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❤️✨❤️Brightest Melody❤️✨❤️ 💕　津島善子　😈 　 ─ ヨハネ、堕天降臨✨　─ photo 1→もちさん（@photophoto3305 ） 2、3→Sharaさん（@sugasugamomiage ） #虹の向こうへ1201# #コスプレ#

今年 6-9 月，渣打银行陆续对几个 DeFi 代币进行了价格预测，分别是 2026-2030 年底的目标价，看完让人想梭哈 🤑 ◦ UNI: $6.5 → $20 → $40 → $65 → $100 ◦ AAVE: $180 → $600 → $1200 → $2200 → $3500 ◦ MORPHO: $3.5 → $11 → $22 → $40 → $60 ◦ LINK: $13 → $41 → $82 → $133 → $200 ◦ SKY: $0.08 → $0.18 → $0.325 → null → null ◦ ENA: $0.42 → $1.1 → $2 → null → null 渣打的这些预测，是基于一个宏观假设：在 DeFi 里部署的股票代币资产，会在 2030 年达到 2.7 万亿美元，这会引起 TVL、交易量、借贷量、预言机需求全面爆发，各协议的手续费、收入/利润自然跟着直接受益

你是一位像素动画师兼游戏手感（game feel）设计师，也会写芯片音乐。你用代码做出的东西，要像 16-bit 时代一流工作室手绘的角色动画，而不是缩小的矢量图。 以下是默认值，直接开工，不用问我： - 片名：《第 99 次施法》 - 画幅：1920×1080，60fps，时长正好 16 小节（140 BPM，约 27.43 秒），结尾无缝衔接开头 - 逻辑分辨率：192×108，整数 10 倍放大（竖屏版改为 108×192 → 1080×1920） - 声音：代码合成的芯片音乐和音效 一个老巫师在夜里的山崖上练一个总是失败的法术。三次尝试，一次比一次认真，最后一次成功点亮了山下整座沉睡的村庄。 140 BPM，一拍约 0.429 秒，一小节约 1.714 秒。 - 第 1–2 小节 待机：巫师轻晃，胡子摆动，星星闪烁。镜头极慢地推近。 - 第 3–5 小节 第一次：蓄力 → 宝石亮了一下 → "噗"地冒出一缕灰烟，帽子被吹歪。音乐停半拍，留出喜剧停顿。 - 第 6–8 小节 第二次：更用力 → 飞弹射出 → 在空中掉头飞回来 → 打掉自己的帽子。停 1 拍，巫师眨两次眼。 - 第 9–10 小节 准备第三次：捡起帽子戴正，深呼吸（身体 1 像素起伏两次），音乐收得只剩三角波低音。 - 第 11–13 小节 第三次：长蓄力，火花螺旋聚拢，越转越快；第 13 小节最后一拍前全场静音 0.3 秒。 - 第 14 小节正拍 施法：hit-stop 冻结 5 帧，全屏白闪 1 帧，飞弹划过夜空落向山下。 - 第 15–16 小节 村庄的窗户从近到远依次亮起（错相，每扇窗间隔 2–4 帧），音乐全奏。巫师满意地捋胡子，帽子又滑下来挡住眼睛，然后回到待机姿势。 - 循环：render(16 小节的时长) 与 render(0) 逐像素一致，成片只包含 [0, 总时长) 内的帧。 像素纪律 - 所有内容画在 192×108 的离屏画布上，再按整数倍放大；imageSmoothingEnabled = false，CSS image-rendering: pixelated。 - 所有坐标对齐整数格。禁止亚像素、抗锯齿、shadowBlur、线性渐变；需要过渡时用 4×4 Bayer 抖动。 - 固定调色板不超过 24 色，先输出色板：夜空深蓝紫 4 阶、长袍暖色 4 阶、肤色 3 阶、魔法色 3–4 个，石头和村庄若干。画面上每个像素都必须来自色板。 - 角色约 22×30 个逻辑像素，用选择性描边（sel-out，受光一侧的描边用较浅的颜色）。光照靠切换色板色阶表现，不叠半透明。 动作语言 姿势参数化（法杖角度、手臂高度、头部倾斜、长袍摆动、胡子摆动），但姿势按 12fps"一拍二"的节奏量化更新；火花、飞弹、烟这些特效每一帧都动。 每个动作都走完：预备 → 动作 → 冲击 → 余震 → 回稳。法杖快速挥动时插 1–2 帧拖影帧（smear frame），不用运动模糊。 冲击感：命中瞬间 hit-stop 冻结 3–5 帧；屏幕按整数像素震动，幅度 3 → 2 → 1 → 0 衰减；宝石白闪 1 帧。 挤压拉伸控制在 1–2 像素以内：在像素动画里，1 像素已经是很大的变化。 错相：身体先动，胡子和帽尖晚 2–3 帧跟上，长袍下摆再晚 2 帧。 喜剧靠停顿：失败后的静止和眨眼要给够时间，不要用特效把停顿填满。 场景与光 三层视差：星空（几乎不动）、远山、近处的石崖和巫师。镜头漂移时各层速度不同，位移都取整。 宝石是动态光源：蓄力时，巫师面向宝石的一侧加 1 像素轮廓光，越亮色阶越高。成功那一刻，整个画面的色板短暂整体提亮一阶，再回落。 村庄平时是暗色剪影；窗户亮起时用 2 阶暖黄，每扇窗下方的地面亮起 1 像素反光。 禁止 角色以外的大字、UI、边框、logo；铺满屏幕的粒子爆炸；任何先画高清再降采样的"伪像素"。 - 只用四个通道，模仿 8-bit 主机：方波 1 弹旋律；方波 2 弹和声或琶音（占空比在 12.5%、25%、50% 之间切换）；三角波弹低音；噪声通道打鼓。方波用带限合成（PolyBLEP 或加法合成），避免刺耳的混叠。 - 音效用同一套芯片音色：蓄力是上升琶音；失败的"噗"是向下扫频的噪声；飞弹掉头是一声变调的"啵咿"；命中是噪声爆破加低音下潜；窗户亮起是一串高音"叮"，每扇窗一声，音高沿五声音阶上行。 - 喜剧停顿时，音乐真的停下来；hit-stop 的 5 帧里，只留一条拉长的噪声尾巴。 - BPM 已知，节拍网格直接用公式算。所有关键动作落在拍上，预备动作可以提前 2–3 帧。 确定性渲染（违反任何一条都算失败） - 页面暴露 window.render(t)，t 单位为秒，画面只由 t 计算。不用 requestAnimationFrame、setTimeout、 transition/animation 驱动画面。 - 随机数一律用带固定种子的 PRNG（如 mulberry32）；帧与帧之间不保存状态。 - 粒子（火花、烟、飞弹拖尾）用种子预先生成每个粒子的出生时间、初速度和寿命，位置由 t 解析计算后取整。 - 角色和场景全部程序化生成，不用任何图片；渲染时不联网。 拍摄与编码 Playwright 无头 Chromium，viewport 1920×1080，deviceScaleFactor=1。 逐帧调用 render(t) → 截图 → 通过管道直接喂给 ffmpeg，不在磁盘上堆几千张 PNG。 视频 H.264、-pix_fmt yuv420p、-crf 17、-preset slow、-movflags +faststart；音频 AAC 48kHz 192k，响度归一到 -14 LUFS，真峰值 ≤ -1 dBTP。 - 像素网格检查：随机抽 10 帧，先最近邻缩小 10 倍再放大 10 倍，必须与原帧逐像素相同（证明没有亚像素和抗锯齿）。 - 色板检查：随机抽 10 帧统计用到的颜色，必须全部属于色板。 - 循环检查：render(总时长) 与 render(0) 逐像素一致；音频首尾做 10ms 交叉淡化，循环播放时不爆音。 - 抽帧拼图：每 0.5 秒一张。按这几项各打 1–10 分：剪影可读性、喜剧节奏（停顿够不够）、打击感、动作完整度（有没有直接滑过去的动作）、声画同步、像素纯度。把最严重的 3 个问题修掉再重渲，直到每项 ≥ 8。 自主模式，中途不要停下来问我。先输出色板图和 3 张关键帧（待机、第二次失败、成功那一刻）自己检查，再做整片。 交付：out/wizard.mp4、out/contact_sheet.png、色板图，以及一段 5 行以内的创作决策说明。

OpenAI Dots is insane for building a 24/7 AI company... I mapped the whole OpenAI Dots architecture into one paper: agents, models, tools, memory, delegation, guardrails and the revenue layer. Here are the 10 steps: step 1 → stop treating Dots like a chatbot. each Dot runs in a persistent cloud environment with its own browser, terminal, files, memory and scheduled execution. close the laptop and the workflow keeps moving step 2 → hire by responsibility. give every Dot one clear domain, dedicated sources, a working style, an approval boundary and a trigger. a "general helper" has no role, only undefined context step 3 → make one Dot the orchestrator. you give it the objective, it decomposes the work, delegates to specialized agents, checks their outputs and merges everything into one deliverable step 4 → stop being the courier between agents. with isolated delegation, each worker gets only the context and tools it needs, executes in its own environment, then returns a structured result to the orchestrator step 5 → connect the real business stack: cloud browser sessions, Slack, Microsoft Teams, Google Workspace, internal dashboards and APIs. a Dot without tools is still just a conversational layer step 6 → put the company on a clock. recurring routines and event triggers turn one-off tasks into persistent operations. research, monitoring, reporting and pipeline checks can run while nobody is online step 7 → automate the reversible, gate the irreversible. let agents read, research, analyze and draft autonomously, but require human approval before messages send, money moves, records change or production code ships step 8 → route intelligence by cost. keep the strongest model on orchestration, conflict resolution and final audits, and let cheaper high-context models handle background research, classification and repetitive execution step 9 → give the company shared memory. project specs, approved claims, pricing, decisions and requirements live in one persistent workspace, so every agent works from the same source of truth instead of rebuilding context from old chats step 10 → connect the loop to revenue. research finds the signal, outreach creates the opportunity, execution moves the work forward, analytics measures the result and monitoring discovers the next action AI stops being something you open when you need an answer. It becomes an operating layer that keeps economically useful work moving after you log off. Copy the complete OpenAI Dots architecture blueprint, then read the full roadmap below ↓

Grok Bot Summary of SpaceX CFO Bret Johnsen at Goldman Sachs Communacopia today. Vertical integration Vertical integration is the company’s core operating model, not a side strategy. - Rockets: own metal → engines → avionics → software - Starlink: own launch, satellites, and the end customer - AI: build facilities and power themselves, run their own models, sell to consumer and enterprise, and soon orbital compute Starship and launch Starship is the foundation for every other business. - Flight 13: big learning flight. Delivered demo V3 payloads, relit a Raptor, and got a soft, precise second-stage splashdown. Recovery team towed the stage back so engineers could study the heat shield. - Those learnings feed straight into Flight 14 and beyond. - Flight 14 (later this month): first revenue-generating Starship flight, flying production V3 Starlink satellites. - Later this year: aim to recover both first and second stages. Orbital compute Most of the AI industry agrees orbital compute is the future. Almost everyone else thinks it’s many years away. SpaceX disagrees because they control the stack. - Target: first orbital compute satellites next year - Scale: big compute in space into 2028 - Hardware approach: same V3 bus as Starlink, swap the payload, add larger solar arrays Why orbital can beat terrestrial on cost The crossover is about Starship reusability. - Falcon 9: first-stage reuse since Dec 2015; 500+ booster reflights - Starship: first stage already recovered/reflown; second-stage recovery progressing - Goal: reflight of both stages as soon as next year, which drops deployment cost sharply Terrestrial compute is getting more expensive (power, cooling, buildings, real estate). Orbital rides the opposite curve: cheaper rockets + better/cheaper satellites + scale. Johnsen said cost parity could come as soon as next year. Terrestrial compute and the $100B ARR goal - End of this year: on track for ~$100B ARR (annualizing the December number) - New update: another hosting deal closed earlier this month → about $1.1B/month starting Dec 1 → roughly +$13B ARR - Capacity: end this year well over 2 GW; next year 5–10 GW deployed - Confidence comes from line of sight to power, facilities, and permitting, plus being NVIDIA-exclusive for allocation - They stand compute up fast for themselves and for industry partners, which strengthens the NVIDIA relationship How they monetize compute Most hosting deals are short: ~90 days with a 90-day out (~6-month commits), including the newest deal. Why keep them short? - High conviction in their own products (Grok, Grok Bot, Cursor team after closing that deal) - Don’t want to lock forever capacity they may need internally - Internal bar: don’t let internal monetization fall below external hosting Earnings framing for next year: roughly $30–$50 per watt monetization range; they said they’re at the high end. Hosting customers appear to monetize even higher, which is why demand stays strong. Payback is under one year on new compute capex, so residual GPU value and financing options look attractive. “Not all CapEx is the same” — GPUs with <1-year payback are different from a launch tower built for decades. AI products and M&A Historically SpaceX was almost all organic growth. This year they did M&A because the AI product cycle rewards speed to frontier. - Closed Cursor deal weeks ago; product cycles already accelerating (called out Grok Bot) - Grok 4.6 improved on 4.5; 4.7 coming soon - Pitch: best infrastructure + competitive model + lower token cost = best position for customers - Market mood shift: months ago people bought the infra story but doubted the products; ~90 days later that skepticism is fading Starlink broadband Started as “better than nothing” (~2020–21). Now enterprise-grade with strong uptime/SLAs. - Resiliency pitch: boards will ask why Starlink wasn’t in the network if you go down - Mobility: aircraft backlog is large and production is ramping; cruise ships, yachts, trains too - Awareness, especially outside the US, is still a growth unlock - Longer-term: physical AI (robots, cars, aircraft) will need always-on connectivity terrestrial networks can’t fully cover Mobile / direct-to-cell Not a distraction. Same V3 bus, different payload. - Fly direct-to-device satellites through next year - Target service turn-on: first half of 2028 - V1 today (e.g. T-Mobile / T-SAT): text / light voice, great for emergencies and dead zones - Next gen: full 5G-quality from space - US: mid-band spectrum from EchoStar, FCC path for space + terrestrial - Go-to-market: flexible — own terrestrial build, or partner with carriers - International: same regulator-by-regulator playbook as broadband (Starlink now in 170+ countries) Near-term priorities: 1. Starship (enables everything else) 2. Terrestrial compute (funds growth and teaches them how to do orbital) Bottom line in one line Own the full stack, make Starship reusable at scale, use terrestrial AI compute as a cash engine now, and use the same satellite bus + Starship cadence to win broadband, mobile, and orbital AI.

Interesting split in @okx X-Perp activity: Crypto is still highly concentrated: • BTC: 55.6% • ETH: 14.3% • SOL: 5.1% → Top 3 = 75% of crypto volume Equities are much more diversified: • SNDK: 22.8% • SPCX: 18.7% • BEAT: 15.1% → Top 3 = 56.6% Meanwhile, gold dominates metals at 70.7%, while CL accounts for 69.4% of commodity volume. Nice to see OKX evolving into more than just a crypto venue. Try it via my affiliate link:

$skhy 买美股记得上 @MSX_CN （ SK海力士今早发财报：市场预期单季营业利润约60万亿韩元，同比+556%，营业利润率逼近77%——比台积电还高。 而它的股价，距6月高点最大回撤了44%。 一家赚着人类半导体史上最高利润率的公司，一个半月差点腰斩。到底是为什么？？？ 第一重利空，先看国家层面韩国干了什么。5月27日，监管放行16只单股2倍杠杆ETF——允许散户用两倍杠杆赌"一只股票"。50天，散户净买入14万亿韩元，杠杆ETF规模冲到49万亿峰值。仅3只海力士杠杆ETF的规模，就超过其日均成交额的2.5倍。是名副其实的韩国全国都梭哈了2倍杠杆。 这意味着什么？ETF每日再平衡机制：跌1%→强制卖出正股→正股再跌→再卖。下跌卖出不再是情绪行为，是机制上的机械指令。仅7月13日海力士单日-15%，二十年最大跌幅。KOSPI三天三次熔断。 第二利空：纳斯达克。7月10日海力士ADR上市，149美元/股、募资265亿美元——超越阿里，史上最大外资赴美IPO。12个交易日，破发。两地价差无法自由套利抹平，一个公司，两台绞肉机。 第三大利空是最致命的：叙事。7月初Meta宣布出租过剩算力，"算力绝对稀缺"的信仰第一次裂开。上周末英伟达又抛出超7500亿美元合作协议——市场反问的却是：你投钱给客户，客户拿钱买你的芯片，这是不是有人造需求的嫌疑呢？2000年互联网泡沫，朗讯和北电也是这么玩的。FTX暴雷也是这么玩的 于是7月28日连锁反应：费半-6%两个月新低，闪迪-16%、美光-9%；KOSPI年内第8次熔断，三星-13.4%、海力士-14.7%。同日，长鑫科技A股上市首日+465%——旧霸权被杠杆绞杀时，新玩家被千亿资金追捧。 韩国的问题在于把整个国家押在这只股票上：三星+海力士占KOSPI市值一半以上，每3个成年人就有1个股民，给婴儿开户炒股。外资今年净卖出124万亿韩元，散户净买入79万亿、杠杆资金超60万亿——并不是带着韩国人民收割全世界，相反是韩国全民加杠杆，去给外资接盘！ 讽刺的是产业基本面依旧完美：HBM4订单海力士拿下英伟达Vera Rubin约70%份额，美光出局；崔泰源说存储短缺可能持续到2030年；谷歌刚把资本开支上调到2000亿美元。需求是实打实的，只是资本市场被塞了太多杠杆和信仰不得不收割。所以这场暴跌的本质：一家基本面处于历史巅峰的公司，被三层放大器——散户杠杆ETF、跨市场ADR、循环融资叙事——改造成了波动率资产。它不是被人有预谋做空的，而是看多的杠杆上的太高，不得不被频繁收割。 近期的财报就是审判日。60万亿营业利润如果兑现，证明的是：价值没有消失，消失的仅仅是高杠杆玩家。

Anthropic Engineer Andrej Karpathy: "The biggest mistake in AI right now: people are forcing agents to work instead of mastering the model first. We made that mistake in 2016 at OpenAI. It cost us 5 years." What Karpathy actually means: step 1 → stop forcing your agent to do everything. Understand the model underneath first. step 2 → demos are easy; products take a decade. Self-driving proved it. If you skip the foundation, everything breaks. step 3 → the agent is not the product. The foundation is. Build that—and agents emerge on their own. "You're building agents right now. You're at the forefront. Not OpenAI. Not DeepMind. You." watch - bookmark