Armin was once one of the most prolific programmers in Python.
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@gugurumbe I'm not saying people *should* use it for summarize and explore, I'm saying that's a different category of concern, if done with a local model.
However, I'll also point out you were trying to debug LaTeX, which I would argue is a nearly impossible task no matter how many resources are thrown at it
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Armin was once one of the most prolific programmers in Python. Says he never writes code anymore. Seeing more and more people like him write stuff like this on what are supposedly computer programming forums. https://lobste.rs/s/qmjejh/ai_is_slowly_munching_away_my_passion#c_jcgdju
Notably, once a person crosses this threshold, I see them still hang out on programming forums, but they never talk about any of the puzzles of programming anymore. Only about running agents. Which feels strange and sad. Why hang out on the forums at all then?
@cwebber maybe they need attention? They need to talk about something -- anything. They talk about what they do, like before.
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Feeling FOMO about AI? Well here's my advice!
Stay on top of what's happening. Which doesn't really require *using* the tools. Just see what people are doing.
Whether or not you do use it, stay a practitioner. And don't fall for the FOMO.
Your career won't end because you're not making the choice to use AI. (If your employer makes you use it, that's another thing.)
If you use AI, use it for "summarize and explore" tasks. DO NOT use it for *generate* tasks. That's a different thing.
If you want to differentiate yourself, *learning skills* is the differentiation space right now.
These things are easy to pick up. You can do it whenever. But keep learning.
If you see generated examples, don't paste or accept them. Type them in by hand! The hands on imperative: actually trying things congeals core ideas.
And if it doesn't help your career... well, your consolation prize is: you'll stay interesting.
@cwebber In reality these machines sabotage the will to learn and the human spirit. People are lessened by using them. And most cannot resist their allure.
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@cwebber this is so dark and depressing
@zkat Very much so seconded.
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Steve Klabnik also had an interview on lobste.rs. There's a lot in it! It's a cool read! https://alexalejandre.com/programming/steve-klabnik-interview/
And then it gets to the AI part and he's just like "oh I don't write code anymore".
And notably Steve Klabnik has a lot to say about code, but it's *all in the past*.
Lots of brilliant people are becoming non-practitioners.
@cwebber im still wondering how much of all of this can be blamed on the industry-manufactured 'programmer ideal' to become a manager - past programmer, now seniority justifying bossing others around instead, even if the 'others' is simply a simulacrum of the subordinate
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@cwebber What's telling, I think, is that all these people go on about how much they're doing and how great AI is to help them build more *but there's no actual demonstrable stuff being done.* I mean, if AI was some kind of Nx multiplier you'd think we'd be getting N times more actual functionality out of software but mostly it seems like the N multiplier only applies to blog posts about how AI multiplies their programming.
@wordshaper @cwebber Also, the point of writing is understanding things a little bit better (and the joy of finding the perfect words and using them in the right order).
So you're not outsourcing your writing, but rather your thinking as well as your understanding of things.
(Unless you write copy or something. Then it doesn't matter as much. Although you will be worse at writing over time, but that is – or absolutely should be – more of a preference than thinking, I'd say.)
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Feeling FOMO about AI? Well here's my advice!
Stay on top of what's happening. Which doesn't really require *using* the tools. Just see what people are doing.
Whether or not you do use it, stay a practitioner. And don't fall for the FOMO.
Your career won't end because you're not making the choice to use AI. (If your employer makes you use it, that's another thing.)
If you use AI, use it for "summarize and explore" tasks. DO NOT use it for *generate* tasks. That's a different thing.
If you want to differentiate yourself, *learning skills* is the differentiation space right now.
These things are easy to pick up. You can do it whenever. But keep learning.
If you see generated examples, don't paste or accept them. Type them in by hand! The hands on imperative: actually trying things congeals core ideas.
And if it doesn't help your career... well, your consolation prize is: you'll stay interesting.
@cwebber Also, don't use it for "summarize" because it literally can't do that.
https://ea.rna.nl/2024/05/27/when-chatgpt-summarises-it-actually-does-nothing-of-the-kind/
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@cwebber Also, don't use it for "summarize" because it literally can't do that.
https://ea.rna.nl/2024/05/27/when-chatgpt-summarises-it-actually-does-nothing-of-the-kind/
“ChatGPT trust is risky, as a recent study by the European Broadcasting Union (EBU) shows. The association of 68 public broadcasters from 56 countries systematically tested the reliability of the most popular AI systems. The alarming result: ChatGPT, Gemini, and other chatbots invent up to 40 percent of their answers and present them as facts.”
EBU – European Broadcasting Union (2025) News Integrity in AI Assistants. An international PSM study, https://www.ebu.ch/Report/MIS-BBC/NI_AI_2025.pdf
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“ChatGPT trust is risky, as a recent study by the European Broadcasting Union (EBU) shows. The association of 68 public broadcasters from 56 countries systematically tested the reliability of the most popular AI systems. The alarming result: ChatGPT, Gemini, and other chatbots invent up to 40 percent of their answers and present them as facts.”
EBU – European Broadcasting Union (2025) News Integrity in AI Assistants. An international PSM study, https://www.ebu.ch/Report/MIS-BBC/NI_AI_2025.pdf
@Rainer_Rehak @jwcph @cwebber
In reality, LLMs invent 100% of their answers, it's just that what they make up turns out to be true approximately half the time, and they're only that good because they're mostly just copying other sources without attribution. -
@Rainer_Rehak @jwcph @cwebber
In reality, LLMs invent 100% of their answers, it's just that what they make up turns out to be true approximately half the time, and they're only that good because they're mostly just copying other sources without attribution.@StarkRG @Rainer_Rehak @jwcph @cwebber people that think they'll get better with more R&D really don't understand how they work. they'll get worse as more and more of the web that companies crawl to train their models on fills with LLM generated garbage.
