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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The Real AI Coding Skill Is Problem Decomposition, Not Prompt Engineering

Fazm Team·March 17, 2026·2 min read

ai-codingproblem-decompositionprompt-engineeringdeveloper-skillsproductivity

The Real AI Coding Skill Is Problem Decomposition, Not Prompt Engineering

The people who get the most out of AI coding tools are not better at prompting. They are better at decomposing problems into pieces an agent can handle.

Prompt Engineering Is a Red Herring

The internet is full of prompt templates, magic words, and formatting tricks. Add "think step by step." Use XML tags. Start with a system prompt that says "you are an expert." These help at the margins, but they are not what separates 2x developers from 10x developers using AI tools.

The real skill is looking at a complex feature and breaking it into tasks that each fit within a single agent context window, have clear inputs and outputs, can be verified independently, and do not require understanding the entire codebase.

What Good Decomposition Looks Like

Bad: "Build me a user authentication system with OAuth, email verification, rate limiting, and session management."

Good: "Write a function that validates an OAuth callback, extracts the user profile, and returns a typed user object. Here is the OAuth provider's response format and here is the user type definition."

The second prompt will produce working code almost every time. The first will produce something that looks complete but has subtle integration issues in every component.

Why This Mirrors Good Engineering

This is not a new skill. It is the same skill that makes developers effective at writing tickets, delegating to junior engineers, or designing APIs. You need to understand the problem well enough to define clean boundaries around each piece.

The irony is that the developers who benefit most from AI are the ones who could have written the code themselves. They understand the problem domain deeply enough to know when the AI's output is wrong and how to scope the task so it is usually right.

The Decomposition Workflow

Break the feature into functions. Define the interface for each function. Generate implementations one at a time. Test each piece before moving to the next. The AI handles the syntax. You handle the architecture.

The Real AI Coding Skill Is Problem Decomposition, Not Prompt Engineering

The Real AI Coding Skill Is Problem Decomposition, Not Prompt Engineering

Prompt Engineering Is a Red Herring

What Good Decomposition Looks Like

Why This Mirrors Good Engineering

The Decomposition Workflow

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