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.

AI Scientific Research - Marine Biology, VARS-ML & FathomNet

Scientific research generates enormous amounts of specialized data that requires navigating domain-specific databases, reading technical documentation, and synthesizing findings across multiple sources. Fazm is an AI desktop agent for macOS that assists researchers working with specialized systems like VARS-ML, FathomNet, and other domain-specific databases.

The Hidden Time Cost of Domain-Specific Research

Researchers working with specialized scientific databases face a unique challenge: the tools and data systems they use are often highly domain-specific, requiring significant context to use effectively. Marine biology databases like VARS-ML and FathomNet contain millions of annotations and localizations, but understanding how to navigate, query, and interpret their data structures takes time that could otherwise go toward actual analysis.

MBARI's VARS-ML system, for example, now contains over 1.5 million localizations - deep-sea video annotations built up over decades of ROV dives. FathomNet serves as the public-facing distribution layer for this kind of data, allowing the broader research community to access, search, and contribute annotations. Understanding the relationship between these systems, how to access specific datasets, and how to interpret the data formats requires reading through multiple documentation sources and often experimenting with queries.

Fazm accelerates this orientation process. It can navigate these systems, read their documentation, extract key structural information, and help you understand the data landscape before you spend hours going down the wrong path.

Scientific Research Tasks You Can Give Fazm

✓"Search MBARI VARS-ML and read up on this dataset"

✓"What is the relationship between VARS-ML and FathomNet?"

✓"Find the latest localization statistics for this species"

✓"Read this research paper and summarize the methodology"

✓"Search Google Scholar for papers on deep-sea annotation systems"

✓"Document this research workflow from the terminal output"

These are based on real research workflows Fazm users have brought to it. Fazm handles specialized terminology and database systems the same way it handles general web research.

How Fazm Assists with Scientific Research

Orient to the data landscape

Tell Fazm about the databases or systems relevant to your research. It will navigate to the relevant portals, read documentation, and build an understanding of the data structures available - so you can ask informed follow-up questions.

Search and extract specific data

Fazm can query databases through their web interfaces, read search results, extract statistics and dataset descriptions, and compile what it finds. For systems like FathomNet, it navigates the search interface and reads annotation details.

Synthesize across multiple sources

Good scientific research requires triangulating information. Fazm can cross-reference what it finds in one database against documentation from another source, papers in Google Scholar, or existing notes you have shared with it.

Create structured outputs

Fazm can organize research findings into structured summaries, comparison tables, or annotated lists. For longer research sessions, it can draft a written summary document that captures key findings, data sources, and open questions.

Why Researchers Use Fazm for Domain-Specific Work

No pre-built integrations needed

Fazm works with any database or research portal accessible through a browser. It does not need an API or plugin. If you can open it in Chrome on your Mac, Fazm can navigate and read it.

Handles technical terminology

Fazm can work with domain-specific language - species names, annotation systems, database schemas, and research methodology terminology. It reads and interprets specialized documentation accurately.

Runs locally and privately

All research processing happens on your Mac. Sensitive unpublished data, proprietary datasets, or confidential research details do not leave your machine during a Fazm session.

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Frequently Asked Questions

Can Fazm work with highly specialized scientific databases?

Yes. Fazm works with any website or database accessible through a browser on your Mac. It does not require pre-built integrations. Real researchers have used it to navigate MBARI's VARS-ML system and FathomNet, reading documentation and data structures that would take significant time to parse manually.

What is the difference between VARS-ML and FathomNet?

VARS-ML is MBARI's internal annotation and data system for deep-sea video footage, containing over 1.5 million localizations. FathomNet is the public-facing platform for sharing ocean imagery and annotations with the broader research community. VARS-ML data is the source; FathomNet is the public distribution layer.

How does Fazm help with scientific literature review?

Fazm can search Google Scholar, PubMed, and domain-specific databases, open papers, read abstracts and key sections, extract methodology and findings, and compile a structured literature summary. For large review projects, it can work through dozens of papers and organize findings by theme.

Can Fazm help document and annotate research data workflows?

Yes. Fazm can read existing workflow documentation, extract the steps it describes, and help create structured summaries or process documentation. It can also read terminal output from data processing pipelines and help interpret results.

Accelerate Your Scientific Research Workflows

Download Fazm for macOS and let AI navigate specialized databases, read technical documentation, and synthesize research findings on your behalf.

Download Fazm

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