271–280 of 280 posts
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aider jams the backend on my PC, i have to kill the tcp connection or python to stop it running a GPU on the backend, from time to time. I can't imagine paying for tokens and not knowing if it's working or wasting money.
The loops and constant useless changes drive me nuts haha
because even a rudimentary, garbage implementation would be fun to have, i think.
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every time you respond to an AI model "no, you got that wrong, do it this way" you provide a very valuable piece of data to train on. With reasoning tokens there is just a lot more of that data to train on now
Users can be adversarial to the “truth” (to the extent it exists) without being adversarial in intent. Dinosaur bones are either 65 million year old remnants of ancient creatures or decoys planted by a God during a 7 day creation, and a large proportion of humans earnestly believe either take. Choosing which of these to believe involves a higher level decision about fundamental worldviews. This is an extreme example,…
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No they aren't. Every arc problem is novel - that's why it resisted deep learning for so long (and still does to a degree). We just don't know how much the model seeing what an arc problem is on the first place boosts its ability to solve them - that limited statement is all the author is making.
The ARC prize was created last year. Arc hasn't resisted AI for very long. See; https://en.wikipedia.org/wiki/Fran%C3%A7ois_Chollet
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The ARC prize was created last year. Arc hasn't resisted AI for very long. See; https://en.wikipedia.org/wiki/Fran%C3%A7ois_Chollet
Huh? It was made in 2019
I was following Wikipedia: In 2024, Chollet launched ARC Prize, a US$1 million competition to solve the ARC-AGI benchmark. I guess the ARC benchmark appeared in 2019 (https://arcprize.org/). So (shrug)
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aider jams the backend on my PC, i have to kill the tcp connection or python to stop it running a GPU on the backend, from time to time. I can't imagine paying for tokens and not knowing if it's working or wasting money.
The loops and constant useless changes drive me nuts haha
I haven't been able to get it to do anything but waste my tokens with deepseek itself as the backend (aider --model deepseek[/deepseek-reasoner|/deepseek-chat] i think but am not certain).
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our collective netflix thumbs up indicators gave investors and netflix the confidence to deploy a series of adam sandler movies that cost 60 to 80 million US dollars to "make". So depending on who you are, the system might be working great.
Through analytics Netflix should know exactly when people stop watching a series, or even when in a movie they exit out. They no doubt know this by user. They know exactly what makes you stay, and what makes you leave. I would not be surprised if in the near future movies and series are modifed on the fly to ensure users stay glued to their screens. In the distant future this might be done on a per user level.
Hamlet says "to be or not to be", implying it's simply a choice. I choose to be!