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Clear out junk files and repair common Windows errorsFree Scan →Fix the driver behind crashes, sound loss and screen glitchesFind Drivers →In a comparison of 602 DEV Community articles published near the author’s own posts and carrying the same tag, 134 had at least one reaction (22.3%) and 60 had at least one comment (10.0%). That is a local benchmark, not a platform-wide engagement rate—and it suggests that a post receiving no reaction is common in this sample, not proof by itself that the post or channel failed.
What the 602-article comparison found
Listwright’s September 22, 2026 article reports counts from neighboring DEV Community posts selected around eight of the author’s older articles. Of the 602 comparison articles, 134 received at least one reaction and 60 received at least one comment. The corresponding shares, calculated from those reported counts and rounded, are 22.3% and 10.0%.
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In other words, 468 of the 602 posts had no reaction, and 542 had no comment, according to the reported counts. These figures describe this selected sample; they do not estimate the rate for all DEV posts. DEV Community
How the author selected comparison posts
The author began with 14 published articles: eight old enough for the page-view counter to have caught up and six newer ones. The six newer posts were excluded because the author’s comparison window extended both before and after publication, and the future half had not elapsed. The exercise therefore covers the eight eligible posts, not all 14.
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For each eligible post, the author counted other articles with the same tag published within a 24-hour interval centered on the author’s publication time. The author’s own article was excluded from its neighboring set. Pooling those sets produced the 602-article sample.
The reported reaction shares across the individual windows ranged from 17.5% to 27.5% in windows with enough neighbors to interpret: python, 17.5% of 120; webdev, 20.0% of 30; opensource, 22.5% of 89 and 24.0% of 125; programming, 22.9% of 179; and discuss, 27.5% of 51. Two other windows contained only four neighbors each. The author reported one reaction in each, but treated those groups as too small to read as meaningful rates.
What a zero-reaction post does—and does not—tell you
Listwright reports that their own 14 articles had no reactions and three comments in total. Their eight older posts had accumulated 103 page views. The comparison provides context for the lack of reactions: in the selected neighboring sample, most posts also had none. It does not show why any particular post received no reaction.
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Neighboring posts may differ in topic, quality, audience, author reach, and age. The exercise is observational, so it cannot establish that a particular writing choice or publishing channel caused an outcome. The author’s conclusion is appropriately limited: these results do not prove the writing is bad, and they do not prove the channel works.
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Use a fair comparison window
Wait until the window is complete
A centered 24-hour window includes time after publication. Comparing a post before that future portion has passed leaves part of its potential comparison period unobserved. The author excluded posts that were too new rather than treating incomplete windows as finished results.
Set a minimum sample size
After seeing two windows with only four neighboring posts apiece, the author set a 30-neighbor minimum. That is the author’s practical rule, not a statistically validated threshold. The useful lesson is to label a very small comparison group “too thin to interpret” rather than turning a volatile percentage into a verdict.
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Compare like with like, and keep the metric consistent
Same-tag posts near the same publication time are a narrower comparison than all posts on the platform, but they are still not guaranteed to be equivalent. Count the same kind of outcome across the comparison set: this analysis used whether an article had at least one reaction or at least one comment. It did not compare the author’s page views with neighbors’ reactions or comments, because the author had no corresponding public view-count data for those posts.
Limits on what readers can verify
The published article reports the sample size and aggregate counts but does not expose the 602 underlying rows or a calculation artifact. Readers therefore cannot independently recalculate the pooled result from the published material. Treat the figures as the author’s reported measurements, not as a reproduced dataset or an official DEV statistic.
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The author also describes a counter-threshold lesson: a nonzero view count showed that the counter had updated in their implementation, while age mattered when the count remained zero. That is an account of how the author handled their own measurements, not a guarantee about how DEV analytics counters behave for every post.
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