hadeson

I don't think it's easier, a bad poker bot will lose a lot over a large enough sample size. But maybe it's easier to incorporate exploitation into your strategy - exploits that rely more on human psychology than pure statistics?

From my experience, their hallucination when playing poker mostly comes from a wrong reading of their hand strength in the current state. E.g., thinking they have the nuts when they are actually on a nut draw. They would reason a lot better if you explicitly give out their hand strength in the prompt.

"Magni, inquit, fures minorem puniunt" - Appeared in Pseudo-Caecilius Balbus.

I like the Tree of Thoughts theory that treat each chain of thoughts 'branch' as a possible hypothesis. They might trained a search system that quickly explore some of these branches and by some metric choose the most likely to be the right one at the moment to answer.

Up to six-player no-limit Hold’em poker with Pluribus.

This was a thing until mining ETH is profitable again.

It seem this setup get close to the Jetson Nano 2Gb price range ($59), which from my experience is order of magnitude faster than rpi 4 for computer vision/video processing tasks. Is there any advantage of using Motion over nvidia-deepstream?

It could be used to accelerate Convolutional Neural Nets training [0]

[0] https://arxiv.org/abs/1312.5851

I guess object detection is the bottleneck of the pipeline, which highly optimized YOLOv4-tiny[0]could get to 39 FPS on Jetson Nano. Also camera decoding with Nvidia Deepstream is a lot faster than OpenCV.

[0] https://github.com/pjreddie/darknet/issues/2201

Cooking is really an art with lots of tacit knowledge that vary too much to put in a simple recipe.

Most kinds of professional applications I could think of would better off with a green screen. On the other hand, using this for editing videos in the wild with messy background still not good enough.

Yandex seem to give even more precise matches than Google. Maybe image of someone less popular would work better.

Twitter investigation suggest that this is a coordinated social engineering attack [0]. The idea that the hackers are some non state actors and not from the US seem unlikely. [0] https://twitter.com/TwitterSupport/status/128359184646423347...

Really just curious about this approach and want to test it since most old scraping methods failed on Facebook data. My take is that it is possible with enough resources since it is actually pretty hard to separate this from real usages.

I tried using OCR to scrap Facebook profiles by simulate web browsing behavior. It helps a lot in avoid account blocking but still too slow to be practical.