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.

API Endpoints That Stay Alive - Health Checks, Heartbeats, and Warm Connections

Fazm Team·March 18, 2026·3 min read

api health-checks reliability agent-integrations infrastructure

API Endpoints That Stay Alive - Health Checks, Heartbeats, and Warm Connections

A door with a pulse is an API endpoint that is alive. Not just responding with 200 OK, but genuinely ready to handle real work. The distinction matters enormously for AI agents that depend on external services to function.

The Difference Between Alive and Responsive

A health check that returns {"status": "ok"} tells you almost nothing. The endpoint is reachable. The web server is running. But can it actually process a request? Is the database connection pool healthy? Are downstream services available?

For AI agents, this is not an academic concern. An agent that calls an LLM API, gets back a 200 response with an empty completion because the model is overloaded, and then tries to parse that empty response as instructions - that agent is about to do something unpredictable.

Heartbeats for Long-Running Agent Sessions

Desktop agents often maintain long-running connections to multiple services - LLM providers, memory stores, MCP servers, local databases. These connections go stale. TCP keepalives help but are not sufficient.

Application-level heartbeats solve this. Every 30 seconds, the agent sends a lightweight ping to each service it depends on. If a service stops responding, the agent knows before it tries to use that service for a real task. It can reconnect, switch to a fallback, or pause and notify the user.

Connection Warmth Matters for Latency

Cold API connections add latency that compounds across multi-step agent workflows. An agent that needs to make 15 API calls to complete a task - hitting the accessibility API, querying a knowledge graph, calling an LLM, updating a database - cannot afford connection setup overhead on every call.

Connection pooling and persistent HTTP/2 connections keep things warm. The first request might take 200ms to establish. Subsequent requests on the same connection take 20ms. Over a complex workflow, that difference adds up to seconds of saved time.

Build for Degraded States

The best agent architectures assume some endpoints will be temporarily dead. They have fallback paths, cached responses, and graceful degradation. An agent that crashes because one API is down is an agent that cannot be trusted with real work.

API Endpoints That Stay Alive - Health Checks, Heartbeats, and Warm Connections

API Endpoints That Stay Alive - Health Checks, Heartbeats, and Warm Connections

The Difference Between Alive and Responsive

Heartbeats for Long-Running Agent Sessions

Connection Warmth Matters for Latency

Build for Degraded States

More on This Topic

Related Posts

Uptime Lies - Co-Failure Patterns in AI Infrastructure

Invisible Infrastructure in AI Agent Systems - The Scripts That Run Silently

What Actually Makes Agent Networks Work - The Boring Stuff