How much time will I actually save with Fazm?

Most users report saving 45-90 minutes per day on repetitive tasks. The biggest time savings come from email management (drafting replies, sorting, archiving), form filling (expense reports, CRM updates, applications), research (comparing products, gathering data from multiple sites), and social media (cross-posting, scheduling, replying). For example, booking a flight that normally takes 10 minutes of clicking through airline sites takes a single voice command with Fazm. Filing an expense report that requires opening a PDF, extracting numbers, and entering them into a spreadsheet becomes one spoken sentence. The more repetitive and multi-step your daily work is, the more time you get back.

What exactly can Fazm control?

Fazm controls everything you can do manually on your Mac. That includes mouse movements and clicks, keyboard typing, browser navigation and form filling, app switching, file management in Finder, and system-level actions. It uses direct browser DOM control for web interactions - meaning it reads and manipulates the actual page structure rather than taking screenshots and guessing where to click. For native macOS apps, Fazm uses accessibility APIs to interact with buttons, menus, text fields, and other UI elements. This works across any application: Chrome, Safari, VS Code, Slack, Figma, Terminal, Mail, Calendar, and more.

How is Fazm different from ChatGPT or Claude?

Chatbots like ChatGPT and Claude give you answers in a conversation window. Fazm takes action directly on your computer. When you say 'reply to Sarah's email,' Fazm opens your email app, finds Sarah's message, writes a contextual reply, and sends it. A chatbot would tell you what to write, but you still have to do all the clicking and typing yourself. Fazm moves your mouse, types on your keyboard, clicks buttons, and navigates between apps. It is a computer agent, not a chat assistant.

How does the memory layer work?

Fazm builds a personal knowledge graph by extracting information from your files, browsing history, conversations, and daily activity. Over time, it learns your contacts (names, emails, relationships), accounts and credentials, preferences (formatting, tone, scheduling habits), and frequently used workflows. In your first week, you might say 'Reply to Sarah's email, she's my cofounder, her email is sarah@acme.com.' By week four, you just say 'Reply to Sarah' because Fazm already knows who she is. By week eight, Fazm can draft the reply before you even ask. All of this data stays locally on your Mac - your knowledge graph never leaves your machine or gets sent to any cloud service.

Is Fazm safe? Can it mess up my computer?

Safety is built into every layer of Fazm. You watch every action in real-time on your screen - Fazm moves your mouse and types visibly, so there is nothing hidden. You can stop any action at any point by pressing a keyboard shortcut. Fazm also shows you what it plans to do before executing destructive actions like deleting files or sending emails, giving you a chance to confirm or cancel. Because Fazm is open source, the code is fully auditable on GitHub. There is no hidden behavior, no background processes making changes you cannot see, and no actions that bypass your screen.

What platforms does Fazm support?

Fazm currently supports macOS, running natively on both Apple Silicon (M1, M2, M3, M4) and Intel Macs. It is built with Swift and uses macOS-specific frameworks like ScreenCaptureKit for screen analysis and Accessibility APIs for app control, which is why macOS is the primary platform. Windows and Linux support are on the roadmap but do not have release dates yet. You can also control your Mac remotely from your phone using Fazm Remote, which gives you voice access to your desktop while you are away from your computer.

How does Fazm voice input work?

One keyboard shortcut activates push-to-talk. Hold the key, speak naturally about what you need done, and release. Fazm transcribes your speech locally using on-device models, so there is no network latency for the voice recognition step. You do not need wake words like 'Hey Siri' or 'OK Google' - just press and talk. You can also type commands into the floating toolbar if you prefer text input. Fazm understands natural language, so you do not need to memorize specific commands. Say 'book me a flight to Tokyo next Thursday' or 'find the cheapest AirPods on Amazon' and Fazm figures out the steps.

Is my data sent to the cloud?

Fazm processes screen analysis and memory entirely on your machine. The personal knowledge graph, file indexing, and screen capture all run locally. The only data that leaves your Mac is the intent - a text description of what you want to accomplish, which is sent to the AI model (Claude or GPT) for action planning. Your actual screen content, file contents, browsing history, and personal knowledge graph never leave your computer. Fazm is also open source, so you can verify exactly what data is transmitted by reading the code on GitHub. For users who need complete air-gapped operation, local model support is planned.

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Build a Local-First AI Agent with Ollama - No API Keys, No Cloud, No Signup

Fazm Team·March 11, 2026·3 min read

ollamalocal-firstprivacymacostutorial

Build a Local-First AI Agent with Ollama

The most common friction point with AI tools is setup. Create an account. Add a credit card. Generate an API key. Configure rate limits. Handle billing alerts.

What if you could skip all of that?

With Ollama running on your Mac, you can run AI models locally with zero cloud dependency. No account. No API key. No credit card. No data leaving your machine. Just download and run.

The Setup

# Install Ollama
brew install ollama

# Pull a model
ollama pull qwen2.5:14b

# It is running
ollama list

That is the entire setup. The model runs on your Apple Silicon GPU. Inference stays on your machine. Your data never touches a remote server.

What Works Well Locally

For desktop automation tasks - the kind where an agent fills in forms, navigates apps, and executes multi-step workflows - local models in the 7-14B range are surprisingly capable. They handle:

Action planning. "Open Safari, go to this URL, click this button" - straightforward sequences that smaller models handle reliably.
Text extraction. Reading structured data from screen content and reformatting it.
Simple reasoning. Deciding which app to open, which field to fill, what value to enter.

Where local models struggle:

Complex multi-step reasoning. A 20-step workflow with branching logic might need a larger model.
Nuanced writing. Drafting a sensitive email or crafting a specific tone - cloud models are still better here.
Vision tasks. Local vision models exist but are significantly behind cloud offerings.

The Hybrid Approach

You do not have to choose one or the other. Fazm supports both local models via Ollama and cloud models like Claude. The practical approach:

Local for routine tasks. Form filling, app navigation, file organization - run these on Ollama with zero latency and complete privacy.
Cloud for complex tasks. Multi-step reasoning, nuanced text generation, vision-heavy workflows - use Claude when accuracy matters more than privacy.
Your choice, per task. There is no reason to commit to one approach for everything.

Getting Started with Fazm + Ollama

Install Ollama and pull a model
Download Fazm and build it
Set the model provider to Ollama in settings
Start automating - fully local, fully private, no API keys

Fazm supports both Ollama (local) and Claude (cloud) for maximum flexibility. Open source on GitHub. Discussed in r/ollama.

Build a Local-First AI Agent with Ollama - No API Keys, No Cloud, No Signup

Build a Local-First AI Agent with Ollama

The Setup

What Works Well Locally

The Hybrid Approach

Getting Started with Fazm + Ollama

Related Posts

Why Local-First AI Agents Are the Future (And Why It Matters for Your Privacy)

On-Device AI on Apple Silicon - What It Means for Desktop Agents

Local LLMs Are Not Just for Inference Anymore - Real Workflows on Your Machine