The use and operation of open-weight models and locally or privately run AI, including control, adaptation, licensing, and operational tradeoffs.
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9 articles in this tag.
A team has an open-weight model in production. The outputs are wrong in a specific, repeatable way. Someone says the words "fine-tuning run," and suddenly…
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The demo is no longer the hard part. The hard part is the fifth revision, when the client wants the same character, the same lighting, and one changed word…
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A leaderboard rank tells you how a model performed under someone else's test conditions. It cannot tell you whether the model will survive yours.
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You have a model file, a GPU, and a demo that works on your laptop. Now answer the only question that matters: what breaks first when this leaves your…
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The model loads. The first prompt returns in two seconds. Then the context grows, a second request arrives, and the whole thing crawls.
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The demo ends the moment the model answers. The operations commitment begins the moment it answers twice, at 2 a.m., on a Tuesday, while the one engineer…
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The model runs. The eval looks good. Then someone on the call asks the question nobody scheduled time for: are we actually allowed to ship this?
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A prototype works on a hosted API, then someone says "let's just run it locally." That sentence usually bundles three different decisions into one, and…
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A model that fits in VRAM, loads in seconds, and answers instantly — then fails on the one task you actually needed.
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