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GitHub Trending
4/10

WhatRunsOnMyMac

An interactive, plain-English visual explainer that shows curious non-engineers exactly what happens when an AI model runs on their Mac versus in the cloud, and the privacy, cost and speed trade-offs.

Target user

Mac-using professionals and curious students who keep hearing "on-device AI" but don't know what it means

Features
  • Interactive CPU vs GPU vs Neural Engine visual walkthrough
  • "Can your Mac run this model?" local checker using real Apple Silicon specs
  • Privacy and energy trade-off simulator
  • Side-by-side monthly cost calculator: local vs cloud inference
Why now

Inference School is trending on GitHub, signalling mainstream curiosity about Apple-Silicon AI; meanwhile every Mac app now markets an "AI" feature and users can't tell what's local.

Signals · overall 4/10
Demand
4/10

Multiple comparison articles are being written (The Verge, KernelAI, Koombea, Stackviv, Naloseed) and the GitHub Inference School (134★) confirms rising curiosity, but no direct metric shows non-engineers are actively seeking a dedicated visual tool — most users rely on existing journalism.On-Device AI vs Cloud AI: Key Differences Explained (2026)Here's how Apple's AI model tries to keep your data private

Whitespace
3/10

Direct niche competitor ai-on-mac.com already publishes privacy matrices, original diagrams, comparisons, hardware guides, and a subscription, occupying the 'explain Apple/Local AI on Mac' position with multiple published articles dated through May 2026.Apple Intelligence vs. Local AI on Mac: Privacy ComparedLocal AI vs Cloud AI (2026): Benefits, Cost & Privacy Compared

Monetization
3/10

The closest competitor offers a subscription, proving some willingness to pay, but consumer educational explainers historically monetize weakly without a corporate audience; pure one-shot interactive content rarely converts.Apple Intelligence vs. Local AI on Mac: Privacy Compared

Longevity
6/10

The on-device vs cloud debate is durable while Apple ships Apple Intelligence features per macOS release and PCC evolves, but every visual/inference claim must be re-validated frequently, raising maintenance cost.On‑Device vs. Cloud AI: WWDC 25 vs Google I/O 25

Feasibility
6/10

Building a web-based interactive explainer with sliders/simulations is straightforward, but accurately modeling token-level routing between on-device, PCC, and ChatGPT paths requires ongoing research as Apple updates its stack.Apple Intelligence vs. Local AI on Mac: Privacy Compared

A hands-on Swift and Metal course for building LLM inference from first principles on Apple silicon, with 48 guided lessons, runnable exerci · ★ 134GitHub Trending · 2026-07-15 (9d ago)