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Relevance
2 min read · 462 words
Relevance is the operator’s filter for which inputs warrant attention and which do not.
The volume of available input far exceeds what the system can process. The filter has to run constantly, deciding what gets attended to and what gets dismissed. Most of the time the filter runs below conscious awareness. The operator notices the input that passed the filter and acts on it; the inputs that didn’t are not noticed. The filter’s calibration determines what the operator’s experience consists of.
The cultural environment has degraded most operators’ relevance filters. The systems delivering input are engineered to capture attention regardless of relevance, exploiting the system’s reflexive interest in novelty, threat, and social signal. The operator who runs without deliberate filter management ends up attending to whatever was best engineered to capture attention, which often correlates poorly with what would actually matter to that specific operator’s life. The filter has been outsourced to the engineering of the input streams.
The cost: the operator’s bandwidth gets consumed by inputs that don’t serve them. The actual matters of the operator’s life — the relationships, the work, the body, the questions worth thinking about — get less attention than the engineered inputs that automatically pass the eroded filter. Across years, this produces an operator whose life has been pulled toward what surrounding systems wanted them to attend to, and away from what the operator would have chosen if they were running their own filter consciously.
From the chair: run the filter deliberately. The diagnostic question for any input: does this connect to anything in the operator’s actual life — the work that matters, the relationships that matter, the conditions of the operator’s actual situation. If the answer is no, the input is probably not relevant, regardless of how interesting it appears. The interest may be the engineered hook, not actual relevance.
The other discipline: shrink the input streams that consistently produce low-relevance output. The news source that mostly produces concern about distant events that the operator cannot influence. The social media that produces attention-grabbing input mostly disconnected from the operator’s actual life. The notifications that interrupt focused work to deliver low-relevance content. Each of these can be reduced or removed, and the bandwidth recovered redirects to the actual relevant material that was being crowded out.
The relevance filter, when operating well, produces a smaller input stream that more closely matches what the operator would choose if they were choosing consciously. The filter, when degraded or outsourced, produces a larger stream that mostly serves whoever engineered it. The operator’s job is to run their own filter, not to inherit the one the input ecosystem has installed.