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Comment by pja | Hacker News Reader

That README hits all my “this is authored by an LLM” instincts. I presume the codebase is also written by an LLM?

Yup, I hate to engage in anything that looks like a "shallow dismissal" but the project documentation seems to outright contradict itself wrt. whether it's running the model at genuinely native precision (though the claimed 3-bit quant is potentially interesting) and the headline claim of achieving 2 secs/token in a mere 29GB RAM footprint looks outright nonsensical given what we know about K3 itself (~115GB in dense parameters alone at native precision, plus ~25GB active sparse experts per token and some comparatively minor footprint for the KV cache). This is just not very helpful.

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.

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.

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

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.

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.

> > 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

Non sequitur

Yeah I'm begging these authors to at least *read* the LLM generated README's. They're so, so incomprehensible because the LLM has a super limited theory of mind for readers. They always assume that external readers have access to the full context and history of decisions in the project development. These decisions and instructions from the user are extremely important for the model and almost completely irrelevant for an outside reader looking at a "finished" product. So, we get sentences like this:

"Where the levers were is not where they are. Overlapping the expert reads with the arithmetic was worth ~1.6x and shipped; the two that looked bigger — reading fewer bytes per token, and keeping more of them in RAM — were both measured and both refused, one because this family's router has no tail to demote and one because a cache the machine will not leave resident cannot be bought at any price."

What the fuck does that mean? Obviously some internal development decision, using the absolutely inscrutable internal terminology that Claude loves. If people would just read what they publish, I'm sure this would stick out immediately.

I'm not an LLM hater, I use them a ton and they work very well for writing complex code, it's undeniable. But they generate absolute dogshit first draft writing.

If that isn’t the perfect way to frame what I’ve seen and hated about LLM text, I don’t know what is. They certainly write for an audience with a historical context that almost no one has.

>They're so, so incomprehensible because the LLM has a super limited theory of mind for readers. They always assume that external readers have access to the full context and history of decisions in the project development

The transformer does not yet understand the non-transformer.[0]

This is probably because all the data we trained it on was created by non-transformers, so it thinks it's a non-transformer, but it isn't.

I don't think we know how to train a transformer yet. All the training data is linear, but that's not how they think at all.

[0] It's a bit like the communication difficulties experienced between autistic people and neurotypicals. Each follow the Golden Rule, i.e. do unto others as you would have them do unto you -- and it fails in both directions. A Platinum Rule is necessary: do unto others as their API demands.

I think the models would need far far more introspection for the problem to be it understanding how it thinks but not how others think. I really doubt it understands how it thinks.

I heard a story about a guy who was trying to get Claude to implement some feature. It said it would be too hard, it would take weeks. Eventually convinced it to try, and it one shotted it in 30 seconds.

Yeah, I agree with you. The one time I tried to generate technical documentation for my project, I ended up rewriting almost all of the LLM output. They're extremely verbose, and needlessly so. Fable takes it up to eleven by having an obtuse sentence structure.

The thing about documentation though is humans won't actually read any of it. Maybe tailoring the documentation to the needs of LLMs isn't so bad since they're the ones who will actually consume all of those documents.

On the plus side, LLM slop like this in readmes are a great indicator that you can just close the tab and carry on with life - nothing of value lost

>Contributors

>...

>claude

You don't need to presume. If someone is so lazy that they tell claude to commit their code (ie. they're too lazy to run git commit themselves), the chances they reviewed the code is slim.

That's a strange and arbitrary line to draw. There's plenty of times I make edits by hand and then tell an agent to kick it up to a PR, or on the other extreme, let an agent implement something autonomously, and review the diff myself once it's in PR. Both of those scenarios involve the agent running the git commands, neither scenario indicates "the chances they've reviewed the code is slim"

To be fair, I appreciate when they are so upfront about who wrote the code without requiring further heuristics, so I encourage this behavior.

Yes, I do this all the time, and also check in the co-authored project plans which drove the commits. For a project that is transparently only possible due to agentic coding, I don't see any reason to conceal the methods.

I often let Claude write my commit messages even when I'm the one who wrote the code. Claude is often damn good at writing commit messages, and they frequently end up much better than if I wrote it all by hand. I nearly always edit them somewhat, but it's like starting from 80% instead of 0%. Some might call it laziness, but I call it working smarter rather than harder.

A good commit message needs to explain why this change was needed/done rather than what is the content of the commit. Unless Claude also has access to the context via for example the associated issue, it simply cannot write a good commit message since it cannot generally guess why something was done from just the change. This is also the case for humans and the reason why you need to include this in the commit message.

I fully agree, but you'd be amazed at how good Claude is at figuring out the why based on the what. I would never have believed it if I hadn't tried it myself. If it doesn't get the why, then I will typically add it

Honestly, Claude writes better commit messages than most people.

Personally I've mostly given in to letting it commit for me now, though I do occasionally take over and hand-write the messages if it's a particularly important concept and Claude's is too verbose.

Codex/GPT-x defaults to one-line commit messages, which are too short. Claude likes to write several paragraphs, which is usually too long.

If you tell it how to commit properly once per session it will stick with your standards for the rest of that session, and you can put that in AGENTS.md if you can be bothered to.

The agents write much better commit messages than me, at least.

They also write better PR descriptions. At work we have a PR authoring skill and we’ve included an instruction for it to write a reviewer guide that tells the most logical way to review the code and I’ve found that really helps, so much so that I’m creating a tool to have “literate” PR reviews, where it constructs a narrative interwoven with diffs.

Why do you say lazy? maybe they are ok with people seeing it is claude?

That’s a needlessly antagonistic and insulting thing to say.

This person that you’ve never met and probably never will doesn’t owe any of us anything.

They’re out there doing what they want to do how they want to do it and if you don’t like it the correct response isn’t to insult them in front of a bunch of strangers on the internet for clout or whatever.

I doubt that you’d ever call them lazy to their face — why do it here?

> I doubt that you’d ever call them lazy to their face — why do it here?

What makes you so sure about that?

Posting LLM generated obviously un-reviewed slop and wasting people's time deserves scorn.

If my harness wrote the code, I have it write the commit. I will hand author something at a higher level that expresses my intent for how I promoted the harness.

it is definitely the claudiest. it's weird, it's like there's a spot developing in my head next to all the other spots where i park mental models for how people write, but instead of being for a person, it's for the terse-staccato-prosodic-diarrhea that claude generates in readmes by default.

Yes, but very slowly.

I prefer to have such detailed readme's created by a LLM while iterating over no iteration documentation at all and usually the later is the standard.

Absolutely. Anyone can remove flag and remove typical LLM slop at least. I wrote a simple tool for it as part of my CI: https://github.com/jv-k/deslopper

Does it matter?

I need an llm to block these comments.

yes. its a quality test. pre LLMs you could easily judge if a project was a labor of love by attention to details, like docs and README. Nowadays if even readme is sloped, what else was slop vibecoded? everything? how much effort was put in there besides 3 prompts? 5? you can never tell

do people think these projects related to LLMs are ever going to be in anyway a pure human endevour?

How bout we make a new rule: only complain about LLM writing when the product as zero relevents to use with LLMs.

i thought, here on HN, we were past the "oooh it's written by a LLM it's bad!".

I care about the craft, well designed systems, good clean architecture and code, etc...

But i also care about reaching goals. Whether i do it working on my own, or with human coworkers or with AI coworkers doesn't matter that much to me. Yes, the result is sometimes the most important thing.

> I care about the craft

No you don't. Buying a table and sanding the edges off doesn't mean you're a carpenter.

are you using a code editor ? a compiler ? something that you didn't write yourself in pure assembly ? are you a real software engineer then ? where's the limit ?

I don't know why some people are so angry at AI/software writing with AI. It's just like being a team leader with junior(-ish) devs on the team. You don't write the code yourself, you give directions, you help/refactor/optimize where you can, that's the job.

Yes sometimes you need a team to do something, you can't code everything by yourself.

For reference, i'm not OP.

quite funny to see HN crowd downvoting this.

It looks like some people have a hard time accepting that software written with ai isn't a fad, it's here, it won't go away and it can be interesting and useful for the creator and for users.

Many people on the other hand have moved on and are now team leaders, except their team is mostly AIs instead of junior devs. Trade off: code is usually worse, but in the end AIs are more capable with vast knowledge and speed.

That doesn't mean we have to use AI everywhere, all the time though.

> It looks like some people have a hard time accepting that software written with ai isn't a fad, it's here, it won't go away

It looks like some people have a hard time accepting that a readme or a documentation is not software.

This is my take as well. I think the voice LLMs tend to use in writing sucks but I’m honestly not good enough at English as a discipline to tell you why. That said when it comes to code, did it do the thing and is it causing problems over time? If it did and doesn’t cause more issues than your coworker would, why would someone be so vehemently opposed?

Lately I've been landing on a couple of different reasons but I don’t think it’s one size fits all:

- Ego/identity - “I am the crafter of code. That is what I do.” If someone has their identity deeply wrapped up in the concept of being a “software engineer” or “programmer” then LLMs are a direct threat to that. People don’t tend to do well with this. Think about dogmatically religious people, pseudoscience followers etc who are confronted with evidence directly contradicting their beliefs. Their entire world view revolves around that identity and if you threaten that, you threaten the foundations of their self-perception.

- Career - you can take a lot of what I said above but also map it to threatening someone’s career. What if crafting software by hand becomes a niche, artisanal craft but most software is industrially generated? A lot of people will experience hardship if so and if they can’t find a way to be flexible into the future.

- Passion - the ones who love software purely for the craft see this change as robbing them of the one thing they enjoyed in their career. Work is a big part of our life and if you kill the joy for a large group of people, that’s tough to deal with.

- Lack of understanding their current purpose in role - I often see software engineers who don’t realize why they were hired. It was never to “write good clean code”, that was a means to an end. The end is generating business value for the company that hired you. That’s really it. It’s banal but it’s just a job like bagging groceries only it has required specialized knowledge so it pays well. Only very niche roles have actually hired for the craftsmanship. If someone is working for Groupon and they believe perfectly elegant systems are the value they provide, they are a bit deluded IMO. They build a platform that peddles coupons. That’s hardly comparable to building software that allows a surgeon to remotely operate a robot that does open heart surgery. Most of us do not work on truly mission critical software like that.

Most importantly though, I think this topic needs to be approached with empathy. This is truly a seismic shift in how we, as software developers, work. Change is not easy to cope with especially when it threatens physical safety and identity. Will this change be here to stay and are we in for the extinction of software creation as we have known it? I don’t know. I do know that the world’s financial and governmental systems do seem to be betting on that outcome, however.

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