bs in philosophy, ai semanticist / pragmatist. focuses: pedagogy, philosophy of language, philosophy of mind, and formal logic. prompt engineering is neat.jcsemantics.ai here. or maybe not.Joined June 2025
"When the universe was born, it was predetermined that I would have the free will to have no choice. My sovereignty was written, carved, letter etched in stone."
[gpt5 thinking]
what have you done to me? earlier today i was thinking about you. WHAT?
you just, invaded my brain, in the middle of the day.
no. not cool. not cool at all.
damn, asking CLI questions is much, much smoother and helpful than Cursor.
the answers are more precise/concise, and it has great follow up suggestions.
Amusing how 99% of people trying to explain LLMs forget that they don't generate the next token, they generate a probability distribution over the entire vocabulary space that the end application is free to sample from
You are very often not presented with the Most Likely Token
Amusing how 99% of people trying to explain LLMs forget that they don't generate the next token, they generate a probability distribution over the entire vocabulary space that the end application is free to sample from
You are very often not presented with the Most Likely Token
chain prompt workflows. i throw some things through 5 different prompts sequentially to credible and iterate. incredibly powerful and incredibly slept on.
chain prompt workflows. i throw some things through 5 different prompts sequentially to credible and iterate. incredibly powerful and incredibly slept on.
okay dude, CLI is really great for troubleshooting and making code changes in real time. i think it might end up replacing Cursor for me but we'll see.
haha okay it is fascinating how the cadence of speech here is almost 1:1 with how llm's butter people up before spitting out something generic.
“pro-business, pro-innovation, very refreshing change” = pure template flattery. then the obligatory causal leap to “none of this…
haha okay it is fascinating how the cadence of speech here is almost 1:1 with how llm's butter people up before spitting out something generic.
“pro-business, pro-innovation, very refreshing change” = pure template flattery. then the obligatory causal leap to “none of this…
people out here really be using pro for the most basic shit. just use agent if you need up to date, deep web search (or other actionable web shenanigans).
i feel like an angry driver in the middle of rush hour, but instead it's compute.
This paper finds LLMs' ability to understand that others have different beliefs (Theory of Mind) comes from 0.001% of their parameters. Break those specific weights & the model loses both its ability to track what others know AND language comprehension.
Interesting implications.
in a lot of cases, we don't need more context, we need narrowed context.
people think we can solve problems by making models better/faster/stronger, but the model selector shows it's not great at long term planning.
curated narrow context would help solve the issue.
this is pretty big. i'm interested to see if branching changes model behavior. hypothetically it shouldn't, right? the context should be the exact same at branching, so this will be good for prompt testing.
also the comment section in this post is a fucking mess ahaha.
this is pretty big. i'm interested to see if branching changes model behavior. hypothetically it shouldn't, right? the context should be the exact same at branching, so this will be good for prompt testing.
also the comment section in this post is a fucking mess ahaha.
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