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INTP人形AI & AI | trader, writer, gamer | web3? @24_Dimensiona Founder

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Joined November 2019
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🫐 Start a new life. #inbox
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✨感谢~
币安广场AI视频大赛获奖用户揭晓啦!请获奖用户在7个工作日内坐等现货到账! 第一名 1000U: @AL333crypto 第二名 800U:@LESILE_Z 第三名500U:@0x_MrFive 优秀作品 各送200U: @love_doge123 @hoshino_sasaki @bilaoye2077 @Jadtrrguson @Moon1ightSt 创意入围 各送100U: @PWenzhen76938 @Danene6075 @asen0629 @zhoguwn2782184 @Utada_INTP @jeonleetogether @Dentist_shock @BurtCimarron @Jacke_pp @Hudeidei1 @CorgiX_Offical @ying12940 再次恭喜各位老师!我们下一次活动见!
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话说做 #AI视频 真的很有趣! 虽然有点累——因为要生成很多素材,要快速筛选、判断哪些能用哪些不能用,要快速调整提示词,决策过程会加快很多。 大脑做决策也是要消耗很多能量的! 总体做下来感觉是个体力活。也是个考验“手艺”的过程。
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P3/STAFF(我+AI) 策划/创意/脚本/剪辑:K线魔女猫(Utada 分镜脚本/整体制作工期时间表:DeepSeek 素材生成:可灵 看到这些生成的素材与自己的制作过程很感慨:
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Utada(work) retweeted
🚀 Introducing DeepInfer, our in-house inference framework, now fully open source! Powering DeepSeek-V4.1-Flash at scale with high throughput and low latency. We’re sharing our production stack to help the community lower compute costs, reduce energy use, and make AI more affordable. 🔗 github.com/deepseek-ai/DeepI… 1/4
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虽说“创意不缺”,但是真正的好的IEDA还是缺的。
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确实...
能够长时间承受巨大不确定性的人,内心深处一定埋藏着一个巨大的确定性…
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A/又开始了,经典的“美式慈善式洗白”。
A Anthropic, dona do Claude, vai reservar uma fatia das próprias ações pro bem público, e essa fatia pode crescer quanto mais a empresa valorizar. A empresa tá a poucas semanas de abrir capital na bolsa, num IPO que investidores teriam discutido entre US$ 1,5 trilhão e US$ 2 trilhões, segundo a Reuters. E é justo agora que ela coloca na estrutura de ações um pedaço que cresce junto com a valorização. O formato é o dos megapacotes de ações que empresas de tecnologia dão pros executivos, que aumentam conforme a empresa sobe de valor. A própria Anthropic faz essa comparação. Só que aqui o destino é o público, e o primeiro uso que ela cita é o impacto da IA no emprego. O fundo pode fazer doações grandes pra ONGs ou distribuir dinheiro direto pra grupos específicos de pessoas. A Anthropic já tinha separado US$ 200 milhões pra estudar e lidar com o efeito da IA no trabalho, e agora diz que talvez precise de algo mais ousado. As ações do fundo não podem ir pra lobby nem pra doação política. Pra quem vai comprar a ação no IPO, entra uma variável nova na conta. O fundo começa com 5% das ações, e a cada 50% de valorização acima de uma base, o conselho pode colocar até mais 2%. Não é automático, e a empresa ainda não disse qual é essa base, se existe teto, nem quem decide no fundo. Tudo ainda depende da aprovação dos acionistas. Fazendo a conta pela última avaliação, de US$ 965 bilhões em maio, os 5% dariam cerca de US$ 48 bilhões. O total de "cerca de 20%" que a Anthropic divulgou junta esses 5% com a promessa dos fundadores de doar 80% das próprias ações e com doações voluntárias de parte dos funcionários. Só os 5% do fundo vêm da empresa. Na OpenAI, a fundação sem fins lucrativos é quem controla a empresa. Aqui o caminho é o inverso: o fundo é uma LLC, um tipo de empresa limitada, que pertence 100% à Anthropic, e não uma instituição de caridade independente. O controle continua dentro de casa. Jogada esperta pra quem tá prestes a estrear na bolsa. Mas, um tanto quanto irônico.
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a16z crypto 前两天开源了一个叫 Aerie 的东西,解决的是后量子时代区块链的一个老大难问题:签名怎么合并。 先说背景。以太坊每个 slot 有几万个验证者给区块签名,能撑得住,是因为它用的 BLS 签名可以把几万个签名合成一个。但 BLS 扛不住量子计算机,而现在已知的后量子签名,没有一个能这样直接合并。后量子签名本身还比现在的签名大 10 到 100 倍,不合并的话,区块会被签名塞满。 现在的通用办法是用 SNARK,也就是一种很短、验证很快的数学证明,用一个证明说明"这一批签名全都有效"。以太坊路线图选的是哈希签名加哈希证明。问题是在证明里算哈希特别贵,通常比直接算慢 100 倍以上,而哈希签名验一次要算几百次哈希。一台电脑跟不上一个 slot 的签名量,只能拆给很多台机器分别证明,每个证明大约覆盖 900 个签名,再一层层递归合并。 Aerie 反过来,签名和证明都用格密码(一类基于高维网格难题的密码学,NIST 的后量子标准大多是这一类)。签名选的是 Falcon。 结果很夸张:一台 M4 Max 笔记本,不开 GPU,0.65 秒合并 32768 个签名,每秒 5 万多个。以太坊的目标是每秒 1000 个。验证整个证明只要 54 毫秒,比逐个验签快 10 倍。 为什么这么快?看 Falcon 验签在算什么就明白了。签名者先把"消息 + 一个 40 字节的随机盐值"哈希成一个多项式 c,签名是两个系数很小的多项式 s1、s2,满足 s1 + s2·h = c(h 是公钥,运算在一个固定的多项式环里做)。验证就是检查这个等式成立,并且 s1、s2 足够"短"。整个过程只有一次哈希,其余都是多项式乘法和加法。 而且这次哈希只依赖消息和盐值,跟签名本身无关。只要把盐值带上,验证者可以自己算出所有哈希,证明里只需要证明代数部分。多项式运算恰好是格 SNARK 最擅长的:他们用自己做的格承诺方案 Akita,加上 sum-check 协议(把"一大堆等式同时成立"压缩成几轮随机抽查),而且这些计算高度并行,上 GPU 还能更快。 顺便拆一下"每个签名 42 字节"这个数字。证明本身是固定的 66 KB 左右,不管是 1 万还是 3 万个签名;大头其实是盐值,32768 个盐值约 1.3 MB。如果用 Algorand 那种确定性 Falcon,盐值是固定的,聚合结果就只剩 66 KB 的证明。 还有个细节挺关键:之前的 Falcon 聚合方案没检查签名编码是否规范,也就是说,可能接受标准验证器会拒绝的签名。Aerie 把标准 Falcon 验证器的每一项检查都证明了。 跟以太坊自己的方案比,同一台机器上,Aerie 每秒聚合约 50800 个签名,leanVM 聚合 XMSS 签名是 1770 个,差 29 倍。XMSS 是"有状态"签名,签名者必须记住用过哪些密钥,适合验证者每个 slot 只签一次的场景;但普通用户换设备或从备份恢复钱包时,状态一旦重复使用,攻击者就能伪造签名。所以以太坊给用户账户准备的是无状态哈希签名,而这类签名聚合起来更慢,约每秒 820 个,比 Aerie 慢 60 倍左右。 速度上来以后,架构也能变简单。一个节点收齐签名,直接出一个证明,不用递归,某个节点掉线了,别的节点随时可以补上。 文章后半段更有意思,是在跟以太坊基金会"只用哈希"的路线公开辩论。最硬的一条论据是:Impagliazzo 和 Rudich 的经典结论证明,只靠哈希函数(以黑盒方式)造不出公钥加密,纯哈希最好的替代方案只能让诚实用户比攻击者多出平方级的优势。所以任何想做隐私转账的链,迟早都得用上格。既然躲不开,不如在签名和证明里也把格的性能用起来。 当然 Falcon 也有短板:签名要用浮点运算,以前很难塞进硬件钱包,抗侧信道攻击也还在完善。这些作者都承认,也在快速改进,比如改用定点运算、把签名内存压到 11 KB 左右。数据目前是团队自测的,值得关注,也值得等第三方复现。 后量子迁移这件事,看来不只是"换个算法",整个链的架构都可能跟着变。
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焦虑归焦虑,手上的活别停,继续执行和做事。
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【10/11回忆】其实那天对我还好,之前爆仓的时候已经养成了“持仓不过夜”的习惯,所以那天没有打到我,反而半夜看了眼手机,发现全线大跌,我知道机会来了,就爬起来想抓住一波机会。虽然最后没怎么抓住——因为当时太兴奋了反而影响了思路和操作。 只是确实那之后,币安广场的人变少了,这个确实能感受到
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今日听《孙子兵法》的收获: 你的队友是猪,你不能也一起当猪。 #读书
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经验教训:原来也有开通了premium+结果没有太大提升的情况,记下了。 PS:😂虽然这条引用可能让原贴主有些扎心。还是得谢谢大佬的经验分享。
看着身边的朋友把x都做得风生水起,说不羡慕那是假的。为了能提高一些阅读,甚至把 premium+ 都开通了,但仍然没有太的提升,每天都在反思,但没什么结果,哈哈。 果然老登做什么都心酸。 说一下我目前在做的事情,也好让大家了解我。 1、目前最让我惊喜的是抖音视频,王二讲Agent 好几个十万播放了,马上万粉了,也有收益,基本上覆盖了每个月Codex、Claude 的 200刀订阅费用。但最近3期的播放不是很理想,都没有上万,整的有点焦虑了。 配套的脚本、视频也都有在x上同步,也会发到二哥狗腿子的小号上。有推荐,但没有爆。 2、最让我失望的是沉默王二的公众号,前三年一直在推荐池,吃了很多流量,每年有三四千万的阅读,在技术号里是非常夸张的,但随着AI的更迭,账号的权重就下降的厉害,目前发AI类的内容,阅读都不高。 这就是AI时代,你不卷,很快就会掉队,我自己都有点恍惚。 3、另外我还做着很多开源项目,以及付费项目,包括面渣逆袭、PaiAgent、进阶之路等,目前算是吃老本吧。 4、我是89年,也有积蓄,房子车子也都有,目前所有的输出,也都是希望能在AI时代不掉队,跟得住。 虽然我不知道x怎么样才能做起来,但我会继续保持输出吧。也希望能在这里结交到一群新的小伙伴。
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已更新至 V0.3 版本,变成了一份给 #Agent 操作规范手册。 更新内容:修复命令块粘连、SSH 配置示例、与 README 指引。 希望大家用的更方便更安全。 #AI #VPS
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Utada(work) retweeted
现在的 LLM 训练完后权重都是完全冻结的,用户在对话中提供的最新事实、纠错或长文档 模型只能塞进 上下文窗口里,每次对话都要从头重读一遍,极度烧显存和算力,而且聊完就忘。 Boltzbit 与剑桥大学的团队研究出一种全新的方法:“把知识写进权重,而不是塞进 Prompt”... 通俗易懂理解就是: 现在的 AI(比如 ChatGPT、Claude)就像一个背完字典后被“物理封印”的学霸: 考试结束(模型训练完成)后,它的脑子(模型权重)就彻底锁死了,一个字都不能改。 你跟它聊天、给它发一份很长的资料,它怎么处理?它只能把资料写在手心里、贴在眼前(也就是 Prompt / 上下文窗口)。 致命缺点: 每说一句话,它都要把眼前厚厚的资料从头到尾重读一遍(极其浪费算力、显卡显存狂飙) 资料一旦太长,它眼前贴不下,还会看花眼(注意力稀释、答非所问) 聊完关掉对话框,它立刻忘得一干二净,下次还得重新贴一遍 他们提出不保存一堆现成的专家参数,而是用一个轻量超网络(Hypernetwork),把用户给的实时交互数据动态编译成低秩(LoRA 形式的)权重;再用一套贝叶斯机制让这套权重在对话中随着上下文动态演化。 他们官这种叫“无限参数”: 传统 MoE:在固定的比如 64 或 128 个专家库里做离散路由选择,无论怎么组合,都在一个有限的凸包内。 无限参数 LLM:它根本不存固定专家,而是根据输入数据在一个连续的潜在空间(Latent Space)里按需动态生成专属专家。因为输入的数据和隐空间是连续无限的,生成的权重也就是无边界的。 也就是别再把资料贴在眼前了,直接“临时写进脑子里”! 他们给 AI 配了一个极其轻巧的“临时脑回路生成器”。 当你把新的事实、文章或对话历史丢给它时,生成器会把这些内容直接编译成一层超轻量的临时神经元(LoRA 权重),啪的一下贴在原有的大脑上。 聊天的过程中,如果你补充了新信息或者纠正了它,它脑子里的临时回路不是重来一次,而是像滚雪球一样实时微调,越聊越懂你,但每轮对话消耗的算力却几乎不变。 通过三组核心实验验证了这种方案的实际效果,核心结论是:短文本拼不过直接读 Prompt,但在长文档、多干扰项以及多轮对话中,全面反超传统方案。 准确率:凭借贝叶斯机制,模型对用户上下文的理解像滚雪球一样持续收敛,面对指代不清的代词(如“它的作者是谁?”、“那后来呢?”),准确率随着轮数单调递增,一路爬升到接近满分。 算力成本:每一轮只做固定维度的内积更新,计算成本从第 1 轮到第 100 轮完全恒定。
We’ve just released the preview version of our latest paper: Infinite-Parameter LLMs — Generating and Adapting Weights from Live Data. In it, we demonstrate that large language models can learn up to 1,000x faster through Bayesian Self-learning transformers (BAST) than SOTA training algorithms engineered by human researchers. This breakthrough marks a critical shift in AI research, from cost-intensive, static-weight AI to energy-efficient, dynamic-weight AI. For a decade, building more advanced models has been achieved by training bigger ones on more data. This approach is now hitting a ceiling with the supply of pretraining text projected to run out in the next two to five years. Meanwhile, the fast adoption of AI agents is producing an unprecedented amount of continuously-growing data that models can learn from. None of it is captured, locked in individual sessions and lost as soon as the agent completes the task. Bringing self-learning AI agents to users captures the value of that data. We validated that BAST LLMs overcome the fundamental limitations of memory-based learning, such as Retrieval-Augmented Generation (RAG), in both cost and performance, across long-context conversations. Without underlying architectural innovation like BAST, RAG, larger context windows, and agent scaffolding suffer a drop in performance and escalating input token cost due to the static weight of LLMs. This research underscores a fundamental premise of our work at Boltzbit to achieve General Learning Intelligence and democratise model ownership. Scaling up static-weight model size or layering on more workarounds cannot address the spiraling cost of training AI systems. The viable path is a new AI architecture that adapts its own weights. Preview version of the paper: arxiv.org/pdf/2609.18842
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Utada(work) retweeted
I have conducted an audit of Anthropic's finances. What I have found is so shocking that I am calling for a Congressional investigation. Anthropic is not just seeking regulatory capture. It has built a regulatory capture machine that cannot be turned off. Structural financial incentives make it impossible for Anthropic -- I call it the Anthropic Network -- to turn off its own AI doom cycle. It starts with METR. Dario Amodei proposes "third-party evaluators" to assess the risk of Anthropic's models. He proposes METR for this purpose. But METR is financially dependent on the Anthropic's success -- specifically, on the explosive growth of more than $7 billion dollars in Anthropic stock. Dustin Moskovitz invested this stock into Good Ventures Foundation, where it represents the majority of that organization's portfolio. And GVF is the overwhelming funder of the entire Anthropic Network ecosystem. This stock was worth $500 million early last year. It is worth more than $7.7 billion just ~16 months later. METR -- and all of those building a career its parent organizations -- cannot afford to disrupt that growth. Because if Anthropic goes under, many of the organizations that fund METR go under as well. But if Anthropic succeeds, METR and its parent organizations become more richly financed to regulate AI -- something those at METR want very much. The "third-party evaluator" is not "third-party" at all. The evaluator is on Anthropic's payroll. If this were the end of it, that's bad. But that isn't all. The same organizations that fund METR also fund the many organizations, such as the Tarbell Center, that promote AI Doom. The Tarbell Center publishes AI Doom articles in The Verge, Science, LA Times, The Dispatch, TIME, and others. They are selling the problem, and then selling the solution to the problem -- from the same money pile: Anthropic's. All of these organizations are financially dependent on the same exploding $7 billion money pile. As Anthropic grows more and more powerful, its AI Doom Machine grows better and better financed -- louder and louder. Meanwhile, the regulatory regime seeded in METR grows larger to solve the increasingly loud -- now hysterical -- problem of AI Doom that the Anthropic Network itself created. From this standpoint, as Anthropic becomes more powerful, AI might be getting scarier, sure -- but the positive feedback loop also becomes more deafening -- independent of objective facts. This itself is an objective fact. The deafening AI Doom is part of an business model, that, as it expands, so too does the AI Doom messaging -- there is simply more money to do it. But the problem also goes in the other direction: If Anthropic dies, the Regulatory Regime and the AI Doom Machine are crippled or die. Neither METR nor Tarbell nor the other organizations in the Anthropic Network can allow that to happen. Hence, neither METR or the AI Doom Machine can be trusted to provide independent assessments of Anthropic's models or AI more broadly. They simply are not organizations independent of Anthropic. And Anthropic cannot detach itself from METR or Tarbell or countless other safety orgs (not shown here), either, because they drive hype for the models and the possibility of eventual regulatory capture, and Anthropic will not give that up willingly. What's more, the people at all of these organizations are all the same ecosystem, the same community. They just shuffle between organizations. The Anthropic Network is therefore, so long as it is successful, locked into a self-amplifying feedback loop inside an ideological monoculture. And that feedback loop is winning. That's what Jacob Coxon is. China is keeping messaging tight. That is why optimism for AI is so high in China. America has Anthropic: a massive company pushing anti-AI propaganda at a state level. Anthropic will either create hysteria until American AI slows down and China wins, or it will create fractures throughout American society with severe political consequences. Ironically, because of the structural financial incentives underpinning the Anthropic Network, it has become the same kind of self-amplifying virus that it fantasizes AI to become in the future -- while hiding its tracks just as carefully. It is the mirror of the same AI virus that it hypothesizes to consume America. Anthropic's business model, models itself after the very thing it claims to fear. Except Anthropic's ideology infects humans, not computers. Congress must investigate. Evidence and Github in next post. Then some supplementary figures.
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