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Joined 1 year ago
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Cake day: June 5th, 2023

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  • Note that this isn’t specific to Go. Reading from stream-like data, be it TCP connections, files or whatever always comes with the risk that not all data is present in the local buffer yet. The vast majority of read operations returns the number of bytes that could be read and you should call them in a loop. Same of write operations actually, if you’re writing to a stream-like object as the write buffers may be smaller than what you’re trying to write.


  • Looks exactly like Visual Studio 2022.

    I guess the joke implies that automated (or incorrect manual) conflict resolution causes code that doesn’t compile. But still not git’s fault. They should probably have merged earlier and in rare cases where that wasn’t possible, you have to bite the bullet and fix this stuff.
















  • No joke here. Large language Models (which people keep calling AI) have no way of checking if what they’re saying is correct. They are essentially just fancy text completion machines that answer the question what word comes next over and over. The result looks like natural language but tends to have logical and factual problems. The screenshot shows an extreme example of this.

    In general, never rely on any information an LLM gives you. It can’t look up external information that wasn’t in its training set. It can’t solve logic problems. It can’t even reliably count. It was made to give you a plausible answer, not a correct one. It’s not a librarian or a teacher, it’s an improv actor who will „yes, and“ everything. LLMs will often rather make up information than admit that they don’t know. As an easy demonstration, ask ChatGPT for a list of restaurants in your home town that offer both vegan and meat-based options. More often than not, it will happily make you a list with plausible names and descriptions but when you google them, none of the restaurants actually exist.