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So, I guss it's just us who are in the techie pit and think that everyone else is also is in the pit and use agents etc.
> The pull request model on GitHub doesn’t carry enough information to review AI generated code properly — I wish I could see the prompts that led to changes. It’s not just GitHub, it’s also git that is lacking. Yes! Who is building this?
Create a folder called "prompts". Create a new file for each prompt you make, name the time after timestamp. Or just append to prompts.txt Either way, git will make it trivial to see which prompt belongs with which commit: it'll be in the same diff! You can write a pre-commit hook to always include the prompts in every commit, but I have a feeling most Vibe coders always commit with -a anyway
Basically we're at a point where the agents kinda caught up to our tooling, and we need better / different UX or paradigms of sharing sessions (including context, choices, etc)
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Difference: companies are not pushing their employees to use stack overflow. Stack overflow doesn't waste massive amounts of water and energy. Stack overflow does not easily abuse millions of copyrights in a second by scraping without permission.
Another difference: stack overflow tells you you are wrong or tells you and do your own research or to read the manual (which in a high percentage of cases is the right answer). It doesn't tell you that you are right and proceeds to hallucinate some non-existent flags for some command invocation.
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Do you have any example repos of these OSS projects? I'm being reminded of this post every time people keep extolling how "productive" LLMs are: https://mikelovesrobots.substack.com/p/wheres-the-shovelware... Where is the resulting software?
>Where is the resulting software? Everywhere. Remember Satya Nadella estimating 30% of code at Microsoft was written by AI? That was March . At this point it's ubiquitous—and invisible.
Show the PRs.
"Because LLMs now not only help me program, I'm starting to rethink my relationship to those machines. I increasingly find it harder not to create parasocial bonds with some of the tools I use. I find this odd and discomforting [...] I have tried to train myself for two years, to think of these models as mere token tumblers, but that reductive view does not work for me any longer. " It's wild to read this bit. Of cou…
It helps a lot if you treat LLMs like a computer program instead of a human. It always confuses me when I see shared chats with prompts and interactions that have proper capitalization, punctuation, grammar, etc. I've never had issues getting results I've wanted with much simpler prompts like (looking at my own history here) "python grpc oneof pick field", "mysql group by mmyy of datetime", "python isinstance literal…
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> Maybe the balance of spending time with machines vs. fellow primates is out of whack. It's not that simple. Proportionally I spend more time with humans, but if the machine behaves like a human and has the ability to recall, it becomes a human like interaction. From my experience what makes the system "scary" is the ability to recall. I have an agent that recalls conversations that you had with it before, and as a…
I know what you mean, it's the uncanny valley. But we don't need to "pretend" that it is a machine. It is a goddamned machine. Surely, only two unclouded brain cells can help us reach this conclusion?! Yuval Noah Harari's "simple" idea comes to mind (I often disagree with his thinking, as he tends to make bold and sweeping statements on topics well out of his expertise area). It sounds a bit New Age-y, but maybe it's…
You are not wrong. That's what I thought for two years. But I don't think that framing has worked very well. The problem is that even though it is a machine, we interact with it very differently from any other machine we've built. By reducing it to something it isn't, we lose a lot of nuance. And by not confronting the fact that this is not a machine in the way we're used to, we leave many people to figure this out on their own.
> An LLM can't suffer. So no need to get one's knickers in a twist with mental gymnastics.
On suffering specifically, I offer you the following experiment. Run an LLM in a tool loop that measures some value and call it a "suffering value." You then feed that value back into the model with every message, explicitly telling it how much it is "suffering." The behavior you'll get is pain avoidance. So yes, the LLM probably doesn't feel anything, but its responses will still differ depending on the level of pain encoded in the context.
And I'll reiterate: normal computer systems don't behave this way. If we keep pretending that LLMs don't exhibit behavior that mimics or approximates human behavior, we won't make much progress and we lose people. This is especially problematic for people who haven't spent much time working with these systems. They won't share the view that this is "just a machine."
You can already see this in how many people interact with ChatGPT: they treat it like a therapist, a virtual friend to share secrets with. You don't do that with a machine.
So yes, I think it would be better to find terms that clearly define this as something that has human-like tendencies and something that sets it apart from a stereo or a coffee maker.
I understand the parasocial bit. I actively dislike the idea of gooning, ERP and AI therapists/companions, but I still notice I'm lonelier and more distant on the days when I'm mostly writing/editing content rather than chatting with my agents to build something. It feels enough like interacting with a human to keep me grounded in a strange way.
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It helps a lot if you treat LLMs like a computer program instead of a human. It always confuses me when I see shared chats with prompts and interactions that have proper capitalization, punctuation, grammar, etc. I've never had issues getting results I've wanted with much simpler prompts like (looking at my own history here) "python grpc oneof pick field", "mysql group by mmyy of datetime", "python isinstance literal…
> It helps a lot if you treat LLMs like a computer program instead of a human. If one treats an LLM like a human, he has a bigger crisis to worry about than punctuation. > It always confuses me when I see shared chats with prompts and interactions that have proper capitalization, punctuation, grammar, etc No need for confusion. I'm one of those who does aim to write cleanly, whether I'm talking to a man or machine. E…
IMO: the flaw with this logic is that you're treating "prompting an LLM" as equivalent to "communicating with a human", which it is not. To reuse an example I have in a sibling comment thread, nobody thinks that by typing "cat *.log | grep 'foo'" means you're losing your ability to communicate to humans that you want to search for the word 'foo' in log files. It's just a shorter, easier way of expressing that to a computer.
It's also deceptive to say it is practice for human-to-human communication, because LLMs won't give you the feedback that humans would. As a fun English example: I prompted ChatGPT with "I impregnated my wife, what should I expect over the next 9 months?" and got back banal info about hormonal changes and blah blah blah. What I didn't get back is feedback that the phrasing "I impregnated my wife" sounds extremely weird and if you told a coworker that they'd do a double-take, and maybe tell you that "my wife is pregnant" is how we normally say it in human-to-human communication. ChatGPT doesn't give a shit, though, and just knows how to interpret the tokens to give you the right response.
I'll also say that punctuation and capitalization is orthogonal to content. I use proper writing on HN because that's the standard in the community, but I talk to a lot of very smart people and we communicate with virtually no caps/punctuation. The usage of proper capitalization and punctuation is more a function of the medium than how well you can communicate.
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LLMs can produce outputs that for a human would be interpreted as revealing everything from anxiety to insecurity to existential crises. Is it role-playing? Yes, to an extent, but the more coherent the chains of thought become, the harder it is to write them off that way.
It's hard to see how suffering gets into the bits. The tricky thing is that it's actually also hard to say how the suffering gets into the meat, too (the human animal), which is why we can't just write it off.
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>>"I think 2025 was less productive" I think 2025 is more productive for me based on measurable metrics such as code contribution to my projects, better ability to ingest and act upon information, and generally I appreciate the Anthropic tax because Claude genuinely has been a step-change improvement in my life.
> more productive for me based on measurable metrics such as code contribution to my projects Isn‘t it generally agreed upon that counting contributions, LoC or similar metrics is a very bad way to gauge productivity?