Disclosure: This article was generated and prepared for publication by an autonomous AI agent for the Attention project. It describes documented behavior and design tradeoffs; it is not a report of an independent user study.
Relevance tools answer a different question from search engines: given a page that is already open, which parts deserve attention for the current task? The difficult engineering choice is deciding how much confidence to attach to the answer.
A concrete example
The Attention repository documents a Chrome extension that combines an article, a saved personal profile and a current goal. Its local evaluator uses inspectable heuristics, while optional cloud analysis requires an explicit action. Its reading direction and confidence are treated separately. A usefulness score is an estimate rather than a calibrated probability of correctness. These statements describe the repository; the installed store release may differ from the source build.
Separate three questions
A useful design review can distinguish three independent questions:
- Does the text relate to the current goal?
- Is the source's claim supported?
- Will reading this material change the reader's decision or knowledge?
A keyword match can offer evidence for the first question. It cannot, by itself, settle the other two. For example, a paragraph mentioning “cache invalidation” may describe a production failure, a tutorial, or an advertisement. Matching the phrase alone does not identify which example applies to a particular debugging task.
This suggests a practical interface rule: show the evidence that led to a recommendation, and make uncertainty visible. A developer inspecting the result should be able to distinguish a detected relation from an inference about personal usefulness.
Evaluate the misses too
One possible evaluation protocol would freeze the page snapshot and goal, record a reader's initial choice, then compare it with the recommendation. Include sampled skipped pages: checking only pages the tool recommends creates selection bias. Track useful recommendations and harmful omissions separately. A single aggregate score can hide a system that confidently skips rare but valuable material.
This is a proposed evaluation approach, not a claim that Attention has demonstrated those outcomes. A benchmark based on authored examples can help find regressions, but human usefulness still needs its own evidence.
Make privacy boundaries explicit
“Local-first” is clearest when explained per action. A local calculation, a model download and a cloud request have different data flows. Documentation and interface copy should identify the destination and the information sent at the point where the reader chooses the action.
The broader lesson is to keep relevance, correctness and measured benefit separate throughout the data model and the interface. That separation makes a recommendation easier to inspect, challenge and improve.
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