Editorial methodology

Modelwire is a curated index of AI news with original summaries and, on select stories, a short analysis layer that connects coverage to what we have already published. This page explains how that works.

Sources

We ingest from a fixed catalog of RSS feeds and official APIs (e.g. Hacker News, arXiv, GitHub, YouTube). Each source has an editorial weight that nudges buzz scoring; it does not guarantee placement. See Our sources for the full list.

Buzz score

Every candidate item is triaged by an AI model (Claude Haiku) with our rubric: timeliness, breadth of interest, originality, and source credibility. The model outputs a 0–100 score, which we then adjust slightly using the source weight so trusted outlets are not systematically under-ranked. Items below our publish threshold stay in our database for audit but are not shown to readers.

Headlines and summaries

Each story gets an original Modelwire headline; the publisher’s own headline is shown on the article page as attribution. Summaries (typically 80–120 words) are written in our own words from the headline, snippet, and metadata we have at ingest time. We do not store or republish full article bodies. The “Read full story” link on every page points to the publisher.

Editorial analysis

Every published story qualifies for a second analysis pass; higher scoring stories use a more capable model (Claude Sonnet). It receives a short list of similar recent Modelwire pieces ranked by embedding similarity so the analysis can reference real prior coverage instead of inventing connections. Output is checked by automated gates: length, banned hype phrases, repetition against the summary, and consistency with any cited related story. When a story fails those gates, the page ships without the analysis block and we exclude it from search indexes and skip advertising on it until a later pass upgrades it.

Daily Landscape

Each morning, our editorial system analyzes the top stories from the preceding 24 hours to generate a cohesive “Daily Landscape” briefing. This synthesis provides our readers with an overarching narrative of the industry’s current direction, hardware shifts, and research trends, ensuring the homepage offers value beyond a simple list of links.

AI disclosure

Modelwire uses Anthropic Claude and Amazon Titan embeddings as tools. They do not replace human judgment on policy: we set thresholds, prompts, and quality rules. Where analysis appears, the article page states that it was generated by Modelwire’s editorial layer and links here.

Corrections

We correct factual errors in our own text (summaries and analysis) promptly. See Corrections policy.