Building a timeline analyzer with Jev: 12 structured judgments per post
The author has Jev output 12 typed bool, score, or choice fields with confidence for each post, averaging 203ms per answer and producing 1,318 judgments across 30 posts.
Nothing is impossible with Jev now, I just proved it myself. I built a Jev X Feed Analyser, it reads my timeline while I scroll and judges every post on the screen Here is how it actually works: > every post gets the same 12 typed questions > not a summary, not a vibe check, a schema > hook_archetype, topic, reach_band, is_shill, reads_like_ai, claim_needs_source, regret_if_quoted, worth_reading, worth_reply, bookmark_worthy, opens_with_number, mute_author > each answer is a bool, a score or a choice from a fixed list > each answer carries its own confidence, 0 to 1 > 203ms average per answer, so a full post is judged before I finish reading it The output is not prose, it is a stream of typed JSON, that is the whole point: {"is_shill": 0.36, "confidence": 0.44, "ms": 91} {"mute_author": 0.80, "confidence": 0.46, "ms": 276} A model that writes paragraphs cannot be sorted, filtered or trusted twice, a model that emits typed fields with confidence can be turned into a rule, and one scroll proved it: > 30 posts, 1,318 judgments, keep 10, reply 4, skip 10, mute 3, 52% of my feed was worth the scroll, the rest I never have to see again Then I ran the same 12 questions across a batch corpus of 100,000 viral posts, to have a baseline for what a good post even looks like > 16.5M tokens in > $0.042 per million > total cost, $0.693 > output tokens are free 7 hook types ranked by 1M+ reach, superlative claims are most common, and convert poorly, receipts/proof drops are used half as much, yet hit the top 1% more often. Your feed is not an algorithm problem, it is a labeling problem, Jev just made labels cheap enough to run on everything you read
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