Editorial methodology
Modelwire is an automated AI news index with an added layer of summaries, archive context, and cross-story synthesis. This page explains what goes into that coverage and what the system can miss.
1. Collect source material
The collection system checks a maintained catalog of RSS feeds and APIs for new items. It records the source headline, link, available feed snippet, publication time, and source information. It does not store full article bodies. The source directory shows the catalog and source weights.
Different sources establish different things. A lab announcement states the lab’s own claims; a research preprint may not have completed peer review; community votes show attention, rather than independent verification. Inclusion in the catalog is not an endorsement of every claim a source publishes.
2. Select and rank stories
An AI model screens for AI relevance, spam, and low-signal material. It assigns a 0–100 buzz score using a rubric for the scale of a development, novelty, and likely interest to AI readers. Source weighting adjusts that score; available community engagement and video-view signals can also affect ranking. Candidates below the current publication threshold of 60 are hidden from the public feed. URL, title, and semantic similarity checks help reduce duplicate coverage.
Buzz is a selection signal, not a probability that a story is true, a benchmark score, or a recommendation to buy a product. A major company can attract a high score while its claims still need independent testing. Duplicate detection can also miss a repeated story or incorrectly group two distinct developments.
3. Write a summary from the available inputs
Language models generate a Modelwire headline and summary from the source headline, snippet, and metadata. The summary aims to explain the development and its implications in original wording. The source’s own headline and a link to the original appear on the article page; older entries may use the source headline as their display title.
This is not a full-text reading of every linked article. A short or missing feed snippet limits the evidence available to the model. Details such as price, availability, benchmark conditions, and exceptions can be absent from our inputs. The original publication remains the place to check those details.
4. Add context from the archive
Qualifying stories can receive a second analysis pass. The model receives the current summary and a small selection of related Modelwire archive entries retrieved by semantic similarity. It is asked to explain what is new, how the development fits earlier coverage, and a concrete milestone to watch next. When an archive story is referenced, it is linked in the article’s context section so readers can inspect the connection.
Automated gates check required fields, length, repeated phrases from the summary, banned hype language, and whether a cited archive reference belongs to the supplied set. A failed pass is omitted. Search indexing and advertising apply additional content thresholds; some readable entries are deliberately excluded from search or ads.
These checks do not independently fact-check the prose or prove that an inferred connection is sound. Archive entries contain generated summaries too, so a link to earlier Modelwire coverage provides context, not independent corroboration. Analysis and forward-looking milestones should be read as interpretation rather than established outcomes.
5. Synthesize the day and week
Daily briefings draw on selected recent stories. The weekly homepage overview uses up to twelve highly ranked stories, with selection spread across sources and topics, from a completed week. It generates a narrative, themes, and developments to watch, and links highlighted stories. These briefings are AI-generated; they are not staff-written essays or a complete account of everything that happened in the period. A briefing’s covered period and the linked stories help readers judge its scope.
Accountability and corrections
Modelwire uses language models and embedding models in its automated publishing process. Zacaria Solis operates the site and sets its catalog, prompts, and rules. Individual items are not routinely reviewed by a person before publication. Automated checks reduce some errors but cannot guarantee accuracy, completeness, or originality in every output.
Report a factual error, unsupported connection, or inaccurate attribution through our corrections policy. For more guidance on interpreting our coverage, read the editorial notes.