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.

Does Fazm work in languages other than English?

Yes. Fazm works in any language - English, Russian, Spanish, German, Japanese, and more. You can speak or type in your preferred language and Fazm will understand and act. Some of our most active users run their entire workday in Russian, issuing voice commands to manage Jira tickets, write Slack messages, and navigate internal tools. There is no language setting to configure - just speak or type naturally and Fazm handles it.

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.

Wonder Behind a Load Balancer - Routing Models by Task Complexity

Matthew Diakonov·March 18, 2026·2 min read

load-balancing model-routing task-complexity cost-optimization ai-agents

Wonder Behind a Load Balancer - Routing Models by Task Complexity

Not every task needs the most powerful model. Renaming a file does not require the same reasoning capacity as refactoring a distributed system. Yet most AI agents send every request to the same model, paying premium prices for trivial tasks.

The Routing Principle

A load balancer for AI models routes requests based on task complexity:

Simple tasks (file operations, formatting, data extraction) go to fast, cheap models
Medium tasks (code generation, content writing, data analysis) go to mid-tier models
Complex tasks (architecture decisions, debugging, multi-step reasoning) go to the most capable model

This is not theoretical. Teams implementing model routing report 60-80% cost reductions with minimal quality degradation.

How to Classify Task Complexity

The challenge is determining complexity before sending the request. Practical heuristics:

Token count - shorter prompts tend to be simpler tasks
Tool requirements - tasks needing multiple tools are usually more complex
Domain signals - "rename this file" vs "refactor this module" are clearly different tiers
Historical data - if similar past requests were handled well by a cheaper model, route there

You do not need perfect classification. Even a rough split captures most of the savings.

Implementation for Desktop Agents

A desktop agent handles a wide range of task complexities in a single session. Opening an app is trivial. Debugging why that app is crashing requires deep reasoning. A well-implemented agent should:

Classify each action by complexity
Route to the appropriate model
Escalate to a more capable model if the cheaper one fails
Track which model handled which task for future optimization

The result is an agent that feels just as capable but costs a fraction of what a single-model approach would.

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

Wonder Behind a Load Balancer - Routing Models by Task Complexity

Wonder Behind a Load Balancer - Routing Models by Task Complexity

The Routing Principle

How to Classify Task Complexity

Implementation for Desktop Agents

More on This Topic

Related Posts

Multi-LLM Agent Routing - Using Different Models for Different Subtasks

Tips for Secondary Models - When to Use Haiku vs Opus in AI Agents

Optimizing 23 AI Agent Cron Jobs from $14/Day to $3/Day