
v0.7.0
LLM inference in C/C++. Contribute to ggml-org/llama.cpp development by creating an account on GitHub.
Stay updated with the latest developments in Artificial Intelligence

LLM inference in C/C++. Contribute to ggml-org/llama.cpp development by creating an account on GitHub.
Reconstruct last week from your calendar, tasks, and work journal. Verify outcomes before writing a review, status update, or invoice.
Automatic time trackers log work without manual timers. Compare app logging, screen-based journals, and AI memory tools, then choose the right model.
Time blindness means the hours vanish without a trace. An automatic work journal replays where they went - no timers to remember, no logging to forget.
Use a daily standup template with a completed example and editable draft. Turn work history into yesterday, today, and blockers without inventing outcomes.
Review a planned focus block against your calendar and work history, then choose one practical change for next week.
Capture and AI analysis have different resource costs. How to compare Dayflow's local and cloud modes on your Mac without relying on unsupported battery estimates.
Learn what separates private self-tracking from employee surveillance, which data each tool collects, and how local-first time tracking protects your work.
Where Dayflow stores recordings, what each AI mode sends off-device, and which behavior you can verify in the open-source Mac client.
Three ways to get an honest record of your Mac time - the built-in Screen Time report, an open-source app logger, and an AI work journal - with the setup for each.
Timekeeping software for Mac that fills itself in - how automatic timesheet tools work, which ones suit billing vs self-tracking, and a setup that takes five minutes.
Showing 10 of 10 stories
You're all caught up.
Get the top AI news and tools straight to your inbox every week.
Weekly · AI news from Dayflow Blog
Join 15,000+ AI enthusiasts