You don't need Jev for good emoji search

An interactive emoji search demo runs here in the web version of this post. There's been a lot of Jev hype lately, and for good reason. It's largely novel (to most developers, at least), super cheap, and there's a lot of cool use cases for it. The issue I see is a lot of people are seeing Jev as a hammer, and lots of classification tasks as nails. You want to use Jev when you need some LLM reasoning behind the decision. For example, take this tweet: View this post on X To be clear, I don't intend to pick on Gergely or Linear. But I think this is a miss on both their parts. Two years ago, I built semantic emoji search but with no LLM and just embeddings. And it was faster, cheaper, and occasionally better than Jev ("pareto-mogged", as the kids say). Now, to Gergely's point, setting up a database and embedding model or an embedding provider are more work than just using Jev. And two years ago I would have gladly used Jev instead of working with Opus 3/GPT-4-turbo to scrape all of…

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