discounted competitor could cheat. they can offer subpar model response and sell it as deepseek-v4. it is uneconomical to prove inference providers are cheating, so they get away with it. cheating inference provider does not care if their customers stay.
it takes extra effort to open source a model even if you had it running internally. even traditional software takes extra effort to get released as open source.
there is an algorithm called quick-select. getting median of an array should not require full sort of the whole array, only a partial sort is needed to get the median. quick-select does this.
24-bit was created because microphone want to record large dynamic range without gain switching circuit.
96kHz was created to better reproduce 20kHz high frequency, so the digital noise shaping filter does not need to be super sharp right at the Nyquist frequency.
Both were introduced for a sound technical reason. beyond that, most are marketing non-sense to cheat consumers.
Those resellers are simply just selling Kimi K2.5 or GLM5.1 as counterfeit Opus. We, Chinese, know how to play the counterfeit game for a long time in so many industry.
in a few more months, when Chinese model gets to Mythos capacity and Fable still locked down. What Anthropic will say? Why can they just admit they are not the only people who know how to train an LLM model.
At one point, I was thinking that if any of my customer send me a snail mail with an actual physical stamp on it, we will call the customer immediately and solve their problem.
There are better and superior alternative of NE5532 these days. People should just move on. OPA1612 is the king in highest-end audio performance, at least on datasheet paper.
I think what this actually means is that you can apply permanent residency in the US, but you can only get the physical green card outside of the US when the case is approved. So, the last step to get the card need to from outside the country.
This is not surprising at all. The biggest benefit of cloud model in terms of energy efficiency is that when running more than 1 requests, the power consumption of said GPU roughly stayed the same. The more concurrency requests the server can handle, the less power each request consume. The server GPU is already likely more energy efficient than local GPU, concurrency make the cost structure unbeatable by local hardware. It is generally assumed the local hardware only run 1 request, but if the local engine is meant to serve a small business with meaningful concurrency, the economy might still work out.