Guides
Practical, step-by-step explanations of how on-device AI works and how to get the most out of it — from quantization and context windows to running a model with no connection at all.
Which iPhones Can Run Local AI? A Device Compatibility Guide
Which iPhone and iPad models can run on-device language models, how much RAM each model actually requires, and how to work out what your specific device will run.
Private AI for Writers: Drafts That Stay Unpublished
Unfinished work belongs in one place. How novelists, journalists, and academics use on-device models without their manuscripts leaving the device.
Offline AI for Travel: Translation and Guidance With No Signal
A local model on your phone works on the plane, in the tunnel, and in the country where you have no data. What it does well while travelling, and what it can't replace.
Private AI Journaling: Thinking Out Loud Without an Audience
Journaling with a model that responds is genuinely useful — but only if the space is actually private. How it works, how to set it up, and where the limits are.
What People Actually Use Private AI For
The real workflows where on-device AI beats a cloud assistant — not because the model is better, but because you can use it without editing yourself first.
AI Chat With No Account, No Sign-Up, and No Subscription
Why almost every AI app wants an email address, what an account actually enables, and how on-device inference makes the whole requirement unnecessary.
On-Device Vision: How Your iPhone Reads Images Without Uploading Them
Multimodal models can describe, read, and analyse photos entirely on-device. Here's how vision models work, what they can and can't do at small sizes, and why the metadata question matters.
Thinking Mode: How Reasoning Models Work on Device
Reasoning models spend extra tokens working through a problem before answering. Here's what that buys you, what it costs, and when to turn it off.
Tokens Per Second: What AI Speed Numbers Actually Mean
Why benchmark speeds never match what you see, the difference between time-to-first-token and throughput, and the threshold where a local model stops feeling slow.
Context Windows Explained: How Much Your Local AI Remembers
What a context window is, why conversations slow down as they grow, what happens when you hit the limit, and how local models handle memory differently from cloud assistants.
LLM Quantization Explained: Why a 9B Model Fits in 5.6GB
Quantization is the compression that makes on-device AI possible. Here's what 4-bit weights actually mean, how much quality it costs, and why it makes models faster as well as smaller.
How LLMs Actually Run on Your iPhone
The complete technical explanation of on-device language models — unified memory, the Neural Engine, quantized weights, the KV cache, and why a 5.6GB model fits in your pocket at all.
On-Device AI: The Complete Guide to Running LLMs on Your iPhone
Everything you need to know about running large language models directly on your iPhone — how it works, which models run well, what the trade-offs are, and why it matters for your privacy.
What Is Apple MLX? The Framework Powering On-Device AI
Apple MLX is an open-source machine learning framework built specifically for Apple Silicon. Learn how its unified memory architecture enables fast, private on-device AI inference on iPhone and Mac.
How to Use AI Without Internet Access
A practical guide to using AI assistants with no internet connection — which apps work offline, how to set them up, and why going offline is often the smarter choice for sensitive conversations.
How to Run AI Completely Offline: A Practical Guide
A comprehensive guide to running AI locally on your devices — how offline inference works, what hardware you need, which use cases benefit most, and how to get started today without giving up your data.
Open Source AI Models: Why They Matter and How to Use Them
Open source AI models like Llama, Qwen, Mistral, and Gemma give anyone the ability to run capable, auditable language models without paying API fees or trusting a third party with their data. This guide covers what open source means for AI, who builds the models, how licensing works, and how to actually run them on your own hardware.
Small Language Models: Why Smaller Can Be Smarter
Small language models — models under 10B parameters — have gone from compromise to genuine alternative in two years. This post explains why they're improving faster than large models, how quantization and distillation work, and what it means for running capable AI privately on your phone.