This is an AI event reminder solution built on Feishu Bitable and automated workflows. Its core capabilities are centralized collection of AI competitions and bootcamp information, deadline-based sorting, and scheduled reminder delivery. It solves a common problem: saving opportunities and then forgetting them before the deadline. Keywords: Feishu Bitable, workflow automation, AI event reminders.
Technical Specifications at a Glance
| Parameter | Description |
|---|---|
| Platform | Feishu Bitable / Feishu Workflow |
| Implementation | No-code automation orchestration |
| Trigger Protocol | Scheduled trigger + table record query |
| Stars | Not applicable (not an open-source repository project) |
| Core Dependencies | Bitable fields, filter conditions, message delivery nodes |
This solution closes the loop between event collection and reminders
The original solution does not focus on complex AI reasoning. Instead, it turns information management into automated action. As AI competitions, hackathons, bootcamps, and call-for-paper opportunities continue to grow, it becomes difficult to track them consistently with bookmarks alone.
This agent stores the event title, link, start date, end date, and status in a single Bitable. Then, a daily scheduled workflow filters records that are close to their deadlines and sends reminders automatically. As a result, developers only need to maintain the event table instead of checking everything manually over and over again.
AI Visual Insight: This image shows the master event table interface. The key idea is to centrally manage multiple AI events as structured records, usually including fields such as title, time, and link. That makes it a suitable data source for later filtering, sorting, and automated reminders.
This data structure is the foundation of the entire automation chain
Bitable is more than a simple notebook. It acts as a lightweight database. The more stable your field design is, the easier it becomes to reuse the workflow later.
activities = [
{
"title": "AI 黑客松示例", # Event title; recommended as the primary field
"url": "https://example.com", # Event link for direct navigation from reminders
"status": "进行中", # Select field for easier filtering
"start_date": "2026-05-01", # Start date
"end_date": "2026-05-20" # End date used for deadline reminders
}
]This code snippet illustrates the minimum viable data model for a Bitable using structured fields.
The twice-daily reminder mechanism makes information easier to act on
The original article shows two automated reminder scenarios: one sends near-deadline events at 6:00 PM, and the other sends an AI news briefing at 9:00 AM. The first supports execution, while the second supports information intake, forming a combination of information gathering and milestone tracking.
For developers, the value of this design lies in reducing context switching. You do not need to open multiple group chats, bookmark folders, and calendars. Instead, you receive consolidated results at fixed times.
AI Visual Insight: This image shows the actual push result of scheduled reminder messages. The key information typically includes the event name, deadline, and priority, which demonstrates that the system has transformed raw table data into a summary format optimized for quick reading.
AI Visual Insight: This image shows the push format of the AI news briefing. It highlights that the same automation framework can handle not only deadline reminders but also information aggregation and distribution, making it suitable for building a personal AI intelligence dashboard.
The implementation breaks down into table initialization and workflow orchestration
The first step is to create a new Bitable and define the key fields clearly. At minimum, it is recommended to keep six field categories: event title, event link, status, start date, end date, and notes. Among them, the end date is the core of the reminder logic.
AI Visual Insight: This image shows the entry point and initialization screen for creating a new Bitable in Feishu. It indicates that the entire solution can be built from scratch without any external scripting environment, which makes it suitable for no-code or low-code users.
AI Visual Insight: This image shows the table structure after field configuration. It emphasizes the typed design of fields such as title, status, and date. Once field types are constrained correctly, automated filtering and sorting can run reliably.
It is best to use the end date as the default sort dimension
As the number of events grows, the sorting strategy determines whether the system remains truly useful. Sorting by end date in ascending order keeps the most urgent tasks at the top and reduces the chance of missing important milestones.
sorted_activities = sorted(
activities,
key=lambda item: item["end_date"] # Sort by end date ascending so near-deadline events appear first
)This code corresponds to the core management logic of sorting by end date in the original solution.
The workflow connects four nodes into a complete reminder pipeline
The workflow section is the most reusable part of the entire solution. It consists of four nodes: trigger, record lookup, formatting, and reminder delivery. The structure is both simple and clear.
AI Visual Insight: This image shows the entry point for attaching a workflow under a Bitable. It demonstrates that the data layer and the automation layer are tightly coupled, making it easy to build trigger logic directly around table records.
AI Visual Insight: This image shows the full workflow orchestration diagram. It clearly reflects the pipeline structure from scheduled trigger to record retrieval, content processing, and message distribution, which makes the workflow easy to standardize and reuse.
The trigger node defines the reminder cadence
The original solution sets the trigger time to 6:00 PM every day, which works well for handling registrations and submissions before the workday ends. If you prefer morning planning, you can change it to 9:00 AM instead.
AI Visual Insight: This image shows the scheduled trigger configuration screen. It indicates that the automation does not depend on an external cron service, because reminder frequency and timing can be configured entirely within Feishu.
The record lookup node filters valid events precisely
This node determines whether the reminders are accurate. Common conditions include a status of In Progress, an end date within the next few days, and complete records with valid links.
AI Visual Insight: This image shows the configuration of record filtering conditions. The technical focus is Boolean filtering based on field values, which determines whether the reminder message keeps only recent opportunities that are still actionable.
def should_notify(item, today, days=3):
# Notify only events that are currently in progress
if item["status"] != "进行中":
return False
# Trigger a reminder when the end date falls within the next N days
return 0 <= (item["end_date"] - today).days <= daysThis code abstracts the filtering logic behind near-deadline reminders.
The formatting node determines message readability
After filtering, you should not send raw fields directly to the user. It is better to compress the title, deadline, and link into a readable summary. That allows users to make decisions quickly inside the chat window.
AI Visual Insight: This image shows the message formatting node. It demonstrates that the system can reformat query results before output, making them better suited for IM-friendly cards or summary text.
The delivery node completes the final distribution loop
The last step is to specify the sender and destination. That destination can be a personal chat or a group. At this point, data maintenance, rule evaluation, and message notification form a complete closed loop.
AI Visual Insight: This image shows the message delivery configuration screen. Technically, it means the workflow now has an execution endpoint and can reliably deliver processed structured results to specific users or groups.
AI Visual Insight: This image shows the final automated reminder effect after the build is complete. It indicates that the system has evolved from a static record table into a continuously running personal event-tracking agent.
This kind of agent is especially useful for people who track AI opportunities frequently
If you regularly participate in AI competitions, follow open-source events, maintain a content opportunity pipeline, or keep remembering submissions only right before the deadline, this solution offers strong practical value. At its core, it is a personal opportunity management system rather than just a spreadsheet.
Compared with more complex agents, this lightweight automation is easier to deploy in real workflows. Its advantage is not that it is smarter, but that it is stable, low-maintenance, and immediately usable.
FAQ
1. Can a Feishu Bitable solution replace traditional calendar reminders?
Yes, especially for batch event-tracking scenarios. Calendars are better for single-point event reminders, while Bitable is better for centrally managing multiple opportunities, field-based filtering, and bulk notifications.
2. If there are many events, how can I reduce reminder noise?
Add more conditions in the record lookup node. For example, keep only records that expire within three days, are still in progress, and have not yet been registered for or submitted. This reduces low-value reminders.
3. Can this solution be extended into a more complete AI intelligence system?
Yes. In addition to deadline reminders, you can add news briefings, event scoring, automatic categorization, and submission tracking, allowing it to evolve into a personal AI opportunity and content operations hub.
Core Summary: This article reconstructs an AI event reminder solution based on Feishu Bitable and workflows. Its core capabilities are collecting event data, sorting by end date, and sending scheduled daily reminders, which helps developers avoid missing competitions, bootcamps, and submission deadlines.