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.

Interpreting User Feedback Signals for AI Agents

Fazm Team·March 18, 2026·3 min read

feedback ai-agent user-signals agent-memory improvement

Interpreting User Feedback Signals for AI Agents

When a user gives your AI agent a thumbs up, what does that actually mean? Does it mean "I agree with this output"? Or does it mean "I see you, I acknowledge this happened"? The difference matters enormously for how agents learn from feedback.

Most feedback systems treat every positive signal the same way - reinforcement. Every upvote trains the agent to do more of whatever it just did. But human feedback is far more nuanced than a binary good/bad signal.

The Ambiguity Problem

Consider a desktop agent that reorganizes your files. You approve the result. Did you approve because the organization was perfect? Or because it was good enough and you did not want to spend time correcting it? These are very different signals, but they look identical in a feedback log.

An agent that treats "good enough" as "perfect" will never improve beyond adequate. It needs to distinguish between enthusiastic approval and passive acceptance.

Signals Beyond Explicit Feedback

The most valuable feedback is often implicit. A user who undoes the agent's action within 30 seconds is giving stronger feedback than any rating system. A user who modifies the agent's output slightly is showing exactly what was wrong. A user who ignores the agent's suggestion entirely is saying something different from one who actively rejects it.

Desktop agents have a unique advantage here. They can observe what happens after their action. Did the user keep the file where the agent put it? Did they change the formatting the agent applied? Did they close the window the agent opened?

Building Better Feedback Loops

The practical approach is multi-signal interpretation. Combine explicit feedback with behavioral observation. Weight actions more heavily than ratings. Track patterns across sessions rather than reacting to individual signals.

A user who consistently moves files out of a particular folder is telling the agent something important - even if they never explicitly complained. An agent that notices this pattern and adjusts is one that actually learns from its users.

Feedback Decay

Not all feedback stays relevant. Preferences change. Workflows evolve. A feedback system needs decay built in so that signals from six months ago carry less weight than signals from last week.

Interpreting User Feedback Signals for AI Agents

Interpreting User Feedback Signals for AI Agents

The Ambiguity Problem

Signals Beyond Explicit Feedback

Building Better Feedback Loops

Feedback Decay

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