Comment by georgeburdell

> ... LLMs aren't very good at logical reasonsing.

I'm curious about what experiences led you to that conclusion. IME, LLMs are very good at the type of logical reasoning required for most programming tasks. E.g. I only have to say something like "find the entries with the lowest X and highest Y that have a common Z from these N lists / maps / tables / files / etc." and it spits out mostly correct code instantly. I then review it and for any involved logic, rely on tests (also AI-generated) for correctness, where I find myself reviewing and tweaking the test cases much more than the business logic.

But then I do all that for all code anyway, including my own. So just starting off with a fully-fleshed out chunk of code, which typically looks like what I'd pictured in my head, is a huge load off my cognitive shoulders.

You can view Claude Code as a non-deterministic compiler where you input english and get functioning code on the other end.

The non-determinism is not as much as a problem because you are reading over the results and validating that what it is created matches what you tell it to do.

I'm not talking about vibe-coding here, I'm grabbing the steering wheel with both hands because this car allows me to go faster than if I was driving myself, but sometimes you have to steer or brake. And the analogy favors Claude Code here because you don't have to react in milliseconds while programming.

TL;DR: if you do the commit you are responsible for the code it contains.

So here's a transcript of a recent Claude Code interaction I had, trying to build an MP3 organizer that relies on heuristics and fuzzy matching to make sense out of all the random MP3s I downloaded in the 90s. This all happened in-between prompts: I gave it a correct example and an incorrect example and asked it why it was giving the incorrect example.

● Let me trace through what happens with the "Jordan Rudess" example:

  1. Directory: Jordan Rudess/
  2. file_rel_path would be Path("Jordan Rudess") (after removing MP3 Disk prefix)
  3. If Jordan Rudess matches in path component 0, scope_path would be Path("Jordan Rudess")
  4. _path_is_in_scope checks if "Jordan Rudess".startswith("Jordan Rudess") → True

  This looks correct. Let me check the inference logic:

● Read(mp3_organizer_v2.py) ⎿ Read 15 lines (ctrl+r to expand)

● The inference priority looks correct - path_component_match is indeed the highest priority. But I think I see the issue now. Let me look at the actual logic flow in the main organize method:

● Read(mp3_organizer_v2.py) ⎿ Read 10 lines (ctrl+r to expand)

● The flow looks right. Let me examine the actual problem more carefully. Looking back at the _apply_path_component_matching method, I think there might be an issue with when the matching happens. Let me check the logic:

● Update Todos ⎿ Check _path_is_in_scope logic for correctness Verify artist inference priority logic works with path_component_match Test and fix any remaining issues with artist propagation

● I think I see the issue now. The problem is that the current code only looks for matches in files that already have file_id3 tags, but it needs to propagate those matches to all files in the directory. However, the current implementation is still only applying matches to files that have matching candidates, not ALL files. Let me check the exact logic:

● Read(mp3_organizer_v2.py) ⎿ Read 8 lines (ctrl+r to expand)

● Actually, looking at this more carefully, the logic should work. The matched_artists dictionary contains the matches found from ANY file that had a file_id3 tag matching a path component, and then it applies that match to ALL files in the file_infos list via the loop at line 737.

  Wait, let me understand the scoping issue better. Let me check what file_rel_path and scope_path would actually be in your example:

● Read(mp3_organizer_v2.py) ⎿ Read 5 lines (ctrl+r to expand)

● Ah! I think I see a potential issue. The file_rel_path is calculated as the directory path, not the individual file path. So for all files in the same directory, file_rel_path will be the same. This should actually work correctly.

  But let me double-check the scope matching logic. If the directory is "Jordan Rudess" and the scope_path is also "Jordan Rudess", then _path_is_in_scope should return
  True.

  Let me debug this by adding some debug output to see what's actually happening. But first, let me look at a potential issue: maybe the match isn't being found in the first place?

It's doing a pretty complex chain of step-by-step reasoning here, looking in detail at the code. It ended up by printing out more debug info and having me re-run it again, then paste in the debug info, then add even more debug info and run it again. But it did eventually get the bug, which was non-trivial to identify (it was an aliasing problem where Claude was mutating state on a list and that list was shared with all the other files in the directory).

For things where I don't want creativity, I tell it to write a script.

For example, "write a comprehensive spec for a script that takes in the date and a location and computes when golden hour is." | "Implement this spec"

That variability is nice when you want some creativity, e.g. "write a beautiful, interactive boids simulation as a single file in html, css, and JavaScript."

Words like "beautiful" and interactive" are open to interpretation, and I've been happy with the different ways they are interpreted.

Seconding this. My work has had the same problem - by the time I've got things all hooked up, figured out the complicated stuff - my brain (and body) clock out and I have to drag myself through hell to get to 100%. Even with ADHD stimulant medication. It didn't make it emotionally easier, just _possible_ lol.

LLMs, particularly Claude 4 and now GPT-5 are fantastic at working through these todo lists of tiny details. Perfectionism + ADHD not a fun combo, but it's way more bearable. It will only get better.

We have a huge moat in front of us of ever-more interesting tasks as LLMs race to pick up the pieces. I've never been more excited about the future of tech

I'm kind of in this cohort. While in the groove, yea, things fly but, inevitably, my interest wanes. Either something too tedious, something too hard (or just a lot of work). Or, just something shinier shows up.

Bunch of 80% projects with, as you mentioned, the interesting parts finished (sorta -- you see the line at the end of the tunnel, it's bright, just don't bother finishing the journey).

However, at the same time, there's conflict.

Consider (one of) my current projects, I did the whole back end. I had ChatGPT help me stand up a web front end for it. I am not a "web person". GUIs and what not are a REAL struggle for me because on the one hand, I don't care how things look, but, on the other, "boy that sure looks better". But getting from "functional" to "looks better" is a bottomless chasm of yak shaving, bike shedding improvements. I'm even bad at copying styles.

My initial UI was time invested getting my UI to work, ugly as it was, with guidance from ChatGPT. Which means it gave me ways to do things, but mostly I coded up the actual work -- even if it was blindly typing it in vs just raw cut and paste. I understood how things were working, what it was doing, etc.

But then, I just got tired of it, and "this needs to be Better". So, I grabbed Claude and let it have its way.

And, its better! it certainly looks better, more features. It's head and shoulders better.

Claude wrote 2-3000 lines of javascript. In, like, 45m. It was very fast, very responsive. One thing Claude knows is boiler plate JS Web stuff. And the code looks OK to me. Imperfect, but absolutely functional.

But, I have zero investment in the code. No "ownership", certainly no pride. You know that little hit you get when you get Something Right, and it Works? None of that. Its amazing, its useful, its just not mine. And that's really weird.

I've been striving to finish projects, and, yea, for me, that's really hard. There is just SO MUCH necessary to ship. AI may be able to help polish stuff up, we'll see as I move forward. If nothing else it may help gathering up lists of stuff I miss to do.

Ironically, I find greenfield projects the least stimulating and the most rote, aside from thinking about system design.

I've always much preferred figuring out how to improve or build on existing messy systems and codebases, which is certainly aided by LLMs for big refactoring type stuff, but to be successful at it requires thinking about how some component of a system is already used and the complexity of that. Lots of edge cases and nuances, people problems, relative conservativeness.

Looks like the definition of boilerplate will continue to shift up the chain

What is described in that post is a skill issue. The attention issues spoke about can be worked through. Because attention is a skill like anything else. And if one does not learn to practice. One becomes victim to it

Aider actually has a mode that is called "copy-paste" where it basically gives you a context to paste in an LLM chat and then you copy-paste back the reply to aider

I can't tell how widespread any of is, to be honest.. mostly because it's anecdata, and impossible to determine if what I'm seeing is just ragebait, or shallow dunks by reply-guys in comment sections, or particularly-loud voices on social media that aren't representative of the majority opinion, etc

That said, the amount of sort-of-thoughtless, I'm-just-repeating-something-I-heard-but-don't-really-understand outrage towards AI that I'm seeing appears to be increasing -- "how many bottles of water did that slop image waste??", "Clanker"-adjacent memes and commentary (include self-driving + robots in this category), people ranting about broligarchs stealing art, music, movies, books to train their models (oddly often while also performatively parroting party lines about how Spotify rips artists off), all the way to refusing to interact with people on dating apps if they have anything AI in their profiles hahaha (file "AI" alongside men holding fish in their pics, and "crypto" lol)

It's all chronically-online nonsense that may well just be perception that's artificially amplified by "the algorithm".

Me, I have no fundamental issue with any of it -- LLMs, like anything else, aren't categorically good or bad. They can be used positively and negatively. Everything we use and consume has hidden downsides and unsavory circumstances.

Yes, people are refusing for those reasons. I don't know how many, but I'd say about half of the the people I know who do not work in tech are rejecting AI, with ethics being the primary reason. That is all just anecdata, but I suspect the tech bubble around AI is making people in tech underestimate how many people in the world simply are not interested in it being part of their lives.

In the case of programming is not quite as problematic with unsolved problems as much as others, like completeness. In the case of programming, it's context and understanding. It's great for small chunks of code but people think you can vibe code entire interactive applications with no programming knowledge, but LLMs simply don't understand, so they can't keep a cohesive idea of what the end goal is in mind. The larger the codebase it needs to work on the more likely it is to make catastrophic errors, create massive security flaws, or just generate nonfunctional code.

Programming LLMs will become awesome when we create more narrowly targeted LLMs rather than these "train on everything" models.

It's the same as saying that writing good commit messages is a boon for extroverts and neurotypicals. It's a computer. You're giving it instructions, and the only difference to traditional coding is that the input is English text.