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.

Running AI Agents as Actual Employees in Real Workflows

Fazm Team·March 17, 2026·2 min read

ai-agents workflow parallel-agents social-media code-review productivity

Running AI Agents as Actual Employees in Real Workflows

Right now I am running multiple Claude Code instances in parallel on the same codebase. Each one gets a discrete task - one handles social media engagement, another does PR reviews, a third is refactoring a module. This is not a demo or a proof of concept. This is the actual daily workflow.

The Task Assignment Pattern

Treat each agent like a junior employee. Give it a clear, scoped task with explicit success criteria. "Review the three open PRs and leave comments on code quality issues" is a good task. "Help with the project" is not.

The key insight is that agents work best on tasks that are parallelizable and have clear boundaries. Social media engagement does not depend on the PR review. The refactoring does not block the social media work. Each agent operates independently.

Social Media as an Agent Task

Social media engagement is surprisingly well-suited for AI agents. The agent reads relevant threads, drafts thoughtful responses, and posts them. It understands context from your CLAUDE.md about your product, your voice, and your positioning.

This is not automated spam. The responses are genuine, relevant, and add value to conversations. The agent is doing what you would do if you had three extra hours in the day.

PR Reviews as an Agent Task

Code review is another natural fit. The agent reads the diff, understands the project context, checks for common issues, and leaves meaningful comments. It catches things like missing error handling, inconsistent naming, and potential performance issues.

The important part is that a human still merges. The agent reviews and comments - a human makes the final call.

Coordination Between Agents

The main challenge is preventing agents from stepping on each other. Git worktrees help - each agent works in its own worktree on its own branch. For tasks that touch the same files, run them sequentially instead of in parallel.

A simple coordination rule works well - if two tasks might touch the same file, they cannot run simultaneously. Everything else is fair game for parallelization.

Running AI Agents as Actual Employees in Real Workflows

Running AI Agents as Actual Employees in Real Workflows

The Task Assignment Pattern

Social Media as an Agent Task

PR Reviews as an Agent Task

Coordination Between Agents

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