@PANewsCN 练习时长2年半的实习生,互联网 街溜子 & 碎嘴 I 人 关注我!了解更多金融八卦和亏钱小妙招~~ (有爆料,直接私信!)

Joined April 2022
前段时间 #三省六部制手机桌面 还挺火的,实习生必须整个币圈版! 也是当上皇帝了!朕的朝廷: 一、三省 中书省(策略决策):X、微信、Telegram、Deepseek、千问、ChatGPT 门下省(监察审核):SlowMist、CertiK、 尚书省(总理执行):OKX、币安、Bitget、Bybit、Coinbase、Hyperliquid、Aster
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翻译翻译 谁在这上面赚钱了
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孙哥在互联网上留下的素材太丰富了
👀之前火出圈的 「金明 X 孙宇晨」访谈下半场来了! 速览一下,这些观点很“孙哥”👇 1️⃣ 自称「宾大第四巨头」:人生打法是巴菲特、马斯克、特朗普三人的结合体。投资跟巴菲特学风控,看科技增长跟马斯克学,宣传传播跟特朗普学,英语口语就是看《飞黄腾达》(The Apprentice)练的 2️⃣ 跟巴菲特吃午餐时,曾建议他收购特斯拉。巴菲特回他:会让自己上报纸头版的公司从来不投 3️⃣ 起诉 WLFI:称这是「在商言商」的正常投资者诉讼,国内有点夸大了,诉讼标的约四五亿美元,「对投资组合不是要命的事」;同时表示仍是特朗普加密政策的坚定支持者,持续重仓美国科技股 4️⃣ 谈感情风波:公开写文章是因为诉讼一提交就会上网,「我没有不说的选项」;分手那个决定是 Claude 帮他做的,现在人生重大决策都有 Claude 参与 5️⃣ 立下健身 flag:20 天练 10 次,体脂从 26.8% 降到 25.8%,目标一年降到 20% 以下,明年发无 P 图接受监督。还当场被教了波比跳
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昨天早上看到余弦老师发的时候还没怎么上心 今天早上已经乱成一锅粥了 趁热喝了吧,诸君
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💬对话Robinhood 高级副总裁:Meme 热潮退了,我们看的是五年、十年以后! TOKEN2049 新加坡期间,PANews @tongtongbee 专访了 Robinhood @RobinhoodApp 高级副总裁兼加密业务总经理 @JohannKerbrat 。 Robinhood Chain 上线才约 100 天,链上热度已不如早期,面对这个问题,Robinhood 的答案是:不看日交易量,看五到十年。 他还谈了 Robinhood Chain 的定位、对竞争对手的看法,以及股票代币、永续合约、AI Agent 等下一步布局👇 🎥 视频时间线 00:08 加密,不该只属于工程师 02:25 刚起步的链,想做新金融的骨干 05:36 不抢份额,只想带人上链 07:51 新人入场,下一步押注永续合约 09:39 AI Agent,替你 24/7 盯盘 10:37 寄语中文社区
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PANews 疯狂实习生(吃瓜版) retweeted
SpaceX三季度营收大增161%,马斯克透露已与特斯拉成立特别委员会讨论合并 SpaceX今天公布上市后的第三季度财报:营收145.75亿美元,同比增长161%,高于市场预期的128.7亿美元;净利润44亿美元,去年同期还亏了10.5亿美元。 其中最大的进账来自AI业务"超级智能",营收75.6亿美元,超过了星链的52.48亿美元。 最大的看点在电话会上。据CNBC报道,马斯克说SpaceX和特斯拉已分别成立特别委员会,讨论两家公司合并。"我认为这是个好主意,但这取决于两家的特别委员会。"目前双方还没达成任何协议。 他还透露,两家合资的芯片厂Terafab计划2028年三季度量产,但他自己也说这个时间表"非常激进",误差范围很大。
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高端的实习生往往只需要简单的烹饪手法
Alexandr Wang 晒合影:HRT 实习生帮又聚齐了! Meta 首席 AI 官 Alexandr Wang 发了一张和 Hyperliquid 创始人 Jeff Yan、Cognition 联创兼 CEO Scott Wu 的三人合影,配文「HRT 帮重新聚齐了!」 三人都是量化交易公司 Hudson River Trading(HRT)2015 年首届实习生,那一届只招了 10 个人,同届还有 Decagon 创始人 Jesse Zhang。 Alexandr Wang 19 岁生日拍摄的老合照今年被网友称为「新一代 PayPal 黑帮」。
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在抖音刷到孙宇晨 @sunyuchentron 的 Coser 了😂 不得不说还真有几分神似! 按照目前这个孙学的发展速度,说不定真能在互联网有口饭吃🤣
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没有比直接发钱更好的了
马斯克建议 Anthropic 把股份直接给公众:我自己就会这么做 Anthropic 宣布设立「Anthropic 公益基金」,在股东批准后先注入公司 5% 的股份。公司估值在基准线之上每增长 50%,董事会可再追加 2%。 基金可用于向非营利组织大额捐赠,或直接向特定人群发放,比如应对 AI 对就业的冲击。加上联合创始人和员工此前的捐赠承诺,约 20% 的股份将用于公益。 马斯克随后在 X 上回应:「我建议把股份直接给公众。」随后他又补了一句:「这就是我将要做的。」
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当 Claude 说 我不会帮你全文翻译 的时候 我想起了,停止订阅了的 GPT,它向来不问就直接做了 当你结束了上一段订阅,投向了A家大小姐,她更强,但也更傲娇,有自己的小脾气。 不像平凡的前妻,虽然笨,但是从来不会说不 还好偷偷养了个 grok bot,但是额度总不够用——走API,那是有钱人做的事
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开上 特斯拉 Roadster :
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飞行汽车——个人飞船
120 年前,人类就在电影里幻想过飞行汽车 1906 年,英国无声短片《The "?" Motorist》里,一辆汽车开上墙壁,穿过云层,绕过月亮和土星环。 120 年后,特斯拉在新一代 Roadster 的发布会现场,把这个想象变成了现实:倒挂爬坡上墙,空中转体,飞跃障碍区。 科技在指数级加速,@SpaceX 已经让火箭助推器回收复用成为常态,还用发射塔的机械臂在空中接住了星舰助推器。2018 年,一辆特斯拉 Roadster 就已经被猎鹰重型火箭送进了太空。 也许下一辆穿云上月、遨游太空的特斯拉,也不再遥远。 @elonmusk @Tesla
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PANews 疯狂实习生(吃瓜版) retweeted
专访Bitget谢家印:UEX布局与八年成长背后的担当与野心 在 @bitget 成立八周年之际,PANews @tongtongbee 对话Bitget大中华区 @Bitget_zh 负责人谢家印@xiejiayinBitget 。从行业新人到交易所高管,从“证明自己”到“表达自己”,他回顾了八年成长,以及对个人、平台与团队关系的理解。 此外,他还和我们分享了Bitget布局UEX全景交易所的原因,以及又如何在竞争与焦虑中持续创新? 📹时间轴: 00:33 危机应对:回应、担当与信任 01:56 家印的个人成长:从证明自己到表达自己 03:42 理解用户:从个人经历到服务实践 06:32 面对行业焦虑,如何持续创新 07:34 UEX:走向多资产布局 10:45 Bitget 八周年:产品、服务与长期主义 12:05 未来目标与团队成长
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只能 卧槽 来形容了
😱特斯拉 Roadster 直接飞起来了! 比科幻电影还科幻!在特斯拉 Roadster 演示会现场,Roadster 冲上一座弧形坡道,一路爬到顶部,车身翻转到几乎倒立,倾角最高达到 170°,下压力约 4700 公斤。 第二次冲坡时,Roadster 从高处腾空飞出,车底的冷气推进器喷出压缩气体,随后落回地面,扬起一片白烟。全程高度读数最高约 69 英尺(约 21 米)。
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有时候检查作业比写作业更麻烦!
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PANews 疯狂实习生(吃瓜版) retweeted
I don't recommend anyone scramble to move their funds to new wallets today. But we should take the risks to cryptography from AI-accelerated math seriously, and minimize our exposure to not just quantum-vulnerable cryptography, but also potentially AI-vulnerable cryptography. The core new area of risk from this viewpoint is, unfortunately, ML-DSA / FHE / lattices. (and it's also another reason, along with quantum, why ECDSA might fall even faster than expected, hence the "fresh address" recommendation) So far most people have been in the mode of thinking "elliptic curves broken, hashes safe, lattices safe". But there is a good chance that the concrete security of lattices will take serious hits from the next two years of AI math. The basic threat model is: factoring is something that naively takes 2^(n/2) time, but over decades smart people have found and optimized number field sieves, and degraded that to 2^O(n^(1/3)), which is why RSA keys and signatures need to be ~400 bytes (and not 64 bytes). What if there are skeletons in the closet like that, both for elliptic curves and lattices, that we are simply not smart enough to discover - but bots soon will be? This is a major part of the reason why for the past year ethereum's lean roadmap has been going in the "hash-only" direction: no lattices, no ML-DSA, no Falcon, no lattice-based commitments inside ZK proofs, etc. Signatures in lean ethereum are all hash-based, either WOTS or SPHINCS-. For signatures and proofs, we already know how to go hash-only. The bigger challenge is for *public-key encryption* - and this goes far beyond blockchains. Secure communication, anonymizing protocols, lots of things need public-key encryption. And unfortunately there are long-standing mathematical theorems showing why public-key encryption cannot be done with hashes alone. You have to have some kind of trapdoor object that has at least one form of usable "structure" - either group theory (incl. isogenies) or lattices or code-based or potentially in the future even more newfangled and spooky things (local mixing?). But for anything that has structure, you should assume that AI will make at least some progress in breaking that structure. Here, one reasonable inference is that if you want to make something plausibly long-term secure, multiply the key sizes by 10. To me that's a very plausible world and something not at all extreme to predict. If AI will bring us 50 years of math in 2 years, then that 50 years of math may very plausibly include a "naive factoring -> GNFS" level of improvement to our ability to break lattices. In that world, lattices will still exist, but they will have to be significantly bigger to guarantee the same level of safety. And at those new larger sizes, hash-based constructions will beat lattice-based constructions on concrete efficiency in every use case where hash-based constructions are possible at all. Theoretically, of course it's possible that hashes are broken too (eg. P = NP would imply that). But I think P = NP is very unlikely. And intuitively, it's much more likely that a mathematical object has exactly no exploitable structure (like hashes are intended to), than that a mathematical object has exactly ~3 forms of exploitable structure (for elliptic curves: associativity, Schoof, pairings) and not some secret fourth form of structure we have not yet discovered that greatly degrades its security (for elliptic curves, ECDLP and pairing security). Similar for LWE, SVP, RLWE and the zoo of lattice problems. For this reason, we do not yet see any reason to worry and start padding the byte size of hashes (if we start to worry more, we would pad the round count first before doing anything to the byte size). Concrete TLDR, my own personal views: * Hash-based > lattice-based, in those situations where hash-based is possible at all * For anything lattice-based, be much more paranoid on param sizes. Remember that blockchains are only a small portion of the cryptography story; this point goes far beyond blockchains and applies to eg. access to websites, secure messaging, Tor / VPNs ... * For privacy protocols, strongly favor NOT putting encrypted notes onchain. Instead, send them offchain through some third-party mechanism. * If it's not difficult for you, keeping your funds in addresses which have not yet been used to make a transaction is a good idea. If it's easy for you, do it. **But be careful about migrations; I personally have lost more money in botched migrations than I have lost in all hacks combined**. * For multisig wallets, doing confirmations offchain is better than onchain, because this way the signatures of signer wallets do not get exposed to the public, so if ECDSA falls to AI much faster than expected, at least the multisig "gracefully degrades" to a 1-of-1 where the 1 is whoever was gathering the signatures - a much better place to be than "anyone can take the money" firefly.social/post/x/210783…
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好主意,说不定这样就能对抗 达里奥 了😂
说个刚才洗澡想到的思路 字节是不是可以考虑把百度给收购了,拿上互联网历史上巨量的语料去和百度网盘里的信息去训练模型。。。 毕竟市值也就7-8个月利润
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人说谎的时候小动作真的很多啊
🚨 NOW: Anthropic chief DARIO just walked up and said President Trump helped put his Super Intelligence worries at ease "If we do this right, we work with the President and everyone here, we can win safely!" Imagine that, all it took was DONALD J. TRUMP getting involved and the tables are turning! No more SI dooming, no more surrendering to China. America will remain number one BOTH for national and economic security, as long as Trump is president 👏🏻
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PANews 疯狂实习生(吃瓜版) retweeted
中秋刚好在思考这个问题,其实也是个老观点了,借机发出来。 -To C 产品研究的是人性,因为它的决策单元是个体,成功取决于能否触发欲望、情绪和认知偏差; 所以 To C 产品必须研究人性: 贪婪:优惠、红包、返现、免费额度; 懒惰:一键下单、自动续费、语音输入; 恐惧:安全焦虑、健康焦虑、FOMO; 虚荣:等级、勋章、排行榜、社交货币; 从众:销量排序、好友都在用; 损失厌恶:限时折扣、连续签到、即将过期。 我们看到的现象级移动互联网产品,包括币圈成功产品,都是人性机制的产品化。 -To B 产品研究的是权力,因为它的决策单元是组织,成功取决于能否嵌入预算权、审批权、否决权和责任链。 To B 产品的典型设计:组织架构、角色权限、审批流、审计日志、单点登录、数据看板、汇报关系、合规留痕。 这些本质上是权力的数字化: 谁看得见? 谁批得了? 谁改得了? 谁导得出? 谁背锅? To B 产品不是让所有人满意,而是让关键权力者放心。 所以不同方向的创业者要研究的课题不一样,To C 是行为设计学,To B 是组织政治经济学。
This quoted post is unavailable.
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PANews 疯狂实习生(吃瓜版) retweeted
Opus 5.5 created an animation highlighting one of the biggest risks for superintelligence...
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