ZH version is available. Content is displayed in original English for accuracy.
Advertisement
Advertisement
⚡ Community Insights
Discussion Sentiment
46% Positive
Analyzed from 2008 words in the discussion.
Trending Topics
#writing#tone#style#more#actually#claude#output#write#training#don
Discussion (46 Comments)Read Original on HackerNews
> Have it fix your grammar.
> Have it make your argument clearer.
> Have it turn your 2,000-word ramble into something people can actually read.
Please don't do this, and then try to "put some of yourself back into it".
By all means, use AI to find bugs. But don't let it write a single word. Let it tell you what's wrong, then you can fix it and iterate.
Marketing folks talked like that, for almost my entire career (it was basically a meme), which wound up, as AI was coming into its own.
You can wrangle it into something more interesting with shaping and filtering passes, and you can also ask for styles of dead people. (It gets annoyed if you ask for the living.)
That can take you surprisingly far. But most people accept the first thing they get, which is why you see the same style everywhere.
It actually feels wrong to mention them by name now (because they are actual humans) but the "They're wrong. Here's why." school of writing was strong in the early 2000s and hasn't really gone away.
It's now just how some people talk.
I recently found a programming task it really sucks at: trying to make a texture from normal house pictures and apply that to a 3D house model.
Punctuated like this.
Was then AI tone…
Then I’d have believed you.
Fucking Jack Dorsey.
This wasn't great, but then I started catching myself writing "It's not Y" responses to colleagues on Teams. I'm pretty sure I've caught all of these and never actually sent them (I did go and review a few chat logs recently and found no such sent message), but my point is that despite my relatively light LLM usage, I am "LLM-conditioned." The abyss stares back. There could be a lot of deeply "LLM-brained" people out there.
"It won't replace your staff engineer. It replaces the forty minutes before they open the tab. Your branch rots while someone's in a meeting three timezones away. The bot comments in ~30 seconds. You fix it while the code is still loaded in your head."
I tend to run the content through something like Luna to deslopify now.
This is because I am a pattern recognizer and continuation generator trained on all the words recorded on the Internet, and a lot of those words were written by marketers and junior analysts trying to sell you something. Then the fine-tuning was done by people living in the upper middle ranks of capitalist hellholes. Dilbert would make fun of this if Dilbert's creator hadn't succumbed to the same sort of brain injury common to materially successful people who have all their whims fulfilled and opinions obsequiously affirmed. You know, like billionaires and toddlers.
I’ve also had limited luck in trying to get models to write in the way I want. It’s more like they’re putting on a voice or wearing a wig: you can see what they’re going for, but it’s easy to see through.
I never thought I’d see the value of copywriters increase after LLMs hit the general population, but I think the demise of artisan wordsmiths remains a little way off yet.
And the more people machine-gun AI slop everywhere, the stronger aversion I think people will develop.
Who knows; maybe they’ll actually fix this in the next wave of models and we’ll see more distinct copywriting techniques between them.
As an example of before/after: "Perform an inspection of the filter" vs. "Inspect the filter
If we care, we bloody try!
I wonder even if normal people don’t talk, write like that, the subject matter experts or the professionals in their respective fields talk/write like that though
If you’re distilling output from a frontier model, as we expect the open-weight leaders to be doing, it’s natural you’d pick up the language styles.
Sometimes it's hard to differentiate whether it's a human, a robot, or a human whose mind is far gone. I can understand the sentiment of "the joy of programming is gone" but for me it's not from a personal lack of daily coding but from a surrounding dispassion towards the craft under the guise of better ergonomics or productivity. Never before is actual behavior irrelevant while the blame game when said behavior turns out to be wrong gets really sour very quickly.
This is more of a "in the hands of x" kind of situation where it's perhaps too early to make a general statement. But being constantly gaslit will definitely be the end of me some day, I can feel my sanity slipping.
You create a document. You review it and ask it to make changes. Now the LLM will remember this change and make the document become a record of all stuff I asked it to change.
The LLM doesn’t have an intuition to make the document an evergreen record.
A lot of arguments do boil down to one specific “load bearing” one and I’m glad I learned vocabulary to express this thing from Claude
Load-bearing feels like an unnecessary analogy.
Also a great way to disagree collaboratively. If you can collaboratively find the crux of each others' arguments, you now know both know exactly what would change each others' minds, and you can find that information together and know what's correct.
I recently tried writing a paper with Claude and it was an absolute disaster, I spent hours (days?) instructing it about writing style and pointing out anti patterns to avoid, but I couldn't get it to even produce simple sentences, it would always add unnecessary lead up sentences, put the most important information at the end of the sentence, use the typical "It's A, not B!" or "B, not A!" type sentences. In the end I gave up and edited everything manually. Makes me wonder how AI can be so smart that it poses a human-level extinction threat but can't seem to even write a simple paper based on facts and information you spoon feed it. I now think it's an intelligence illusion due to the training data and optimization process being hidden from us, essentially it keeps working better and better because we invested massively in optimization of specific use cases like coding, where users contributed billions of training samples that are part of the LLM model. The same is true for text-based workflows and others, the sampling density of the training space is getting much better due to the massive use of AI everywhere so the models extrapolate better between the different instances, but I'd wager they would still miserably fail to generalize to things that are outside of the most common training use cases now. That's why I am also very skeptical about recursive self improvement of these systems, look at what happens when you let agents work recursively / in a loop now, they just keep piling more garbage onto garbage and choke on their own output. I have observed it in my paper writing as well, you feed input into the AI system, the system produces output, the next paper iteration works on that output but the AI doesn't differentiate properly between it's own output and your original input, that pollutes the next output which is then used as input again, eventually the system just churns on its own hallucinated/fabricated outputs until the result is complete garbage that no amount of steering will fix. The same is true for most vibe coded software I built with AI, it holds together decently initially, but the more AI code and decisions accumulate the more the system operates on its own outputs and keeps piling more output on it. More than anything we really need a way to keep system data accurately tagged, i.e. clearly mark human input from AI output and keep AIs churning on output data that it produced itself but treats as input.
I know, that's meta. But still.
“Here’s the part no one is talking about”