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OnTimer.app
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OnTimer is a calendar reminder app designed to make sure you are never late for meetings, calls, or appointments. While standard calendar notifications are easy to miss or can fail due to silent mode, focus settings, or device limitations, OnTimer adds a reliable reminder layer that ensures you always know when it is time to act.
OnTimer connects to Google Calendar and Microsoft 365 to monitor your upcoming events and send persistent alerts shortly before meetings begin. These alerts are designed to stand out so you do not overlook them during busy workdays.
In addition to meeting reminders, OnTimer includes Time-to-Leave alerts for events that have a location. The app calculates when you should leave based on travel time and real-time traffic conditions, helping you arrive on time whether you are walking, driving, or commuting.
Instead of relying on a single fragile notification, OnTimer acts as a safety net for your schedule. Professionals use it as a backup reminder system to prevent missed meetings, late arrivals, and costly no-shows.
Key features include:
โข Persistent meeting reminders you canโt miss โข Calendar integration with Google Calendar and Microsoft 365 โข Time-to-Leave alerts based on travel time and traffic โข Customizable reminder timing โข Simple setup that works with your existing calendar
If you rely on your calendar to manage your day, OnTimer helps make sure you actually show up on time.
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"""Clones a single branch of a GitHub repository into a temporary directory for local scanning.""" Import logging Import shutil Import subprocess Import tempfile Logger = logging.getLogger(__name__) Class RepoFetchError(RuntimeError): """Raised when the target repository/branch cannot be cloned.""" Class RepoFetcher: """Shallow-clones a single branch of a repository so its tests can be scanned... - Source: dev.to / 5 days ago
# video-meta.yml Topic: > Walking through how we cut cold-start time on a Lambda-backed GraphQL API from 2.4s to under 400ms, including the two things that didn't work. Target_keyword: lambda cold start optimization Audience: backend devs who already ship serverless, not beginners Model_channels: - the three channels currently ranking for this keyword Links: repo: https://github.com/... slides:... - Source: dev.to / 6 days ago
Import struct, json, urllib.request REL = "https://github.com/{owner}/{repo}/releases/download/{tag}/" PART = ["...part1.zip.001", "...part2.zip.002", "...part3.zip.003"] SIZE = [1992294400, 1992294400, 1893639808] # from the releases API Def grab(part, start, end, out): # HTTP range fetch req = urllib.request.Request(REL + PART[part], Headers={"Range":... - Source: dev.to / 11 days ago
Is published at https://github.com/.keys so an SSH server to which you connect could do a reverse lookup. This is the reason why my ~/.ssh/config has those 2 lines at the end:- Source: Hacker News / 18 days agoHost *.
All of this assumes you can actually inspect what the agent did โ the real inputs after resolution, the real tool outputs, the real intermediate steps. That is the other half of the workflow. AgentLens captures the trace: every model and tool step, resolved inputs, raw outputs. agent-eval scores and gates the output; AgentLens gives you the unforgeable, agent-didn't-author trace data for Tier 1+2 to score against... - Source: dev.to / 18 days ago
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