From the rise of AI topics to publishing patterns and writing habits.
From December 2022 to January 2026, developers published over one million ar...
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My reaction to most posts: either ignore or moderate as low quality and flag to admins as spam. In 2022 DEV had to incentivize users to write more. Now it's time to do the opposite. I'd also recommend everyone add the #ai hashtag to their blocklist, only that doesn't seem to work anymore. Slop detection heuristic might also filter out long titles with dashes and tropes like "here is what I learned" ... if that wouldn't filter false negatives like this interesting post that somehow passed my human spam filter. Thanks for sharing!
Thanks for commenting and sharing your perspective, totally get it! It’s interesting how platform dynamics shift so quickly. I think your idea of using the ai hashtag sparingly makes a lot of sense, it gave me something to think about 🙂
This is exactly the kind of data analysis I love seeing! The Azure pipeline architecture is clean Functions for ingestion, Blob for raw storage, Databricks for transformation Power BI for viz.
Question about the engagement paradox: did you normalize reactions by follower count or account age? Newer authors might have smaller audiences, which could explain the per-article drop even as total volume rises. Also curious if you looked at quality signals like saves or clap ratios vs just raw reactions.
The 38 days of nonstop reading stat is wild. 😅 Great work.
Thank you so much for reading and for your kind words! You made a very valid point: the decrease in engagement could indeed be due to the appearance of new authors who haven’t yet built an audience. I haven’t looked into it yet, and now that you mention it, it seems like the right next step to check. Appreciate the thoughtful suggestion! 🙂
This is a super cool analysis, great work!
Thanks a lot! I’m glad you liked it 🙂
Wow! Great analysis of DEV Community. You really have strong analytical skills. Thank you for sharing! 😄
Thanks for your kind words! I’m really glad you found the analysis interesting. DEV has a lot of fascinating data to explore, so it was fun to dig into it and share the insights 😄 One shocking part for me was discovering that we now write such short articles, on average only about a 3-minute reading time. Really did not see that coming!
The shocking part for me is that posts are increasing, but reactions and comments are decreasing. I hope the community becomes more active.
I agree, more engagement would be great! I have to admit I also used to think reactions or comments weren’t that important. Now I realize just showing that the article is useful really matters for the author and encourages more people to share their knowledge. Even a quick comment can make a difference :)
I think so too! However, the problem is that it takes time for me to write a comment. I wish I could write more smoothly. 😅
This is super cool. I've been all over this data recently and I still learned things from your analysis! 🙌 Thanks for sharing
Glad you enjoyed it, and awesome to hear you found it useful!
I’ve also shared some additional insights based on the same dataset that I thought might be interesting: link
Omg please tell me you anonymized the data….🤣🤣hate to see what mine looks like lol
Thanks for your comment, that’s actually a very important point!
I definitely won’t analyse or share any data about individual authors or specific posts, that’s completely out of the question. No individual data is exposed, so you’re completely safe 😄
The 3% to 23% jump in AI content is wild but makes total sense from the builder side. When I look at what developers are actually shipping right now, AI is the common thread — but most of the focus is on coding tools and chatbots.
What I find underexplored is AI applied to communication workflows. I'm building a Mac app (Drafted) that uses AI to pre-draft email replies in your writing style. It's a completely different application of the same LLM capabilities that power coding assistants — instead of understanding code context, it's understanding communication context and tone.
The data point about engagement peaking early and then plateauing is also interesting. Feels like AI articles need to move past "look what AI can do" into "here's what I actually built with AI and what I learned." The articles in that second category seem to have much longer shelf life.
Curious — did the data show any patterns around which AI sub-topics get the most engagement? Like AI for developer tools vs AI for end-user products?
Thanks for your comment! Love this perspective, especially the shift from “AI demos” to real use cases. Your app sounds like a great example of that second category.
I did look at the engagement, but not around AI sub-topics particularly. It's a great idea for a follow-up analysis!
Wow, amazing insights. Awesome article.
Thank you so much! I'm very happy you liked it 😊
Very insightful post, thanks for putting this together.
Thanks for reading and commenting! Happy you liked it 😊