与「PoolX」相关的搜索结果

好久都没有撸交易所的毛了，今天在看 Bitget 母情节活动的时候，看到 Bitget 的PoolX板块有两个空投。 $BILL 这个池子给的是真多，交易池的年化达到11.41%。 多数人估计都参与不了「交易池」，这里要科普一下，交易池是给用 Bitget 交易的用户准备的，分配的额度更高。

#Bitget# 昨天上线了 $NIGHT 的 Launchpool 重点给小伙伴们捋一下： 活动总奖励 1200 万枚 NIGHT，价值50万美金 时间就一周，从 4 月 9 日到 4 月 16 日 参与方式两种： 第一种，用 BGB 锁仓，目前年化31% 这是大头池子，占了 1125 万枚 门槛也不高，5 个 BGB 就能参与 第二种，用 NIGHT 锁仓，目前年化83% 这个池子小一点，75 万枚 顺便说一下，#Bitget# 的PoolX，也可以用ETH跟BTC挖 $NIGHT 相比较当下的市场，这个年化已经算是很高的了 这种机会不一定是暴富型，但至少节奏没那么焦虑 参与流程图片，我放在下面了👇

Air Force One is wheels up. Shortly before departure staff took all badges and pins issued by China from reporters. They then put them and WH staff burner phones in a bin, per pool

Trump and Xi lunch menu: Minced Codfish in Seafood Soup Crispy and Stir-Fried Lobster Balls Pan-Seared Beef Fillet Stuffed with Morel Mushrooms Kung Pao Chicken and Scallops Braised Seasonal Greens, Bamboo Shoots, Mushrooms, and Beans Stewed Beef in a Bun Steamed Pork and Shrimp Dumpling Chocolate Brownie Fruits and Ice Cream Coffee/Tea Meanwhile the traveling White House press pool got this

🚨 NOW: President Trump has just sat down with PRESIDENT XI inside Zhongnanhai in Beijing The two just held a PRIVATE, off-camera meeting away from the press 🔥 Trump says they've made "fantastic trade deals," per WH pool 👏🏻

Stop testing and rewriting prompts manually! Most teams run evals, look at failures, guess what's wrong, rewrite the prompt, then repeat. It's slow and you never know if your rewrite actually fixes the root issue. The better way is evolutionary optimization. Instead of manual rewrites, you use genetic algorithms to analyze eval feedback and rewrite prompts automatically. The algorithm maintains diverse prompt candidates that excel at different problem types, not just one "best" version. DeepEval does this using GEPA - Genetic Evolution with Pareto Selection. You provide a prompt template, test cases, and metrics to optimize for. The optimizer handles the rest. Here's how it works: It splits your test cases into validation and feedback sets. The validation set scores every prompt fairly. The feedback set provides training signals for mutations. Then it starts evolving. It selects a parent prompt, runs it on a minibatch of test cases, collects metric feedback on what failed, and uses an LLM to rewrite the prompt addressing those issues. If the rewritten prompt scores better, it gets added to the candidate pool. After several iterations, it returns the highest-scoring prompt. Key capabilities: • Works with 50+ built-in metrics - answer relevancy, hallucination, bias, task completion, and more. • Supports multi-objective optimization - optimize for multiple metrics simultaneously without forcing tradeoffs. • Configurable iterations and minibatch sizes - control search thoroughness and compute cost. The best part? It's 100% open source. Link to DeepEval in the comments!

Travel pool note #18# - Banquet menu Banquet menu, per WH Hors d’Oeuvres Lobster in Tomato Soup Crispy Beef Ribs Beijing Roast Duck Stewed Seasonal Vegetables Slow-Cooked Salmon in Mustard Sauce Pan-Fried Pork Bun Trumpet Shell-Shaped Pastry Tiramisu Fruits and Ice Cream Danny Kemp AFP Thu. May 14, 6:53 PM CST

300 BNB. One tournament. Are you in? 🫵 Trade $LAYER on Binance and compete for a share of the prize pool. Early Bird Boost available for early traders. Act now →

Earlier today a vulnerability in Huma’s legacy v1 contracts on Polygon was exploited for 101,400 USDC. No user funds at risk and PST is not impacted. Huma’s v2 system on Solana is a complete rewrite and this issue does not apply to v2 systems. The teams were already in the process of sunsetting all the legacy v1 pools, and have paused v1 completely now.