Zuck in founder mode.Obviously what's he saying about coding makes no sense, watching a screen isn't as information dense as just reading the finished code. So my intuition is he is building a model that uses a front end as a front end. Just like humanoid robots make sense because our environment is already built for the human form, like we don't want to make humaniods with wheels because they won't be able to handle every staircase. And we don't want to build every staricase, so we build robots
6:13
x.comAlmaCap
Zuck in founder mode.Obviously what's he saying about coding makes no sense, watching a screen isn't as information dense as just reading the finished code. So my
AlmaCap (@AlmaCap114204). 8 likes. Zuck in founder mode.Obviously what's he saying about coding makes no sense, watching a screen isn't as information dense as just reading the finished code. So my intuition is he is building a model that uses a front end as a front end. Just like humanoid robots make sense because our environment is already ...
2.4M views1 month ago
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LEAKED AUDIO: In an all-hands meeting on April 30, Mark Zuckerberg tells employees that he's training AI on them ahead of mass layoffs."The AI models learn from watching really smart people do things... The average intelligence of the people who are at this company is significantly higher than the average set of people that you can get to do tasks.So if we're trying to teach the models coding, for example, then having people internally build tools or solve tasks that help teach the model how to
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I just tested Claude Mythos Fable 5 and told it to build a fully functional Jarvis AI from a single prompt. No coding. No setup. Just one instruction.
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Andrej Karpathy's explaination of Bigram Language Model explained Bigram LMs, though simple, it provides powerful insight into the inner mechanics of how tokens are processed in language models. This is a pre-amble for what's next: GPT, which is the 2nd part of the series The Bigram model here incorporates: Tokenization, Vocabulary, Negative Loss Function, Cross Entropy, Logits, SoftMax, Optimizer, and AdamW These are essential ingredients to understand in order to build our knowledge on how LLM
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Andrej Karpathy's explaination of Bigram Language Model explained Bigram LMs, though simple, it provides powerful insight into the inner mechanics of how tokens are processed in language models. This is a pre-amble for what's next: GPT, which is the 2nd part of the series The Bigram model here incorporates: Tokenization, Vocabulary, Negative Loss Function, Cross Entropy, Logits, SoftMax, Optimizer, and AdamW These are essential ingredients to understand in order to build our knowledge on how LLM
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