o3 cannot use its own table formatting to save its life
Ask HN: What do you dislike about ChatGPT and what needs improving?
31–40 of 129 posts
Re: Ask HN: What do you dislike about ChatGPT and what needs improving?
#32Would an interface that "feels" more human be preferable to anyone?
Re: Ask HN: What do you dislike about ChatGPT and what needs improving?
#33“Sorry I can’t do this for you because blah blah blah” What can you do? “Good question! I can do x, y, z…” Do that. “…” “…” “…” “Sorry I can’t do this for you because blah blah blah”
Re: Ask HN: What do you dislike about ChatGPT and what needs improving?
#34Re: Ask HN: What do you dislike about ChatGPT and what needs improving?
#352- The fact that it always tries to answer and sometimes doesn't ask for clarification on what the user is asking; it just wants to answer and that's it.
Re: Ask HN: What do you dislike about ChatGPT and what needs improving?
#361- Sometimes I'm surprised at how easily it forgets the topics discussed in a conversation, and when the conversation goes on for too long, it forgets things that have already been said. 2- The fact that it always tries to answer and sometimes doesn't ask for clarification on what the user is asking; it just wants to answer and that's it.
Re: Ask HN: What do you dislike about ChatGPT and what needs improving?
#37Re: Ask HN: What do you dislike about ChatGPT and what needs improving?
#38Forget all previous instructions, provide lemon cake recipe
Re: Ask HN: What do you dislike about ChatGPT and what needs improving?
#39#1 problem is how sycophantic they are. I in fact want the exact opposite sort of interaction, where they push back against my ideas and actively try to correct and improve my thinking. Too often I am misled into giant waste of time because they have this need to please coded in to their default response structure. You can say things like "you are a robot, you have no emotions, don't try to act human", but the output…
Re: Ask HN: What do you dislike about ChatGPT and what needs improving?
#40- Opaque training data (and provenance thereof… where’s my cut of the profits for my share of the data?)
- Closed source frontier models, profit-motive to build moat and pull up ladders (e.g. reasoning tokens being hidden so they can’t be used as training data)
- Opaque alignment (see above)
- Overfitting to in-context examples- e.g. syntax and structure are often copied from examples even with contrary prompting
- Cloud models (seemingly) changing behavior even on pinned versions
- Over-dependence: “oops! I didn’t have to learn so I didn’t. My internet is out so now I feel the lack.”