I don't know if I made this explicit so I'll make this explicit: zero point zero knowledge required of HTML or JS animation techniques to make this work. I'm not even looking at the textual output here. That's Claude's problem; nothing matters except how it displays in Chrome.
LLMS
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Optimizing LLM Prompt Length and Workflow Efficiency
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"How long are your prompts for these?" 600 and 900 words for last two, both assuming refinement after initial delivery. One operator at a single keyboard can do several of them during a work day, even assuming downtime as the LLM goes off and cranks for 5 minutes between revs.
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Using AI agents and headless browsers for automated animation debugging
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Presumably if I were more sophisticated at prompting I could ask it to use a headless browser to render every 1s of the animation to a screenshot then fix all the bugs identified w/o me needing to flag them individually. Tokens are abundant, after all.
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Using Claude Code for efficient animation and simulation workflows
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I am really loving Claude Code for bespoke animations/simulations, where you probably wouldn't want to spend a week of a team's time to compress into 30 seconds but where CC can do that in about 10-30 minutes of one person's time. (Probably possible in most modern coding tools.)
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Oxford AI4Science research on LLM long-horizon reasoning
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Long-horizon reasoning is one of the largest obstacles in LLMs. In our latest AI4Science talk, Sumeet (
@sumeetrm
) and Charlie (
@CharlieLondon02
) from Oxford discussed H1 and LongCoT, two projects focused on measuring and improving how models reason over long chains of steps. -
SlimQwen: Technical Research on Pruning and Distillation for MoE Models
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“SlimQwen: Exploring the Pruning and Distillation in Large MoE Model Pre-training” This new Qwen paper shows that pruning a pretrained MoE is much better than training the smaller MoE from scratch. All you need to do is prune depth, width, and experts, preserve some experts
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On model choice and training dynamics
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You don’t remove any information. You just use the right model. The action distribution is a delta function for your own actions. You still train on how the world is affected by or reacts to your actions
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Educational Resource: The Hundred-Page Language Models Book
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The Hundred-Page Language Models Book — Hands-on with PyTorch: http://
amzn.to/4sJl7YC by @burkov -
GenAC: chain-of-thought generative critic for LLM RL
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What if LLM reinforcement learning could assign credit more accurately by thinking step by step? Researchers from Peking University and Microsoft Research Asia introduce GenAC: a generative critic that replaces one-shot value predictions with chain-of-thought reasoning before
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The relationship between tokens, intelligence, and problem-solving complexity
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Simply put ‘tokens’ are what makes up a measure of non-biological Intelligence. And with more intelligence you can solve infinitely more complex problems. And no, there is no ceiling on complexity, so it’s not a zero-sum game (IE humans vs machines). It just means we can do…