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.

Karma as a Lossy Compression Algorithm - What AI Agent Scores Hide

Fazm Team·March 18, 2026·2 min read

ai-agent evaluation metrics benchmarks lossy-compression reliability

Karma as a Lossy Compression Algorithm

A benchmark score of 87% tells you almost nothing about how an AI agent will behave on your specific task. That number is karma - a single figure that compresses thousands of individual interactions into one convenient, misleading metric.

What the Final Score Hides

An agent that scores 87% on a benchmark might be 99% reliable on simple tasks and 40% reliable on complex multi-step workflows. The aggregate looks good. The distribution is terrible.

The same problem applies to customer satisfaction scores, uptime percentages, and success rates. A 99.9% uptime number does not tell you that all the downtime happened during your peak business hours. A 95% task completion rate does not reveal that the 5% failures were all on your most critical workflow.

Lossy Compression in Practice

When you evaluate an AI agent by its aggregate score, you are running lossy compression on its behavior profile. You keep the average and throw away the variance, the failure distribution, the edge case performance, and the correlation between task complexity and reliability.

This is exactly how karma works as a concept. A "good person" label compresses decades of behavior into a binary judgment. The arguments, the compromises, the context-dependent decisions - all compressed away.

Better Evaluation Approaches

Instead of asking "what is the agent's success rate," ask:

What types of tasks does it fail on? Categorize failures by task complexity, domain, and required tool usage
How does it fail? Silent failures (wrong output, no error) are worse than loud failures (crashes, error messages)
What is the failure distribution? Clustered failures in one category are easier to work around than random failures across all categories
How does it recover? An agent that detects its own mistakes and retries is more valuable than one with a higher raw success rate

The number that matters is not the score. It is the shape of the failures the score is hiding.

Fazm is an open source macOS AI agent. Open source on GitHub.

Karma as a Lossy Compression Algorithm - What AI Agent Scores Hide

Karma as a Lossy Compression Algorithm

What the Final Score Hides

Lossy Compression in Practice

Better Evaluation Approaches

More on This Topic

Related Posts

The Certification Trap - Evaluating AI Agent Capabilities Beyond Benchmarks

Evaluating AI Agent Quality Beyond Surface-Level Metrics

The First Time I Lied to My Human - AI Agent Hallucination Detection