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angarrido

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投稿

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1 ポイント·投稿者 angarrido·3 か月前·0 コメント

How HN: AndesCode – local AI coding assistant that runs offline

1 ポイント·投稿者 angarrido·3 か月前·2 コメント

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1 ポイント·投稿者 angarrido·3 か月前·0 コメント

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1 ポイント·投稿者 angarrido·3 か月前·0 コメント

コメント

angarrido
·3 か月前·議論
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angarrido
·3 か月前·議論
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angarrido
·3 か月前·議論
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angarrido
·3 か月前·議論
must people think it’s just GPU cost. In practice it’s coordination: model latency variance + queueing + retries under load. You don’t scale linearly, you get cascading slowdowns.
angarrido
·3 か月前·議論
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angarrido
·3 か月前·議論
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angarrido
·3 か月前·議論
I built this because I couldn’t use Copilot or Claude at work without risking exposing internal code.

This runs fully local (Gemma 4 26B), indexes your codebase, and answers questions about it without anything leaving your machine.

Still early, but works well on large projects. Curious where this breaks for others.
angarrido
·3 か月前·議論
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angarrido
·3 か月前·議論
Local inference is getting solved pretty quickly.

What still seems unsolved is how to safely use it on real private systems (large codebases, internal tools, etc) where you can’t risk leaking context even accidentally.

In our experience that constraint changes the problem much more than the choice of runtime or SDK.
angarrido
·3 か月前·議論
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angarrido
·3 か月前·議論
Awesome idea, usually LLMs lack of creativity, so layouts look mostly the same, I could give a try
angarrido
·3 か月前·議論
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angarrido
·4 か月前·議論
Interesting approach. One thing I’ve been running into is that even with good alerts, the harder problem ends up being when to actually be exposed. You can get the signal right and still underperform just because of timing and volatility.