Chris Karani

I built Wax, a single-file crash-safe memory engine for AI agents on Apple Silicon: WAL durability, hybrid RAG, bitemporal facts, no cloud dependency.

I built Swarm's Swift web tool for 4,096-token on-device LLMs.

A 3B on-device model with a 4K context window produced a 2,336-word grounded research report using live web search.

Built EdgeRunner in pure Swift and Metal from scratch over a weekend.

A throughput research postmortem from Espresso: what we tested, what failed, what stayed slower than baseline, and where the real architectural blockers are.

How local telemetry transforms AI reliability, privacy, and performance for applications running on edge devices.

We fully documented EdgeRunner, a Metal LLM inference engine for Apple Silicon.

We built ContextCore to manage conversation context on Apple Silicon without cloud or battery drain.

If you've shipped AI features in an iOS app recently, you know the drill.

After building a multi-agent framework in Swift, I have thoughts on why it might actually be a better fit than Python or TypeScript for this kind of work.

Colony is a Swift framework that orchestrates AI agent loops on iOS and macOS using Apple's Foundation Models.

How chain-of-thought prompting and simple techniques transformed large language models from pattern-matchers into genuine problem-solvers.

Exploring the wild probability estimates from AI researchers, the behavioral and architectural evidence of machine consciousness, and why this might be the biggest moral challenge of our era.

Why the serious agent stacks are converging on files as the primitive for machine behavior, and what that means for the future of AI systems.