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bitcoin:native shares a very classic long-term macro on-chain indicator for Bitcoin: STH-RP to TMMP Ratio The ratio of the realized price to the real market average price for short-term holders This indicator clearly reflects the interplay between short-term market speculation and the fair value of the overall market. Current core data: -BTC current price and short-term costs: Currently, both values ​​are around 77.4K. This means that short-term investors who entered the market in recent months, The overall situation is at the break-even point. -Ratio trend: The current ratio is 0.8998, and the 7-day and 30-day moving averages are showing a fluctuating downward trend. On-chain data is clear. Short-term holders' cost basis is converging towards the true market average. The market as a whole is in a phase of deflating the bubble and clearing out speculative capital. Historically, when this ratio spikes, it usually corresponds to a period of market overheating. When this ratio falls below the 0.75 red line (the historical lows of 2015, 2018, and 2022), it indicates the establishment of an absolute bear market bottom. Currently, this ratio is at a relatively neutral to low level of around 0.9. And the trend is still downward. This indicates that the market's blind optimism has been effectively suppressed. The chips are undergoing a thorough turnover. At the break-even point where short-term holders find it unprofitable and even begin to feel anxious. It is often a crucial window for accumulating strength and choosing a direction. Be patient and watch for further crossovers of the cost line. #Onchaindata# #welinkBTC# bitcoin:native

倒查 8 年税务。彭博援引消息人士透露，北京、上海和广州的一些个人被告知，针对他们的税务调查，可能至少会追溯到 2018 年。 被盯上稽查税务的人，通常都很富有，账户里往往有超过 3000 万美元资产。 有一些甚至是华裔人士，拿到了外国护照，但后来又回到中国生活。

🦅稳定币的"前三守门员"，正在被一个 2025 年才出生的新人撞门 ▪️USDT 诞生于 2014 年 10 月，今年 12 岁。 ▪️USDC 诞生于 2018 年 9 月，今年 8 岁。 ▪️USDS 诞生于 2024 年 9 月（由DAI升级而成，1:1兑换，实际8岁，比USDC早9个月） ▪️USD1 诞生于 2025 年 4 月，13 个月。 ▍这个牌桌上，USD1 是唯一一个真正从零开始的新人，却已经稳稳坐在第四把椅子上 →USDT 用 12 年换来了全球 CEX 默认交易对和 OTC 灰色市场的事实结算地位。 →USDC 用 8 年换来了美国合规标杆和 BlackRock、Visa 这一票 TradFi 巨头的深度集成。 →哪怕去中心化路线的 USDS，也是在 DAI 的 7 年积累上做的升级。 这三家共同的故事告诉所有新人一句话：稳定币这门生意吃的是网络效应 + 流动性深度 + 监管信任，每一样都不能用钱在短期内砸出来。 ▍USD1 用 13 个月把这条规矩撞碎了 储备 100% 由美债和现金背书、BitGo 官方托管、机构级合规格式，让 TradFi 资金可以直接打款来。MGX 对 Binance 的 20 亿美元投资采用 USD1 结算，成为史上最大规模稳定币结算样本 Binance 一年内连续多轮 WLFI 空投激励持有 USD1，最新一轮 13M 美元奖池 5/15–6/12 正在派发，年华6-7% 这套打法不再依赖加密原生增长曲线，靠的是机构 + 交易所 + 政策三股力量的合力。 第四已经坐稳，第三只差一步。USDS 在 Sky 迁移阶段供给增长偏缓，去中心化叙事红利消退，机构端基本不碰它。 而 $USD1 的飞轮还在加速！🚀

三笘選手が食べに行ってた鰻屋さん！ 気になってはいたけどまだ行ってないから今度行ってみよう！ ケガで残念だけど、その分、田中碧選手が頑張ってくれるはず！！

这算是90年代内地的顶流女明星吧？ 中国明星足球队官方女子啦啦队成员， 那英正是这支明星啦啦队的队长。 2018年那英在微博重新发布这张20年前旧照，感慨岁月友情，引爆全网！

最近，风华高科因RC/RS系列0402、0603晶片电阻订单激增，暂停部分新订单。 虽然只是是贴片电阻吗，但重要的，这是，高端、小尺寸、高一致性被动器件，的供需失衡信号。 0402 / 0603是封装尺寸。它既可以是MLCC，也可以是贴片电阻、电感、EMI滤波器。AI服务器真正需要的，是“小尺寸下还能维持高频、高一致性和长期可靠性”的器件。 AI的VRM复杂度提升、PDN越来越复杂。于是系统开始大量消耗：高频MLCC、小尺寸电阻、高频电感、钽电容、HSC。 这些器件单价不高，但属于“缺一个，整机就无法出货”的东西。 AI服务器里的需求本身是分层的。最核心的位置，比如GPU核心供电、HBM附近、ASIC substrate附近，仍然高度依赖：Murata Manufacturing、TDK Corporation、Taiyo Yuden。因为这里要求极低ESL、极低ESR、高频响应和长期可靠性。 但AI服务器并不只有核心位置。PSU、BBU、NIC、SSD、光模块、交换机，同样会消耗海量0402/0603。重要的是，AI正在先抽紧最顶级MLCC产能，然后压力开始向中高端0402/0603扩散。 最近大火的MLCC和0402电阻看起来是不同器件，但背后共享的是“小尺寸精密制造能力”。包括精密印刷、烧结、AOI检测、高频测试、超小尺寸良率控制、精密材料处理。 这和HBM产业链很像。最开始缺的是HBM，后来CoWoS、ABF、substrate、电源、散热、测试一起开始紧张。MLCC现在也开始出现类似现象。 真正最容易缺货的，往往不是最顶级料号，而是“能量产、能过验证、还能部分替代”的中高端规格。 2018年被动器件超级周期就是典型案例。当时车规MLCC先缺，高频小尺寸规格先涨，随后0402/0603全面涨价。因为一旦高端规格开始缺货，客户就会提前备货、长单锁产能、替代采购、超额下单，最后整个产业链一起紧张。 现在AI行业，可能正在重复这个过程。 更重要的是，这种紧缺会向二线供应商传导。当Murata Manufacturing、TDK Corporation、Taiyo Yuden 优先保障AI服务器和车规客户后，订单开始向：Vishay Intertechnology、Yageo Corporation、Bel Fuse Inc.、Fenghua Advanced Technology 溢出。 AI数据中心，正在把整个电子产业重新拉回“工业品逻辑”。从GPU，到HBM，到光模块，到电源，再到0402电阻，整个链条都在同时变紧。 这可能意味着，被动器件行业，正在进入新一轮量价周期。甚至可能是超级周期。 免责声明：本人持有文章中提及资产，观点充满偏见，非投资建议，dyor

@shayu_ura 🔞から普段の日常に戻る展開大好きなんですよね…😭文字化ありがとうございます🙇 一緒に食べてくれなきゃヤダっていう A▽さん可愛くてニヤケが止まらない☺️

当你坚持不住时，请看看elonmusk 8年前的创业情景： .场景：特斯拉工厂开放办公区，水泥地、杂乱线缆。 • 状态：侧身蜷缩、熟睡、一脸疲惫，没有枕头，直接躺桌下。 • 时间：2018 年 Model 3 产能地狱时期 @elonmusk

I asked Claude to apply a capital cycle analysis to $MU. Here's what it came up with: Net reading: 11 of 14 capital cycle signals are bearish or strongly bearish. The framework reads this as late-cycle, not early/mid-cycle. The two unambiguously bullish signals (equipment lead times, industry concentration) are eroding rather than strengthening. Insights Yielded by Capital Cycle Analysis: 1) "Structural change" rhetoric is itself diagnostic. The capital cycle framework treats coordinated industry-wide CEO claims of regime change as evidence of late-cycle euphoria. The same language was deployed by the same CEOs (Mehrotra at Micron specifically) in 2017–2018 and was wrong. Bayesian base rates argue against accepting the current claims at face value. The previous analysis under-weighted this base-rate evidence. 2) Look at total capital flowing into the supply curve, not just incumbent capex. The structural-change analysis focused on Big Three capex. The capital cycle lens forces aggregation of all capital flowing into memory output: a) Incumbent capex: ~$104B in 2026 across DRAM + NAND; b) CXMT IPO proceeds: ~$4.2B (with state-aligned co-financing many multiples larger); c) YMTC capacity additions (privately financed) d) Substitute technology capital (Cerebras, photonic startups, CXL controller designers) — billions of dollars of equity raised to reduce HBM intensity per dollar of AI compute deployed. When aggregated, total effective supply-side capital formation in 2026 is materially higher than the Big Three capex alone suggests. The supply response is being underestimated. 3) The customer base is doing exactly what late-cycle customers do. Hyperscalers locking in 3–5 year LTAs, pre-ordering 2027 NAND, building strategic inventory — these are not signs of confident long-cycle visibility, they are signs of late-cycle scarcity panic. Historically (DRAM 2017–2018, oil 2008, shipping 2007), customer pre-buying at peak prices is followed by sharp inventory destocking when prices roll over. The structural-change narrative frames LTA penetration as a benefit; the capital cycle frames it as a peak signal. 4) Multiple expansion + earnings expansion = asymmetric downside. The previous analysis flagged the 15x NTM P/E multiple as aggressive (referring to UBS PT raise). The capital cycle framework sharpens this: when both earnings and multiple are at peak, the compound drawdown when either reverts is severe. Memory historically goes from 60% gross margin to negative gross margin and from 10x P/E to <5x P/E. Even a modest reversion to 35% gross margin and 8x P/E from current levels implies a 60–75% equity drawdown for the memory primaries — without any disorderly cycle. 5) Supply lag is real but not unique. The bullish point about EUV/TSV/hybrid bonding lead times is correct but mis-weighted. The capital cycle history of other capital-intensive industries (oil refining, shipbuilding, semiconductor wafer fab) shows that long lead times increase the eventual amplitude of the down-cycle: capital decisions made at peak are not reversible when conditions soften, leading to capacity overhang. Long lead times delay the down-cycle; they do not abolish it. 6) China is the textbook capital-cycle disruptor. In Chancellor's historical case studies (steel, shipbuilding, solar, panels, batteries), state-backed Chinese entrants repeatedly compressed margins of consolidated Western/Korean/Japanese oligopolies once technology gaps narrowed. The U.S. equipment restrictions on China have created the illusion that this dynamic is paused, but the data shows CXMT doubled DRAM share in 18 months and is targeting domestic HBM3. The structural-change analysis appropriately flagged this; the capital cycle framework would weight it heavier as the single most important multi-year risk. 7) Substitute capital formation is its own supply curve. The capital cycle framework treats financing flows into substitutes as a parallel supply expansion. Cerebras' $5.5B IPO, Marvell's $5B Celestial acquisition, the Sandisk/SK hynix HBF JV, and the CXL ecosystem (ALAB, MRVL, MCHP) are collectively financing "HBM intensity reduction." Even if HBM unit demand is met, the value capture per dollar of AI compute is diluted. Capital is flowing in adjacent to the memory primaries to reduce the share of AI spend that ends up in their P&L. 8) The bull case relies disproportionately on demand visibility. The capital cycle warns against demand-anchored theses. The bull case requires AI capex to continue at current levels or accelerate, hyperscaler ROI economics to remain favorable, sovereign AI to scale, and inference workloads not to migrate to non-HBM architectures. Each of these is plausible; the joint probability that all hold through 2028 is materially lower than the headline narrative suggests. 9) Sell-side estimate trajectory is itself a signal. UBS's PT trajectory ($535 → $1,625, a 3x increase in one revision) is historically associated with peak euphoria. Estimate revisions of this magnitude have a poor forward record. The framework would treat the velocity of estimate revisions as a contra-signal. 10) Where the asymmetry sits. The capital cycle framework reframes the risk/reward calculation. Even if the bull thesis is right and earnings hold through 2028, the upside from current levels is modest (multiple expansion has already happened). If the bull thesis is partially wrong — say, 2028 brings 25% peak-to-trough EPS decline rather than 50% — the equity drawdown is still material because multiples will compress simultaneously. The asymmetry is not favourable at current valuations. Bottom line: The structural change thesis was directionally correct but materially overweighted by the original analysis. The capital cycle framework appropriately reweights toward supply-side caution and treats current peak conditions, peak valuations, peak management confidence, and accelerating capital inflows as a coherent set of late-cycle signals. The memory industry has undergone real and beneficial structural change in shape, but the empirical base rate against the "cycle has been abolished" claim is overwhelming. The economic characteristics of memory businesses have improved but have not been transformed into stable, compounding, low-volatility ones — and the next 18–30 months are statistically more likely to mark the end of this up-cycle than a transition to a new regime.