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Working With AI: A concrete example

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Re: Working With AI: A concrete example

#21

Post by rst »

jdlshore wrote: post #10

Carson’s experience matches mine: AI is good at analysis and boilerplate, but not good at the kind of critical thinking necessary for good designs. If it were human, I would say that it jumps to solutions to quickly, rather than stepping back to consider the big picture and how everything should fit together to make a cohesive whole. It’s not human, of course, and I think this problem actually relates to the fact tha…

One partial mitigation is to ask it to use plan mode -- and then very carefully review the plan before allowing it to execute.

Re: Working With AI: A concrete example

#22

Post by saagarjha »

rst wrote: post #21
jdlshore wrote: post #10

Carson’s experience matches mine: AI is good at analysis and boilerplate, but not good at the kind of critical thinking necessary for good designs. If it were human, I would say that it jumps to solutions to quickly, rather than stepping back to consider the big picture and how everything should fit together to make a cohesive whole. It’s not human, of course, and I think this problem actually relates to the fact tha…

One partial mitigation is to ask it to use plan mode -- and then very carefully review the plan before allowing it to execute.

At that point I would rather just write the plan myself

Re: Working With AI: A concrete example

#24

Post by ekidd »

waffletower wrote: post #7

I disagree with the trope -- (AI effects) "the slow dulling of our intellects". I am old enough to remember my career change, being a developer in the Apple ecosystem, confident with Objective-C and native system libraries in iOS and MacOS. I changed direction using a very different software stack in cloud services as a data engineer with deep utilization of Clojure. I have personal projects that I occasionally would…

> I saw what I forgot immediately; but soon after, with engagement, I saw how quickly I was able to remember.

We actually have pretty good models for how long it takes to forget things. It's the same basic math that powers Anki. To oversimplify, if you force yourself to remember something right before you would have otherwise forgetten it, you will remember it roughly 2.5 times as long before forgetting it again. (This changes at both the shortest time intervals and the longer ones, so treat it as a rough rule of thumb, not an exact formula.)

But this provides a handy bound! If you've been doing something professionally for 20 years, you should expect to remember it for another 50. At which point you're likely well into old-age, and memory performance may decrease for other reasons.

Where AI kills you is actually at the other end: initial learning. You are much less likely to need to recall something after 1 day, 2.5 days, 6.25 days, etc. And thanks to the lack of the "testing effect", memory formation will be much weaker.

In other words, I would naively expect AI to make long-used skills a bit rusty, but to drastically impede formation of new skills and knowledge.

Re: Working With AI: A concrete example

#25

Post by AloysB »

recursivedoubts wrote: post #2

hello all, this is an article I wrote up on my interaction with an agent, Claude, in fixing a bug in the hyperscript parser it was a rather mundane bug, but i thought the interaction was interesting and worth analyzing to show where AI is very strong and where it is not as strong

I very much love your work Carson, it has always been and remain a fresh breath of air.

The example is mundane but to the point; and I very much enjoyed this article. It's a concrete example which is rare to read when it comes to using LLMs.

To the risk of being told that we "hold it wrong", it resonates with my experience of using LLMs.

Re: Working With AI: A concrete example

#26

Post by thunky »

oulipo2 wrote: post #15
jdlshore wrote: post #10

Carson’s experience matches mine: AI is good at analysis and boilerplate, but not good at the kind of critical thinking necessary for good designs. If it were human, I would say that it jumps to solutions to quickly, rather than stepping back to consider the big picture and how everything should fit together to make a cohesive whole. It’s not human, of course, and I think this problem actually relates to the fact tha…

Exactly, LLM is good at "code inpainting" : define clear structures and goals, and it will fill the boilerplate. But it doesn't work for reasoning and abstraction, so it fails to synthesise and propose novel views. But that's integral to the way it's designed and has been trained, to do a kind of "averaging" which limits it's capacity to explore novel designs

> But it doesn't work for reasoning and abstraction, so it fails to synthesise and propose novel views

I disagree. Have a conversation with it about your problem and work through design decisions with it. When I do that, I find it gives me a lot of good ideas.

Disclaimer: I'm not working on anything groundbreaking (like most people)

Re: Working With AI: A concrete example

#27

Post by bob1029 »

rst wrote: post #21
jdlshore wrote: post #10

Carson’s experience matches mine: AI is good at analysis and boilerplate, but not good at the kind of critical thinking necessary for good designs. If it were human, I would say that it jumps to solutions to quickly, rather than stepping back to consider the big picture and how everything should fit together to make a cohesive whole. It’s not human, of course, and I think this problem actually relates to the fact tha…

One partial mitigation is to ask it to use plan mode -- and then very carefully review the plan before allowing it to execute.

I've been in a lot of situations where I could step gpt5.x through a big refactor if I spoon feed it one type name at a time. If I let it try to do the whole thing at once it will refuse or get stuck in apply patch loops.

Planner / executor separation can make a huge difference in performance. LLMs are fantastic at coming up with a lot of elaborate narratives regarding what should be done. They are terrible about doing that prescribed work all at once. This impedance mismatch is best resolved with a simple role separation. Placing a shared collection of tasks between these roles is how you can decouple them. The executors need significantly more tokens than your planners to get the job done. It's probably in the range of 10-100x more for really complicated jobs with a lot of iterations through compiler feedback, sql provider errors, etc. This is why you can't do both things in the same context very well.

Re: Working With AI: A concrete example

#28

Post by piskov »

thorum wrote: post #5

Interesting read! Creating tests is highlighted as something Claude did well, but it strikes me that all the weaker rejected solutions could have been avoided if it were really good at designing intelligent tests for itself. For example, the first solution “was very specific to the reported bug and wouldn’t have fixed the general case” and the third suggestion “prevented the perfectly valid use of as conversion expre…

As humans we have a concept of viscosity. That resistance, like being in quicksand or a swamp, is how you “easily” identify a code smell, something that needs to be refactored, etc. Part of it is human laziness, part of it some concept of elegance, an itch of being not quite tidy as it can be, etc.

LLM, being a tiresome little helper, will gladly output hundreds of lines, hacks, and what have you.

I don’t think any amount of tests, prompts, harnesses and other “my shaman is a better shaman” will help it to acquire this trait. Some other AI architecture someday maybe — just not today.

And that’s why it is good at what it is and really bad at stuff like code “design” (unless it is a well-known solution being baked in the training set)

Re: Working With AI: A concrete example

#29

Post by recroad »

jdlshore wrote: post #10

Carson’s experience matches mine: AI is good at analysis and boilerplate, but not good at the kind of critical thinking necessary for good designs. If it were human, I would say that it jumps to solutions to quickly, rather than stepping back to consider the big picture and how everything should fit together to make a cohesive whole. It’s not human, of course, and I think this problem actually relates to the fact tha…

Have to disagree with this as it's excellent at helping you wide and broad before converging. I suggest trying OpenSpec and use /ospx:explore to state your problem and go from there.

Re: Working With AI: A concrete example

#30

Post by recursivedoubts »

wiremine wrote: post #17

It's a good write up, but it's lacking some details, the most important one is: which Claude model was used? The second issue is: what was tooling and the prompt approach? (To be clear, I have no problem with the premise of the write up. But without some details like this, it's sort of like saying "I had a bad board on my deck, and my tape measure wasn't able to help me remove the nails. What a bad tape measure."

Opus 4.whatever (it was last week) via a command line interface in the IntelliJ Claude plugin.

The series of prompts weren't particularly interesting or innovative on my part: a paste in of the user report then a few back and forths on fixing it, me reviewing the changes and coming up with the final answer.

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