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こんばんは🌇 #コンサートホール志村# です‼ いよいよ明日 3⃣月1⃣6⃣日（月） am10:00 🎉🎉🎉🎉🎉🎉🎉🎉🎉🎉🎉 🎉 リニューアルオープン‼ 🎉 🎉　　～BOOST OVER～　　🎉 🎉🎉🎉🎉🎉🎉🎉🎉🎉🎉🎉（予定） 🔭Find🔭 ☄️☄️☄️　☄️☄️☄️　☄️☄️☄️ 　　　SINCE　1994.8.19 　　 ☄️☄️☄️　☄️☄️☄️ 気になる機種配置は この後2⃣0⃣時のポストを 要チェックΣ☝️ フォローと通知のONも 忘れずに😘 【入場抽選のご案内】 　"当店HP"か"公式LINE"の 　リッチメニューにてご確認を👇 　当店HP（P-World）： 　公式LINE： みなさまのご来店を 心よりお待ちしておりま～す👋 #リニューアルオープン# #BoostOver# #コンサ志村の挑戦# #Find#

Starship transiting the Sun this morning during Flight 14 — prior to this morning's mission, this has never been captured before with the world's largest and most powerful rocket. I’ve ached over this shot for years, mainly for two reasons: what does methane exhaust look like silhouetted against the Sun, and how violent are the acoustic energy waves from 33 Raptor engines on the Super Heavy booster? Well...now we have an answer. 📸 - @NASASpaceflight

🚀 LIFTOFF! SPACEX JUST SENT STARSHIP ROARING INTO ORBIT FROM TEXAS SpaceX launched its massive Starship rocket on its first orbital test flight — carrying Starlink V3 satellites into space. 🔥 The booster successfully splashed down in the Gulf of America. Another massive step forward for @ElonMusk and SpaceX. 🇺🇸🚀

Blue Origin is great and if we had nothing to compare it with, we'd be incredibly impressed with what they built, having raised just $30B. But let's just use BO to put SpaceX's capital efficiency into perspective; — SPCX invented the orbital class reusable booster — flew + landed it like 650 times — built Starlink; 10M+ customers — built Dragon; launched like 60 humans to the ISS — got ~95% of the way to making a fully reusable Starship operational — acquired xAI, positioning them to do Starmind All of this on just $10B worth of private equity Like what the fuck

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.

From Digital Twins to Data Engine: Cutting the Real-Data Burden with Sim-Powered Robot Learning Teaching a robot a new task could take hundreds of teleoperated demonstrations. For foundation models, adapting to an entirely new robot can cost orders of magnitude more — dedicated hardware, trained operators, months of engineering. On @boosterobotics' dual-arm robot, we studied this at two levels: ✱ Specialist: Can task-aligned simulation mixed with a small set of real demonstrations reduce the real-data burden? ✅ Yes. With only 10 real demos, the policy made no contact at all in physical rollouts (0/20). Adding 50 simulated trajectories brought contact to 17/20. ✱ Foundation: Can data accumulated across tasks build a reusable starting point (a Booster-specific model prior)? ✅ Yes. After full-parameter continued pretraining, a model adapted with just 30 demonstrations per task beat the original given twice as many: 14/16 vs 10/16 in simulated evaluation. Before any task-specific adaptation, in zero-shot simulation, it was already roughly 3× closer to the target (17.27 cm → 5.78 cm). This work runs on Axis Suite, our Physical AI solution across different robot embodiments. Distributed contributors generate task-aligned sim data on Axis Hub at scale, reducing real-data needs for specialist adaptation while powering cross-embodiment generalist training. Read the full blog:

昨天抢的Tmx不拿了，卖了34U，勉勉强强吧！ 我只想说做过Booster任务的都亏大了，2分换来3U，还有恶心的Dc验证，手机还验证不了，我还是用电脑验证的，纯纯的恶心人！

其实机器人学习的真正瓶颈在于数据采集的成本，而非模型设计。每次新任务，几百次遥操作起步，换个场景再来一遍，永远在重复最贵的劳动。 而 @axisrobotics 与Booster的合作表明，任务对齐的模拟能够在不改变任务结构的前提下，大范围改变光照、视角和物体外观，教会策略应对各种变化。而真实的演示则专注于物理接触与执行噪声，两者互补。 实验显示，加入50%-67%的模拟数据后，策略提升率比纯真实数据高出10个百分点。仅用10次真实演示加上50条模拟轨迹，目标接触率便从0跃升至85%。 更关键的是通过持续预训练构建Booster专用基础模型，不仅使手腕-目标距离缩短67%，还让小样本微调极为高效，每任务30次演示的表现已超过原始模型60次的结果。 这一闭环数据引擎让每一次任务积累都强化整体先验，从而使得下一个任务的构建更加轻松，机器人开发由此从一次性昂贵探索走向可持续复用的工程化流程。