Could iOS/Android OS do some kind of on-device ML for liveness detection and securely, privately cryptographically sign/certify actions as coming from a live, real person?
@karpathy
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llm.c Memory Efficiency Advantages Over PyTorch
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If you match the parameters you're actually under-estimating the improvement, because llm.c takes up a lot less space so you can crank up the batch size. I haven't fully dug into why PyTorch takes up that much space, slightly worried I'm doing something wrong but not sure what
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Karpathy highlights extraordinary moment in AI at World’s Fair
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The @aiDotEngineer World's Fair in SF this week https://
ai.engineer/worldsfair Reminded of slide #1 from my most recent talk: "Just in case you were wondering…
No, this is not a normal moment in AI" -
MLX Framework as Optimized Alternative to PyTorch MPS
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I'd have a look at MLX, cc @awnihannun , from some recent chatter I understand it is a lot more optimized that PyTorch mps atm. This would need a re-write of the build-nanogpt code into mlx, but possibly it's not that involved. I'd be happy to accept a PR for mlx clone.
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In-Context Learning as Testbed: Example Order and Label Effects
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I quite like it as a nice/intuitive testbed of in-context learning, and the experiments around example order, label names, label flipping, etc., which give a sense of the strength of the prior, and ICL as an optimizer. Does the performance here also correlate with other LLM
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LLMs as Memory-Based Answerers Without Reference Materials
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The way to think about asking a factual question to an LLM is that it's a bit like asking a person who read about the topic previously, but they are not allowed to reference any material and have to answer just from memory. LLMs are a lot better at memorizing than humans, but the
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LLM Interfaces Need Built-in Proof and Verification Features
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One built-in UI/UX feature of LLM interfaces I'd love is proof. I almost always do this manually – for example if the LLM recommends running some commands with some switches, I manually look up and verify the API in the docs to make sure those switches are correct and that I
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Micrograd: 94 Lines to Train Neural Networks
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These 94 lines of code are everything that is needed to train a neural network. Everything else is just efficiency. This is my earlier project Micrograd. It implements a scalar-valued auto-grad engine. You start with some numbers at the leafs (usually the input data and the
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Building a Reading Companion App for Books with AI Context
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Nice! I really want to build a reading companion app for books. E.g. I am re-reading LoTR again, you could imagine stuffing all of it (and discussion boards related commentary and chatter) into context and making it very easy to ask questions, clarifications, discussions. There's
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nanoGPT for Education: Exploring Production-Ready Alternatives
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btw nanoGPT is meant for education, possibly have a look at some of the slightly bit more "prod" repos i link to it in the readme, e.g. litgpt or tinyllama. When you look at the code it will look quite nanoGPT-like and recognizable, but possibly a bit more battle-tested.