GPT-4o with scheduled tasks (jawbone) is available in beta

Original Article ↗ Hacker News Discussion ↗

Feature description & docs

  • New “GPT-4o with scheduled tasks (jawbone)” model adds automation that runs prompts on a schedule.
  • Tasks have three core parameters: title, prompt, and schedule, with schedules defined using iCalendar VEVENT syntax (e.g., RRULE, optional DTSTART).
  • Official help and changelog pages exist, but many find them sparse, confusing, or incomplete; some see “Invalid DateTime” in the changelog.

User experience & discoverability

  • Several users report the UI gives almost no hints about how to use scheduled tasks.
  • People discover functionality only after clicking avatar → “Tasks” or experimenting with phrases like “remind me…”.
  • Mobile and desktop apps behave inconsistently; some capabilities (e.g., editing tasks, Canvas) appear only on web.

Functionality & behavior

  • Tasks can send notifications via email and/or push (user-configurable).
  • They can run web searches as part of the scheduled job, though enabling “Search” during task creation can confuse the model.
  • Some see this as “scheduled tasks for the AI” (periodic research, summaries) rather than just user reminders.

Reliability & technical issues

  • Reports of incorrect time zones (defaulting to UTC), time deltas added to requested times, missed executions, and absent push notifications.
  • Some users receive “task couldn’t be completed” emails or generic errors.
  • Rollout appears flaky; the model sometimes doesn’t show up or errors in the UI (e.g., source map errors).

Use cases & value debate

  • Supporters see this as an early step toward agents, workflow automation, and “AI cron” for non-developers.
  • Critics dismiss it as a glorified reminder/todo app, especially given existing tools with better calendars and sync.
  • Some argue free or local models and simple cron + API are sufficient; others value having this inside ChatGPT they already pay for.

Comparisons & strategic context

  • Comparisons made to Siri, Google’s ecosystem (Gmail/Calendar/Maps), and other LLM providers; mixed confidence in each company’s execution.
  • Some view this as a tech demo to acclimate “normal users” to agents and long-term AI memory.
  • Skeptics question OpenAI’s product quality, QA, and long-term moat; others see steady capability improvements and inevitability of agent-based workflows.

Ethical / political concerns

  • At least one commenter refuses to use OpenAI for political/leadership reasons, noting similar issues likely exist across major tech platforms.