like! tiny idea tiny code, strips away the formalism except the high level idea (iterative denoising on a schedule)
AI
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BetterTwitter Adds Blue Verified Filter for Code Quality
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i've now added the "blue_verified" filter to filter out the riffraff that paid for their blue ticks https://
github.com/sw-yx/bettertw
itter/commit/79096938a53e3787f6093030d362d5964e1c7852
… love using 3 year old code that "just works" -
How Technologies Become Standard: Adoption Process Framework
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first they do it as part of their job then they do it as a stepping stone then it wins
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Replit Training Data Access Terms Review
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i had a look at the terms. looks fine but replit doesnt have access for training data?
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Replit Terms: Training Data Access Rights Unclear
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i got excited about this but then went through their terms. nothing in there says Replit has the right to access them for training data, though public Repls are MIT by default, many of these won't be public i'm guessing
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Data Quality and Curriculum Learning for LLM Training
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"Obviously anything that looks useless (like SHA hashes or other noise) is not worth training on and is just wasting training capacity and time"
"You may want to start with simpler topics and work up to more complex later, just like in human school" -
Concerns about GPT alignment with human values and safety
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"Finally, we are very concerned that this GPT could be unaligned with humans. This would be bad. We want this to be a nice GPT that deeply loves all humans and is always considerate and helpful. Thanks"
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GPT Training Framework with Dataset and Sampling Tools
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Prompt: "You are a GPT and you're in charge of training an even better GPT, congrats! You have a dataset here . You can train it on document chunks like this: and sample its current understanding like this: . And here's a calculator and a scratchpad . Begin:"
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Growing Up in the Virtual Fifties: Nostalgia and Tech Evolution
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I like to say that we grew up in the (virtual) 50s.
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Meta-learning policies for LLM attention management during training
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Feels like a lot of fertile ground is left in managing the "attention" of an LLM during its training via a meta-learning policy, instead of the typical "memorize dataset uniformly at random" strategy. And giving it a calculator and a scratch pad.