Inoreader Intelligence or an AI monitor: where the model sits
Inoreader uses models to summarize what your rules let in. AI monitors such as AyeWatch use them to decide what reaches you at all.
Editorial team
The ClaudeGPT Si newsroom covers artificial intelligence: new models, tools and apps, the companies behind them and what they mean for the people who use them.
153 stories
Inoreader uses models to summarize what your rules let in. AI monitors such as AyeWatch use them to decide what reaches you at all.
changedetection.io lets you plug an LLM into its diff pipeline. AyeWatch makes the model the core of how it judges change.
Keyword line filters decide by string match. A language-model monitor like AyeWatch decides whether a change matches what you asked for.
NewsWhip models attention to predict which stories will grow. Meaning-based monitors like AyeWatch judge each item against what you asked for.
Meltwater puts an AI assistant on top of a media suite. AyeWatch makes the language model the filter that decides what you hear about.
Brandwatch uses AI to help analysts read what a query collected. AyeWatch uses AI to decide what reaches you at all.
OnWebChange adds AI summaries to rule-based alerts. AyeWatch lets the model decide whether a change matters at all.
Dataminr layers fusion AI, generative AI and agents over 1M public sources for institutions. AyeWatch lets each user write the rule instead.
Google Alerts matches strings in new search results. AyeWatch lets a language model judge each item against a sentence you wrote.
Feedly puts AI inside a reader to rank articles. AI monitors such as AyeWatch put it in front of the web and decide whether to alert at all.
Language models can now read a policy change and say whether it matters to you. That changes what a terms watcher needs to be.
Trellis works on your own social data, billed in credits per seat. An AI monitor reads the outside web for you.
Meta moved public data access to approved researchers; tools that let a model judge each post by meaning now serve many of the people left out.
The Apache 2.0 model activates 2.5B parameters per token and, the company says, leads open models under 50B on agentic coding benchmarks.
Language models have no link index of their own; pair an agent with a link data tool and let the model do the sorting.
rel nofollow is a hint for search ranking, but the sentence around the link stays on the page for readers and AI tools alike.
You can count AI referrals on your own site with source filters; for a rival, no outside tool can see them.
A model has no link index, but given real lists it can label link pages, spot unlinked mentions and draft the ask, and AI citations add a second gap.
A data tool supplies the rankings, an agent sorts and drafts, and people decide intent and what a page adds.
A model reading a rival's pages infers intent; a model calling a ranking data tool over MCP can report positions and visits.
ChatGPT, Perplexity and Gemini are good at the brands people compare, as long as each answer is treated as one sample.
AI answers keep their own scoreboard. How to find which rivals ChatGPT and AI Overviews cite, and what today's tools can and cannot measure.
The brands assistants recommend, and the pages they cite, form a competitor list of their own.
One topic lookup shows which sites Google AI Overviews and ChatGPT cite, and a rival or brand that is not cited is a finding in itself.