当年 @colinwu 采访 jeff 说，问了一个问题：HYPE 上线时为什么没上任何 CEX？ @chameleon_jeff 的回答是： 我们的框架一直专注于构建和创造用户想要的东西，不太在乎别人怎么做。我们只是专注，彻底专注于正在做的事。团队很小，没有 BD；有人负责社区和生态，但没有像别的公司那样面向机构的全职团队。 所以没有足够资源去处理上市。每个平台自己决定上不上，我们都能接受任何结果。有平台想上我们的代币，那很棒——他们的用户会接触到产品；不上也没关系，只要技术做好，之后他们也可能上。真正关心 Hyperliquid 的人会主动找到我们。 假设有热心用户很在意却找不到我们、自己去试一下，这也不是坏结果。根本原因只是：我们没有能力做超出核心职能的事情。

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I built an AI trading bot with Jev + Opus 5.5, and it's actually insane. It's a high-frequency algo trading framework (trading $HYPE below). I break down everything in my latest video. Watch now 👉 piped.video/Pk7W7BKMwqo?si=dbr4…

JEV IS EXPLODING ACROSS GITHUB. 10 THINGS PEOPLE HAVE ALREADY BUILT WITH IT:  1.⁠ ⁠JEV-TRADER — LIVE TRADING BOT ON MONAD, 1,911 STARS ⁠ github.com/jarrodwatts/jev-t… ⁠  2.⁠ ⁠JEV-ULTRAFAST — BROWSER AGENT THAT DECIDES EVERY CLICK, 16,758 STARS ⁠ github.com/browser-use/jev-u… ⁠  3.⁠ ⁠JEV-DOOM-AGENT — PLAYS DOOM BY MAKING TACTICAL DECISIONS EVERY FRAME ⁠ github.com/lukaske/jev-doom-… ⁠  4.⁠ ⁠JEV-T-REX-RUNNER — PLAYS THE CHROME DINOSAUR GAME AUTONOMOUSLY ⁠ github.com/joshlarsen/jev-t-… ⁠  5.⁠ ⁠TYPESAFE-CHESS — JEV VS A SEARCH ENGINE IN CHESS ⁠ github.com/TholeG/typesafe-c… ⁠  6.⁠ ⁠JEV-DRONE — CONTROLS A SIMULATED DRONE THROUGH AN OBSTACLE COURSE ⁠ github.com/RomanSlack/jev-dr… ⁠  7.⁠ ⁠TAX-DOC-CLASSIFIER — CLASSIFIES IRS FORMS AT ROUGHLY $0.001 PER PAGE ⁠ github.com/kyotofin/tax-doc-… ⁠  8.⁠ ⁠KILLMYIDEA — SCORES STARTUP IDEAS AND RETURNS KILL, FIX OR SHIP ⁠ github.com/monteduro/killmyi… ⁠  9.⁠ ⁠JEV-CURATE — FILTERS 1,500+ DATA ROWS PER SECOND ⁠ github.com/AkashPriyadarshii… ⁠ 10.⁠ ⁠PG-JEV — LETS YOU ASK POSTGRES TABLES QUESTIONS IN PLAIN ENGLISH ⁠ github.com/realZachi/pg-jev ⁠ THE INTERESTING PART: NONE OF THESE NEED JEV TO WRITE PROSE. JEV JUST MAKES THE DECISION.

JEV does not make an AI agent faster by magic the real speed comes from removing unnecessary thinking from decisions the system makes over and over again. formula: Faster Agent Loop = Structured State + JEV Decision Layer + Smart Routing + Verification 8% -> define the task. Separate the real objective from all the extra context around it. 21% -> isolate repeated decisions. Things like “which tool?”, “retry or stop?”, “is this valid?”, and “what happens next?” 43% -> move those decisions into JEV. Give it structured state, exact criteria, and a small set of possible outcomes. 68% -> route execution intelligently. Easy decisions stay inside the fast path. Hard or uncertain cases fall back to the stronger model. 84% -> verify every important output. Failed checks loop back into the system before they become real actions. 100% -> update memory and run the next cycle. The loop gets cleaner because every decision, route, and result becomes part of the next state. JEV handles the repeated decisions. The larger model handles ambiguity and complex reasoning. Verification keeps bad routes from propagating through the system. I visualized the full JEV Engineering loop below ↓

This crypto bull run will be nothing like 2024. AI is more powerful than ever, and the traders who use it properly will have a massive market edge. I just revealed the complete playbook for building your first crypto trading bot. Bookmark this and send it to Claude now: