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$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!

New data shows 𝕏 actually GREW its U.S. unique visitors between June 2025 and March 2026 Meanwhile, over the exact same period: - Threads dropped by 43% - Bluesky dropped by 44% While the media predicted that 𝕏 will collapse but now other platforms bleed users, 𝕏 continues to grow People are massively voting with their attention, and they are choosing 𝕏

起因是有个观众说有人盗我照片冒充我搞诈骗被人揭穿后diss了我，但观众说盗我照片冒充我并diss我的原贴找不到了，然后观众给我发了这个小姐姐，然后我看了看小姐姐发言内容有点不理解 threads上的超妒忌魔怔小姐姐莫名其妙想搞雌竞但无力雌竞的样子好好玩

I’ve been noticing how the flow between tools starts feeling smoother when each one handles a different gap in your day. Spending feels natural on @useTria, research threads stay alive inside @TheARCTERMINAL, and liquidity on @RiverdotInc keeps moving instead of sitting still. What stands out is how none of it feels forced. Just quiet pieces that fit together: easy payments persistent context assets that stay productive Feels like the first time these systems are starting to match how we already live online. The shift usually starts subtle, then becomes normal before anyone notices @wallchain

台北旅行圆满结束，惯例讲讲这次看到的信息差。 这次旅行有几个Highlight： - 见到了黄仁勋爸爸，喝了Jensen给的台湾啤酒 - 约出来很多很久没见/第一次线下见的朋友 @SaBiBro666 @94cho94134 @0xMalingshu @yoaka__ - 感谢大家带我见了超多人 - 参访了六间Web2公司，并且跑了好多会。 - 之前在泰国grab上坐摩托跑会本来被疯狂司机搞出心理阴影了，后面跟着周周老师的摩托跑了一天会之后痊愈了。 - 在各种地方探讨 的更新 - 见到了传说中的Ray，也见证了大D哥在匹克球上吊打所有人。 话不多说，我们直接开聊 一、关于Web3： 在生态层面上。台湾的Web3生态和我之前听到的一样，以 $SUI 为中心。很多大学生都参与过 $SUI 的黑客松，或者直接在 $SUI 的项目方工作。近期有听到一些风声，说 $SUI 生态在新加坡也在很努力的BD。 看得出来， $SUI 在亚洲区还是有很努力的BD的。 在交易所层面上，台湾与香港有一些细微的相似之处，即，有交易用的交易所，以及OTC用的合规所。在台湾，最为普遍的交易用大所就是 @BingXZH 和 @BitgetTC，两者都通过大量 KOC 进行了有效的推广。 至于合规所，台湾有一系列包括 Hoyabit 在内的合规所，用于出金 - 这些合规所在定位上跟香港的 @HashKey_Global 和 @osldotcom 类似，以合规，可以让散户出入为主。 但是，Hoyabit 等等本地合规所在渠道上明显比香港的合规所更加亲民，以Instagram/Threads营销为主，能够真正触及到Web2的潜在用户。 在KOL层面上，台湾的本土KOL明显是比香港的本土KOL更多的。虽然很多KOL住在香港，但是粤语区 + 能够触及本地用户的KOL实际上两只手就数得过来。 *甚至可以说 @monsterblockhk 和 @852Web3 相关人士就基本占了所有香港KOL的半壁江山 相比之下，台湾明显有更多的KOC和KOL走到潜在用户当中，这也为本地交易所打出自己的市场空间提供了初步的入场点。 跟香港类似的点，在于这些KOC和KOL非常善于在Facebook和Instagram上起号，有些甚至在这些平台上反而粉丝更多。 Mass Adoption这件事情在执行上，台湾一带确实有很多可以学习的点。 最后说点刺激的 - 有听传言说不少当地的黑社会仍然盛行，会专门挑有钱的kol下手勒索。所以大家跑会时可能还是要尽量少穿merch，少显露财富。或许这也是不少KOL不露脸的原因。 二、关于AI： 这次是在英伟达的Claw活动上见到Jensen的。除了再一次听到孔总办的深圳活动被点名表扬之外，也有不少机会和当地人交流AI。 整体感觉下来，两岸对Agent目前的focus大差不差，都是在讨论怎么加入工作流，怎么更好的融入现有的企业体系。 而这却是也符合英伟达这次活动推广的Nemo Claw以及Open Shell的专属功能 - 即，更方便企业进行信息转移，权限继承，以及信息隔离的claw。 三月份的时候，我就稍微体验过nemo claw。不过用下来感觉除了免费，速度和工作能力没有很多亮眼的地方，甚至不如我直接问codex。 本来期望这次可以看到一些nemo claw更有趣的应用，没想到演示小哥现场翻车了好几次。或许Nemo Claw也不是我们这些初创阶段的startup适合用的。 除此之外，这次探访了华硕和几家Startup（包括SaaS，用户端产品，ESG审计公司，以及一些ToB技术服务），大家也很明显在试图寻找AI时代新的PMF，包括新的硬件，新的叙事，等等。 印象比较深刻的一个叙事案例就是一个共享车位供应商，就在为“成为自动驾驶汽车的上游供应商”准备。这个还是蛮有意思的。 三、关于台股： 说一个题外话 - 我是直到这次旅行，才发现台股也有 +-10% 的限制。 这次见的不少圈内朋友都有在交易台股，而且很多都有赚到钱，让我也忍不住想要研究。 台股的盘子实际上也确实很大，目前已经是全球第五大股票市场。像台湾电信，台湾大哥大等等巨头也投了很多数十亿台币的轮次 - 包括见的六家企业中的其中一家。 由于盘子足够大，所以也有不少本地企业会选择先上台股，再上美股的策略。 所以，一个这次比较直观的感受，就是台湾地区已经产生了独特的VC投资生态，模式，以及上市和退出的common sense。 最后，软件上，大家用的主要还是富途，和一个忘记名字了的本地软件。（@Live_2_Earn和@jhaninvest有跟我提，但是我忘了） 四、 关于生活，生活成本，以及娱乐 台湾的生活确实很舒适，而且超便宜。礼拜六的时候，帮朋友订了宜兰的别墅，带电梯+两层楼，可以容纳20人，居然只需要 16000台币（大概人均30u） 然后虽然台北很多地方感觉有点点破，但是新北一带看房子的外观还是蛮新的。 平常吃饭的成本也比较低。有个朋友约喜欢的女生出来吃饭，找了家蛮有名的店，最后两个人买单也才15u左右。 娱乐上，积分制的扑克是合法的。（师父 @gokunocool 这不得去一下） 这次踩点了Ace8，CTP，6Bet几个赛点。第一天就在CTP打了60人的锦标赛。本来打到第10，结果一手冤家牌三条allin被原地送走。 *草泥马chipchip，怎么每次都是冤家牌？？！ 6Bet的话感觉大家都比较鱼，所以倒是很快收集到筹码。 最后Ace8是和 @SaBiBro666 两兄弟一起去的，没想到这次我彻底成了鱼，被彻底干碎。 路边还有跟多六合彩店，进店可以花钱开刮。朋友直接给干没3000台币。 然后就是按摩。这次去的都是正经的按摩 - 为什么要强调正经的？因为我搜索按摩的时候，真的出现了几家不正经的按摩，就在谷歌地图上。 第一天去了一个叫Villiage的Spa，大概1500台币60分钟，手法非常赞，感觉技师是懂穴位的，按的我骨头全程发响，当晚一躺上床就睡着了。水平超过了不少我在深圳水会体验的按摩。 最后是交通。周周老师的摩托确实非常方便，穿梭于车水马龙之中，解决了所有堵车的问题。 本来是要尝试捷运的，结果明明写着visa可以拍，却完全用不了。哭了。 剩下的时间就是坐uber，价格比香港便宜不少，横跨半个台北也不到20u，司机态度也都很不错。 印象比较深的就是有个司机吐槽，从必胜客下班过来开uber，就是因为打工赚的太少，一个月每天12小时，才赚不到3万台币（约1000u） 五、关于夜市和夜店： 由于住在宁夏夜市旁边，台湾的夜市小吃这次也是吃了个遍，简单评价一下印象深的： 卤肉饭：感觉不如在香港吃到的好吃，但确实有家常菜的味道，而且胜在便宜（约2u） 地瓜球：给到夯。炸的外酥内软，而且不油腻。吃的很上瘾。 牛肉粒：特别喜欢这个，可以说是入口即化。450台币可以买四人份，吃肉爱好者的不二之选。 麻油鸡/麻油猪肝：听第一天司机师傅推荐吃的，感觉麻油味不够重，或许喜欢清淡口味的人会喜欢。猪肝倒是非常爽脆好吃。 酒吧上，台湾有很多不错的选择。不过建议大家有空一定要去之前币安，以太坊等等都办过活动的Sitdown酒吧看看。有很多有意思的调酒，东西也很好吃。每一支鸡尾酒，都是歌的名字。 地址： 如果是 @0xajc @0xAgata 这样的男同，据说西门町一带则有不少Gay Bar，堪比成都。Agata老师的名号更是在台北的Gay Bar被反复提及。 至于夜店，不少当地的朋友和我推荐了fix sober和Ruff，可惜最后去的时候人满为患。不过确实很多好看的小姐姐在排队，没去成很可惜。 最后我去的一家叫Wave，要坐一个超大的电梯上楼。内部非常大，有两层楼，以及可以上下驱动的天花板。价格也很实惠，只要15000台币最低消费就可以开台（约500u） Wave的DJ质量一般，但是光效很不错，至少吊打80%的香港夜店。每次干冰出来的效果也很high。 结语： 总之，这次旅行超级fruitful。 非常推荐还没有去过台湾的朋友有空就去看看，体验一下当地的氛围，以及独有的商业文化和圈子。 可靠消息说，今年的TBW会取消，改成Future Summit，同时做AI和Web3。感兴趣的朋友也一定要去看看！ 照片credit @94cho94134

台湾作家杨双子创作、由金翎英译的作品《台湾漫游录》上周获得国际布克奖。周三（5月27日），中国国台办发言人陈斌华被问及中国是否有可能引进这本书、是否有言论审查考虑；对此，他称"两岸作家都应站稳民族立场"，并且正视日本侵略带给中国人和世界人民的巨大伤害。 这个提问是由台湾中央社记者在中国国台办例行记者会所提出。值得注意的是，陈斌华回应时，首先强调了用词差异："台湾的书籍不是在'中国'，（而是）在'大陆'出版。" 上周，杨双子接受法新社采访曾指出，这本书目前在中国还买不到，但她知道仍有中国读者想读；而"如果这本书能够用各种方式进到中国"，就有机会促进中国人理解台湾人的想法。她表示，台湾人想要的未来，"跟中国人所想像的并不相同"。 在台湾人常使用的Threads平台上对这本书有许多讨论；中国的豆瓣平台到5月27日为止，亦有200多名网友留下短评。部分读者关注这本书强调的台湾主体性，也有人批评此书"美化殖民"或者"恋殖"。 美国亚马逊（Amazon）的有声书平台Audible也上架了《台湾漫游录》。不过，有网友发现这本书的书籍页面包含了"中国"的标签，遭质疑不尊重捍卫民主自由的台湾人、否定了台湾的身份认同。 你读过这本书了吗？留言分享你的读后感！

内容号真正累的不是写，是发。 一篇内容要改成 X、LinkedIn、Threads、小红书、公众号、Telegram，不同平台长度不一样，标题不一样，发布时间也不一样。 你手动搞，几天就废了。 Postiz 现在这个方向很猛：开源内容分发平台，还把 OpenClaw / Hermes / Claude 这类 Agent 工作流直接摆到台面上。 我更看重的是它能变成一个“内容流水线”： Codex 批量改写选题； Postiz 排期发布； 数据回来再让 AI 复盘哪种标题更能打。 做矩阵号、出海内容、AI 工具号、项目推广的人，这种东西早晚都要用。 你缺的可能不是灵感，是一个能每天稳定发出去的机器。 🔗

なんだ、このエグいグラフィック？？？ 「クロノ・トリガーの再来 × 最新HD-2D」 UE5で描かれた2.5D世界にドットキャラが動き回る、時空を旅する狂気のターン制RPG。恐竜が闊歩する古代やサイバーパンクな未来で、ただひたすらこのイカれたドット絵世界に引きこもる没入感がマジでイイ。海外の古参ゲーマーたちが「これはJRPGだ！」「いやキャラが鼻ピアスしてるから西洋ゲーだ！」と解像度のおかしいブチギレ方で派閥争いしてて最高。 『Threads of Time』