9 ms·

This seems to confirm my feeling when using AI too much. It's easy to get started, but I can feel my brain engaging less with the problem than I'm used to. It c

by mcv 9mo ago

This seems to confirm my feeling when using AI too much. It's easy to get started, but I can feel my brain engaging less with the problem than I'm used to. It can form a barrier to real understanding, and keeps me out of my flow. I recently worked on something very complex I don't think I would have been able to tackle as quickly without AI; a hierarchical graph layout algorithm based on the Sugiyama framework, using Brandes-Köpf for node positioning. I had no prior experience with it (and I went in clearly underestimating how complex it was), and AI was a tremendous help in getting a basic understanding of the algorithm, its many steps and sub-algorithms, the subtle interactions and unspoken assumptions in it. But letting it write the actual code was a mistake. That's what kept me from understanding the intricacies, from truly engaging with the problem, which led me to keep relying on the AI to fix issues, but at that point the AI clearly also had no real idea what it was doing, and just made things worse. So instead of letting the AI see the real code, I switched from the Copilot IDE plugin to the standalone Copilot 365 app, where it could explain the principles behind every step, and I would debug and fix the code and develop actual understanding of what was going on. And I finally got back into that coding flow again. So don't let the AI take over your actual job, but use it as an interactive encyclopedia. That works much better for this kind of complex problem.

isolli 9mo ago
This mirrors my experience exactly. We have to learn how to tame the beast.
- sevenzero 9mo ago
  [flagged]
- sothatsit 9mo ago
  I think we all just need to avoid the trap of using AI to circumvent understanding. I think that’s where most problems with AI lie. If I understand a problem and AI is just helping me write or refactor code, that’s all good. If I don’t understand a problem and I’m using AI to help me investigate the codebase or help me debug, that’s okay too. But if I ever just let the AI do its thing without understanding what it’s doing and then I just accept the results, that’s where things go wrong. But if we’re serious about avoiding the trap of AI letting us write working code we don’t understand, then AI can be very useful. Unfortunately the trap is very alluring. A lot of vibe coding falls into the trap. You can get away with it for small stuff, but not for serious work.
  - orenp 9mo ago
    I'd say the new problem is knowing when understanding is important and where it's okay to delegate. It's similar to other abstractions in this way, but on a larger scale due to LLM having so many potential applications. And of course, due to the non-determinism.
    - sothatsit 9mo ago
      My argument is that understanding is always important, even if you delegate. But perhaps you mean sometimes a lower degree of understanding may be okay, which may be true, but I’d be cautious on that front. AI coding is a very leaky abstraction. We already see the damage of a lack of understanding when we have to work with old codebases. These behemoths can become very difficult to work in over time as the people who wrote it leave, and new people don’t have the same understanding to make good effective changes. This slows down progress tremendously. Fundamentally, code changes you make without understanding them immediately become legacy code. You really don’t want too much of that to pile up.
  - player1234 9mo ago
    [dead]
  - alfalfasprout 9mo ago
    I'm writing a blog post on this very thing actually. Outsourcing learning and thinking is a double edged sword that only comes back to bite you later. It's tempting: you might already know a codebase well and you set agents loose on it. You know enough to evaluate the output well. This is the experience that has impressed a few vocal OSS authors like antirez for example. Similarly, you see success stories with folks making something greenfield. Since you've delegated decision making to the LLM and gotten a decent looking result it seems like you never needed to know the details at all. The trap is that your knowledge of why you've built what you've built the way it is atrophies very quickly. Then suddenly you become fully dependent on AI to make any further headway. And you're piling slop on top of slop.
Archer6621 9mo ago
That's a nice anecdote, and I agree with the sentiment - skill development comes from practice. It's tempting to see using AI as free lunch, but it comes with a cost in the form of skill atrophy. I reckon this is even the case when using it as an interactive encyclopedia, where you may lose some skill in searching and aggregating information, but for many people the overall trade off in terms of time and energy savings is worth it; giving them room to do more or other things.
- chairmansteve 9mo ago
  "I reckon this is even the case when using it as an interactive encyclopedia". Yes, that is my experience. I have done some C# projects recently, a language I am not familiar with. I used the interactive encylopedia method, "wrote" a decent amount of code myself, but several thousand lines of production code later, I don't I know C# any better than when I started. OTOH, it seems that LLMs are very good at compiling pseudocode into C#. And I have always been good at reading code, even in unfamiliar languages, so it all works pretty well. I think I have always worked in pseudocode inside my head. So with LLMs, I don't need to know any programming languages!
  - Archer6621 9mo ago
    It's even more general than that: LLMs seem to be exceedingly good at translating bodies of text from one domain to another. The frontier models also have excellent natural language translation capabilities, far surpassing e.g. Google translate. In that sense, going from pseudocode to a programming language is no different from that, or even translating a piece of code from one programming language to another.
- scyzoryk_xyz 9mo ago
  If the computer was the bicycle for the mind, then perhaps AI is the electric scooter for the mind? Gets you there, but doesn't necessarily help build the best healthy habits. Trade offs around "room to do more of other things" are an interesting and recurring theme of these conversations. Like two opposites of a spectrum. On one end the ideal process oriented artisan taking the long way to mastery, on the other end the trailblazer moving fast and discovering entirely new things. Comparing to the encyclopedia example: I'm already seeing my own skillset in researching online has atrophied and become less relevant. Both because the searching isn't as helpful and because my muscle memory for reaching for the chat window is shifting.
PatronBernard 9mo ago
> a hierarchical graph layout algorithm based on the Sugiyama framework, using Brandes-Köpf for node positioning. I am sorry for being direct but you could have just kept it to the first part of that sentence. Everything after that just sounds like pretentious name dropping and adds nothing to your point. But I fully agree, for complex problems that require insight, LLMs can waste your time with their sycophancy.
- TheColorYellow 9mo ago
  This is a technical forum, isn't pretentious name dropping kind of what we do? Seriously though, I appreciated it because my curiosity got the better of me and I went down a quick rabbit hole in Sugiyama, comparative graph algorithms, and learning about the node positioning as a particular dimension of graph theory. Sure nothing ground breaking, but it added a shallow amount to my broad knowledge base of theory that continues to prove useful in our business (often knowing what you don't know is the best initiative for learning). So yeah man, lets keep name dropping pretentious technical details because thats half the reason I surf this site. And yes, I did use ChatGPT to familiarize myself with these concepts briefly.
  - fatherwavelet 9mo ago
    I think many are not doing anything like this so to the person who is not interested in learning anything, technical details like this sound like pretentious name dropping because that is how they relate to the world. Everything to them is a social media post for likes. I have explored all kinds of graph layouts in various network science context via LLMs and guess what? I don't know anything much about graph theory beyond G = (V,E). I am not really interested either. I am interested in what I can do with and learn from G. Everything on the right of the equals sign Gemini is already beyond my ability. I am just not that smart. The standard narrative on this board seems to be something akin to having to master all volumes of Knuth before you can even think to write a React CRUD app. Ironic since I imagine so many learned programming by just programming. I know I don't think as hard when using an LLM. Maybe that is a problem for people with 25 more IQ points than me. If I had 25 more IQ points maybe I could figure out stuff without the LLM. That was not the hand I was dealt though. I get the feeling there is immense intellectual hubris on this forum that when something like this comes up, it is a dog whistle for these delusional Erdos in their own mind people to come out of the wood work to tell you how LLMs can't help you with graph theory. If that wasn't the case there would be vastly more interesting discussion on this forum instead of ad nauseam discussion on how bad LLMs are. I learn new things everyday from Gemini and basically nothing reading this forum.
foxes 9mo ago
Is this a copilot ad?
- gambiting 9mo ago
  It does read like one.
  - hobofan 9mo ago
    It reads like an anti-ad for both. "I didn't use the Copilot IDE because I lack control over the context provided" and "I used Copilot 365 because it for sure doesn't have any context of anything because connecting things to it is hard/expensive".
vidarh 9mo ago
My "actual job" isn't to write code, but to solve problems. Writing code has just typically been how I've needed to solve those problems. That has increasingly shifted to "just" reviewing code and focusing on the architecture and domain models. I get to spend more time on my actual job.
- thefaux 9mo ago
  This feels like it conflates problem solving with the production of artifacts. It seems highly possible to me that the explosion of ai generated code is ultimately creating more problems than it is solving and that the friction of manual coding may ultimately prove to be a great virtue.
  - Difwif 9mo ago
    This statement feels like a farmer making a case for using their hands to tend the land instead of a tractor because it produces too many crops. Modern farming requires you to have an ecosystem of supporting tools to handle the scale and you need to learn new skills like being a diesel mechanic. How we work changes and the extra complexity buys us productivity. The vast majority of software will be AI generated, tools will exist to continuously test/refine it, and hand written code will be for artists, hobbyists, and an ever shrinking set of hard problems where a human still wins.
    - hluska 9mo ago
      I’ll be honest with you pal - this statement sounds like you’ve bought the hype. The truth is likely between the poles - at least that’s where it’s been for the last 35 years that I’ve been obsessed with this field.
      - paulcole 9mo ago
        They may be early but they’re not wrong.
        9mo ago
jstummbillig 9mo ago
> But letting it write the actual code was a mistake I think you not asking questions about the code is the problem (in so far it still is a problem). But it certainly has gotten easy not to.
exodust 9mo ago
Similarly I leave Cursor's AI in "ask" mode. It puts code there, leaving me to grab what I need and integrate myself. This forces me to look closely at code and prevents the "runaway" feeling where AI does too much and you're feeling left behind in your own damn project. It's not AI chat causing cognitive debt it's Agents!
jeffreygoesto 9mo ago
Elk (Eclipse Layout Kernel) is a very good package solving that, you might want to check it's Javascript port https://github.com/kieler/elkjs https://github.com/kieler/elkjs
- mcv 9mo ago
  I'd briefly come across Elk, but couldn't tell how it was better than what I was using. The examples I could find all showed far simpler graphs than what we had, and nothing that seemed to address the problems we had, but maybe I should give it another look, because I've kinda lost faith that dagre is going to do what we need. If I can explain briefly what our issue is: we've got a really complex graph, and need to show it in a way that makes it easy to understand. That by itself might be a lost cause already, but we need it fixed. The problem is that our graph has cycles, and dagre is designed for DAGs; directed acyclic graphs. Fortunately it has a step that removes cycles, but it does that fairly randomly, and that can sometimes dramatically change the shape of the graph by creating unintentional start or end nodes. I had a way to fix that, but even with that, it's still really hard to understand the graph. We need to cut it up into parts, group nodes together based on shared properties, and that's not something dagre does at all. I'm currently looking into cola with its constraints. But I'll take another look at elk.
  - jeffreygoesto 9mo ago
    Our graphs are hierarchical, can contain cycles, too and have a bunch of directed subgraphs. We reach 500 nodes with 20k ports and 10k edges and "getting the graph" is still possible but takes a bit of practice. Cycle breaking is okish for us, because there is a strong asymmetry between many "forward" and much less "backwards" edges that makes the heuristics succeed often.
deadbabe 9mo ago
I think a good rule of thumb is, only have AI write some code when you know exactly what it should look like and are just too lazy to type it out, or, if it is code that you would have otherwise just pulled down from some open source library and not written yourself anyway.
anthonypasq 9mo ago
seems like the actual issue is that you were using a Copilot IDE plugin.
waterhouse 9mo ago
Has anyone here switched roles from programmer to manager (of human programmers), and is it a similar feeling?
- cowlby 9mo ago
  Would be curious about this too. It’s a mental shift to go from understanding everything about the code, to trusting someone else understands everything and we just make decisions.
- deleted 9mo ago
  [deleted]
stronglikedan 9mo ago
> It's easy to get started Funny - that's the hard part for me. I have yet to figure out what to use it for, since it seems to take longer than any other method of performing my tasks. Especially with regards to verifying for correctness, which in most cases seems to take as long or longer than just having done it myself, knowing I did it correctly.
alexjplant 9mo ago
When I used Copilot autocomplete more I noticed myself slipping a bit when it comes to framework and syntax particulars so I instituted a moratorium on it on Fridays to prevent this. Claude Code seems to be a much better paradigm. For novel implementations I write code manually while asking it questions. For things that I'm prototyping I babysit it closely and constantly catch it doing things that I don't want it to do. I ask it questions about why it built things certain ways and 80% of the time it doesn't have a good answer and redoes it the way that I want. This takes a great deal of cognitive engagement. Rule nombre [sic] uno: Never anthropomorphize the LLM. It's a giant pattern-matching machine. A useful one, but still just a machine. Do not let it think for you because it can't.
- archy_ 9mo ago
  4b-model take. LLMs are far more intelligent than you give them credit for. Every new layer of abstraction allows us to develop software better and faster. People constantly ragged on OOP yet it is the foundation of modern computing. People while about "bloat" but continue to buy more RAM. Compilers are a black box and meaningfully inhibit your ability to write asm but these days nobody cares. I see LLMs as the next logical evolution in computing abstractions.
omneity 9mo ago
I just went through an eerily similar situation where the coding agent was able to muster some pretty advanced math (information geometry) to solve my problem at hand. But while I was able to understand it enough to steer the conversation, I was utterly unable to make any meaningful change to the code or grasp what it was doing. Unfortunately, unlike in the case you described, chatting with the LLM didn’t cut it as the domain is challenging enough. I’m on a rabbit hunt now for days, picking up the math foundations and writing the code at a slower pace albeit one I can keep up with. And to be honest it’s incredibly fun. Applied math with a smart, dedicated tutor and the ability to immediately see results and build your intuition is miles ahead of my memories back in formative years.
Telemakhos 9mo ago
I see the opposite effect with AI: I quickly find some error that it has made, because it always makes errors in my field, and that keeps me from disengaging with the problem, because it helps define what can be wrong. I mainly use AI like I used to use my blog, for writing out my ideas in prose that I think is comprehensible and organized. Neither AI nor my old blog ever solved a problem for me, but they help me figure out how to talk about problems. I'll solve them on my own, but being able to describe a problem well is an important step in that.
- mcv 9mo ago
  I think that's still in line with what I mean. Letting the AI solve the problem doesn't work, but I've had several times that simply trying to explain the problem to the AI helped me solve it. Sometimes it's not an interactive encyclopedia, but an interactive rubber duck. That works too. Don't outsource the thinking to the AI, is what I mean. Don't trust it, but use it to talk to, to shape your thoughts, and to provide information and even ideas. But not the solution, because that has never worked for me for any non-trivial problem.
agumonkey 9mo ago
I noticed a few states: - why bother, ask the llm - relief.. i can let the llm relay me while i rest a bit and resume with some progress done - inspiration.. the llm follows my ideas and open weird roads i was barely dreaming of (like asking random 'what if we try to abstract the issue even more' and get actual creative ideas) but then there day to day operations and deadlines
h4kunamata 9mo ago
>but I can feel my brain engaging less with the problem than I'm used to With me it has been the opposite, perhaps because I was anti-AI before and because I know it is gonna make mistake. My most intense AI usage: Goal: Homelab is my hobby and I wanted to setup a private tracker torrent via Proton VPN, fully. I am used to tools such Ansible and Linux operating system, but there were like 3 different tools to manage the torrents, plus a bunch of firewall rules so in case Provon VPN drops, everything stops working instead of using my real IP Address snitching me to my ISP. I wanted everything to be as automated as possible, Ansible, so if everything catches on fire, I can run Ansible playbook and bring everything back online. The whole setup took me 3 nights and I couldn't stop thinking about it during the day, like how can I solve this or that, the solution Perplexity/ChatGPT gave me broke something else so how could I solve that, etc. I am using these tools more like a Google Search alternative than AI per se, I can see when it made mistakes because I know what I am asking it to help me with, homelab. I don't wanna to just copy and paste, and ironically, I have learned a ton about Promox ( where I run my virtual containers and virtual machine ). I always say that I don't wanna just answers, show me how did you get to that conclusion so I can learn it myself. As long as you are aware that this is a tool and that it makes mistakes the same way as somebody's reply in any forum, you are good and should still feel motivated. If you are using AI tools just for copy/paste expecting things to work without caring to understand what is actually happening (companies and IT teams worldwide), then you have a big problem.

by mcv 9mo ago

isolli 9mo ago

This mirrors my experience exactly. We have to learn how to tame the beast.

sevenzero 9mo ago
[flagged]
sothatsit 9mo ago
I think we all just need to avoid the trap of using AI to circumvent understanding. I think that’s where most problems with AI lie. If I understand a problem and AI is just helping me write or refactor code, that’s all good. If I don’t understand a problem and I’m using AI to help me investigate the codebase or help me debug, that’s okay too. But if I ever just let the AI do its thing without understanding what it’s doing and then I just accept the results, that’s where things go wrong. But if we’re serious about avoiding the trap of AI letting us write working code we don’t understand, then AI can be very useful. Unfortunately the trap is very alluring. A lot of vibe coding falls into the trap. You can get away with it for small stuff, but not for serious work.
- orenp 9mo ago
  I'd say the new problem is knowing when understanding is important and where it's okay to delegate. It's similar to other abstractions in this way, but on a larger scale due to LLM having so many potential applications. And of course, due to the non-determinism.
  - sothatsit 9mo ago
    My argument is that understanding is always important, even if you delegate. But perhaps you mean sometimes a lower degree of understanding may be okay, which may be true, but I’d be cautious on that front. AI coding is a very leaky abstraction. We already see the damage of a lack of understanding when we have to work with old codebases. These behemoths can become very difficult to work in over time as the people who wrote it leave, and new people don’t have the same understanding to make good effective changes. This slows down progress tremendously. Fundamentally, code changes you make without understanding them immediately become legacy code. You really don’t want too much of that to pile up.
- player1234 9mo ago
  [dead]
- alfalfasprout 9mo ago
  I'm writing a blog post on this very thing actually. Outsourcing learning and thinking is a double edged sword that only comes back to bite you later. It's tempting: you might already know a codebase well and you set agents loose on it. You know enough to evaluate the output well. This is the experience that has impressed a few vocal OSS authors like antirez for example. Similarly, you see success stories with folks making something greenfield. Since you've delegated decision making to the LLM and gotten a decent looking result it seems like you never needed to know the details at all. The trap is that your knowledge of why you've built what you've built the way it is atrophies very quickly. Then suddenly you become fully dependent on AI to make any further headway. And you're piling slop on top of slop.

Archer6621 9mo ago

That's a nice anecdote, and I agree with the sentiment - skill development comes from practice. It's tempting to see using AI as free lunch, but it comes with a cost in the form of skill atrophy. I reckon this is even the case when using it as an interactive encyclopedia, where you may lose some skill in searching and aggregating information, but for many people the overall trade off in terms of time and energy savings is worth it; giving them room to do more or other things.

chairmansteve 9mo ago
"I reckon this is even the case when using it as an interactive encyclopedia". Yes, that is my experience. I have done some C# projects recently, a language I am not familiar with. I used the interactive encylopedia method, "wrote" a decent amount of code myself, but several thousand lines of production code later, I don't I know C# any better than when I started. OTOH, it seems that LLMs are very good at compiling pseudocode into C#. And I have always been good at reading code, even in unfamiliar languages, so it all works pretty well. I think I have always worked in pseudocode inside my head. So with LLMs, I don't need to know any programming languages!
- Archer6621 9mo ago
  It's even more general than that: LLMs seem to be exceedingly good at translating bodies of text from one domain to another. The frontier models also have excellent natural language translation capabilities, far surpassing e.g. Google translate. In that sense, going from pseudocode to a programming language is no different from that, or even translating a piece of code from one programming language to another.
scyzoryk_xyz 9mo ago
If the computer was the bicycle for the mind, then perhaps AI is the electric scooter for the mind? Gets you there, but doesn't necessarily help build the best healthy habits. Trade offs around "room to do more of other things" are an interesting and recurring theme of these conversations. Like two opposites of a spectrum. On one end the ideal process oriented artisan taking the long way to mastery, on the other end the trailblazer moving fast and discovering entirely new things. Comparing to the encyclopedia example: I'm already seeing my own skillset in researching online has atrophied and become less relevant. Both because the searching isn't as helpful and because my muscle memory for reaching for the chat window is shifting.

PatronBernard 9mo ago

> a hierarchical graph layout algorithm based on the Sugiyama framework, using Brandes-Köpf for node positioning. I am sorry for being direct but you could have just kept it to the first part of that sentence. Everything after that just sounds like pretentious name dropping and adds nothing to your point. But I fully agree, for complex problems that require insight, LLMs can waste your time with their sycophancy.

TheColorYellow 9mo ago
This is a technical forum, isn't pretentious name dropping kind of what we do? Seriously though, I appreciated it because my curiosity got the better of me and I went down a quick rabbit hole in Sugiyama, comparative graph algorithms, and learning about the node positioning as a particular dimension of graph theory. Sure nothing ground breaking, but it added a shallow amount to my broad knowledge base of theory that continues to prove useful in our business (often knowing what you don't know is the best initiative for learning). So yeah man, lets keep name dropping pretentious technical details because thats half the reason I surf this site. And yes, I did use ChatGPT to familiarize myself with these concepts briefly.
- fatherwavelet 9mo ago
  I think many are not doing anything like this so to the person who is not interested in learning anything, technical details like this sound like pretentious name dropping because that is how they relate to the world. Everything to them is a social media post for likes. I have explored all kinds of graph layouts in various network science context via LLMs and guess what? I don't know anything much about graph theory beyond G = (V,E). I am not really interested either. I am interested in what I can do with and learn from G. Everything on the right of the equals sign Gemini is already beyond my ability. I am just not that smart. The standard narrative on this board seems to be something akin to having to master all volumes of Knuth before you can even think to write a React CRUD app. Ironic since I imagine so many learned programming by just programming. I know I don't think as hard when using an LLM. Maybe that is a problem for people with 25 more IQ points than me. If I had 25 more IQ points maybe I could figure out stuff without the LLM. That was not the hand I was dealt though. I get the feeling there is immense intellectual hubris on this forum that when something like this comes up, it is a dog whistle for these delusional Erdos in their own mind people to come out of the wood work to tell you how LLMs can't help you with graph theory. If that wasn't the case there would be vastly more interesting discussion on this forum instead of ad nauseam discussion on how bad LLMs are. I learn new things everyday from Gemini and basically nothing reading this forum.

foxes 9mo ago

Is this a copilot ad?

gambiting 9mo ago
It does read like one.
- hobofan 9mo ago
  It reads like an anti-ad for both. "I didn't use the Copilot IDE because I lack control over the context provided" and "I used Copilot 365 because it for sure doesn't have any context of anything because connecting things to it is hard/expensive".

vidarh 9mo ago

My "actual job" isn't to write code, but to solve problems. Writing code has just typically been how I've needed to solve those problems. That has increasingly shifted to "just" reviewing code and focusing on the architecture and domain models. I get to spend more time on my actual job.

thefaux 9mo ago
This feels like it conflates problem solving with the production of artifacts. It seems highly possible to me that the explosion of ai generated code is ultimately creating more problems than it is solving and that the friction of manual coding may ultimately prove to be a great virtue.
- Difwif 9mo ago
  This statement feels like a farmer making a case for using their hands to tend the land instead of a tractor because it produces too many crops. Modern farming requires you to have an ecosystem of supporting tools to handle the scale and you need to learn new skills like being a diesel mechanic. How we work changes and the extra complexity buys us productivity. The vast majority of software will be AI generated, tools will exist to continuously test/refine it, and hand written code will be for artists, hobbyists, and an ever shrinking set of hard problems where a human still wins.
  - hluska 9mo ago
    I’ll be honest with you pal - this statement sounds like you’ve bought the hype. The truth is likely between the poles - at least that’s where it’s been for the last 35 years that I’ve been obsessed with this field.
    - paulcole 9mo ago
      They may be early but they’re not wrong.
      - 9mo ago

jstummbillig 9mo ago

> But letting it write the actual code was a mistake I think you not asking questions about the code is the problem (in so far it still is a problem). But it certainly has gotten easy not to.

exodust 9mo ago

Similarly I leave Cursor's AI in "ask" mode. It puts code there, leaving me to grab what I need and integrate myself. This forces me to look closely at code and prevents the "runaway" feeling where AI does too much and you're feeling left behind in your own damn project. It's not AI chat causing cognitive debt it's Agents!

jeffreygoesto 9mo ago

Elk (Eclipse Layout Kernel) is a very good package solving that, you might want to check it's Javascript port https://github.com/kieler/elkjs https://github.com/kieler/elkjs

mcv 9mo ago
I'd briefly come across Elk, but couldn't tell how it was better than what I was using. The examples I could find all showed far simpler graphs than what we had, and nothing that seemed to address the problems we had, but maybe I should give it another look, because I've kinda lost faith that dagre is going to do what we need. If I can explain briefly what our issue is: we've got a really complex graph, and need to show it in a way that makes it easy to understand. That by itself might be a lost cause already, but we need it fixed. The problem is that our graph has cycles, and dagre is designed for DAGs; directed acyclic graphs. Fortunately it has a step that removes cycles, but it does that fairly randomly, and that can sometimes dramatically change the shape of the graph by creating unintentional start or end nodes. I had a way to fix that, but even with that, it's still really hard to understand the graph. We need to cut it up into parts, group nodes together based on shared properties, and that's not something dagre does at all. I'm currently looking into cola with its constraints. But I'll take another look at elk.
- jeffreygoesto 9mo ago
  Our graphs are hierarchical, can contain cycles, too and have a bunch of directed subgraphs. We reach 500 nodes with 20k ports and 10k edges and "getting the graph" is still possible but takes a bit of practice. Cycle breaking is okish for us, because there is a strong asymmetry between many "forward" and much less "backwards" edges that makes the heuristics succeed often.

deadbabe 9mo ago

I think a good rule of thumb is, only have AI write some code when you know exactly what it should look like and are just too lazy to type it out, or, if it is code that you would have otherwise just pulled down from some open source library and not written yourself anyway.

anthonypasq 9mo ago

seems like the actual issue is that you were using a Copilot IDE plugin.

waterhouse 9mo ago

Has anyone here switched roles from programmer to manager (of human programmers), and is it a similar feeling?

cowlby 9mo ago
Would be curious about this too. It’s a mental shift to go from understanding everything about the code, to trusting someone else understands everything and we just make decisions.
deleted 9mo ago
[deleted]

stronglikedan 9mo ago

> It's easy to get started Funny - that's the hard part for me. I have yet to figure out what to use it for, since it seems to take longer than any other method of performing my tasks. Especially with regards to verifying for correctness, which in most cases seems to take as long or longer than just having done it myself, knowing I did it correctly.

alexjplant 9mo ago

When I used Copilot autocomplete more I noticed myself slipping a bit when it comes to framework and syntax particulars so I instituted a moratorium on it on Fridays to prevent this. Claude Code seems to be a much better paradigm. For novel implementations I write code manually while asking it questions. For things that I'm prototyping I babysit it closely and constantly catch it doing things that I don't want it to do. I ask it questions about why it built things certain ways and 80% of the time it doesn't have a good answer and redoes it the way that I want. This takes a great deal of cognitive engagement. Rule nombre [sic] uno: Never anthropomorphize the LLM. It's a giant pattern-matching machine. A useful one, but still just a machine. Do not let it think for you because it can't.

archy_ 9mo ago
4b-model take. LLMs are far more intelligent than you give them credit for. Every new layer of abstraction allows us to develop software better and faster. People constantly ragged on OOP yet it is the foundation of modern computing. People while about "bloat" but continue to buy more RAM. Compilers are a black box and meaningfully inhibit your ability to write asm but these days nobody cares. I see LLMs as the next logical evolution in computing abstractions.

omneity 9mo ago

I just went through an eerily similar situation where the coding agent was able to muster some pretty advanced math (information geometry) to solve my problem at hand. But while I was able to understand it enough to steer the conversation, I was utterly unable to make any meaningful change to the code or grasp what it was doing. Unfortunately, unlike in the case you described, chatting with the LLM didn’t cut it as the domain is challenging enough. I’m on a rabbit hunt now for days, picking up the math foundations and writing the code at a slower pace albeit one I can keep up with. And to be honest it’s incredibly fun. Applied math with a smart, dedicated tutor and the ability to immediately see results and build your intuition is miles ahead of my memories back in formative years.

Telemakhos 9mo ago

I see the opposite effect with AI: I quickly find some error that it has made, because it always makes errors in my field, and that keeps me from disengaging with the problem, because it helps define what can be wrong. I mainly use AI like I used to use my blog, for writing out my ideas in prose that I think is comprehensible and organized. Neither AI nor my old blog ever solved a problem for me, but they help me figure out how to talk about problems. I'll solve them on my own, but being able to describe a problem well is an important step in that.

mcv 9mo ago
I think that's still in line with what I mean. Letting the AI solve the problem doesn't work, but I've had several times that simply trying to explain the problem to the AI helped me solve it. Sometimes it's not an interactive encyclopedia, but an interactive rubber duck. That works too. Don't outsource the thinking to the AI, is what I mean. Don't trust it, but use it to talk to, to shape your thoughts, and to provide information and even ideas. But not the solution, because that has never worked for me for any non-trivial problem.

agumonkey 9mo ago

I noticed a few states: - why bother, ask the llm - relief.. i can let the llm relay me while i rest a bit and resume with some progress done - inspiration.. the llm follows my ideas and open weird roads i was barely dreaming of (like asking random 'what if we try to abstract the issue even more' and get actual creative ideas) but then there day to day operations and deadlines

h4kunamata 9mo ago

>but I can feel my brain engaging less with the problem than I'm used to With me it has been the opposite, perhaps because I was anti-AI before and because I know it is gonna make mistake. My most intense AI usage: Goal: Homelab is my hobby and I wanted to setup a private tracker torrent via Proton VPN, fully. I am used to tools such Ansible and Linux operating system, but there were like 3 different tools to manage the torrents, plus a bunch of firewall rules so in case Provon VPN drops, everything stops working instead of using my real IP Address snitching me to my ISP. I wanted everything to be as automated as possible, Ansible, so if everything catches on fire, I can run Ansible playbook and bring everything back online. The whole setup took me 3 nights and I couldn't stop thinking about it during the day, like how can I solve this or that, the solution Perplexity/ChatGPT gave me broke something else so how could I solve that, etc. I am using these tools more like a Google Search alternative than AI per se, I can see when it made mistakes because I know what I am asking it to help me with, homelab. I don't wanna to just copy and paste, and ironically, I have learned a ton about Promox ( where I run my virtual containers and virtual machine ). I always say that I don't wanna just answers, show me how did you get to that conclusion so I can learn it myself. As long as you are aware that this is a tool and that it makes mistakes the same way as somebody's reply in any forum, you are good and should still feel motivated. If you are using AI tools just for copy/paste expecting things to work without caring to understand what is actually happening (companies and IT teams worldwide), then you have a big problem.