7 ms·

AI coding is addictive. Engineers are paying the price

46 points by sefrost 3mo ago

vijayst 3mo ago
I can't agree more. I spent 4 hours debugging an issue with Claude from 10PM to 2AM which I will never do - before Claude.
- eithed 3mo ago
  Same; the most infuriating parts - Claude caused the issue and Claude misdiagnosed the issue, making me spend more time than if I was debugging it myself.
  - hoppp 3mo ago
    I basically generate and then remove 80% manually. Way overbuilt and doesn't follow my thinking that well.
  - bigstrat2003 3mo ago
    That's been my experience. LLMs cost me time, they don't save it.
    - weezing 3mo ago
      Glad to hear that. Gives some hope for the future.
      - deleted 3mo ago
        [deleted]
    - sph 3mo ago
      Then why tf are you using it? I mean, seriously. Truly sounds like addiction.
      - batshit_beaver 3mo ago
        I think the issue is that obtaining empirical proof of AI or manual coding being more efficient is very difficult, since true costs and outcomes aren’t known for months and often years. AI _is_ faster at producing short term results though, so naturally, given the state of the industry, everyone is piling both money and time into AI driven workflows and manual work is met with suspicion, if not outright discouragement. Engineers might hate AI, but they need their paychecks.
      - bigstrat2003 3mo ago
        Because my employer forces me to. I wouldn't be using it if not for that.
    - whattheheckheck 3mo ago
      You may be wise beyond your years and are sitting on a goldmine of consulting opportunity if you can actually get tasks done faster without ai given the exact same inputs
      - bigstrat2003 3mo ago
        Not really. It just is a very sucky tool, contrary to what the hype says.
        whattheheckheck 2mo ago
        Can you share what other "sucky tools" would be on the same level? I understand they do kind of suck holistically but to explain it better to laymen how would you describe the whole thing?
  - lambdaone 3mo ago
    This happens all the time to me. The model emits thousands of lines of high-quality code, making vast progress very quickly, but on the way does a few very, very stupid things a human being would never have been silly enough to do. Then it takes hours or days - sometimes weeks - to find and fix the AI-induced problems. If you very, very tightly constrain the AI by using structured processes and unit tests you can work wonders with it, but you do start to wonder to what extent this is better than if you had simply coded it yourself.
xenophonf 3mo ago
https://archive.is/2pKhN https://archive.is/2pKhN
paxys 3mo ago
> AI is keeping engineers at their desks longer, not freeing them up. Random rewards, dopamine hits, and no natural stopping points create a loop comparable to casino gambling. > The fix is deliberate habits, not restricted tools. Time-box sessions, separate exploration from execution, and treat recovery as maintenance. Getting tired of AI slop telling me about AI.
- whattheheckheck 3mo ago
  Yeah do people not know how to do deep knowledge work? Look up Cal Newport habits
hirvi74 3mo ago
What type of AI coding? I do not have the attention span to sit there and let these LLMs just churn away. I have to be the one doing the typing or nothing will be accomplished. I tried playing Claude Code and Codex a bit. While impressive in their outputs (at times), I just find the workflow to be so dissatisfying. One other aspect of LLMs that I do not enjoy when it comes to development is the fact that LLMs minimize my contributions. I do not feel like I can take credit for anything I create if I technically did not create it. However, I absolutely adore LLMs for learning new concepts and for troubleshooting. To me, that is where they shine the brightest.
- bigstrat2003 3mo ago
  Yeah I feel much the same way, though I think LLMs suck ass at troubleshooting. They are almost always going down wrong paths and making bad guesses that I waste time disproving, or suggesting solutions that don't wind up lining up with the symptoms I'm having. Where I do get value out of LLMs is in two main areas. One is generating short bits of code that I can more or less instantly recognize as correct. Bash scripts are a good example - I can read bash well enough but I'm not great at writing it, so Claude can generate a 20-line script very quickly and I can equally quickly understand the generated code. Writing such scripts would probably take me 15-20 minutes, so I'm not saving huge time, but it's there. The other use case I have is asking the LLM for code review on my personal projects. I don't let it write code (that would destroy the whole fun of the personal project, for one thing), but sometimes I have some code I'm pretty sure sucks and I'll ask ChatGPT to suggest better ways to accomplish the same thing. I learn a lot reviewing its suggestions.
yungtunafish 3mo ago
Just started at a company and the amount of irresponsible AI use is appalling. I asked an employee whose job involves AI adoption/training how large their diffs are for pull requests. They told me that their diffs are "As much as the model can produce given its reasoning level". In the end, this is going to create unmaintainable code that no one understands. It also discourages reviewing the code because no dev can meaningfully review 1000s of lines of code in a day while also accomplishing their tasks. NOTE: I am still pro AI, just like I am pro heavy machinery. I just don't want people to cut off their legs...
- dominotw 3mo ago
  why do you care how large the diffs are. isnt there any other way to measure if ai is producing value?
  - chrisandchris 3mo ago
    Not OP, but I think it's not about producing value now, but how much it will cost in the long term. If you have unmaintaable code that is N times larger than a hand-written codebase, what is the cost to be?
  - yungtunafish 3mo ago
    Its about the team being able to review the code to tell if its slop or not. It's hard to meaningfully review huge changes to a codebase for one PR. Just imagine if there are 5 PRs a day with 1000+ insertions. It leads to the production codebase being somewhat of a black box imo
- hoppp 3mo ago
  Yeah, I dread going back to work as my position has transformed from full stack dev to Ai code unfucker...
  - Terr_ 3mo ago
    I'm not scared of trying to unfuck LLM code per se, but I'm very afraid of doing it in a company that's still simultaneously adding 10x more...
devld 3mo ago
Or "paying the price" literally. I'm returning after a small break and into new GitHub Copilot prices. A simple question / request / analysis on Sonnet - that will be 29 cents, please. I can't imagine how much it will cost to do actual development with it.
chopete3 3mo ago
The problem with articles like this one is, they give ways to become efficient at handling more addiction, at the individual level. Nothing for others part of this, companies developing the software and organizations employing these tools. Summary of the addiction management tips from the article. 1. Time-box your AI coding sessions with a clear goal and a hard end time. 2. Separate exploration (testing ideas) from execution (shipping code) to avoid losing focus. 3. Prioritize sleep, hard stops, and actual recovery as essential maintenance, not just wellness. 4. Invest in structured training to move from basic usage to advanced multi-agent workflows. 5. Personalize your AI workflow to fit your needs while actively avoiding common anti-patterns. - When a developer stops writing code and starts using Claude to handle multiple projects at once, they are essentially managing the outcomes. They have become 10x engineering managers. The context strain and emotional strain is overwhelming.
- andrekandre 3mo ago
  > Nothing for others part of this, companies developing the software and organizations employing these tools. lack of systems-thinking and over focus on the individual has been a trend since a long time in business. its all on the individual worker to fix things for themselves...
otekengineering 3mo ago
agentic coding is a soft drug, so taking 'the only way out is through' approach is pretty viable. once you figure out how to swim and have claude running for an hour+ at a time and only bugging you with either high-level taste decisions or 'done, how's it look?' it's pretty low stress if you're overloaded with PRs, build LLM-based systems to take the load off. don't be a senior engineer, be an engineering manager.
kcoul 3mo ago
Has anyone else found a similarity between how you feel at the end of a long AI coding session, and getting off of a long haul flight? I think the reasons are similar. On the flight, it's not exactly like you directly feel the wind going through your hair as you travel 1000km/hr, but your body still knows that you did. You feel the lag immediately, not really due to a time zone difference but due to how unnatural it is to move so far in so short a time. I feel the same way after a highly productive AI coding session. I used to anecdotally mention to others that I liked to maintain and use older machines because it felt nice to get little breaks here and there while the machine took longer to open a browser/app, return search results, render a file, etc. This is the opposite of that. Everything is happening so fast, your mind is taxed differently than if you are responsible for typing everything yourself... no matter how fast you could type code. That said, I don't think it's entirely my increased cognitive load that makes me feel drained after a session, it's as though you can somehow feel the token burn, the water/electricity use, just as you somehow felt the wind shear on the airplane you were just in for many hours.
- slicktux 3mo ago
  Maybe a system running a local model is not so bad. The tokens per second will allow for that break? Also, at what point does AI take over for all the thinking and white board planning? AI should be a rubber duckie one can use to posit and assist when hit a brick wall. But if using it for all code generation, planning and troubleshooting then one is not in control; they’re just prompt drones…
  - kcoul 3mo ago
    This gets into how advanced the prompt engineering actually is. I anticipate discussion around what "state-of-the-art" prompts look like, since, as the OpenClaw founder suggested, Prompt Requests may well replace Pull Requests when a set of small tweaks to the prompt may yield vastly improved output. This of course needs to be coupled with actually staying accountable for what the entirety of the codebase looks like. I imagine many people are unwilling to slow down enough to actually do that accounting/review, and the architecture might gradually shift towards entropy.
- 3mo ago

46 points by sefrost 3mo ago

I can't agree more. I spent 4 hours debugging an issue with Claude from 10PM to 2AM which I will never do - before Claude.

eithed 3mo ago
Same; the most infuriating parts - Claude caused the issue and Claude misdiagnosed the issue, making me spend more time than if I was debugging it myself.
- hoppp 3mo ago
  I basically generate and then remove 80% manually. Way overbuilt and doesn't follow my thinking that well.
- bigstrat2003 3mo ago
  That's been my experience. LLMs cost me time, they don't save it.
  - weezing 3mo ago
    Glad to hear that. Gives some hope for the future.
    - deleted 3mo ago
      [deleted]
  - sph 3mo ago
    Then why tf are you using it? I mean, seriously. Truly sounds like addiction.
    - batshit_beaver 3mo ago
      I think the issue is that obtaining empirical proof of AI or manual coding being more efficient is very difficult, since true costs and outcomes aren’t known for months and often years. AI _is_ faster at producing short term results though, so naturally, given the state of the industry, everyone is piling both money and time into AI driven workflows and manual work is met with suspicion, if not outright discouragement. Engineers might hate AI, but they need their paychecks.
    - bigstrat2003 3mo ago
      Because my employer forces me to. I wouldn't be using it if not for that.
  - whattheheckheck 3mo ago
    You may be wise beyond your years and are sitting on a goldmine of consulting opportunity if you can actually get tasks done faster without ai given the exact same inputs
    - bigstrat2003 3mo ago
      Not really. It just is a very sucky tool, contrary to what the hype says.
      - whattheheckheck 2mo ago
        Can you share what other "sucky tools" would be on the same level? I understand they do kind of suck holistically but to explain it better to laymen how would you describe the whole thing?
- lambdaone 3mo ago
  This happens all the time to me. The model emits thousands of lines of high-quality code, making vast progress very quickly, but on the way does a few very, very stupid things a human being would never have been silly enough to do. Then it takes hours or days - sometimes weeks - to find and fix the AI-induced problems. If you very, very tightly constrain the AI by using structured processes and unit tests you can work wonders with it, but you do start to wonder to what extent this is better than if you had simply coded it yourself.

xenophonf 3mo ago

https://archive.is/2pKhN https://archive.is/2pKhN

paxys 3mo ago

> AI is keeping engineers at their desks longer, not freeing them up. Random rewards, dopamine hits, and no natural stopping points create a loop comparable to casino gambling. > The fix is deliberate habits, not restricted tools. Time-box sessions, separate exploration from execution, and treat recovery as maintenance. Getting tired of AI slop telling me about AI.

whattheheckheck 3mo ago
Yeah do people not know how to do deep knowledge work? Look up Cal Newport habits

hirvi74 3mo ago

What type of AI coding? I do not have the attention span to sit there and let these LLMs just churn away. I have to be the one doing the typing or nothing will be accomplished. I tried playing Claude Code and Codex a bit. While impressive in their outputs (at times), I just find the workflow to be so dissatisfying. One other aspect of LLMs that I do not enjoy when it comes to development is the fact that LLMs minimize my contributions. I do not feel like I can take credit for anything I create if I technically did not create it. However, I absolutely adore LLMs for learning new concepts and for troubleshooting. To me, that is where they shine the brightest.

bigstrat2003 3mo ago
Yeah I feel much the same way, though I think LLMs suck ass at troubleshooting. They are almost always going down wrong paths and making bad guesses that I waste time disproving, or suggesting solutions that don't wind up lining up with the symptoms I'm having. Where I do get value out of LLMs is in two main areas. One is generating short bits of code that I can more or less instantly recognize as correct. Bash scripts are a good example - I can read bash well enough but I'm not great at writing it, so Claude can generate a 20-line script very quickly and I can equally quickly understand the generated code. Writing such scripts would probably take me 15-20 minutes, so I'm not saving huge time, but it's there. The other use case I have is asking the LLM for code review on my personal projects. I don't let it write code (that would destroy the whole fun of the personal project, for one thing), but sometimes I have some code I'm pretty sure sucks and I'll ask ChatGPT to suggest better ways to accomplish the same thing. I learn a lot reviewing its suggestions.

yungtunafish 3mo ago

Just started at a company and the amount of irresponsible AI use is appalling. I asked an employee whose job involves AI adoption/training how large their diffs are for pull requests. They told me that their diffs are "As much as the model can produce given its reasoning level". In the end, this is going to create unmaintainable code that no one understands. It also discourages reviewing the code because no dev can meaningfully review 1000s of lines of code in a day while also accomplishing their tasks. NOTE: I am still pro AI, just like I am pro heavy machinery. I just don't want people to cut off their legs...

dominotw 3mo ago
why do you care how large the diffs are. isnt there any other way to measure if ai is producing value?
- chrisandchris 3mo ago
  Not OP, but I think it's not about producing value now, but how much it will cost in the long term. If you have unmaintaable code that is N times larger than a hand-written codebase, what is the cost to be?
- yungtunafish 3mo ago
  Its about the team being able to review the code to tell if its slop or not. It's hard to meaningfully review huge changes to a codebase for one PR. Just imagine if there are 5 PRs a day with 1000+ insertions. It leads to the production codebase being somewhat of a black box imo
hoppp 3mo ago
Yeah, I dread going back to work as my position has transformed from full stack dev to Ai code unfucker...
- Terr_ 3mo ago
  I'm not scared of trying to unfuck LLM code per se, but I'm very afraid of doing it in a company that's still simultaneously adding 10x more...

devld 3mo ago

Or "paying the price" literally. I'm returning after a small break and into new GitHub Copilot prices. A simple question / request / analysis on Sonnet - that will be 29 cents, please. I can't imagine how much it will cost to do actual development with it.

chopete3 3mo ago

The problem with articles like this one is, they give ways to become efficient at handling more addiction, at the individual level. Nothing for others part of this, companies developing the software and organizations employing these tools. Summary of the addiction management tips from the article. 1. Time-box your AI coding sessions with a clear goal and a hard end time. 2. Separate exploration (testing ideas) from execution (shipping code) to avoid losing focus. 3. Prioritize sleep, hard stops, and actual recovery as essential maintenance, not just wellness. 4. Invest in structured training to move from basic usage to advanced multi-agent workflows. 5. Personalize your AI workflow to fit your needs while actively avoiding common anti-patterns. - When a developer stops writing code and starts using Claude to handle multiple projects at once, they are essentially managing the outcomes. They have become 10x engineering managers. The context strain and emotional strain is overwhelming.

andrekandre 3mo ago
> Nothing for others part of this, companies developing the software and organizations employing these tools. lack of systems-thinking and over focus on the individual has been a trend since a long time in business. its all on the individual worker to fix things for themselves...

otekengineering 3mo ago

agentic coding is a soft drug, so taking 'the only way out is through' approach is pretty viable. once you figure out how to swim and have claude running for an hour+ at a time and only bugging you with either high-level taste decisions or 'done, how's it look?' it's pretty low stress if you're overloaded with PRs, build LLM-based systems to take the load off. don't be a senior engineer, be an engineering manager.

kcoul 3mo ago

Has anyone else found a similarity between how you feel at the end of a long AI coding session, and getting off of a long haul flight? I think the reasons are similar. On the flight, it's not exactly like you directly feel the wind going through your hair as you travel 1000km/hr, but your body still knows that you did. You feel the lag immediately, not really due to a time zone difference but due to how unnatural it is to move so far in so short a time. I feel the same way after a highly productive AI coding session. I used to anecdotally mention to others that I liked to maintain and use older machines because it felt nice to get little breaks here and there while the machine took longer to open a browser/app, return search results, render a file, etc. This is the opposite of that. Everything is happening so fast, your mind is taxed differently than if you are responsible for typing everything yourself... no matter how fast you could type code. That said, I don't think it's entirely my increased cognitive load that makes me feel drained after a session, it's as though you can somehow feel the token burn, the water/electricity use, just as you somehow felt the wind shear on the airplane you were just in for many hours.

slicktux 3mo ago
Maybe a system running a local model is not so bad. The tokens per second will allow for that break? Also, at what point does AI take over for all the thinking and white board planning? AI should be a rubber duckie one can use to posit and assist when hit a brick wall. But if using it for all code generation, planning and troubleshooting then one is not in control; they’re just prompt drones…
- kcoul 3mo ago
  This gets into how advanced the prompt engineering actually is. I anticipate discussion around what "state-of-the-art" prompts look like, since, as the OpenClaw founder suggested, Prompt Requests may well replace Pull Requests when a set of small tweaks to the prompt may yield vastly improved output. This of course needs to be coupled with actually staying accountable for what the entirety of the codebase looks like. I imagine many people are unwilling to slow down enough to actually do that accounting/review, and the architecture might gradually shift towards entropy.
3mo ago