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cheesecompiler

261 karma · joined June 24, 2025

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cheesecompiler··on Depression reduces capacity to learn to actively avoid aversive events
depression, like alcoholism, is a crutch and a symptom of a deeper issue, not an independent behaviour in a vacuum.
cheesecompiler··on Depression reduces capacity to learn to actively avoid aversive events
The authors take a leap in assuming there's a difficulty with overriding a response. But in my and others' experience from group therapy, I can confidently say it's more like either apathy or being numbed out, to the point of either not noticing or caring that the aversive sound is happening.

This is how SSRI has felt for me also, incidentally: an even deeper apathy, setting in from lack of any emotion at all.

cheesecompiler··on Apple Photos app corrupts images
The walls of the Google garden have been slowly going up.
cheesecompiler··on Ask HN: Anyone using augmented reality, VR, glasses, helmets etc. in industry?
Wow no one at all with a Vision Pro?
cheesecompiler··on Gemini CLI
In my experience Gemini is consistently more conservative and poor at reasoning/regurgitates, like a local Llama instance.
cheesecompiler··on The Bitter Lesson is coming for Tokenization
But this uses the transformers model to justify its own reasoning strength which might be a blindspot, which is my original point. All the above shows is that transformers can simulate solving a certain set of problems. It doesn't show that they are the best tool for the job.
cheesecompiler··on Is mathematics mostly chaos or mostly order?
The overarching point still stands: our formal systems are just models built to describe the patterns we observe. In that sense, math “just is.” The fact that some models aren’t compatible with others doesn’t undermine that—it just shows they’re incomplete or context-dependent views into a larger structure.
cheesecompiler··on The bitter lesson is coming for tokenization
The reverse is possible too: throwing massive compute at a problem can mask the existence of a simpler, more general solution. General-purpose methods tend to win out over time—but how can we be sure they’re truly the most general if we commit so hard to one paradigm (e.g. LLMs) that we stop exploring the underlying structure?
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