AI news rarely arrives as a neat collection of settled facts. A lab announces a model, a repository appears, a paper reports a result, and a discussion post sends the story around. Those are different kinds of evidence. They can all be worth reading, but they should not be presented as if they prove the same thing.

AINewsIQ is a public AI news desk from Triuna Labs, led by Paul Woll. We built it to make that distinction easier to see. Readers can scan current developments in a live feed and radar, search older items, and follow a link back to the original paper, repository, report, or announcement. A short brief can help someone decide what to inspect next. It is not a substitute for the original source.

Find the change while it is still new

The desk looks for the freshest useful changes because AI research, products, and open-source tools can move between a reader's usual check-ins. The radar's rolling 24-hour and seven-day windows make new signals visible while they are still timely; the search archive lets readers return later. Recency tells us when something surfaced, not whether it is important or correct.

That search includes recent research and primary announcements, newly discovered repositories in tracked GitHub topics, and published releases from tracked repositories. A product launch or update is worth a look when its original page can be inspected. A new repo is a lead, not evidence that the software is mature. Topic discovery may rank newer repos by total stars; measured star growth requires at least two daily observations. An ordinary code push does not become a news signal merely because it happened.

AINewsIQ uses purpose-built rubrics so one number does not have to answer every question. The public radar separates time, observed attention, a reader's interest match, source trail, and a documented source-evidence lens. Each has a stated method and missing information remains visible. Popularity can explain discussion, but it cannot raise a claim's evidential credibility. For organizations, versioned custom rubrics are a proposed for-leaders extension ↗: a team could define which source findings matter to its stack, cost, policy, or security decisions, with its criteria and unknowns shown beside the result. That team assessment is not a claim that today's public feed knows a company's private context.

FIG. 01 / AINEWSIQFrom fresh lead to an inspectable signal
A time window helps readers find a change; source checks and separate rubrics show what is known. The path does not independently verify a claim.AINewsIQ editorial diagram · Source ↗

The evidence should travel with the headline

The source trail belongs close to the story. If a company reports a benchmark, the reader should know the company made the claim. If a result has not been independently replicated, that gap should remain visible. Observed attention around a link can explain why people are discussing it; it cannot tell us whether the underlying claim is true. AINewsIQ's source method ↗ describes those boundaries, and the radar ↗ keeps these questions in separate views.

A short brief can help someone decide what to inspect next. It is not a substitute for the original source.

— Paul Woll, AINewsIQ editorial

Personalization has a place here too. Readers can follow topics and save items, and those choices can help shape their own view. The public source brief and its caveats remain shared. You can still browse the wider desk even when a story falls outside your followed topics.

Now make room for the writer

We also want AINewsIQ to be a home for original reporting and considered argument. A source-linked brief answers a useful first question: what does the source say? A writer can go further by asking what changed, who is affected, what comparison is fair, and what evidence would change the conclusion. That work should have a named byline and a durable article page that readers can cite. It should also show the writer's sources and unresolved questions.

That is why we are inviting authors to apply through the AINewsIQ contributor desk. Applicants provide a public bio, background links, and a writing sample. An editor reviews the author profile before the writer can submit a story, and reviews each story separately before publication. The draft workspace asks for who, what, when, where, why, recency, claim attribution, significant points, and linked evidence. Its live checklist finds missing structure; it does not verify a source or certify a claim.

A published AINewsIQ article can be part of a writer's public portfolio: it has a byline, an author profile, a publication date, and a stable page to share with readers or prospective employers. Publication shows that the piece passed editorial review. It does not mean every claim has been independently confirmed. Writers should be able to point to their work with confidence, and readers should be able to inspect how that work was assembled.

Join the conversation

The desk is still developing. Automated discovery and AI-assisted briefs can miss context or make mistakes. Coverage will never include every important AI development. We invite corrections, competing interpretations, and better evidence. If you have original reporting, research analysis, or a thoughtful editorial to contribute, apply through the contributor desk ↗. If you are reading, follow the source links, question the claims, and tell us what we missed.