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闪迪（SNDK）上半年涨了超850% 这不是炒作，这是 AI 对存储需求有多真实的直接体现 闪迪的 Q3 营收同比涨了 251%，毛利率达到 78.4%，已经比很多软件公司还高 这个数字让我认真重新审视了一下 下半年的配置思路 ════════════════ ▸ 核心主线没有变：AI基础设施 存储和半导体这条线还在 AI 训练和运行都需要大量内存和存储，闪迪只是其中一个例子 美光（MU）背后的逻辑是一样的，这条线我会继续重点持有 AI 的需求还在往下传导 数据中心耗电量巨大，电力设备、能源这块也在受益 服务器、网络、光纤相关的公司也都值得关注。 大科技这边，英伟达、博通（AVGO）、微软、亚马逊、谷歌、Meta 这些公司，要么是 AI 的核心受益者，要么本身就在大量投入 AI 建设 机构普遍认为，只要回调出现，就是加仓机会。 ━━━━━ ∘ ━━━━━ ▸ 次要方向：分散配置一部分 国防和工业这块，全球局势紧张，各国安全支出在增加，这个逻辑比较稳定 能源和材料，一方面是 AI 带来的电力需求，另一方面是对通胀风险的一种对冲 医疗健康，防御属性比较强，长期逻辑也清晰 ════════════════ ▸ 我自己的仓位参考框架 ╭──────────────────────────╮ AI/科技基础设施 50-60% 能源/电力/材料 15-20% 国防/工业 10-15% 现金/防御 10-15% ╰──────────────────────────╯ 这不是固定配方，是我现在思考配置时的大概比例 每个人的风险承受能力不同，比例要自己调整。 ━━━━━ ★ ━━━━━ ▸ 几个操作上的提醒 回调出现就是机会，追高风险大 7 月份有就业数据、美联储会议（美联储就是决定利率的那个机构）、大科技公司的季报陆续出来，这些都可能带来短期波动，也可能带来买点。 不要把全部资金押在一只股票上 可以考虑半导体 ETF 或者 AI 主题 ETF，分散风险。 杠杆要控制好 高估值的环境下，一旦行情反转，杠杆会让损失放大很多倍。 ──────────────── 下半年的核心逻辑没有变：AI 基础设施带动的美股行情还在延续，只是会更挑剔，更看重基本面 闪迪这种故事还在跑，但要持续跟踪需求和供给的平衡，不能只看涨幅买股票。 以上仅供参考，不构成投资建议。DYOR #美股# #半导体# #SNDK# #MU# #AI产业链# #H2配置#

Is Ethereum finally taking the driver's seat from Bitcoin? 📉➡️📈 The $ETH / $BTC ratio just surged to a 3-month high of 0.030, driven by a massive +16% monthly performance spread in favor of Ethereum (+24% vs. +8%). The latest KuCoin blog breaks down the fundamental forces behind the momentum shift: 📊 Reclaiming the 200-Day SMA: The cross-rate has officially reclaimed its 200-day Simple Moving Average for the first time since January, signaling a technical trend reversal. 🌊 ETF Flow Flippening: U.S. Spot Ethereum ETFs recorded $103.9M in weekly net inflows—outpacing Spot Bitcoin ETF inflows by a factor of 3-to-1. 🔒 The Supply Vortex: Public treasury accumulation (Bitmine holding ~4.8% of circulating supply), a zero validator exit queue, and 2.5M ETH waiting in the staking entry queue have squeezed spot liquidity. ⚙️ "Glamsterdam" Horizon: Institutional RWA dominance exceeds $17B on Ethereum, with the H2 2026 "Glamsterdam" upgrade set to introduce parallel processing and slash L1 fees by over 70%. Is this just a short-term liquidity rebound or the start of a full-scale market rotation? Read the full technical breakdown here:

Half-Year 2026: Macro & Bitcoin A full breakdown of the macro forces shaping global markets and where Bitcoin stands in its cycle heading into H2. Read the full report 👇

Feels bad, -49.4% drawdown this month after the recent crash. My portfolio is mainly AI chokepoints and bottlenecks. In the memory, photonics, robotics, and upstream semis, (on margin) which all tend to be higher beta than others. But reduced leverage recently from the crash. I see a lot of people making fun of the drop or AI names, saying it’s obvious that: - “AI is a bubble” - “memory/kospi is a bubble” - “photonics is a bubble” - “humanoids won’t get anywhere” - “neoclouds will get replaced by hyperscalers like Meta” And a bunch of retail + bots saying “sell everything, it’s never going to recover”. But I have conviction that all these themes are backed by structural revenue growth or technological shifts. And I’ve had similar drawdowns back when there admin threatened global tariffs, before markets pulled off a recovery. I personally have a longer horizon + higher tolerance for volatility than others, to see how this plays out. Especially considering a lot of retail view things on a week to week basis: no, my thesis isn’t wrong yet if I project revenue inflection in H2 2027 and it’s 2026 now. Anyway, feels bad short term just wanted to share anyway for transparency.

The BNB Chain 2026 H2 Tech Roadmap is here. After cutting BSC block intervals to 450 ms and nearly doubling benchmark throughput to ~5,200 TPS, the next target is another 2x increase on mainnet. What's next for BNB Chain 👇🧵

tokenized stocks crossed $2B monthly volume on Solana 🤯 six months ago, they did $228M > H2 2025: $775M total volume > H1 2026: ~$4.9B, a 6x jump marketcap up 3.5x, from ~$152.5M to $539M @Backpack's $SPCX (via @Sunrise) did $470M+ in volume in its first week alone, the highest of any tokenized SpaceX version. source: @birdeye_data Solana H1 2026 Report note: data as of June 22, 2026

Windows 11 24H2 版即将抵达生命周期的尽头，微软提醒家庭用户升级到 25H2 版，受影响的主要是家庭版和专业版 / 专业工作站。 企业版因为有额外的 12 个月支持时间，所以到 2027 年 10 月才会结束支持，因此使用企业版和企业教育版的用户还可以继续停留在 24H2 版：

✅✅✅Windows 11 26H2 测试版已经可以体验禁用网络搜索，即搜索面板只搜索本地内容，禁止通过必应搜索内容。 禁用后搜索内容优先搜索本地文件，其次是搜索本地设置，整个搜索面板效率高且非常干净，推荐已经升级 26H2 测试版的用户开启实验性选项试试看。 操作流程：

$AMD| The FOMO to buy @AMD Chips is NOW 🧵 Not Financial Advice! DYOR! Research Purpose Only! The Inference Queen is the biggest winner in Agentic AI where all other CPUs are struggling to compete with a 2yr old EPYC Turin and EPYC Venice is in mass production phase. AMD stresses deployability today on standard x86 platforms (no proprietary architectures required), full software compatibility, and open standards. This positions Venice + Helios as a practical, high-density alternative to competing solutions while underscoring that agentic AI shifts the balance toward CPU-rich racks alongside GPUs, and most importantly, lowering the cost of token to accelerate adoption and innovation. Context: @WSJ yesterday came out with an article that @OpenAI is condiering drasstically lowering the token prices to win more customers from Anthropic. The narrative "they" are trying to exacerbate the current AI selloff won't last long. This is a fundamental misunderstanding of what is going on, or what I already discussed for months and years. Followers and Subscribers already knew this for years, that this day would come, where token cost will bcome the central discussion among enterprises as there is no such thing as unlimited budget or Tokenmaxxing when they use $NVDA chips or In-house Hyperscalers chips. I will link various threads if you are interested in understanding the full picture from supply chain to recent TSMC Rapid 2nm expansion up to 12 Fabs total by 2027/2028. Hyperscalers and AI natives effectively have no choice but to buy more AMD system for Agentic AI as leadership in economical, power-aware, high-volume internal + agentic use. However, due to supply constraints where Supply is far behind Demand, this makes multi-vendor reality along with in-house chips drive faster industry progress, lower overall costs, and better sustainability. NVIDIA’s Vera Rubin cannot compete with a 2 years old EPYC Turin, but AMD under Dr. Lisa Su has engineered the lowest cost-per-million-tokens, highly competitive energy-efficient solutions, and superior CPU orchestration for agentic AI at scale with Helios. Dr. Su has championed this shift since at least 2023, foreseeing the rise of agentic workflows that demand far more orchestration, parallel agents, and balanced compute well before the industry fully embraced it. Her long-term vision of AI moving from simple prompts to always on, multi-agent systems has driven AMD’s investments in high-core EPYC CPUs and integrated rack-scale solutions, perfectly positioning the company for today’s realities. The OpenAI-AMD 1GW Helios deployment (starting H2 2026) represents a pivotal vertical integration move that directly supercharges the inference economics. This isn't incremental; it's a structural shift toward ownership of massive, optimized rack-scale capacity, enabling the lowest token costs and triggering the enterprise adoption flywheel. We need to be honest, $AMD is the only company that made a big bet on Inference since the day Chatgpt became sensational where $NVDA and others were betting big on Training. At the end of the day, Token bill from @AnthropicAI has to obey economics. Meaning the bills rise, companies have to get more out of it to justify the cost. It cannot be an unlimited inference budget, and it has to show up on efficiency, profitability and operating leverage. 1. Tokenomics After you understand this, you will understand why Citi cited @AnthropicAI is likely to sign a deal with $AMD along with Hyperscalers, AI Labs, Sovereign AI like Softbank 5GW in France and many other countries. However, OpenAI and $META are now wanting faster deployment, and they are AMD shareholders now, they have prioritized allocation. Anthropic and Hyperscalers just cannot compete when Helios Rack lower token cost to$0.0003–$0.0005 per million tokens at GW scale. Cost to build 1GW data center 1GW Helios Rack full build is estimated $30-$35B 1GW Rubin Rack full build is estimated $45-$55B Inference (Cost per Million Tokens) ~$NVDA B200 / HGX: ~$0.02–$0.08 on optimized workloads (FP4/MXFP4, speculative decoding). Significant improvement over Hopper but still premium-priced. GB200 NVL72 rack-scale: $0.05–$0.25+ ~$AMD Helios Racks: $0.0003-$0.0005 per M tokens, dramatically lower than NVIDIA equivalents in owned infra. MI355X node-level: Up to 40% more tokens per dollar vs. competing solutions ( B200), driven by higher memory capacity (up to 288GB+ HBM), strong bandwidth, and lower acquisition costs. Training ~$NVDA Rubin Rack is estimated $0.7-$1.2/M Tokens ~$AMD Helios Rack is estimated $0.65-$1.0/M Tokens Now, OpenAI, META and Hyperscalers can lower Inference cost even further with $AMD EPYC Venice "dense rack" or Agentic AI Rack. AMD published a detailed technical blog emphasizing that the future of agentic AI autonomous, multi-step AI systems requiring heavy orchestration, databases, caching, APIs, and control planes demands massive CPU-dense rack-scale infrastructure, not just GPUs. The catalyst prominently positions their upcoming 6th Gen EPYC "Venice" processors as the key enabler for next-generation dense racks, delivering leadership throughput under real-world power, cooling, and density constraints. ~EPYC Venice (Zen 6 architecture, up to 256 cores / 512 threads per socket) is projected to deliver exceptional rack-level performance. In AMD’s modeled 100 kW rack comparisons, Venice-powered systems are expected to achieve ~3.30x the throughput of NVIDIA’s Vera (88-core Olympus) baseline across a broad mix of agentic-supporting workloads. ~This builds on current-generation 5th Gen EPYC "Turin" (up to 192 cores), which already delivers ~2.37x rack throughput vs. Vera and ~1.6x vs. Intel’s Xeon 6980P (128 cores). ~ Liquid-cooled Turin deployments already support >27,000 CPU cores per rack today. Venice is architected to push this beyond 36,000 cores in the same rack class, dramatically increasing concurrent agent capacity and overall infrastructure efficiency. 2. Ownership vs renting compute from Hyperscalers matter to OpenAI and only owning $AMD chips can meaningfully lower token cost for enterprises. ~Eliminates cloud overhead: No provider margins, utilization buffers, or egress fees. Direct control over power contracts, cooling, scheduling, and orchestration at dedicated facilities. ~Helios optimizations at GW scale: Rack-level density (1.4+ exaFLOPS FP8 per rack), high HBM4 bandwidth, EPYC orchestration for agentic workloads, and superior TCO/TDP. AMD's long-standing focus on tokens per dollar/watt shines here 20-40%+ efficiency edges in inference-heavy scenarios. ~At 1GW+ optimized deployment, inference hits $0.0003–$0.0005 per million tokens (community/analyst models tied to Helios metrics). This is dramatically lower than typical rented/cloud equivalents, especially for high-volume output tokens in agentic flows. High token bills today, enterprises running heavy agentic/coding/analysis workloads can face $50-100M+/month at current API rates (flagship models $5-30+/M output, scaled to massive volumes). Post-Helios compression, same volume will drop to $10-15M/month (or better) via lower underlying costs passed through as pricing flexibility, volume tiers, caching, or batch discounts. ROI thresholds collapse. More companies greenlight pilots → production → massive scaling. Agentic AI (autonomous workflows) multiplies token demand exponentially, but affordability removes the friction. OpenAI gains flexibility, Unlike more cloud-dependent rivals (Anthropic), they can lower effective pricing, offer aggressive enterprise bundles, or absorb volume without margin destruction directly tackling "high token bill" complaints while maintaining profitability as usage explodes. 3. Agentic AI Models shifted CPU:GPU Ratio to 1:1 toward 3-5:1 with Explosively Token-Hungry Workloads Agentic AI (autonomous, multi-step agents with planning, tool use, iteration, and self-correction) is fundamentally more compute and token intensive than conversational or single-turn generative AI. Agentic AI. autonomous, multi-step workflows with orchestration, tool use, parallel agents, data movement, and enterprise integration has dramatically increased the importance of strong host CPUs alongside GPUs. This shifts the CPU-to-GPU ratio higher and makes balanced systems critical toward 1:1 to 5:1 as enterprises testing more than 5-10 agents. AMD EPYC Venice excels ~Leadership core density (up to 256 Zen 6 cores per socket) for running many agents in parallel, orchestration layers, and high-throughput control-plane tasks. ~Superior performance-per-core and power efficiency ( up to 2.1x higher perf/core and 2.26x better SPECpower vs. NVIDIA Grace in benchmarks). ~Tight integration in Helios: One Venice CPU + multiple MI450 GPUs per node, enabling efficient data feeding to GPUs ("zero-copy"), parallel execution, and full rack utilization for complex agentic loops. Hyperscalers (Meta, Microsoft, Amazon, Google, Softbank) and AI natives (OpenAI, Anthropic...) are adopting high-core EPYC at scale specifically for these agentic demands, as CPUs now handle a larger share of non-model work (orchestration, policy enforcement, tool calls). This complements AMD’s lower-cost GPUs for overall TCO wins. ~Agents often generate 10–100x+ more tokens per task due to iterative reasoning chains, multiple tool calls, verification loops, and long-context orchestration. ~Goldman Sachs forecasts token consumption multiplying 24x by 2030 (to 120 quadrillion tokens/month) largely driven by agentic adoption in consumer and enterprise. ~Enterprise data shows agent-pattern workloads growing at 680% annualized rates, projected to surpass conversational AI in token volume by Q3 2026. ~Daily enterprise agent token consumption is already in the billions, with complex workflows (coding, workflows, analysis) amplifying this dramatically. 4. Competitive Edge: Winning Customers from Anthropic Anthropic’s Claude models (especially Opus/Sonnet) excel in complex reasoning and agentic coding, commanding premium positioning. However, their higher underlying costs (heavier reliance on third-party cloud with margins) limit pricing flexibility compared to OpenAI’s owned Helios capacity. Anthropic is on track to generate $10.9 billion in Q2 revenue. The company expects to achieve its first-ever quarterly adjusted operating profit of $559 million. However, sustaining full-year profitability remains challenging due to immense computing and model training costs The truth is, Anthropic has no choice but to buy as much $AMD chips as possible if they want to compete with OpenAI or get investors attention. This 5% adjusted operating profit to revenue ratio is just pathetic. Current pricing dynamics (2026): OpenAI already undercuts on many tiers ( flagship output tokens significantly cheaper than equivalent Claude Opus). Nano/mini models offer 5–10x advantages for volume work. Anthropic holds edges in long-context flat pricing and certain reasoning quality. OpenAI after Helios Rack Ownership, At $0.0003–$0.0005/M effective costs, OpenAI gains massive headroom to: ~Aggressively discount high-volume agentic tiers or bundles. ~Offer “unlimited” enterprise plans or usage-based models that Anthropic struggles to match without margin erosion. ~Target cost-sensitive, high-throughput agent deployments (dev tools, automation platforms) where token bills explode. Enterprises facing $ millions in monthly agentic bills will migrate to the provider delivering better economics at scale. OpenAI’s combination of strong models (o-series reasoning) + lowest TCO positions it to erode Anthropic’s enterprise share, especially as agentic becomes the dominant token consumer. Cheaper tokens expand the total addressable market dramatically. This feeds the data/model improvement loop, justifying further capex. AMD benefits from proven scale pulling in more customers (Meta, Oracle, Microsfot, Amazon, Softbank, TensorWave, LumaAI ... already aligned on Helios). Conclusion: Dr. Lisa Su has been laser focused on inference economics since at least 2022–2023, repeatedly emphasizing that the real battleground for AI scalability would be TCO, power efficiency (TDP), and ultimately tokens per dollar and per watt not just raw training FLOPS. While many viewed inference as a secondary, commoditized workload, Dr. Su architected AMD’s roadmap around rack-scale systems optimized for high-volume, sustained inference that would dominate as models matured and usage exploded. Helios represents the culmination of that multi-year bet: a fully integrated, open platform designed precisely for the economics of massive token throughput. This deep, strategic partnership with OpenAI starting with the 1GW Helios deployment in H2 2026 and scaling to 6GW, is the embodiment of that shared vision. Both companies foresaw a future where agentic AI models evolve to become extraordinarily token-hungry: autonomous agents executing complex, iterative workflows with planning, tool use, verification loops, and long-context reasoning. These workloads can consume 100x+ more tokens per task than traditional chat or single-turn generation, driving exponential demand as capabilities improve and enterprises deploy them at scale. By owning and optimizing this massive Helios capacity at GW scale, OpenAI achieves inference costs as low as $0.0003–$0.0005 per million tokens. This structural cost advantage allows OpenAI to absorb the coming token explosion profitably, dramatically lower effective pricing for enterprises, and win high-volume agentic workloads from higher-cost competitors like Anthropic. What was once a prohibitive monthly token bill becomes an affordable accelerator for productivity and innovation. The OpenAI-AMD alliance validates Dr. Su’s prescient strategy and turns the Agentic flywheel into reality: Collapsing inference costs → explosive token consumption → richer data and better models → accelerate greater demand. This partnership doesn’t just address today’s economics, it positions both leaders at the center of the infrastructure buildout that will power AI’s next decade. By delivering the lowest inference economics at scale, OpenAI not only solves enterprise bill pain but gains a decisive weapon to win share from higher-cost rivals like Anthropic. And that is why @OpenAI and $META will deploy EPYC Dense Rack Not Financial Advice! DYOR! Research Purpose Only!