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.

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How to Cut AI Agent Costs 50-70% with Model Routing

Fazm Team·March 17, 2026·2 min read

model-routingcost-reductionollamaclaudeoptimization

How to Cut AI Agent Costs 50-70% with Model Routing

Running every agent action through Claude Opus is expensive. A single complex workflow might involve 50+ LLM calls - reading screen state, deciding what to click, verifying results, handling errors. At API pricing, a heavy user can burn through $20-50 per day easily.

Most of those calls do not need a frontier model. The trick is figuring out which ones do.

Simple Routing Rules

Start with task complexity. Reading a button label and confirming it matches what you expected? That is a local model task. Llama 3 running on Ollama handles simple classification and extraction just fine, and the latency is lower than an API call.

Planning a multi-step workflow that requires understanding context from three different applications? That is Claude territory. The reasoning quality difference between local and frontier models shows up most on tasks that require holding multiple pieces of context simultaneously.

A basic router looks at the task type and routes accordingly:

Screen element identification - local model
Simple yes/no decisions - local model
Text extraction and formatting - local model
Multi-step planning - frontier model
Error recovery and replanning - frontier model
Complex form filling with context - frontier model

State Summarization

The other cost lever is context size. After each step, summarize the current state instead of carrying the full conversation history. An agent that has been running for 20 steps does not need the raw screenshots and tool outputs from step 3. It needs a one-line summary: "Logged into Salesforce, navigated to the Contacts page."

This cuts token usage dramatically. A 50-step workflow with full context might use 500K tokens. With summarization after every 5 steps, you can keep it under 100K.

Context Pruning

Not everything in the agent's context is relevant to the current step. Prune aggressively. If the agent is filling out a form, it does not need the accessibility tree of the menu bar. If it is reading an email, it does not need the state of the sidebar.

Combine routing, summarization, and pruning and you can realistically cut costs by 50-70% without meaningful quality loss on the tasks that matter.

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

How to Cut AI Agent Costs 50-70% with Model Routing

How to Cut AI Agent Costs 50-70% with Model Routing

Simple Routing Rules

State Summarization

Context Pruning

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