241–250 of 328 posts
What matter is not context or the recod token/s you get. But the quality for the model. And it seem Grok pushing the wrong metrics again, after launching fast.
Grok's biggest feature is that unlike all the other premier models (yes I know about ChatGPT's new adult mode), it hasn't been lobotomized by censoring.
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Is this the same AI model that at some point managed to make any single topic about the white genocide in South Africa?
How does this sort of thing work from a technical perspective? Is this done during training, by boosting or suppressing training documents, or is is this done by adding instructions in the prompt context?
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I think they would want a more optimized regex. Like a long list of swears, merged down into one pattern separated by tunnel characters, and with all common prefixes / suffixes combined for each group. That takes more than just replacing one word. Something like the output of the list-to-tree rust crate.
Wouldn't the best approach for that be to write a program that takes a list of words and output an optimized regex? I'm sure an LLM can help write such a program. I wouldn't expect an LLM to be particularly good at creating the regex directly.
What matter is not context or the recod token/s you get. But the quality for the model. And it seem Grok pushing the wrong metrics again, after launching fast.
Indeed. Free grok.com got significantly worse this week and has been on a decline since shortly after the release of Grok-4. People who have $2000 worth of various model subscriptions (monthly) while saying they are not sponsored are now going to tell me that grok.com is a different model than Grok-4-fast-1337, but the trend is obvious.
It's a shame that the top comments are focusing more on Elon Musk, his personality and politics rather than the quality of the model per se. Speaking about Elon, regardless of what you think of him, he really does get things done, despite naysayers -- SpaceX, Tesla, Neuralink and even get Trump elected ( despite subsequent fallout) etc. Even Twitter is finding a second life by becoming a haven for the free speech adv…
This post really has no reason to be flagged. I know Elon is controversial, and I have a lot of gripes with his business practices myself, but this is literally just documentation for a frontier LLM. Can we stay on topic?
This. We like to think about ourselves as engineers. But often behave like a bunch of emotion driven primitives. Honestly this kind of behaviour would be a huge red flag during interviews. I have problems that current LLMs can't solve efficiently due to context window sizes. And welcome any improvement in this space.
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I like grok for noncoding stuff. I find it hasn't been tuned for "Safety" (meaning it isn't tuned much for political correctness). It also seems good at making images and stories up well. I run some choose your own adventures stories with my kids through it. We tell it who each of their characters are and what the theme is for the night and grok gives them each a section of story and 4 choices. They also have the opt…
> it isn't tuned much for political correctness It was tuned to be edgy and annoying though (I mean his general style of speech not necessarily the content).
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I don't think there are any up-to-date leaderboards, but models absolutely degrade in performance the more context they're dealing with. https://wandb.ai/byyoung3/ruler_eval/reports/How-to-evaluate... >Gpt-5-mini records 0.87 overall judge accuracy at 4k [context] and falls to 0.59 at 128k. And Llama 4 Scout claimed a 10 million token context window but in practice its performance on query tasks drops below 20% accur…
That makes me wonder if we could simply test this by letting the LLM add or multiply a long list of numbers? Here is an experiment: https://www.gnod.com/search/#q=%23%20Calcuate%20the%20below%... The correct answer: Correct: 20,192,642.460942328 Here is what I got from different models on the first try: ChatGPT: 20,384,918.24 Perplexity: 20,000,000 Google: 25,167,098.4 Mistral: 200,000,000 Grok: Timed out after 300s…