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Yes, the green flame is caused by burning copper from depleting fuel in the regenerative cooling circuit and going oxygen-rich (closer to stoichiometric temperature) in the main combustion chamber. Slight underperformance of engines on this flight is the reason. The booster is meant to land with tiny residuals, so the solution is to improve engine performance and performance consistency (underway with Raptor V3.2), as well as adjust flight software to do MECO1 about one second earlier to ensure enough propellant for boostback and landing (tiny change). These early ships and boosters are already outdated by improved designs in production or on the test stand, so this booster loss does not change our timeline to reach high volume, fully reusable Starship flights.

This is not as good as it sounds, because Starlink required many satellites in low Earth orbit to achieve this bandwidth improvement globally. That said, there will ultimately be over 100k SpaceX communications satellites, most of which will be V4 or V5. Good chance that this system carries about half of global traffic.

If Jensen gave me his word about compute delivery or I asked for components to be accelerated, he always came through. Sure, we’d put together a purchase order at some point later, but I never for a second thought it wouldn’t happen.

让 Claude Code 跑一会儿额度就见底，翻回去一看，大头根本不是你说的话，是它自己 grep 出来的几百行结果、一整坨日志、重复的 JSON，每一轮都原样塞回给模型。 Headroom 管的就是这块。它在本地把工具输出、日志、文件、RAG 片段先压一遍，再发给模型。 现在 7.9 万多星，今天在 GitHub Python 日榜上，10 号刚发 v0.44.0。 README 里我觉得比较有用的： 1. 不用改代码，headroom wrap claude 一条命令就把 Claude Code 套上，Codex、Aider、OpenCode、Kimi CLI 也能这么接，不想用了 headroom unwrap 撤掉 2. 官方实测四个 agent 场景省了 21% 到 57% 的输入 token，重复的 JSON 和日志能压掉九成以上 3. 压掉的原文都缓存在本机，模型需要细节时自己调 headroom_retrieve 取回来 4. 还能压输出：开 HEADROOM_OUTPUT_SHAPER=1，让模型少说"好的，我来……"这类开场白，默认是关的 压缩在本机跑，内容不会发出去。uv tool install --python 3.13 "headroom-ai[all]" 就能装，Apache-2.0，Cursor 得手动配。 大段正文、本来就很密的输出基本压不动，省多少看你的流量有多啰嗦，跑一下 headroom savings 就知道了。