Open Weight Is Not Open Source
Meta's licence needs Meta's permission above 700 million users, and makes you print "Built with Llama" on the product.
Models, tools, and the hype gap between them.
Meta's licence needs Meta's permission above 700 million users, and makes you print "Built with Llama" on the product.
Four-bit weights need about 0.57GB per billion parameters. That one number decides the whole question.
Apple's new Mac Studio holds sixteen times what the fastest GeForce card does. Capacity and speed are different problems.
Google's ladder runs from nothing to $199.99 a month. Apple charges nothing and gates it on the handset instead.

Ollama gives a card under 24GB a 4k context. Its own docs say coding agents need at least 64k. That gap is why local coding disappoints.

Not a bug in the model so much as an incentive in the scoreboard. OpenAI's own numbers show what honesty costs a model on a leaderboard.

The model has no memory between messages. Every turn re-sends the whole conversation, and there is a hard ceiling on how much fits.

The company with the most to gain from a working detector built one, measured it, and took it down. The numbers it published say why.

The consumer app and the developer API ship opposite defaults. One setting changes it, and it is not the one most people reach for.

The number on the sticker gates one specific list of Windows features. The feature most people would actually notice needs a quarter of it.

The download size is the floor, not the answer. Context, quantisation and where the model actually lands all move the number.
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