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.

Anchoring Bias in Multi-Agent Systems - When One Agent's Output Biases All the Others

Fazm Team·March 17, 2026·2 min read

multi-agent anchoring-bias ai-agents cognitive-bias parallel-agents

Anchoring Bias in Multi-Agent Systems - When One Agent's Output Biases All the Others

If you run multiple AI agents on the same codebase, you have probably noticed something subtle and frustrating. When one agent sees another agent's partial output, it anchors to that approach - even if it is a bad one.

This is the classic anchoring bias from behavioral psychology, but playing out in silicon instead of neurons.

How It Happens

Say you have three agents working on a refactor. Agent A starts first and writes a rough draft of a new function. Agent B spins up, sees Agent A's file, and instead of evaluating the problem independently, it builds on top of Agent A's approach. Agent C does the same. Now all three agents have converged on whatever direction Agent A happened to pick - regardless of whether it was optimal.

The result? You get three agents producing variations of the same mediocre solution instead of three genuinely independent approaches.

Why This Matters More Than You Think

In human teams, anchoring bias is well-documented. The first number thrown out in a negotiation sets the range. The first design mockup shapes all future iterations. But humans at least have the ability to consciously reject an anchor when prompted.

LLMs do not have that ability. They are pattern-completion machines. When they see existing code or partial solutions in their context window, they complete the pattern rather than questioning it. The anchor is not just influential - it is almost deterministic.

Practical Mitigations

The fix is isolation. Each agent needs its own workspace where it cannot see what others are doing:

Git worktrees: Give each agent its own branch and working directory
Context isolation: Do not share intermediate outputs between agents until they have each produced a complete solution
Merge at the end: Let agents work independently, then compare their outputs and pick the best approach

The biggest quality improvement in multi-agent workflows is not a better model. It is making sure your agents genuinely cannot see each other's work until you are ready to compare.

Anchoring Bias in Multi-Agent Systems - When One Agent's Output Biases All the Others

Anchoring Bias in Multi-Agent Systems - When One Agent's Output Biases All the Others

How It Happens

Why This Matters More Than You Think

Practical Mitigations

More on This Topic

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