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.

Does Fazm work in languages other than English?

Yes. Fazm works in any language - English, Russian, Spanish, German, Japanese, and more. You can speak or type in your preferred language and Fazm will understand and act. Some of our most active users run their entire workday in Russian, issuing voice commands to manage Jira tickets, write Slack messages, and navigate internal tools. There is no language setting to configure - just speak or type naturally and Fazm handles it.

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.

Using Desktop UI Agents to Validate Automation Before Building Custom APIs

Fazm Team·March 18, 2026·3 min read

desktop-agent automation api-development mcp validation

Validate with a UI Agent Before Building the API

When you want AI agents to run a logistics platform - or any complex system - the instinct is to build custom API integrations and MCP servers first. That instinct is wrong. Start with a desktop UI agent.

The UI-First Approach

A desktop agent that clicks through your existing web interface does exactly what a human operator does. It logs in, navigates to the right page, fills in forms, clicks buttons, and reads results. No API integration needed.

This sounds hacky, and it is. But it answers the most important question first: does this workflow actually benefit from automation?

Building a custom API for a workflow that turns out to save five minutes a day is wasted engineering. A UI agent prototype takes hours to set up, not weeks, and tells you immediately whether the automation is worth investing in.

What You Learn from the Prototype

Running a UI agent against your real interface reveals things that API planning misses:

Edge cases in the workflow - forms that conditionally show fields, confirmation dialogs, error states
Actual time savings - measured, not estimated
Failure modes - what happens when the page loads slowly, when data is missing, when the workflow changes
Which steps are truly repetitive vs which require human judgment

This information makes your eventual API integration dramatically better because you've already mapped the real workflow.

When to Graduate to APIs

Move from UI automation to proper API integration when:

The UI agent runs the workflow successfully 50+ times
The time savings justify the engineering investment
You need speed - UI automation is inherently slower than API calls
The workflow is stable and unlikely to change

At that point, build your MCP server or direct API integration. You'll know exactly which endpoints you need, what data flows where, and what error handling is required.

The Pragmatic Path

UI agent first, validate the automation, then build the proper integration. This order minimizes wasted engineering and maximizes learning. The opposite order - building APIs for workflows you haven't validated - is how you end up with beautiful integrations that nobody uses.

Fazm is an open source macOS AI agent. Open source on GitHub.

Using Desktop UI Agents to Validate Automation Before Building Custom APIs

Validate with a UI Agent Before Building the API

The UI-First Approach

What You Learn from the Prototype

When to Graduate to APIs

The Pragmatic Path

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