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You seem to be under the impression that I am forced to reply to you.
You are very rude.
I don’t get what you are trying to get to here.
If you read the article then you know exactly what is the article was talking about.
The author is basically talking to people who think that crypto is the solution to everything wrong with the fiat currency.
Did you actually read the article?
Just hours after Willis’s interview with The Independent, House and Senate negotiators revealed a bipartisan compromise spending bill that would ban military health insurance from covering transition care for children. On Wednesday, 50 House Democrats who previously denounced that provision voted in favor, and key Senate Democrats said they would reluctantly back it too.
I meant a community, but I edited it anyway for better clarity.
I talked in my comment about the relvance of the article:
I don’t care about looks, I want the community I participate in to be fair.
I was talking about the post that I linked to.
So, let me be clear here.
The state of media which is owned by billionaires and that defend a CEO who is working at a health insurance company is not politics but news?
To make it clear that this removal is based on the mod mood, not reason:
This is a post I made there, but it did not get removed.
That inconstancy is one of the biggest proofs that this removal is not fair.
The post is deleted by the mod, I will try to add photos here.
It does not bypass the paywall.
I don’t think they would be able to help as their technique for bypassing paywalls does not work at all in this case.
In this case it’s a soft paywall but in other cases that I wanted to bypass it’s a hard paywall.
🌹Thank you for trying to solve this.
Here is a soft paywall example: https://www.levernews.com/regulators-warn-crypto-could-cause-another-financial-crisis/
As far as I can tell it does not work on ghost blogs.
Now that we have discussed the basis of RL, Synthetic Data, Chain-of-Thought, Inference Time Compute and other concepts, let us go through what OpenAI has done with o1 and o1 Pro both during training and during inference. The construction of o1 is unique and doesn’t mirror the papers above. We will also discuss the tokenomics of inference time compute including cost, KV Cache scaling, batching, and more. Lastly, we will explain what OpenAI is doing next with Orion and why the narrative around it being a failure isn’t accurate.
I updated the post :)