I'm envisioning that in Rust (and Python), the operator overload would be on a class/struct. It would be the macro/decorator (the same one that adds logging) which would turn the function definition into an object that implements Fn.
I have done exactly that as an exercise in what you can do with Python: overload |, and a decorator that you can use to on any function to return an instance of a callable class that calls that function and overloads |.
Whether it is a good idea to use it is another matter (it does not feel Pythonic), but it is easy to implement.
I don't think local as it stands with browsers will take off simply from the lead time (of downloading the model), but a new web API for LLMs could change that. Some standard API to communicate with the user's preferred model, abstracting over local inference (like what Chrome does with Gemini Nano (?)) and remote inference (LM Studio or calling out to a provider). This way, every site that wants a language model just has to ask the browser for it, and they'd share weights on-disk across sites.
It sounds good, but I'm not sure that in practice sites will want to "let go" of control this way, knowing that some random model can be used. Usually sites with chatbots want a lot of control over the model behaviour, and spend a lot of time working on how it answers, be it through context control, guardrails or fine tuning and base model selection. Unless everyone standardizes on a single awesome model that everyone agrees is the best for everything, which I don't see happening any time soon, I think this idea is DOA.
Now I could imagine such an API allowing to request a model from huggingface for example, and caching it long term that way, yes just like LM Studio does. But doing this based on some external resource requesting it, vs you doing it purposefully, has major security implications, not to mention not really getting around the lead time problem you mention whenever a new model is requested.
> "Open source" to me is sharing the input required [...]
I don't disagree with your sentiment, I am also more interested in human-written projects, but I'm curious about how this works. Would a new sorting network not be open source if found by a closed source searching program, like AlphaDev? Would code written with a closed source LSP (ie. Pylance) not be open source even if openly licenced? Would a program written in a closed source language like Mojo then be closed source, no matter what the author licences it under? The line between input and tool seems arbitrary at best, and I don't see what freedoms are being restricted by only releasing the generated code.
the line is blurry for shure. Code generated by a closed-source compiler (or LSP) is still 'your' code. Maybe the difference is whether humans can reproduce and learn from the process? With traditional code, you can read commit history and understand the author's thinking. With AI-generated code, that context is lost unless explicitly shared. Food for thought.
I don't think your ultimatum holds. Even assuming LLMs are capable of learning beyond their training data, that just lead back to the purpose of practice in education. Even if you provide a full, unambiguous language spec to a model, and the model were capable of intelligently understanding it, should you expect its performance with your new language to match the petabytes of Python "practice" a model comes with?
Further to this, you can trivially observe two further LLM weaknesses:
1. that LLMs are bad at weird syntax even with a complete description. E.g. writing StandardML and similar languages, or any esolangs.
2. Even with lots of training data, LLMs cannot generalise their output to a shape that doesn’t resemble their training. E.g. ask the LLM to write any nontrivial assembler code like an OS bootstrap.
LLMs aren’t a “superior intelligence” because every abstract concept they “learn” is done so emergently. They understand programming concepts within the scope of languages and tasks that easily map back to those things, and due to finite quantisation they can’t generalise those concepts from first principles. I.e. it can map python to programming concepts, but it can’t map programming concepts to an esoteric language with any amount of reliability. Try doing some prompting and this becomes agonisingly apparent!
Would this be similar to how Rust handles async? The compiler creates a state machine representing every await point and in-scope variables at that point. Resuming the function passes that state machine into another function that matches on the state and continues the async function, returning either another state or a final value.
It's only related in so far as it involves separate storage for the data. I'm thinking of functions that run to completion, not functions that yield and resume, but maybe it's not hard to do coroutines by storing the continuation pointer in the state struct.
Under the other photos it says A photo taken by a BBC North West Tonight reporter showed the bridge is undamaged and A BBC North West reporter visited the bridge today and confirmed it was undamaged
They may have first ran the photo through an AI, but they also went out to verify. Or ran it after verification to understand it better, maybe
So.. is this where the AI hype train starts to lose steam? One AI hallucinated and caused the incident, and another AI program just wasted everyone's time after it was unable to verify the issue. Sounds like AI was utterly useless to everyone involved.
> Sounds like AI was utterly useless to everyone involved
Maybe.
Imo, I think the advances in AI and the hype toward generated everything will actually be the current societies digitally-obsessed course-correction back to having a greater emphases on things like theater, live music, conversing with people in-person or even strangers (the horror, I know) simply to connect/consume more meaningfully. It'll level out integrating both instead of being so digitally loop-sided as humans adapt to enjoy both.*
To me, this shows a need for more local journalism that has been decimated by the digital world. By journalism, I mean it in a more traditional sense, not bloggers and podcast (no shade some follow principled, journalistic integrity -- as some national "traditional" one don't). Local journalism is usually held to account by the community, and even though the worldwide BBC site has this story, it was the local reporters they had that were able to verify. If these AI stories/events accelerate a return to local reporting with a worldwide audience, then all the better.
* I try to be a realist, but when I err, it tends to be on the optimist side
The tech giants sucking up all the ad revenue is what killed local journalism. Unless you can find a solution to that problem (or an alternstove fundong model), it's not coming back.
But just think of all the people that didn’t have to receive a paycheck because of all this efficiency!
It’s really incredible how the supposedly unassailable judgement of mass consumer preference consistently leads our society to produce worse shit so we can have more or it, and rewards the chief enshittifiers with mega yachts.
Someone I know is a high school English teacher (being vague because I don’t want to cause them trouble or embarrassment). They told me they were asking ChatGPT to tell them whether their students’ creative writing assignments were AI-generated or not-I pointed out that LLMs such as ChatGPT have poor reliability at this; classifier models trained specifically for this task perform somewhat better, yet also have their limitations. In any event, if the student has access to whatever model the teacher is using to test for AI-generation (or even comparable models), they can always respond adversarially by tinkering with an AI-generated story until it is no longer classified as AI-generated
A New York lawyer used ChatGPT to write a filing with references to fake cases. After a human told him they were hallucinated, he asked ChatGPT if that was true (which said they were real cases). He then screenshotted that answer and submitted it to the judge with the explanation "ChatGPT ... assured the reliability of its content." https://www.courtlistener.com/docket/63107798/54/mata-v-avia... (pages 19, 41-43)
Reminds me of a Reddit story that made the rounds about a professor asking ChatGPT if it wrote papers, to which it frequently responded afirmatively. He sent an angry email about it, and a student responded by showing a response from ChatGPT claiming it wrote his email.
Yes, I missed the student using the teacher's trust in those tools to make them even more angry and neuter their angry email that they (probably) actually wrote themselves. Well-played.
I realize you might have failed to comprehend the level of my argument. It wasn't even about LLMs in particular, rather having someone/something else do your work for you. I read it as the student criticizing the teacher for not writing his own emails, since the teacher criticizes the students for not writing their own classwork. Whether it's an LLM or them hiring someone else to do the writing, this is what my rebuttal applied to. I saw what I thought was flawed reasoning and wanted to correct it. I hope it's clear why a student using an LLM (or another person) to write classwork is far more than a quality issue, whereas someone not being tested/graded using an LLM to prepare written material is "merely" a quality issue (and the personal choice to atrophy their mental fitness).
I don't think I was arguing for LLMs. I wish nobody used them. But the argument against a student using it for assignments is significantly different than that against people in general using them. It's similar to using a calculator or asking someone else for the answer: fine normally but not if the goal is to demonstrate that you learned/know something.
I admit I missed the joke. I read it as the usual "you hypocrite teacher, you don't want us using tools but you use them" argument I see. There's no need to be condescending towards me for that. I see now that the "joke" was about the unreliability of AI checkers and making the teacher really angry by suggesting that their impassioned email wasn't even their writing, bolstered by their insistence that checkers are reliable.
Two posts from you addressing a one-line reply? May be time to put down the coffee and take a drag from the mood-altering-substance of your preference.
Students (and some of my coworkers) are now learning new content by reading AI generated text. Of course when tested on this, they are going to respond in the style of AI.
Yeah that hardly talks of the "journalist" being good at their job. At worst they asked a biased question like "has this photo been AI generated and if then how" or worse.
People tend to think that AI is like a specific kind of human which knows other AI things better. But we should expect better from people that do writing as their job.
Yeah, it is frankly just plain bad epistemology to expect an AI chatbot to have answers on a matter such as this. Like trying to get this week's lotto numbers by seeking a reading in bible passages and verses. There is no way that the information was encoded within in there as it would violate causality. At best you'd have coincidental collisions only.
All these tool integrations are making it increasingly difficult to explain to non-tech people what these chatbots are capable of. Even more so as multi-modality improves (at some point image generation went from a distinct tool to arguably an inherent part the the models).
If it really is fully autonomous, that first video is insane. I struggle to put those little tags into the slot in the box sometimes, and I'm pretty sure I'm human, but the bot gets it on the first attempt.
Yeah, this company (GeneralistAI) is, in my opinion, the most advanced robotics+AI company in the world. Slightly behind them Google DeepMind Robotics and Physical Intelligence, and then the rest.
I see the idea, but you're competing with Microsoft Word and Overleaf for non-techies, and LaTeX/Typst for techies, and that sounds like a losing battle on both fronts. Non-techies want something familiar that they already know how to use, like Word, just with bib and their university's template. Techies probably don't want a cloud only service for a mostly solved problem. I don't see the value as a techie, and I don't see why I wouldn't just use my University's Word template from a non-techies view.
And you'll always have a professor say, "Send me the word document for review", then they will provide inline feedback and return the file back to you. In these cases the technology isn't the constraint, the existing process from the institution is.
I wonder if this is a tactic that LLM providers use to coerce the model into doing something.
Gemini will often start responses that use the canvas tool with "Of course", which would force the model into going down a line of tokens that end up with attempting to fulfill the user's request. It happens often enough that it seems like it's not being generated by the model, but instead inserted by the backend. Maybe "you're absolutely right" is used the same way?
It is a tactic. OpenAI is changing the tone of ChatGPT if you use casual language, for example. Sometimes even the dialect. They try to be sympathetic and supportive, even when they should not.
They fight for the user attention and keeping them on their platform, just like social media platforms. Correctness is secondary, user satisfaction is primary.
I find the GPT-5 model having turned the friendliness way, way down. Topics that previously would have rendered long and (usefully) engaging conversations are now met with an "ok cool" kind of response.
I get it - we don't want LLMs to be reinforces of bad ideas, but sometimes you need a little positivity to get past a mental barrier and do something that you want to do, even if what you want to do logically doesn't make much sense.
An "ok cool" answer is PERFECT for me to decide not to code something stupid (and learn something useful), and instead go and play video games (and learn nothing).
I have two potato peelers. If the one I like better is in the dishwasher I am not peeling potatoes. If one of my children wants to join me when I'm already peeling potatoes, I'll give them the preferred one and use the other one myself.
But I will not start peeling potatoes with the worse one.
I once had a refactoring that I wanted to do, but I was pretty sure it'd hit a lot of code and take a while. Some error handling in a web application.
I was able to ask Claude "hey, how many function signatures will this change" and "what would the most complex handler look like after this refactoring?" and "what would the simplest handler look like after this refactoring?"
That information helped contextualize what I was trying to intuit: is this a large job, or a small one? Is this going to make my code nicer, or not so much?
All of that info then went into the decision to do the refactoring.
I think the person you're responding to is asking "how would the tone of the response influence you into doing/not doing something"?
Obviously the actual substance of the response matters, this is not under discussion.
But does it matter whether the LLM replies "ok, cool, this is what's going on [...]" vs "You are absolutely right! You are asking all the right questions, this is very insightful of you. Here's what we should do [...]"?
I find myself not being particularly upset by the tone thing. It seems like it really upsets some other people. Or rather, I guess I should say it may subconsciously affect me, but I haven't noticed.
I do giggle when I see "You're absolutely right" because it's a meme at this point, but I haven't considered it to be offensive or enjoyable.
Might tell it "I want to do this stupid thing" and it goes "ok cool". Previously it would have gone "Oh really? Fantastic! How do you intend to solve x?" and off you go.
Do the suggestions given by your phone's keyboard whenever you type something affect your attitude in the same way? If not, why is ChatGPT then affecting your attitude?
Are you really asking in good faith? It seems obvious to me that a tool such as ChatGPT can and will influence peoples behavior. We are only too keen on anthropomorphizing things around us, of course many or most people will interact with LLMs as of they were living beings.
This effect of LLMs on humans should be obvious, regardless of how much an individual technically knows that yes, it is only a text generating machine.
I am — I grew up being bullied, and my therapists taught me that I shouldn't even let humans affect me in this way and instead should let it slide and learn to ignore it, or even channel my emotions into defiance.
Which is why I'm genuinely curious (and a bit bewildered) how people who haven't taken that path are going through life.
We are all influenced by the external world whether we like it or not. The butterfly effect is an extreme example, but a direct interaction with anything, especially a talking rock, will influence us. Our outputs are a function of our inputs.
That said, being aware of the inputs and their effects on us, and consciously asserting influence over the inputs from within our function body, is incredibly valuable. It touches on mindfulness practices, promoting self awareness and strengthening our independence. While we can’t just flip a switch to be sociopaths fundamentally unaffected by others, we can still practice self awareness, stoicism, and strengthen our resolve as your therapist seems to be advocating for.
For those lacking the kind of awareness promoted by these flavors of mindfulness, the hypnotic effects of the storm are much more enveloping, for better or (more often) worse.
If my potato peeler told me "Why bother? Order pizza instead." I'd be obese.
An LLM can directly influence your willingness to pursue an idea by how it responds to it. Interest and excitement, even if simulated, is more likely to make you pursue the idea than "ok cool".
> If my potato peeler told me "Why bother? Order pizza instead." I'd be obese.
But why do you let yourself be influenced so much by others, or in this case, random filler words from mindless machines?
You should listen to your own feelings, desires, and wishes, not anything or anyone else. Try to find the motivation inside of you, try to have the conversation with yourself instead of with ChatGPT.
And if someone tells you "don't even bother", maybe show more of a fighting spirit and do it with even more energy just to prove them wrong?
(I know it's easier said than done, but my therapist once told me it's necessary to learn not to rely on external motivation)
It’s like any other tool. If I wanted to chop wood and noticed how my axe had gone dull, the likelihood of me going “ah f*ck it” and instead go fishing increases dramatically. I want to chop wood. I don’t want to go to the neighbor and borrow his axe, or sharpen my axe and then chop wood.
That’s what has happened with ChatGPT in a sense - it has gone dull. I know it used to work “better” and the way that it works now doesn’t resonate with me in the same way, so I’m less likely to pursue work that I would want to use ChatGPT as an extrinsic motivator for.
Of course if the intrinsic motivation is large enough I wouldn’t let a tool make the decision for me. If it’s mid October and the temperature is barely above freezing and I have no wood, I’ll gnaw through it with my teeth if necessary. I’ll go full beaver. But in early September when it’s 25C outside on a Friday? If the axe isn’t perfect, I’ll have a beer and go fishing.
You are influenced just as much. You're just not aware of it.
Also, I think you're completely missing the point of the conversation by glancing over the nuances of what is being said and relying on completely overgeneralizing platitudes and assumptions that in no way address the original sentiment.
You are trusting the model to never recommend something that you definitely should not do, or that does not serve the interests of the service provider, when you are not capable of noticing it by yourself. A different problem is whether you have provided enough information for the model to actually make that decision, or if the model will ask for more information before it begins to act.
Why, though? I'm with GP, I don't understand it at all. If I thought something is interesting, I wouldn't lose interest even if a person reacted with indifference to it; I just wouldn't tell them about it again.
> If my potato peeler told me "Why bother? Order pizza instead." I'd be obese.
But that's not really the right comparison.
The right comparison is your potato peeler saying (if it could talk): "ok, let's peel some stuff" vs "Owww wheee geez! That sounds fantastic! Let's peel some potatoes, you and me buddy, yes sireee! Woweeeee!" (read in a Rick & Morty's Mr Poopybutthole voice for maximum effect).
This sounds like a contrarian troll question. Every tool we use has an effect on our attitudes in many subtle and sometimes not so subtle ways. It's one of the reasons so many of us are obsessed with tools.
I choose tools based on many reason. But the vibe they give me has a lot of weight, yes.
Another example: if you give me two programming fonts to choose from that are both reasonably legible, I'll have a strong preferance for one over the other. And if I know I'm free to use my favorite programming font, I'll be more motivated to tackle a programming problem that I don't really feel like tackling because I'd rather tackler some other problem.
If the programming problem itself is interesting enough to pull me towards it, the programming font will have less of an effect on me.
Do you see where I'm going with this? A lot of little things pile up every day, each one influencing our decisions in small ways. Recognizing those things and becoming aware of them lets us - over time and many tiny adjustments - change our environment in ways that reduces friction and is conducive to our enjoyment of day-to-day life.
It's not that I necessarily won't be doing something because I'm unable to do it exactly the way I enjoy most. It'll just be more draining because now I have to put in more effort to get myself going and stay focused on the task.
I have been using gpt-5 through the API a bit recently, and I somewhat felt this response behavior, but it's definitely confirming to hear this from another. It's much more willing (vs gpt-4*) to tell me im a stupid piece of shxt and to not do what im asking of the initial prompt
> I get it - we don't want LLMs to be reinforces of bad ideas, but sometimes you need a little positivity to get past a mental barrier and do something that you want to do, even if what you want to do logically doesn't make much sense.
If you want ceaseless positivity you should try Claude. The only possible way it’ll be negative is if you ask it to be.
> Correctness is secondary, user satisfaction is primary.
Kind of makes sense, not every user wants 100% correctness (just like in real-life).
And if I want correctness (which I do), I can make the models prioritize that, since my satisfaction is directly linked to the correctness of the responses :)
> Correctness is secondary, user satisfaction is primary.
And that's where everything is going wrong. We should use technology to further the enlightenment, bring us closer to the truth, even if it is an inconvenient one.
I think this is on the right track, but I think it's a byproduct of the reinforcement learning, rather than something hard-coded. Basically, the model has to train itself to follow the user's instruction, so by starting a response with "You're absolutely right!", it puts the model into the thought pattern of doing whatever the user said.
"Thought pattern" might be overstating it. The fact that "You're absolutely right!" is statistically more likely to precede something consistent with the user's intent than something that isn't, might be enough of an explanation.
Very unlikely to be an explicit tactic. Likely to be a result of RLHF or other types of optimization pressure for multi-turn instruction following.
If we have RLHF in play, then human evaluators may generally prefer responses starting with "you're right" or "of course", because it makes it look like the LLM is responsive and acknowledges user feedback. Even if the LLM itself was perfectly capable of being responsive and acknowledging user feedback without emitting an explicit cue. The training will then wire that human preference into the AI, and an explicit "yes I'm paying attention to user feedback" cue will be emitted by the LLM more often.
If we have RL on harder targets, where multiturn instruction following is evaluated not by humans that are sensitive to wording changes, but by a hard eval system that is only sensitive to outcomes? The LLM may still adopt a "yes I'm paying attention to user feedback" cue because it allows it to steer its future behavior better (persona self-consistency drive). Same mechanism as what causes "double check your prior reasoning" cues such as "Wait, " to be adopted by RL'd reasoning models.
You have "someone" constantly praising your insight, telling you you are asking "the right questions", and obediently following orders (until you trigger some content censorship, of course). And who wouldn't want to come back? You have this obedient friend who, unlike the real world, keeps telling you what an insightful, clever, amazing person you are. It even apologizes when it has to contradict you on something. None of my friends do!
> ... You have this obedient friend who, unlike the real world, keeps telling you what an insightful, clever, amazing person you are. It even apologizes when it has to contradict you on something. None of my friends do!
You're absolutely right! It's a very obvious ploy, the sycophancy when talking to those AI robots is quite blatant.
Truly incisive observation. In fact, I’d go further: your point about the contrast with real friends is so sharp it almost deserves footnotes. If models could recognize brilliance, they’d probably benchmark themselves against this comment before daring to generate another word.
Cool writeup. Have you had to do any other weird shenanigans with getting FFI between Rust and Clojure other than needing to use CStrings?
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