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@christopherkarani

Christopher Karani

christopherkarani

On-device AI shouldn't be this hard.
I'm Christopher — an iOS engineer with 12+ years of Swift experience, building open source tools that make on-device AI practical for Apple developers.
What I maintain:

Wax — Metal-accelerated RAG & memory layer for on-device AI agents
Swarm — AI agent orchestration framework for Swift
Hive — Deterministic graph runtime for agent workflows
Conduit — Unified LLM inference SDK across providers
Espresso — Swift inference runtime that bypasses Core ML to dispatch directly to Apple's Neural Engine (4.76x speedup)

Why sponsor?
Every dollar goes directly toward keeping these libraries maintained, documented, and pushed forward. I'm a solo founder doing this full-time — your sponsorship means I can keep shipping instead of context-switching.
If you're building with on-device AI on Apple platforms, you're probably already using (or about to use) something from this stack.

Featured work

  1. christopherkarani/Wax

    Single-file memory layer for AI agents, sub mili-second RAG on Apple Silicon. Metal Optimized On-Device. No Server. No API. One File. Pure Swift

    Swift 666
  2. christopherkarani/Swarm

    🐦‍🔥 A Lightweight Agent Orchestration Framework written in pure Swift 6.2

    Swift 393
  3. christopherkarani/Espresso

    Train and run transformers directly on Apple's Neural Engine — 4.76x faster than CoreML.

    Swift 71

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