How automation and AI facilitate knowledge sharing
I recently wrote a blog post about building a tech reading list. In it, I mentioned that after reading an article, I decide whether to share it with my colleagues.
Sharing insightful articles with colleagues can stimulate discussions, spark ideas, and promote a learning culture within a team. By disseminating my reading list, I add to our team’s collective knowledge and foster an environment that encourages continuous learning and growth.
In this blog post, I will share insights on how automation and AI can facilitate this process.
“Daily bites” initiative
I’m part of the platform team, and one of our responsibilities is to facilitate knowledge sharing among developers. One of our initiatives is the “Daily bites”, where we share compelling articles each day for colleagues to read at their convenience, perhaps over a coffee break. The aim is to provide not just articles about the most recent updates, but also timeless articles that explain relevant concepts like micro frontends, the Dependency Injection, or the CSS cascade.
Every day, at a set time, a message appears in a dedicated Google Chat space. The message contains a link with a brief description that follows the AIDA pattern.
Let’s examine how automation and AI can enhance this process.
Content gathering
At present, I’m the primary contributor to the initiative. I often read tech articles and use my reading list for this purpose. I discussed the entire process in a previous article.
Content gathering begins with evaluating whether it’s worth sharing an article I’ve just read with my colleagues. As part of my reading routine, I select the “Daily bites” option.
Next, I have an automation that monitors my reading list in Notion each day in the evening.
The scenario involves the following steps:
- Check for any new items marked as shareable.
- Generate message content based on the article URL, adhering to the AIDA framework using Perplexity AI.
- Add a new item to the Airtable database.
- Update the record in Notion indicating that the article has been marked for sharing.
The first view is the “feed”. Every Sunday, I review the “feed” to verify the messages generated by Perplexity AI and select a category for each record. Once a message is verified and its category selected, the record is added to the “backlog”.
Next, I decide when to publish the message, striving to maintain content diversity while delivering the most relevant information as promptly as possible. After selecting a date, the record moves to the “scheduled” view, where it awaits publication.
Content publishing
Now, let’s examine the second scenario responsible for publishing.
The program runs every workday at 9:30. It checks the “scheduled” view for an item with a publish_at date that matches the current day. If it finds a match, a message is sent from the scenario to the webhook created for the Google Chat space.
Finally, the record is updated in Airtable. The is_published box is checked, and the article is moved to the “published” view.
In addition to daily posts on Google Chat, I send a weekly summary every Friday based on the week’s news. The following automation sends a summary to my private Google Chat space, making it easy to incorporate into my email template.
This process collects all records from a given week and assigns a day name to each of them. It executes every Friday at 11:45.
In conclusion, automation and AI enhance knowledge sharing by streamlining content gathering and dissemination, improving efficiency and reliability. They enable focus on content quality, aligning it with team needs, and ensure message consistency via the AIDA framework. However, human intervention is crucial for verifying AI-generated messages and finalizing content classification and scheduling. This synergy makes our “Daily bites” initiative more effective for the team.
I hope you liked my blog post, thanks for reading! 🙂
