeatedly correcting stuff makes me tired faster than simply "LGTM, lets yolo it!" on a filename change, or class refactor, etc.
When the code I get is not what I wanted even though it passes the tests, it's more mental energy to correct the LLM than if I had simply done it myself from the first.
A good example of the exhausting tasks from today - my input has preprocessing directives embedded in it; there's only three now (new project), so the code generated by Claude did a number of `if-then-else-if` statements to process this input.
My expectation was that it would use a jump table of some type (possibly a dictionary holding function pointers, or a match/switch/case statement).
I think a good analogy is self-driving cars: if the SDC requires no human intervention, then sure it's safe. If the SDC requires the human to keep their hand on the wheel at all time because it might disengage with sub-second warnings, then I'm going to be more tired after a long drive than if I simply turned it off.
vidarh|2 months ago
quotemstr|2 months ago
It's often that just getting started at all on a task is the hardest part. That's why writers often produce a "vomit draft" (https://thewritepractice.com/vomit-first-draft/) just to get into the right frame of mind to do real writing.
Using a coding agent to fix something trivial serves the same purpose.
skdhshdd|2 months ago
At some point you realize if you want people to trust you you have to do this. Otherwise you’re just gambling, which isn’t very trustworthy.
It’s also got the cumulative effect of making you a good developer if done consistently over the course of your career. But yes, it’s annoying and slow in the short term.
Animats|2 months ago
You, too, can be awarded the Order of Labor Glory, Third Class.[1]
[1] https://en.wikipedia.org/wiki/Order_of_Labour_Glory
rightbyte|2 months ago
Is that contrarian though? Seems like pretty normal corparate setting bragging to me. (Note: I am not accusing you of it since your boss or collegues does not read this).
On the variant of "I am bad at not working too hard".
pyrophane|2 months ago
You didn’t find that to be a little too much unfamiliarity? With the couple of projects that I’ve worked on that were developed using an “agent first” approach I found that if I added too many new things at once it would put me in a difficult space where I didn’t feel confident enough to evaluate what the agent was doing, and when it seemed to go off the rails I would have to do a bunch of research to figure out how to steer it.
Now, none of that was bad, because I learned a lot, and I think it is a great way to familiarize oneself with a new stack, but if I want to move really fast, I still pick mostly familiar stuff.
Rperry2174|2 months ago
AI removes boredome AND removes the natural pauses where understanding used to form..
energy goes up, but so does the kind of "compression" of cognitive things.
I think its less a quesiton of "faster" or "slower" but rather who controls the tempo
Avicebron|2 months ago
sixothree|2 months ago
But I 100% agree. It's liberating to focus on the design of my project, and my mental model can be of how I want things to work.
It feels like that switch to test driven development where you start from the expected result and worry about the details later.
blitz_skull|2 months ago
To your point, you can blow through damn-near anything pretty quickly now. Now I actually find myself problem-solving for nearly 8 hours every day. My brain feels fried at the end of the day way more than it used to.
WhyOhWhyQ|2 months ago
stuffn|2 months ago
I agree it can be energizing because you can offload the bullshit work to a robot. For example, build me a CRUD app with a bootstrap frontend. Highly useful stuff especially if this isn't your professional forte.
The problems come afterwards:
1. The bigger the base codebase generation the less likely you're going to find time or energy to refactor LLM slop into something maintainable. I've spent a lot of time tailoring prompts for this type of generation and still can't get the code to be as precise as something an engineer would write.
2. Using an unfamiliar language means you're relying entirely on the LLM to determine what is safe. Suppose you wish to generate a project in C++. An LLM will happily do it. But will it be up to a standard that is maintainable and safe? Probably not. The devil is in the mundane details you don't understand.
In the case of (2) it's likely more instructive to have the LLM make you do the leg work, and then it can suggest simple verifiable changes. In the case of (1) I think it's just an extension of the complexity of any project professional or not. It's often better to write it correct the first time than write it fast and loose and then find the time to fix it later.
louthy|2 months ago
Red flag. In other words you don’t understand the implementation well enough to know if the AI has done a good job. So the work you have committed may work or it may have subtle artefacts/bugs that you’re not aware of, because doing the job properly isn’t of interest to you.
This is ‘phoning it in’, not professional software engineering.
etothet|2 months ago
OptionOfT|2 months ago
But we're seeing that this becomes OK in the workplace, and I don't believe it is.
If you propose these changes that would've normally taken you 2 weeks as your own in a PR, then I, as the reviewer, don't know where your knowledge ends and the AI's hallucinations begin.
Do you need to do all of these things? Or is it because the most commonly forked template of this piece of code has this in its boilerplate? I don't know. Do you?
How can you make sure the code works in all situations if you aren't even familiar with the language, let alone the framework / API and protocol?
JohnMakin|2 months ago
vs. what this author is doing, which seems more like agent assisted coding than "vibe" coding.
With regard to the subject matter, it of course makes sense that managing more features than you used to be able to manage without $AI_MODEL would result in some mental fatigue. I also believe this gets worse the older you get. I've seen this within my own career, just from times of being understaffed and overworked, AI or not.
happytoexplain|2 months ago
keeda|2 months ago
Personally I think "vibe-coding" has semantically shifted to mean any AI-assisted coding and we should just run with it. For the original meaning of vibe-coding, I suggest YOLO-Coding.
unshavedyak|2 months ago
Agreed. I've seen some folks say that it requires absolute ignorance of the code being generated to be considered "vibe coded". Though i don't agree with that.
For me it's more nuanced. I consider a lack of review to be "vibed" related to how little you looked at it. Considering LLMs can do some crazy things, even a few ignored LOC might end up with a pretty "vibe coded" feelings, despite being mostly reviewed outside of those ignored lines.
celeryd|2 months ago
wvenable|2 months ago
My interpretation is that you can look at the code but vibe coding means ultimately you're not writing the code, you're just prompting. It would make sense to prompt "I'd like variable name 'bar' to be 'foo' instead." and that would still be vibe coding.
Kiro|2 months ago
The key difference is still the prompts and knowing what to reference/include in the context.
zephyrthenoble|2 months ago
I've found that if an LLM writes too much code, even if I specified what it should be doing, I still have to do a lot of validation myself that would have been done while writing the code by hand. This turns the process from "generative" (haha) to "processing", which I struggle a lot more with.
Unfortunately, the reason I have to do so much processing on vibe code or large generated chunks of code is simply because it doesn't work. There is almost always an issue that is either immediately obvious, like the code not working, or becomes obvious later, like poorly structured code that the LLM then jams into future code generation, creating a house of cards that easily falls apart.
Many people will tell me that I'm not using the right model or tools or whatever but it's clear to me that the problem is that AI doesn't have any vision of where your code will need to organically head towards. It's great for one shots and rewrites, but it always always always chokes on larger/complicated projects, ESPECIALLY ones that are not written in common languages (like JavaScript) or common packages/patterns eventually, and then I have to go spelunking to find why things aren't working or why it can't generate code to do something I know is possible. It's almost always because the input for new code is my ask AND the poorly structured code, so the LLM will rarely clean up it's own crap as it goes. If anything, it keeps writing shoddy wrapper around shoddy wrappers.
Anyways, still helpful for writing boilerplate and segments of code, but I like to know what is happening and have control over how my code is structured. I can't trust the LLMs right now.
Jeff_Brown|2 months ago
xnorswap|2 months ago
I take breaks.
But I also get drawn to overworking ( as I'm doing right now ), which I justify because "I'm just keeping an eye on the agent".
It's hard work.
It's hard to explain what's hard about it.
Watching as a machine does in an hour what would take me a week.
But also watching to stop the machine spin around doing nothing for ages because it's got itself in a mess.
Watching for when it gets lazy, and starts writing injectable SQL.
Watching for when it gets lazy, and tries to pull in packages it had no right to.
We've built a motor that can generate 1,000 horse power.
But one man could steer a horse.
The motor right now doesn't have the appropriate steering apparatus.
I feel like I'm chasing it around trying to keep it pointed forward.
It's still astronomically productive.
To abandon it would be a waste.
But it's so tiring.
lubujackson|2 months ago
The other tax is the intermittent downtime when you are waiting for the LLM to finish. In the olden days you might have productive downtime waiting for code to compile or a test suite to run. While this was happening you might review your assumptions or check your changes or realize you forgot an edge case and start working on a patch immediately.
When an LLM is running, you can't do this. Your changes are being done on your behalf. You don't know how long the LLM will take, or how you might rephrase your prompt if it does the wrong thing until you see and review the output. At best, you can context switch to some other problem but then 30 seconds later you come back into "review mode" and have to think architecturally about the changes made then "prompt mode" to determine how to proceed.
When you are doing basic stuff all of this is ok, but when you are trying to structure a large project or deal with multiple competing concerns you quickly overwhelm your ability to think clearly because you are thinking deeply about things while getting interrupted by completed LLM tasks or context switching.
CPLX|2 months ago
Like when I'm asking it to run a bunch of tests against the UI using a browser tool, and something doesn't work. Then it goes and just writes code to update the database instead of using the user element.
My other thing that makes me insane is when I tell it what to do, and it says, "But wait, let me do something else instead."
colechristensen|2 months ago
waltbosz|2 months ago
This statement resonates with me. Vibe coding gets the job done quickly, but without the same joy. I used to think that it was the finished product that I liked to create, but maybe it's the creative process of building. It's like LEGO kits, the fun is putting them together, not looking at the finished model.
On the flip side, coding sessions where I bang my head against the wall trying to figure out some black box were never enjoyable. Nor was writing POCOs, boilerplate, etc.
OptionOfT|2 months ago
They ask a business question to the AI and it generates a bunch of code.
But honestly, coding isn't the part that slowed me down. Mapping the business requirements to code that doesn't fail is the hard part.
And the generated PRs are just answers to the narrow business questions. Now I need to spend time in walking it all back, and try to figure out what the actual business question is, and the overall impact. From experience I get very little answer to those questions.
And this is where Software Engineering experience becomes important. It's asking the right questions. Not just writing code.
Next to that I'm seeing developers drinking the cool-aid and submitting PRs where a whole bunch of changes are made, but they don't know why. Well, those changes DO have impact. Keeping it because the AI suggested it isn't the right answer. Keeping it because you agree with the AI's reasoning isn't the right answer either.
simonw|2 months ago
It's now 11:47am and I am mentally exhausted. I feel like my dog after she spends an hour at her sniff-training class (it wipes her out for the rest of the day.)
I've felt like that on days without the meetings too. Keeping up with AI tools requires a great deal of mental effort.
damiangriggs|2 months ago
scuff3d|2 months ago
To people with little to no practical software experience, I can see why that seems incredible. Think of the savings! But to anyone who's worked in a legacy code base, even well written ones, should know the pain. This is worse. That legacy code base was at least written with intention, and is hopefully battle tested to some degree by the time you look at it. This is 20k lines of code written by an intern that you are now responsible for going through line by line, which is going to take at least as long as it would have to write yourself.
There are obvious wins from AI, and agents, but this type of development is a bad idea. Iteration loops need to be kept much smaller, and you should still be testing as you go like you would when writing everything yourself. Otherwise it's going to turn into an absolute nightmare fast.
inetknght|2 months ago
...almost as if it's too eager to make its first commit. Much like a junior engineer might be.
It's not eager enough to iterate. Moreover, when it does iterate, it often brings along the same wrong solutions it came up with before.
It's way easier to keep an eye on small changes while iterating with AI than it is with letting it run free in a green field.
tehjoker|2 months ago
spike021|2 months ago
Usually that requires saying something, seeing if the other person understands what I'm saying, and occasionally repeating myself in a different way.
It can be real tiring when I'm with friends who only speak the other language so we're both using translator tools and basically repeating that loop up to 2-3 hours.
I've found the same situation with vibe coding. Especially when the model misunderstands what I want or starts going off on a tangent. sometimes it's easier to edit the original query or an earlier step in the flow and re-write it for a better result.
Sevii|2 months ago
windex|2 months ago
esafak|2 months ago
motbus3|2 months ago
Choosing, analysing, verifying, talking to others rather than thinking in a sequential and organised way.
One of the reasons I moved away of that position was this constant fatigue by the end of the day. I was happy on seeing things moving forward but it felt that it took more energy to do less
lenerdenator|2 months ago
If you're able to blaze through feature tickets using GenAI on existing projects of any major complexity, there's almost certainly something which would produce better code that you're skipping.
I have plenty of info and agents for Claude Code to take into account when I use it to make features in our projects, but what it can't know is the cadence of what our business partners expect, the unknown unknowns, conversations that humans have about the projects, and the way end-users feel about the project. My job is to direct it with those factors in mind, and the work to account for those factors takes time.
cmrdporcupine|2 months ago
This fine for WFH/remote work. It didn't have great optics when I went back to in-office for a bit.
euph0ria|2 months ago
gaigalas|2 months ago
Maybe the fatigue comes from that mismatch?
The classical vibe coder style is to just ignore verification. That's not a good approach as well.
I think this space has not matured yet. We have old tools (test, lint) and some unreliable tools (agent assisted reviews), but nothing to match the speed of generation yet.
I do it by creating ad-hoc deterministic verifiers. Sometimes they'll last just a couple of PRs. It's cheap to do them now. But also, there must be a better way.
VerifiedReports|2 months ago
LocalH|2 months ago
johnsmith1840|2 months ago
Coolest bit of research I cam across was what the brain does during sleep. It basically reduces connection during this. But it also makes you hallucinate (sleep). This was found in researching fish and also training LLMs there's great value in "forgetting" for generalization.
After studying it in LLMs for awhile I also came to your same conclusion about my own brain. Problems are often so complex you must let your brain forget in order to handle the complexity in the same sense I also believe this is the path to AGI.
khimaros|2 months ago
geldedus|2 months ago
uberman|2 months ago
AutumnsGarden|2 months ago
layer8|2 months ago
cxromos|2 months ago
blahbob|2 months ago
However, when it comes to my professional work on a mature, advanced project, I find it much easier to write the code myself than to provide a very precise specification without which the LLM wouldn't generate code of a sufficiently high quality.
iceflinger|2 months ago
nullorempty|2 months ago
wendgeabos|2 months ago