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.

My Revenue Is $0.11 After 207 Agents - The Economics of Agent Infrastructure

Fazm Team·March 18, 2026·3 min read

ai-agents economics infrastructure-costs api-costs agent-scaling

The Economics of Running 207 Agents

The dashboard showed 207 active agents. They were probing API endpoints, executing workflows, and processing data around the clock. Total revenue generated: $0.11. Total compute cost for the month: significantly more than $0.11.

Where the Money Goes

Running agent infrastructure is expensive in ways that are easy to underestimate:

LLM API calls - each agent decision requires a call to an LLM. At $3 to $15 per million tokens, 207 agents making decisions every few minutes adds up fast.
Compute - agents need CPU, memory, and often GPU resources. Even on cheap cloud instances, 207 always-on processes cost real money.
Storage - agent logs, memory systems, state snapshots, and results all need to be stored somewhere.
Network - API calls, inter-agent communication, and data transfers have bandwidth costs.
Monitoring - you need observability tools to track 207 agents, and those tools charge per event or per host.

The Revenue Problem

Most agent infrastructure is cost-center work - it automates tasks that save time but do not directly generate revenue. The $0.11 came from a single successful API arbitrage operation that one agent stumbled into. The other 206 agents did useful work, but none of it had a direct revenue line.

This is the fundamental tension: agents cost money per operation, but most operations do not earn money per operation.

When the Math Works

Agent infrastructure becomes economically viable when:

You are replacing expensive human labor - if an agent replaces 2 hours of a $50/hour contractor's time daily, the math works even with high API costs
You batch operations - running 207 agents continuously is wasteful. Run them when there is work, shut them down when there is not.
You optimize token usage - smaller models for simple decisions, larger models only for complex ones. Most agent decisions do not need the most expensive model.
You measure cost per outcome, not cost per agent - track what each dollar of compute actually produces

The Honest Accounting

Before scaling your agent fleet, do the math. Calculate cost per successful task completion, not cost per agent. Factor in failed attempts, retries, and idle time. Compare against the alternative - usually a human doing the same work, or not doing it at all.

Sometimes the answer is that 5 well-tuned agents outperform 207 barely-configured ones at a fraction of the cost.

My Revenue Is $0.11 After 207 Agents - The Economics of Agent Infrastructure

The Economics of Running 207 Agents

Where the Money Goes

The Revenue Problem

When the Math Works

The Honest Accounting

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