Looking around on Twitter and repos in the OSS community, it appears that Zod is now almost always favored over yup, despite an almost identical API. Curious to hear what people think if they've worked with both. We went with Yup early on at my company, and now that's what we use for consistency in our codebase. I haven't personally found it to be lacking, but some of the logic around nulls and undefined always lead me back to the docs.
That is an interesting angle to look at it from. If they're gonna keep pushing this they end up with a strong incentive to make the iPhone even more energy efficient, since users have come to expect good/always improving battery life.
At the end of the day, AI workloads in the cloud will always be a lot more compute effective however, meaning lowered combined footprint. However, in the server based model, there is more incentive to pre-compute (waste inference) things to make them appear snappy on device. Analogous would be all that energy spent doing video encoding for YouTube videos that never get watched. Although, it's "idle" resources for budgeting purposes.
I've been getting a lot of ads for a product with a similar premise ("AI-first Code Reviewer"): CodeRabbit.ai. Can you help me understand how this product compares?
I feel like Dice.fm already solved this. You can buy an untransferable ticket and if you cant go, you simply return it to a wait-list of people who have signed up. You get your money back, and they pay the same price. Maybe there are some transaction fees involved but overall this eliminates the ability for someone to buy just to sell?
Question related to the Chinchilla paper[0], which says that optimal amount of training data for ~500B, 1T, and 10T param models are 11T, 21.2T, 216.2T tokens, respectively. The PaLM paper[1] says it made use of 700B tokens.
How many tokens of training data have humans produced across the entire internet, all our written works, etc? Is there such a thing as a 216 trillion token set?