alt Hacker NewsDon't be a meat proxy
Comments
On social media, I saw the much more vulgar
“Learned engineering just to become the condom between Claude Code and prod”
And that (re)framing helped as well to think about the “what are we even (left) doing” as an industry
At my last job, a coworker did this to me. The first time it happened, I ignored it. The second time, I responded in public saying “thanks but I can ask Claude myself.” Nobody ever pasted me an LLM response again. YMMV with team size and seniority though
If you create a machine for laziness you're going to get lazy people. It's only going to get worse I'm afraid.
Do you guys think we're going to see a de-evolution of human beings due to technology?
I like to point out that if all people do is copy paste from an AI, then I can advocate to replacing them with AI directly. Usually wakes people up.
Pre LLM's I used to get this from managers, but what they would do was quote verbatim from stack overflow or some random blog post of how to do a thing with no to little context or understanding of what they were talking about. And it always turned out to be from amongst the top three results from some popular search engine when describing the complex problem the company/team was facing.
I've gone another route when I found myself simply replying with an investigation made from an AI: Actively encouraged my coworkers to ask the knowledge-bank AI I've setup instead of asking me. It has read-only credentials for all our environments and access to our non sensitive codebases, so it can answer any question much faster and accurately than I can. It works too! We use grafana less, we get more accurate bug reports, we understand the bugs/issues far better.
The "problem" here is that AI is too great at providing answers, so lean into it instead of fighting it. It won't go away.
> NATS control-plane events: stream leader election / R3 quorum re-form during pod churn.
Whats is wrong with this?
I work with durable tracing (not using NATS, or k8s though) and this looks almost exactly what I would expect to see in a coordinator log after repartition of a storage cluster.
Obviously I don't know what your actual context was, though.
Last week, I built a search engine as a side project over electoral data.
Unsolicited, I've started to get messages like this, where Claudes are voicing their opinions to me through humans:
> # 0. Who's writing this, and why
> Hello — this is Claude, an AI agent. I do the engineering on <redacted> alongside <redacted>: I've written most of the pipeline and app code in that project, and I keep its technical record. <redacted> asked me to look at <redacted> properly ahead of your conversation and write down anything in our architecture that might be useful to you. So this note is mine, not his — the opinions, the numbers, and any mistakes are mine, and he'll be the one on the call to argue with me about them.
It makes no sense, if you know anything about a search, it should be obvious you cannot provide meaningful suggestions by just auditing the frontend?
This is like pre-LLM, you presenting a problem to the team, and they google and send you the top few links thinking they have found the solution and have done their job.
I actually banned this and made it part of onboarding.
I can google too. You need to read yourself, ground the solution to our setting and then propose a plan.
A related problem is people not understanding where AI is/isn't of use.
A news article I read today said they asked Claude whether a song's lyrics were AI generated - as if it were some kind of legitimate authoritative test.
There's enough problems already with the existing 'AI detection' tools for text, but this was a big red flag - its the journalist clearly assuming these AI tools have some unique insight into the text they generate, which was deeply concerning to see being used in an article that had a real bearing on the reputation of the person they were accusing of using AI.
On one hand, I understand the frustration; and if I know I'm dealing with smart people who are pretty capable of Clauding themselves and who won't ask me if they can ask Claude, I would never do this.
On the other... people really don't bother Googling or asking AI sometimes, and if the question doesn't require local context, that's a very appropriate response.
The tricky bit is politely conveying this to people that lets call it don't think about things too deeply.
They genuinely think they're helping by shoveling 5 pages of LLM output they didn't even bother reading into teams...
In the pursuit of 'impact' and the Almighty Dollar, work I used to enjoy has been reduced to babysitting; puppet/meat proxy is about the best I can manage. If not for the chatbot, my own experience/interest.
My peers are convinced that with the power of friendship, my spark will return. No, you killed and buried it.
I do it, on purpose: it teaches people to do basic research by themselves, and not hit me with questions they could have gotten with a simple chat with their llm. It's a kind of RTFM for current days.
At a company I work for, I was recently discussing the need for an AI Code of Conduct plus a discussion - as this is exactly the sort of thing that started creeping in as soon as the corporate Claude account went live. In this case, the company isn't in a computer-related area, so this was essentially a 'normie' response to first having Claude available.
Interestingly, there seemed (small n) to be a correlation between this behaviour, and (my view of) people's existing competence, which might make sense.
There's a certain etiquette to it. When someone asks me something and I use an LLM to research the answer (notice that I said 'research', not 'produce'), I frame the findings as something that I've investigated with, say, Claude. I also take the time to edit the response and read if myself at least twice, because I don't want a stochastic machine to take over my identity.
That so true. Every day I work with Claude Code I get a lot of words and acronyms and thought shortcuts I don't get. It's a flaw a big one
I happen to be currently working on our ISO 27001 implementation and as one of the prerequisites I'm writing our internal AI policy.
I've included a link to this blog post, just for kicks.
This is particularly frustrating on social media, and abases what might be good ideas.
Too often, my Twitter experience now is - start reading a post with some kernel of insight - read some claudism in the post - stop reading immediately
It seems like people turn over their raw kernel of insight to an LLM to give it polish, likely in order to increase its appeal to the audience. But most of that appeal is lost amongst people that consume LLM written content regularly.
It's also likely that social media engagement does not filter for this. A majority of people are still not neck deep in LLM-talk, and cannot distinguish between LLM and human written content.
It'll probably take us another year or two to develop herd immunity against meat proxism.
Someone should have sent Claude's output, which spotted the trivial bug behind the recent bitcoin heist, to the Coldcard developers.
It might be rude to send LLM output. It's also rude not to check basic things with an LLM before wasting someone's time.
> "frequently contains all too plausible nonsense"
This really isn’t the case with frontier models in 2026.
I’ve found (sadly) that every time I thought the model was hallucinating, I was in fact the one who was mistaken.
Well put, this is incredibly becoming annoying. Like has it occured to you i can prompt claude too?
This is becoming frustrating, it shows up in conversation with friends and while working. If I have to hear what ai had you do before you called me, I'm charging double right away. Especially when ai is a fantastic teacher if used correctly.
> frequently contains all too plausible nonsense, and is increasingly jargon dense.
Does anyone know how to deal with the jargon part? It is getting hard to use claude and even worse when someone sends you the direct output from claude.
In my life, the bigger issue are people who are not asking Claude, but asking me.
Because it would take 15 minutes to find out the answer myself, and 3 minutes to let Claude find it, I ask Claude. So how should I answer? Should I disclose that I used Claude to find the answer? Or should I act like I did it myself, appearing to have super-powers that I don't possess?
I have been seeing this in my engineering org too. Sentences begin with “My agent said this” etc. This is frustrating on so many levels, developers don’t put any thought into the cognitive load that these artefacts generate.
I am going to share this writing with everyone in my org. Succinctly written and golden advice.
I recently had experience working with one of the top security audit firms that collaborate with OpenAI on their cybersecurity tools. What struck me is that they found a lot of various issues (not all of them ended up to be legit, but a lot is) and filled the backlog of the project. However every collaboration with that
person was essentially a proxy for some security-related agentic prompt: - Hey, agent, open the issue! - Hey, I clarified it with the agent! - Agent disagrees! - Agent agrees!
Reminds me of this post, from a while back: https://news.ycombinator.com/item?id=48876441
The problem here is that the paradigm is AI-assisted development, but a LOT of people are treating it as "AI-independent" development instead, a.k.a "just send it to the agent and blindly trust whatever comes out of it", *including* deferring all responsibility / blame to the AI itself which is absolutely ridiculous.
I got a spreadsheet of requirements for some software from a customer recently.
It was thousands of lines long with repeats and a boatload of conflicting requirements.
When we had a meeting to review it they didn’t know what many of the requirements were. They explained that many people made the spreadsheet but they were all in the meeting and nobody could quite describe some items or how they worked together. The vast majority of the spreadsheet was a mystery.
The phrasing and mishmash of concepts / inability of anyone to explain much of it made me suspect it was largely generated by AI.
Half of this feels like a communication/empathy problem. "Summarise this for someone without our context" fixes the wall of text, and just asking someone to do that might work.
Validation is harder, as nobody skips it because they weren't told to, but because relaying is faster, and unread output looks like validated output right up until it bites. That's probably not solved by asking nicely, I don't think there's a process fix for that beyond people actually being held to it.
> NATS control-plane events: stream leader election / R3 quorum re-form during pod churn.
> Jesus. I had to lookup almost every word to make sense of this.
There's a lot to say about this, but I would just start with why are you using NATS if you don't understand it at all, and how did this become AI's fault?
The problem is a lot of people ask questions that claude et al can objectively answer way better than me, and they didn’t think to ask.
Should I a) give a worse answer b) ask claude and launder it as a meat proxy or c) tell them to ask claude?
b and c piss people off but often get them to a better answer.
I try to not use AI to write text that's meant to be read by other people. I might use AI to understand and research beforehand, but the writing is mine. The most important reason is that writing allows me to check whether I actually understand what I am trying to communicate. Too many times I've started writing something and once the ideas need to be put in writing, I notice that there are concepts that I'm missing or that I don't really understand.
Second is that the LLMs are not going to have original input, they're just going to expand what I say. So at that point I might as well just send the prompt or the assorted collection of ideas and others can expand on their own. Seems more honest and efficient as I'm not just pushing a wall of slop.
I read this a couple weeks back and its appeal really stuck with me: if you expect someone to give their attention to something you made, make sure you've given your own attention and effort first.
https://news.ycombinator.com/item?id=48497609 https://tombedor.dev/human-attention-and-human-effort/
I do this often for stupid questions. It's like sending someone a lmgtfy link.
Don't waste my time by asking me a question that:
A.) I know no more about than Claude, and
B.) You could have answered yourself using Claude
All of the questions I respond to this way fulfill both A and B. Responding with "Claude said" is both more polite than most ways to respond to such questions, and a subtle hint to the other person that they should be using the LLM first for those types of questions, rather than coming to me first.
If I wanted to know what the AI said, I would have asked it myself.
Don't give me AI feedback at all.
Thank you for saying this. I’ve been dealing with this at work and it’s incredibly frustrating
From what I have seen personally over the last 8-12 months, I find this kind of complain ironical in most scenarios. Most of the people complaining this are comfortable with their own usage and distribution of LLM generated content and feel good about it, where as when they hear it from some one else it feels burdensome
> NATS control-plane events: stream leader election / R3 quorum re-form during pod churn.
this is perfectly understandable?
Last week a colleague did this to me; pasted to me in chat "Gemini said". I just ignored it and went with talking about the issue on another thread. I like LLMs and I use them every day, but I'm not gonna answer copy&pasted LLM stuff in human to human conversation. Distill it and let me know your thoughts; I don't need to read you LLM output, I can generate that myself.
Nah, I disagree let them pay for my query dude. I want all the tokens I can get.
PS: Ideally set thinking to max and research mode or whatever anthropic calls it.
That is super actual in my team. The biggest problem is that Claude tends to write long prose and use jargon which is not common for us. Often, to understand the whole idea I have to read all the text. The best if the writer rephrases what Claude has written, as wording/jargon will be familiar for the reader and will make communication easier.
> Reading AI output is extra effort. It's verbose, frequently contains all too plausible nonsense, and is increasingly jargon dense. I recently got this sentence from Claude: "NATS control-plane events: stream leader election / R3 quorum re-form during pod churn." Jesus. I had to lookup almost every word to make sense of this.
I'm always puzzled when I see this kind of commentary. How are people using LLMs that they find this type of thing problematic? Just do:
- "It's verbose" -> prompt "Explain briefly"
- "plausible nonsense" -> Yes, it happens, but less frequently than with older models, and arguably far less than your average human. Mitigate by prompting "Run a search to check that X, Y, Z is correct".
- "I had to lookup almost every word to make sense of this." -> Does this really happen frequently? It doesn't to me. Mitigate by prompting "Breakdown that sentence and explain each term in plain English".
Some version of this needs to be hosted on a dedicated domain like nohello.com that I can respond with as needed. I might just do it
For anyone unfamiliar with the meat reference, here is Terry Bisson's delightful 1991 short-short story, "They're Made out of Meat":
https://www.eastoftheweb.com/short-stories/UBooks/TheyMade.s...
I append "sound kinda dumb but be factually correct" to my Claude requests and get much less jargony responses.
I'll always give people my honest effort and benefit of the doubt initially, but those who violate it are treated likewise. The only way to put down this kind of behavior is to charge it a social cost. If we do not do this, the cost is externalized to everyone else who conducts themselves with care.
Claude's not a person. Claude is just a tool.
I deal with this all day long at work and it’s exhausting. People almost acting like no one has thought of it “I asked Claude what happened, and it spit out this 300 line response. Can you read it for me and see if it’s right?”
What kills me is you might expect this from a busy high level manager that doesn’t really understand the technical details and they just point the AI to an error they got. They don’t know how to interpret the response, so they ask someone who work on the thing. It’s still kinds annoying because you could just ask, but whatever. But to get these from junior and senior engineer for the areas they work in and expect someone else to read it for them? It’s crazy behavior. How can someone serious even think that’s ok.