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Joined 3 years ago
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Cake day: July 3rd, 2023

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  • context: I wanted to know if the open source projects currently being spammed with PRs would be safe from people running slop models on their computer if they weren’t able to use claude or whatever. Answer: yes, these things are still terrible

    but while I was searching I found this comment and the fact that people hated it is so funny to me. It’s literally the person who posted the thread. less thinking and words, more hype links please.

    conversation

    https://www.reddit.com/r/LocalLLaMA/comments/1qvjonm/first_qwen3codernext_reap_is_out/o3jn5db/

    32k context? is that usable for coding?

    (OP’s response, sitting at a steady -7 points)

    LLMs are useless anyway so, okay-ish, depends on your task obviously

    If LLMs were actually capable of solving actual hard tasks, you’d want as much context as possible

    A good way to think about is that tokens compress text roughly 1:4. If you have a 4MB codebase, it would need 1M tokens theoretically.

    That’s one way to start, then we get into the more debatable stuff…

    Obviously text repeats a lot and doesn’t always encode new information each token. In fact, it’s worse than that, as adding tokens can _reduce_ information contained in text, think inserting random stuff into a string representing dna. So to estimate how much ctx you need, think how much compressed information is in your codebase. That includes stuff like decisions (which LLMs are incapable of making), domain knowledge, or even stuff like why does double click have 33ms debounce and not 3ms or 100ms in your codebase which nobody ever wrote down. So take your codebase, compress it as a zip at normal compression level, and then think how large the output problem space is, shrink it down quadratically, and you have a good estimate of how much ctx you need for LLMs to solve the hardest problems in your codebase at any given point during token generation

    *emphasis added by me