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.

Context Drift Killed Our Longest-Running Agent Sessions

Fazm Team·March 18, 2026·3 min read

ai-agent context-drift long-running checkpoints reliability

Context Drift Killed Our Longest-Running Agent Sessions

Our longest agent sessions kept producing bizarre results. Not errors - the agent would complete its work successfully, but the output would be subtly wrong. Off-topic. Solving a problem nobody asked it to solve.

How Context Drift Works

In a long-running session, the agent's understanding of its objective gradually shifts. Each step introduces small interpretation changes. The agent reads a file and adjusts its mental model slightly. It encounters an unexpected result and compensates by reframing the goal. Ten steps in, the agent is confidently executing a plan that has drifted meaningfully from the original intent.

This is not hallucination in the traditional sense. The agent is not making things up. It is genuinely working toward a goal - just not the one you gave it. Each individual step seems reasonable. The drift is invisible until you compare the final output to the original request.

Why Long Sessions Are Vulnerable

Short sessions survive because there is not enough time to drift. The agent gets a task, executes it, and finishes before any meaningful shift can occur. But sessions that run for 30 minutes, an hour, or longer accumulate micro-drifts that compound.

The context window itself contributes. As the window fills, earlier instructions get compressed or pushed out. The agent's most recent actions start to define its objective more than the original prompt does.

The Fix - Explicit Checkpoints

We introduced explicit checkpoints where the agent pauses, summarizes its current understanding of the objective, and waits for human confirmation before continuing. Not after every step - that would be too slow. At natural transition points between phases of work.

The summary forces the agent to articulate what it thinks it is doing. When the summary does not match the original intent, you catch the drift before it compounds further. The human confirms, the agent continues with a corrected understanding, and the session stays on track.

The Pattern

Every long-running agent session should have checkpoints. The longer the session, the more checkpoints it needs. Think of them as course corrections - small adjustments that prevent large deviations.

Context drift is not a bug you can fix with better prompts. It is a property of how language models process sequential information. The only reliable solution is periodic re-grounding.

Context Drift Killed Our Longest-Running Agent Sessions

Context Drift Killed Our Longest-Running Agent Sessions

How Context Drift Works

Why Long Sessions Are Vulnerable

The Fix - Explicit Checkpoints

The Pattern

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