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>> After a point, I suppose it doesn’t really matter how you make something, what matters is what is made, and how that thing conveys the emotion and the essence.

Somewhat agree with the second half, but disagree with the first. How things are made totally matter... a realistic painting vs a photo, even of the same subject, etc.


With the advances in ML (not just LLMs, but mostly image generation/recognition), getting a cheap EEG machine to read your thoughts and visualize your dreams.


This was presented at NeurIPS 2023: Cinematic Mindscapes: High-quality Video Reconstruction from Brain Activity

https://arxiv.org/abs/2305.11675


was thinking exactly this... and even better, why not on real data, especially since real, clean, data in this field is not at all hard to find.


Real historical data might not cover current market state.

For example, option price among other params depends on the interest rate. For the last decade interest rate was around 0% in Europe and slightly higher in US. If you train on that data only, there is no chance to "learn" option prices in the high-interest-rate environment which we saw for the last few years. Hence, you need synthetic data to learn that region of the market space.


>> * Gives you some time to think what to ask next.

I have found this quite an important part of this strategy actually, especially in negotiations (eg on a termsheet or something like that). having some time to both a) understand what was just offered to you but also b) give enough time to ask questions/respond.


>>One of the most harmful falsehoods about it is that somehow GOTOs and line numbers would turn you into a bad programmer for life.

i had nightmares about avoiding GOTOs in my teens...


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