636 karma · joined May 15, 2008
hn@simplistency.com
It set off all my LLM writing triggers and I noped out after about 3 sentences, because I'm designing/coding with AI all day and I'm 100% sick of its writing. I don't really care if it was written by an LLM or a person who writes in LLM style.
HOWEVER, the surprising fix for me was to ask a number of LLMs to summarize it. I omitted the "is this AI slop?" parts when I pasted it in. Like I said, I don't care about who wrote it, but I hated the writing style.
The AI summaries were quite readable.
They converged at a high level on the summary, but with a good bit of variance in nuance. I had a good follow up discussion with one of the LLMs, so I'm glad I tried it.
“It's a species of anti-blog, as there is no way that you'll get through a post if you suffer from any kind of attention-deficit disorder; even then, you may need a strong cup of coffee and an hour to kill.”
> Rather than a ‘rising tide lifting all boats,’ a higher level of corporate BS in an organization acts more like a clogged toilet of inefficiency.”
and a link to the paper: https://www.researchgate.net/publication/400597536_The_Corpo...
https://www.dolibarr.org/ https://github.com/Dolibarr/dolibarr
I was just fighting ffmpeg earlier today, or rather Gemini and Claude were fighting it. Task: create a video that is a pan across a photo, followed by a scale/zoom.
Probably easy for some people, but I had no clue and the LLMs weren't doing that well either. Things took a turn for the better when I asked Gemini for an alternative tool.
The answer was Vapoursynth - https://www.vapoursynth.com/doc/introduction.html#introducti...
Again, the LLM did the work, but it was able to do so. Since Vapoursynth is driven by python scripts (though with the extension .vpy), it was easy for me to make adjustments.
I also liked that there was a neckband - easy to take the buds out when not needed and leave them hanging, and of course more power in a larger battery.
title: The neural basis for uncertainty processing in hierarchical decision making
abstract: Hierarchical decisions in natural environments require processing uncertainty across multiple levels, but existing models struggle to explain how animals perform flexible, goal-directed behaviors under such conditions. Here we introduce CogLinks, biologically grounded neural architectures that combine corticostriatal circuits for reinforcement learning and frontal thalamocortical networks for executive control. Through mathematical analysis and targeted lesion, we show that these systems specialize in different forms of uncertainty, and their interaction supports hierarchical decisions by regulating efficient exploration, and strategy switching. We apply CogLinks to a computational psychiatry problem, linking neural dysfunction in schizophrenia to atypical reasoning patterns in decision making. Overall, CogLink fills an important gap in the computational landscape, providing a bridge from neural substrates to higher cognition.
I hope they spend a good bit of the $180M on building out their input connectors.
Certainly it suggests that “this time is different” without saying it in a quotable fashion.
The metrics it provides seem useful. What are the metrics it is missing?
At the end Nvidia retains ownership of what are probably very low value assets.
Contrast that with car leases: there is a robust market for used cars.
Nvidia is in effect financing the GPUs by not requiring the full payment up front.
Do the lease payments add up to the total cost?