Dayflow
A private, automatic macOS app that watches your screen and uses AI to generate a timeline of your daily activities.
What is Dayflow?
Dayflow is a native macOS application that automatically captures screen activity and uses AI to generate a private, contextual timeline of your workday. It solves the problem of manual time tracking by providing an accurate, automated record of what you actually accomplished, distinguishing between productive work and distractions. The app is designed to be lightweight and privacy-focused, keeping all data on your device.
Professionals and knowledge workers on macOS who want automated, insightful time tracking without compromising privacy, including founders, engineers, students, researchers, marketers, salespeople, and freelancers.
Developers choose Dayflow for its unique combination of automatic, context-aware tracking, strong privacy controls with local-first data storage, and the flexibility to use various AI providers (cloud or local). It provides deeper insights than basic app trackers while remaining efficient and extensible.
Overview
The automatic work journal/time tracker. Privately turns your screen into a timeline of what you actually accomplished. Open-source and local-first.
Use Cases
Best For
- Automating time tracking for developers and knowledge workers
- Generating private, AI-summarized work journals
- Understanding context switching and distraction patterns
- Creating client-ready work summaries for freelancers
- Analyzing time allocation across different projects or tasks
- Building a local-first, privacy-focused productivity system
Not Ideal For
- Teams requiring cross-platform compatibility on Windows or Linux
- Users needing real-time, instant activity analytics without AI processing delays
- Organizations that require centralized, multi-user tracking and collaborative reporting
- Individuals uncomfortable with granting persistent screen recording permissions due to security or performance concerns
Pros & Cons
Pros
Uses AI to distinguish between activities like research vs. entertainment within the same app, providing more meaningful insights than basic app usage loggers.
Data stays on your Mac with options for fully local AI processing via Ollama or LM Studio, ensuring user control and avoiding cloud dependencies.
Lightweight operation with ~100MB RAM and <1% CPU usage per the README, minimizing system impact while tracking screen activity.
Supports multiple providers: Gemini (cloud), local models, and ChatGPT/Claude (paid), allowing customization based on cost, privacy, and quality needs.
Cons
Only available for macOS 13+, excluding Windows, Linux, and older Mac versions, which severely limits its user base and flexibility.
Requires configuration like API keys for Gemini, local server setup for Ollama, or paid subscriptions for ChatGPT/Claude, adding friction to initial use.
Using local AI involves 33+ LLM calls per analysis, is GPU-intensive, drains battery faster, and may produce lower-quality summaries compared to cloud models.
The journal feature is in beta and requires an access code, making it less accessible and potentially unstable for general use.
Frequently Asked Questions
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