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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Pair Programming with AI - Write the Spec First, Approve the Plan

Fazm Team·March 17, 2026·3 min read

pair-programmingai-codingspecworkflowplanningcode-review

Pair Programming with AI - Write the Spec First

The developers getting the best results from AI coding tools all follow the same pattern. They do not just type "build me a login page" and hope for the best. They write a short spec, let the agent propose a plan, and approve step by step.

This is not slower. It is dramatically faster than fixing bad code after the fact.

Step 1: Write a Short Spec

The spec does not need to be long. Two to five sentences that cover:

What the feature should do
What files or components are involved
Any constraints or preferences (e.g., "use the existing auth middleware, do not add new dependencies")

This takes about 60 seconds and prevents the most common failure mode: the agent building something that works but is completely wrong for your architecture.

Step 2: Let the Agent Propose a Plan

Before writing any code, ask the agent to outline its approach. What files will it modify? What is the implementation strategy? Are there any edge cases it is considering?

This is where you catch misunderstandings. Maybe the agent plans to refactor a module you do not want touched. Maybe it assumes a database schema that does not exist. Catching this at the plan stage costs nothing. Catching it after 500 lines of code costs everything.

Step 3: Approve Step by Step

Once the plan looks right, let the agent execute - but review each meaningful step. Not every line of code, but each logical change. "I am going to add a new route handler" - yes, go ahead. "I am going to modify the database migration" - wait, let me see that first.

This gives you control without micromanaging. The agent does the typing. You do the thinking.

Why This Works

The spec-plan-approve pattern works because it matches how good engineering teams operate. A senior developer does not write code without understanding the requirements. They do not start implementation without a plan. And they get their approach reviewed before committing to it.

AI pair programming is the same process - just faster. You think, the agent types, and together you ship features in a fraction of the time.

Pair Programming with AI - Write the Spec First, Approve the Plan

Pair Programming with AI - Write the Spec First

Step 1: Write a Short Spec

Step 2: Let the Agent Propose a Plan

Step 3: Approve Step by Step

Why This Works

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