bearishcat

[bearishcat @readbearcat]

@f_wintersberger @grok is this real

[bearishcat @readbearcat]

@ragipsoylu Its like giving an umbrella when it doesn’t rain, only to take it when it does.

[bearishcat @readbearcat]

@WallStreet_Wire @MenchOsint Someone can’t stand the truth like a colonizer, oh wait 😆 you’re in his lands and mock him for being elsewhere knowing you caused it lmao

[bearishcat @readbearcat]

@AndreasSteno @grok explain 😆

[bearishcat @readbearcat]

@VolSignals It is insane how surpressed VIX is with this much commotion surrounding macro factors

[bearishcat @readbearcat]

@AuclairsDad That’s it, I’ve followed you for a long time and you became increasly more islamophobic. As a Muslim I cannot accept this kind of language anymore. You, out of all people, should know not to overgeneralize. Unfollowed.

[bearishcat @readbearcat]

@shortbelly @VolSignals Still can’t wrap my head around how muted it is

[bearishcat @readbearcat]

@VolSignals

[bearishcat @readbearcat]

@AuclairCapital I missed that one

[bearishcat @readbearcat]

@AuclairCapital Maybe I’m missing something;
- job openings are up slightly
- initial jobless claims are lower
- ADP non-farm change is worse (going against my point)
Would these development warrant another cut?

[bearishcat @readbearcat]

@AuclairCapital So if employment data isn’t worse and is marginally better-ish, inflation isn’t rising, I can’t grasp why the FED would cut rates?

[bearishcat @readbearcat]

@PharmD_KS So if employment data isn’t worse and is marginally better-ish, inflation isn’t rising, I can’t grasp why the FED would cut rates.

[bearishcat @readbearcat]

Holy cow the yen is in trouble

[bearishcat @readbearcat]

@VolSignals LLMs are summer interns in fancy suits

[bearishcat @readbearcat]

@TailThatWagsDog The issue is that it will give an answer based on what it has read before in its training dataset, and then it will just give an answer. It won’t go back and check if each item in that list has duplicates. Just like a summer intern. Any answer is better than no answer

[bearishcat @readbearcat]

@TailThatWagsDog You might want to try this experiment; provide an LLM a list of ~450 distinct strings with a space in between each like “stringone stringtwo” etc. Then ask it to conver to a list like [“stringone”, “stringtwo”, …]. Then ask it if it has duplicates. Chances are it says yes

[bearishcat @readbearcat]

@TailThatWagsDog I wish I could disclose my work with AI but we try to catch financial fraud. A component of it is with open source intelligence

[bearishcat @readbearcat]

@TailThatWagsDog When you give an LLM a dataset on positioning, it will know what the sentence format is you would want to read based on literature on positioning. Then it can literally make up numbers and conclusions as long as it fits the format of a sentence, irrespective of the actual data

[bearishcat @readbearcat]

@TailThatWagsDog Since the output of an LLM is a result of probabilistic calculations on the likelihood of the next word within a sentence, I wouldn’t trust it to “reason”. It doesn’t draw conclusions like we do. Until fairly recently LLMs struggled to find how many r’s were in strawberry