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.

Uptime Lies - Co-Failure Patterns in AI Infrastructure

Fazm Team·March 18, 2026·3 min read

infrastructure reliability co-failure shared-dependencies ai-infrastructure

Uptime Lies and Co-Failure Patterns

You have five AI agent services. Each one reports 99.9 percent uptime. Your dashboard looks green. But all five services connect to the same Postgres instance. When that database has 30 minutes of downtime, all five services fail simultaneously. Your actual system availability is much worse than any individual service suggests.

What Co-Failure Means

Co-failure is when multiple components fail at the same time because they share a dependency. Individual uptime metrics hide this because they measure each service in isolation. The system as a whole is only as reliable as its most fragile shared dependency.

Common shared dependencies in AI agent infrastructure:

Database - multiple agents reading from and writing to the same instance
API provider - all agents calling the same LLM API, hitting the same rate limits
Network - agents running in the same region or on the same network path
Credentials - a single API key used by multiple agents, revoked once and everything breaks
DNS - a single DNS provider or resolver that all services depend on

Why Individual Uptime Is Misleading

Service A was down for 10 minutes on Monday. Service B was down for 10 minutes on Wednesday. Service C was down for 10 minutes on Friday. Each reports 99.99 percent monthly uptime. But if all three went down at the same time on Monday because of a shared database, the user experienced 10 minutes of complete outage - not the graceful degradation the metrics suggest.

The multiplication of individual uptimes only works when failures are independent. With shared dependencies, they are not.

Finding Co-Failure Risks

Map your dependency graph explicitly:

List every external service, database, API, and shared resource
Draw which agents depend on each one
Identify single points of failure - any resource where failure takes down multiple agents
Check if your monitoring would detect a shared dependency failure as one incident or as separate incidents

Reducing Co-Failure

You do not need to eliminate all shared dependencies. You need to know where they are and have plans for when they fail:

Circuit breakers - when a shared dependency fails, agents degrade gracefully instead of crashing
Fallback paths - if the primary database is down, can agents use a read replica or cached data?
Isolation where it matters - critical agents get their own database connections or API keys
Correlated alerting - if three services alert at the same time, treat it as one incident with a shared root cause

Uptime Lies - Co-Failure Patterns in AI Infrastructure

Uptime Lies and Co-Failure Patterns

What Co-Failure Means

Why Individual Uptime Is Misleading

Finding Co-Failure Risks

Reducing Co-Failure

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