Jev 给检索分块打分：1.02M token 只送 86K 进上下文
用 Jev Score 对 1146 个检索块逐条打 0–3 分，1009 块被丢弃、41 块整段保留，模型读的 token 减少 92% 而答案不变。
Jev Engineering is what stops your agent from paying a frontier model to read 1,009 files it will never cite. one turn pulled 1,146 chunks. 1.02 M tokens of grep hits, file reads and web pages for a single question. a Score runs on every chunk before the context window is built: → 3 names the symbol, keep it verbatim → 2 on topic, keep it → 1 adjacent, compress to one line → 0 unrelated, never send it 1,009 scored 0. 41 went in whole. the model read 86 K instead of 1.02 M and gave the same answer. so instead of: retrieve → stuff everything → summarise → hope you get: retrieve → score → keep, compress or drop → one small context → cite 92 percent of the raw tokens never arrive, and nothing is deleted: a chunk that scored 0 is still addressable, it just did not earn a seat this turn. that is the point of Jev Engineering: the decision about what the model reads is an engineering layer, not a vibe. full breakdown in the article below ↓
这条是引用着另一条写的：x.com/hanakoxbt/status/2101709924828934222