Well, there are only so many nouns, and even fewer "cool-sounding" ones. For better project differentiation, do you think we should instead be naming things "ZurgGlurg327"? I'm sure you can find a completely-unique combo for each thing, but good luck remembering the name!
They work better for coding workloads. Essentially, the more regular the output, the more the faster model gets right, the less the big model has to do.
Writing tends to have more false positives. I haven't tried this particular one, however, but that is the general trend.
I saw EAGLE and thought it's going to be about PCB design. Was left disappointed.
Well, there are only so many nouns, and even fewer "cool-sounding" ones. For better project differentiation, do you think we should instead be naming things "ZurgGlurg327"? I'm sure you can find a completely-unique combo for each thing, but good luck remembering the name!
Are these speculative decoders ok to use for AI coding agents or do they only fit certain workloads?
They work better for coding workloads. Essentially, the more regular the output, the more the faster model gets right, the less the big model has to do.
Writing tends to have more false positives. I haven't tried this particular one, however, but that is the general trend.
I think so, the benchmark is on a coding dataset (SPEED-Bench).