I'm curious though if these training methods are convergent or are models actually different; just like how in the stock market people think they're "diversified" but the truth is their exposure is likely much more risk correlated than one might think.

In certain situations, one right answer is better than a committee discussing the problem, but in others its sometimes nice to have some alternative methods of solving something. Fun project nonetheless.

My approach to using multiple models has been less about CoT but more about time to first token, and how you can use a small model to start interacting with the user while in parallel the more complex model is building a larger more complex thought. My work on this was primarily for voice backed interfaces before the voice models became quite a lot faster.

For example, compute X tokens with model A, then feed those into model B, etc. to get chain of thought through a diverse set of mdoels rather than chain of thought through a heterogeneous chain.

Humans seem to strongly believe echo chambers are bad. Are LLMs the same?

Is creatine safe to supplement for children?

Companies like Bloom Energy are putting up power plants right next to DCs. If datacenter power demand then these companies will sell back to the grid creating an oversupply.

If the debt load is too high they're need to declare bankruptcy, at which point the bond holders will lose their capital, not the public.

Is there any way to use MLX and GPU at the same time? Or does memory become a big problem?

TBH, I never understood Apple hyping these neural cores because I didn't think anyone actually uses them except maybe certain photo/video editing software.

If I can generate voice at the same time as video, that would be useful.

https://www.youtube.com/watch?v=gRBDLE06qNY&t=210

"What does this monstrosity cost?"

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