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说一下我最近在 @predictdotfun 刷世界杯活动的打法，这篇会写得细一点。 1⃣先说我刷这个的目的 我刷 Predict 的世界杯，目的不是在里面赢胜平负、去赚那个比赛结果的收益。 我真正奔着的是它的粉丝积分。 因为这个粉丝积分最后对应的是总价值 200 万美元的奖池，这个奖池才是我真正想拿的东西。 所以我的整个逻辑是： 📌用对冲的方式稳定拿积分，尽量不去赌比赛结果。 2⃣这个活动到底卷不卷 我自己看下来，没有想象中那么卷。 举个例子，以巴西这个组为例，截止我发文的时候有 1 万 7 千多人选了巴西。 但真正拿到积分的有多少人呢？ 我现在是零分，排名第 434，也就是说这 1.7 万人里真正有积分的就 400 多个。 换葡萄牙也类似，8000 人参与，实际有分的也就 89 个。 后面绝大多数人都只有 200 分，只有前三名拿了 1000 分以上，第四往后基本就是 500、200 这种量级。 德国、法国我也看了，实际有分的也就几十个人。 3⃣所以本身难度不算大。 但有一个硬性要求： 📌你选的队伍必须小组赛拿到前两名才有奖金，第三、四名是没有奖金的。 我具体怎么操作的 这里有个关键点，Predict 在币安钱包里也有同样的活动入口，币安钱包里预测并瓜分 200 万奖池的活动，点进去其实就是 Predict。 这等于先天就给了你两个可以用来对冲的账号，一个网页端，一个币安钱包端。 4⃣我昨天踩了个坑跟大家说一下。 我一开始是想直接对冲胜平负，结果发现行不通： 胜平负是三个选项，你买了胜的 Yes 之后，剩下的平和负就锁定了，没法再买。 想完整对冲就必须要三个号，这个路径基本走不通。 5⃣所以我现在改了方式： 买总进球数的 Over / Under。 比如德国这场，我在网页端买 Over，在币安钱包端买 Under，两边对锁。 注意两个账号要选同一个方向去对冲。 这样不管哪个号赢，500 积分我都能稳拿到。 6⃣算一下成本 对冲的磨损怎么看？ 很简单，你把几个选项的价格加起来，比如 77+18+9，最后这个数肯定大于 100，一般在 101、102 左右，多出来的就是你的磨损。 我自己测下来，投入 100 刀，两边对冲的磨损大概是 2.5 美元。 那么单个积分的成本就是 2.5 ÷ 500 = 0.005 美元一分。 7⃣这积分值不值这个价 奖池是这么分的： 小组第一 5 万美元，小组第二 2 万美元，进八强额外 4 万，四强 5 万，决赛 26 万。 📌但有个大前提我得纠正一下自己之前的结论：一定要选强队。 因为如果你选的队没进小组前两名、第三第四被淘汰了，那你积分再多也一分钱拿不到。 所以三四名出局是最要命的情况，必须避免。 基于这点，尽量选巴西、德国、西班牙、法国、葡萄牙、阿根廷这种强队，让它尽可能走得远一些。 8⃣我用 Claude 按约 2500 人参与去估算每分价值（这只是估算，可变数字太多，实际可能更多人，也可能有别的变数）。 大致结论是：如果你能拿小组第一，单分价值能到 1 美元，那是很值钱的，但大多数人拿不到这个排名。 多数人的排名会落在 150～500 之间，对应的单分价值大概在 0.02、0.05、0.1 这种零点几的区间。 9⃣对比我自己 0.005 的成本，猛一看赔率是说得过去的。 我现在很少去算特别精确的赔率了，因为里面波动太多，但只要感觉赔率还行，我就愿意试一下，看看有没有一点盈利空间。 🔟最后提醒 本质上我们参与世界杯不是来赌博的，是来撸它这 200 万奖池的。 能撸到、赔率又还行，那我觉得值得参与。 但我这些计算和预测全是我个人的看法，包括 Claude 算的数据也未必准，只能仅供参考，不构成任何投资建议。 预测市场风险不小，要参与请一定按自己的逻辑和方法来。

本期 @BitMart_zh 世界杯周边开箱Vlog已放送～ 0:11 黑色发带 0:27 足球袜 0:38 币宝感谢信 0:46 运动t恤 1:05 运动毯 1:27 足球包 希亚四处搜刮拿到了2套礼盒🤲 关注希亚并三连，我将在评论区抽两位小伙伴送出周边礼盒喔❤️

恐怖如斯，「先定 10 个大目标」老哥再赚 989.5 万美元！🫢 7 小时前 @Jason60704294 再次平仓 1,365.317 BTC，截止目前已累计平掉 2782.977 枚 BTC 多单，总价值 2.05 亿美元，目前仅剩最后 52.352 BTC，单币交易获利近千万美元 此外，截图显示他 06.04 因做多 $BTC 最终亏损的金额为 668.5 万美元，实际开仓规模 3072.127 BTC

Today on MCG: @ColbySaysHi | @prism_lp | $PRISM $PRISM is the first token where holding is providing liquidity. Each whole $PRISM you hold auto-mints one Prism NFT (a 1/5000 share of the same Uniswap v4 LP position) INSANE TEK Highlights from our convo: 03:18 - Uniswap v4 launched with a hooks whitelist, hooks didn't take off until UniPet (a viral unicorn NFT mint), then a Prism dev took it further 04:50 - How Prism works 05:38 - Full-range concentrated liquidity means fees accrue regardless of price/market cap 06:25 - Token unit economics 08:00 - Spectrum index tokens 14:32 - Colby's own product 15:00 - 10% of all Spectrum index fees 19:24 - Article release: "Retail is right to hate crypto" 22:18 - Why burn vs distribute 27:30 - PMF 30:00 - Spectrum V2 35:00 - @Uniswap team has reached out to learn how hooks are being used in the wild

$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!

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