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A May 2026 engineering audit of a 474-site WordPress publishing network found that 80% of posts were landing on 38 sites, while 249 sites received no posts. The operator attributed the imbalance to separate supply and placement issues and said fixes have been made, with results still pending.
A 28-day engineering audit of a 474-site WordPress publishing network run by Thorsten Meyer AI found that 80% of output was landing on just 38 sites while 249 sites received no posts, exposing a distribution failure hidden by otherwise healthy publishing totals.
The audit found that the network’s top 38 sites, about 8% of the catalog, carried 80% of all posts. The four busiest sites were all technology titles and each received more than 200 articles per week, according to the engineering note.
The operator said the failure did not come from one broken component. DojoClawAI, the content engine, kept selecting the same broad technology sites after stories passed its relevance checks. Stenvrik, the news-intelligence layer, supplied a content mix that did not match the catalog: 53% of incoming material was tech or AI, while only about 13% of the sites were in those categories.
The stated repair has three parts: new placement controls in DojoClawAI, a feed rebalance in Stenvrik, and a scheduler increase. The changes include a per-site weekly cap, a global least-recently-used ordering rule, feed cleanup, new verified feeds across underfed categories, and higher fan-out limits.
Why It Matters
The finding matters because it shows how an automated content network can appear healthy while parts of the network stop receiving material. Total output alone did not show that half the catalog had gone dark.
For publishers using automation, the case points to a measurement gap: volume metrics can hide placement concentration. The operator said the change trades some topical ranking inside already eligible candidates for wider catalog coverage.
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Background
The network described in the note is split between Stenvrik, which ingests feeds, scores stories, and applies tags, and DojoClawAI, which rewrites and places stories across sites. The operator said that split helped identify the issue because content supply and placement demand could be compared separately.
Before the change, rotation occurred inside matched pools. That meant a site that never entered a candidate pool could not receive a turn, even if it had been idle across the wider network.
“The numbers said everything was fine. A 28-day audit said otherwise.”
— Thorsten Meyer AI engineering note
“Not one bug — two independent causes”
— Thorsten Meyer AI engineering note
“The proof is in the next weeks of data”
— Thorsten Meyer AI engineering note
content automation monitoring software
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What Remains Unclear
It is not yet clear how quickly the 249 dormant sites will begin receiving posts, or whether the new caps and feed mix will hold up under normal publishing volume. The operator also said a previously documented intent of about 950 posts per day was not being delivered by the code and remains behind sign-off.
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What’s Next
The next step is monitoring the weeks after the May 2026 changes. The main measures to watch are whether dormant sites shrink, whether output remains spread beyond the former top 38 sites, and whether the higher daily ceiling avoids rebuilding the same concentration pattern.
content scheduling and distribution software
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Key Questions
What happened in the publishing network?
A 28-day audit found that 80% of posts were going to 38 of 474 sites, while 249 sites received no posts.
Was this caused by one software bug?
According to the engineering note, no. The operator identified two causes: repeated placement on broad technology sites and a content supply mix tilted toward tech and AI.
What changes were made?
The operator added placement caps and global recency ordering, cleaned up broken feeds, added verified feeds in underfed categories, and raised fan-out and quota settings.
Has the fix been proven yet?
No. The note says the repair changes future placement behavior and needs the next weeks of data to show whether the network rebalances.
Source: Thorsten Meyer AI
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