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Comment by marcobambini | Hacker News Reader

I wrote tons of software, even a programming language by hand https://github.com/marcobambini/gravity.

I'm using my skills to orchestrate LLMs and agents, and I can write better code much faster. As developers, we can choose to adapt to new technologies or become extinct.

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Please consider writing your Readmes by hand even if the code is computer-generated. I want to read what human authors think about their projects. I virtually never want to read what a computer thought.

Also irrespective of the merits of LLMs, it's simply unpleasant to read LLM generated prose. It can't write well. The annoyance is compounded when you read the same poor writing everywhere.

I don't know how people that shovel AI prose don't realise this. Are they not also reading other people's shitty AI text?

I did see one sloperator who told his agent to copy his writing style. I have no idea if that works but it's got to be better than yet more "Here's the kicker" LinkedIn drivel.

Even worse than that, it's not the LLM, it's the operator. Mimicking your own style given a corpus is the lowest possible bar - you can also select among the great writers (technical or not) from history and have it give you eerily correct stylistic cues. Try Hemingway or Mark Twain next time you feel like generating a doc. Benjamin Franklin if you're feeling particularly Poor Richard.

There's no reason any more to read bad documentation. You can feed any docs in and get them in the precise style you want, so don't impose bad prose on others in your repos. Pick a solid technical writing base, jargon free, and go from there. I sometimes generate architecture proposals from the llm in RFC format and it's word perfect.

Rewrite the readme in my voice, use all of my comments and responses in current context as source. Don't use em dashes or other LLM things.

Done, solved. Never had a problem with a readme or email since.

No idea why you're getting downvoted. Asking the AI to just copy my writing patterns dramatically reduced the amount of code comment and commit message rewriting I had to do. Ten years worth of HN comments finally became useful for something!

> Ten years worth of HN comments finally became useful for something!

This is a great idea! How do you do it practically? I assume you don't feed the whole corpus in every prompt - do you condense it somehow?

I just created a ~/social directory, launched Claude Code in it and told Claude I'd like to start tracking and studying my own social media presence. Before long there were scripts to dump all of my public posts into a SQLite database. I pulled HN, reddit, lobsters, stackoverflow and its sister sites, even mailing lists. Given a corpus inside a database, any sort of analysis can be done.

I started with linguistic analysis to reduce commit message rewriting. That one also used years worth of commit messages as input.

I immediately started doing more though. Sentiment analysis, interlocutors, negativity, positivity, recurring arguments, best and worst threads by voting patterns.

Discovered a lot of fun stuff. For example, it was hilarious and a little terrifying to realize I was randomly interacting with some industry heavy hitters without even realizing who they were. Claude on the other hand seems to know quite a bit about many people on HN.

Mapped out all of my free and open source software contributions. Counted my successes and failures, did post-mortems, saw what I did right and where I could improve. Got some very useful results out of that. For example, I will never attempt to contribute to GNU ever again.

Ask it to steelman every single argument I get into and every single worldview I have. Turned up some excellent literature too. This is great for sharpening my mind, and sometimes Claude even changes my mind.

The database also acts as a preservation archive in case any of these sites get wiped out.

It's also the best search engine I've ever used. Saying something like "I remember posting something along the X and Y lines" is enough to find pretty much anything.

Gets downright nostalgic at times. Claude found my GitHub projects dating to my teenage years.

> I assume you don't feed the whole corpus in every prompt

Claude has the local comment database and runs SQL against it to pull whatever it wants.

The emdash allergy is shortcut sheep mindset. Emdashes are commonly accepted and orthographically correct punctuation in many languages.

Also it looks like English might not be the author’s first language—might not lead to the best training corpus.

I speak English, I don't even know how to create one on my keyboard - is it a mac thing? I literally never saw one until chatgpt started to generate them

Not sure if it was Mac or phone, and I'm not sure if it was strictly emdash or one of the other seemingingly hundreds of different length dashes, but there was a time at least that -- would be rewritten to it. Also macs have this really really annoying habit of changing the dash when copying and pasting (but not always) so the nunver of times copying a bash command errors because it wasn't a dash, or I've copied a command to WhatsApp and the recipient got an error is enough to make me hate these clever dashes forever

This. Code written by LLMs using the new agentic SDLC is fine, can even be good code. But we shouldn't expect users to read sloppy slop, it's just plain lazy.

Look I'll be honest in that I didn't read your code (or even open the link) so this totally might not apply to you, but:

> I'm using my skills to orchestrate LLMs and agents, and I can write better code much faster.

Everyone says that. And most of them are wrong about the better part. Referring to your previous ability to write code is almost an appeal to authority at this point because it's coming with the assumption that you review the code with he same vigor, and that you are just as good as you were before you offloaded these tasks regularly to LLMs. Maybe you do and maybe you are, but I will say that a lot of the people I've worked with who I used to respect and whose code was good now produce absolute crap. The models are getting better so their code is getting a bit better, but I no longer believe they had anything to do with the quality, they no longer seem to care.

I disagree with the naysayers. The README was scannable and sectioned so I could read what I wanted. But some things I wish were clearer, and I’m not sure they would be if you wrote it by hand.

Don’t waste your time rewriting it by hand just because keyboard warriors are up in arms on HN.

The material feedback here reflects the questions who might be from someone who’s been running local LLMs, so ask the LLM to update it with that in mind.

Another poster pointed out it’s not clear what the quantization is. There’s that one paragraph but it’s confusing. What I want to know right away is: are you running the unquantized model or is it quantized? If so, how much? Use terms like Q3 or Q4 or 4-bit or 8-bit. Explain why it’s not practical to quantize less. How much precision loss do you think there is at the quantization selected? What if I had 128GB RAM and wanted to have better precision and not higher token speed—would it be a good idea to choose 4-bit instead of 3-bit for some of these layers?

> I'm using my skills to orchestrate LLMs and agents, and I can write better code much faster.

The fact that the top comment on this thread calls it out, in a negative way, hints at that you aren't.

Non sequitur

> > I'm using my skills to orchestrate LLMs and agents, and I can write better code much faster. > > The fact that the top comment on this thread calls it out, in a negative way, hints at that you aren't.

It's interesting that what I meant as a purely factual question with no prejudice either way has been taken by almost everyone to be explicitly negative & critical.

I need to be a tad less blunt I guess if I don't want to

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