A search-and-inference database from scratch in pure Zig

In one line

Why we rewrote Antfly’s Go engine in Zig while the startup was still early: first principles, caring about the model instead of the embeddings, TigerBeetle-style simulation testing, and what the rewrite made possible.

Opening excerpt

Back to Research My colleague Rowan summarized our ambitious goal for Antfly over a year ago: perfect search! This goal is silly, ambitious, unobtainable, and perfect for us. Hearing grandiose technologists talk about perfect search still strikes me as something out of an episode of Silicon Valley , but I love an impossible goal, and my inner tech hipster appreciates the irony. Aim for perfect search and you miss, but you miss somewhere interesting. So I’ll walk you through why we did the thing you are never supposed to do with a new startup: we rewrote the product. Antfly v0.1 launched a document storage, full-text, vector, and graph indexing engine in Go. Antfly v0.2 launches the same engine in Zig, with zero dependencies (well, none that get to run the show, but more on that below).

I don’t hear people talking about first principles as often as I used to, but they’re still important to me and to how I make decisions. When building the first version of Antfly, the idea was to fill a gap in the database market: a schema-ish, friendly query engine for indexing like Elasticsearch, …

(Excerpted from the original; full article via the source link below.)

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