In coding? Software architecture? Math? General knowledge?

The diversity of model use-cases is so broad that comments about "model x is better" without any context are largely useless

While this model may share much with GPT-style models on the encoder side, it clearly has a different decoder architecture. So is a high-parameter count language model an LLM even when it doesn't have a GPT-style decoder? The definitions are in flux.

For all we know this might be a non-language-generative transformer e.g. a transformer where the decoder produces confidence scores rather than language. Please provide more likely architectures if you know them, I'm genuinely curious.

0.15/0.22 ≈ 0.68, meaning a roughly 32% reduction on inputs. The 50% reduction is only outputs and cached inputs.

Selling a car today gives Tesla the full profits of that hardware production today. Running that car as a robotaxi means that Tesla eats the costs of the hardware today in exchange for a larger total profit collected over several years.

So operating a fleet gives the company access to more long-term profits at the cost of decreasing the bank balance today (negative cash flow), where selling the cars lets the company fill the bank account right now (positive cash flow) at the cost of limiting long-term profitability.

The decision to prioritize immediate cash flow vs long term profits depends on the financial position and overall strategy of the company.

The following from Claude: """ A diff between two randomly chosen commits usually spans years of history, so it's not a few KB — the tree itself is ~1.5 GB of text, and a multi-year span rewrites a large slice of it. Call it 100–200 MB per pair on average: 8.5×10¹¹ pairs × ~2×10⁸ bytes ≈ 10²⁰ bytes, or ~150 exabytes """