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imapenguin.com | ||
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ezyang.github.io
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| | | | | When you're learning to use a new framework or library, simple uses of the software can be done just by copy pasting code from tutorials and tweaking them as necessary. But at some point, it's a good idea to just slog through reading the docs from top-to-bottom, to get a full understanding of what is and is not possible in the software. One of the big wins of AI coding is that LLMs know so many things from their pretraining. For extremely popular frameworks that occur prominently in the pretraining set, an LLM is likely to have memorized most aspects of how to use the framework. But for things that are not so common or beyond the knowledge cutoff, you will likely get a model that hallucinates things. Ideally, an agentic model would know to do a web search an... | |
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techtldr.com
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| | | | | Updated in 2020 to work with Python 3. My company uses multiple git repos that all depend on one another. Often I find myself trying to grep through a few of them at the same time. Regular grep works, but it takes a long time and displays a lot of noise. git grep only searches [...] | |
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www.softdevtube.com
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| | | | | Programming history is filled with bugs that turned out to be features and limitations that pushed developers to make even more interesting products. We'll journey through code that was so 'bad' it was actually good. Along the way we'll look at the important role failure plays in learning. Then we'll tame our inner perfectionists and | |
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jeffmilner.com
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| | | [AI summary] A blog post discussing a humorous scene from a movie and providing a collection of digital content and other miscellaneous topics. | ||
