A mental model for working with coding agents is that they're blind squirrels running into a maze and bumping into walls. You must place the walls (verifiable constraints) strategically so that they end up in the general region you want them in.
@fchollet
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JAX Grows from Niche to Mainstream in Machine Learning
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The JAX package is now around the same level, 20M monthly downloads. Which is incredibly fast growth, because 5 years ago I recall it being below 2M or so. It went from niche to mainstream in the past couple of years. Well deserved success.
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Keras hits record 21M monthly downloads on PyPI
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The Keras package recently crossed 21M monthly downloads on PyPI, an all-time high (the daily ATH is around 900k). I still remember when it first crossed 10M monthly downloads about 5 years ago and I thought it couldn't possibly go any higher…
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The impact of AI on developer productivity and code volume
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The quantity of code that devs ship has roughly 10xed. But net developer productivity (value created by unit of time) is only up by a bit, if at all. Part of it is that the additional code is solving more incremental problems. A bigger part is that the new code is creating
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Symbolic Learning as a Scalable Alternative to Neural Networks
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Symbolic learning is not a replacement for coding agents, it's a replacement for gradient descent & NNs: a low-level, completely general, extremely scalable new learning substrate.
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Agentic coding vs software engineering
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This means that agentic coding isn't exactly a replacement for software engineering. It is a fundamentally different way of producing software, with different best practices and different use cases. Just like ML.
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Agentic coding as machine learning
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Agentic coding is a form of machine learning. Generated code is best treated as a blackbox artifact whose behavior and generalization should be managed via empirical evaluation, like with any ML model.
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Agentic coding use cases by @fchollet
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A few major use cases for agentic coding for me: 1. Adhoc data visualizations. Anytime I have a question that can be answered quantitatively, I generate some code to make a plot. 2. Adhoc data annotation UIs. In ML, "make your own dataset" is often the answer, and that used to
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Inspiration from Bible + Futuristic Tech Mix
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NN learning is not search, and most NN architectures don't encode Turing machines. And gradient descent is not brute force, it's actually very efficient. So this is a lot of nonsense.
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Grok AI Feature Now Available for Premium and Premium+ Subscribers
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There are only two honest metrics when it comes to benchmarking intelligence: novelty and efficiency. You don't need intelligence to solve a known problem (only memory). And you don't need intelligence to solve a problem via brute force. But to solve a novel problem efficiently,