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马斯克 11 月 10 日说 Grok 4.8 预训练完成了 已经进了强化学习阶段 团队把 RL 和推理软件栈 全用 C/C++ 重写了一遍 多花了不少时间 计划本周晚些时候 先给 Grok Bot 用户软发布 看效果再定 注意 这是进 RL、软测 不是正式发布 也不是什么"回归" 9 月 14 日他就说过 4.8 快进 RL 了 这一晃晚了 57 天 你们还等 Grok 4.8 吗 还是已经换主力模型了

SpaceX has just officially unveiled its next-generation @Starlink Mini 2, which now comes with a built-in battery for the first time. • Up to 4.5 hours of connectivity on a single charge with the quick-swap battery (or plug in USB-C). • Weight: 3.53 Ibs (1.6 kg) including battery and kickstand • Dimensions: 10.35 × 12.36 × 0.91 inches (263 × 314 × 23.1 mm) • Field of view: 55° • Wi-Fi: Wi-Fi 6 • Environmental rating: IP67 • Wi-Fi coverage: Up to 1,800 sq ft (167 m2) • Battery: Removable, rechargeable 71 Wh battery • Battery weight: 1.08 Ibs (490g) • Battery attachment: Quick-release mechanism • Charging: USB-C, 60W • Battery power output: USB-C, up to 60W • Power button: Built-in physical power button • Battery indicators: LED status lights • Kickstand: Integrated, foldable kickstand • Tripod mount: Standard 1/4"-20 camera thread • Charger: Included 60W USB-C wall charger • Cable: Included 3-meter (10-foot) USB-C to USB-C cable Starlink is now taking preorders in the U.S.:

We have dumped JAX/SGLang and rewritten the entire RL and inference stack from scratch in C/C++, which took some extra time. Training (aka pre-training) is done. Lot of cleanup was needed on the SFT data to get that mostly right. Now, 4.8 is in RL and the evals are starting to climb. We will do a soft rollout to Bot users later this week and see how it goes. Grok 4.8 will bear no resemblance to 4.7 (thankfully). It is not yet incredible, but, this time, the foundations are built right, so I expect it to evolve to be something special.

TSMC is exploring a role in Terafab, Elon's Texas chip project. Elon's reply was "Just discussions, but something may come of it." @thejefflutz calls it the battery playbook again. Build at a scale nobody else will, and suppliers can't afford to sit out. Intel's already in, Samsung's at the table, and now TSMC. The likeliest deal has TSMC owning and running the fab, with SpaceX investing or committing to buy chips. C.C. Wei says a new fab takes 2–3 years, plus 1–2 more to ramp. Elon got the leverage before a single wafer shipped. 👀 $TSLA $SPCX

AI Drug Discovery Is Becoming a Bottleneck Trade I get excited when an industry starts going through a real regime change. AI drug discovery (“AIDD”) increasingly looks like one of those moments. Two things are happening: 1/ Upstream: the frontier AI labs are piling in. 2/ Downstream: The supply chain is clearly moving. Supply-chain checks suggest the upstream picks-and-shovels of discovery and early preclinical R&D are starting to feel the increase in experimental volume. DNA → protein → assays → sequencing → automation → preclinical testing Names across that stack include $TWST , @GenScript , $ILMN , $TXG , lab-automation vendors and CROs. $TWST expects triple-digit percentage growth in AI-enabled drug-discovery orders in FY26, and another year of triple-digit order growth in FY27. @GenScript's AIDD business doubled YoY in 1H26. Its current platform advertises industrial-scale validation of 4,000+ designs/day, with integrated sequence-to-data workflows. Our channel checks suggest the ramp is moving even faster: roughly 8,000 designs/day currently, with a path toward ~16,000/day by YE26. --- Why does AI drive more wet-lab demand? 1/ AI makes hypothesis generation almost free → way more shots on goal. The bottleneck is moving from expert-driven design to biological validation. 2/ AI models need continuous experimental feedback — and both good and bad data are useful. Traditionally, only the highest-conviction A+ candidates might get pushed into expensive validation. With AI, even the B/C candidates can be valuable because failed experiments generate training data. @GenScript has said its sequence-to-binding workflow can return data in 4–7 days, and that faster cycle times matter because AI models depend on continuous experimental feedback. @Anthropic is a clean example. @claudeai designed 1,320 protein binders. @adaptyvbio converted those digital sequences into DNA, expressed the proteins and tested binding. Only 354 actually bound. And the 966 failures are not wasted. They are useful negative labels: what does not express, what does not bind, what has poor affinity. Those results help train the next model iteration. 3/ Wet labs are no longer just making drugs. They are making training data. $TWST / @GenScript increasingly look like biological data foundries. $TWST explicitly talks about generating model-ready data from AI-designed sequences. In some workflows, the customer may care less about receiving the physical protein than about getting structured experimental results back into the model. Traditional drug discovery asks: “Does candidate X work?” AI drug discovery also asks: “What can this experiment teach the model?” --- TAM of AIDD If AI is simply a better R&D tool, the relevant spending pool is the $300–400B of annual global pharma R&D. If AI meaningfully increases the number of viable drug programs, the opportunity is larger because it expands downstream demand for DNA synthesis, protein production, assays, and preclinical work. Near term, we can also size demand from AI-company spending. If Anthropic reaches $80B of ARR in 2026 and spends just 1% on AIDD, that alone would imply ~$800M of annual investment. --- Trade setup This is a trade that could have long legs. It’s hard to really stop working until PhaseI/II results (2028+) It smells a lot like the bottleneck trade we just saw in semis: GPUs → HBM → networking → power/cooling. In biology, It basically follows the drug discovery process downstream: AI models → designs → DNA/protein → assays → preclinical capacity. After the upstream picks-and-shovels, animal testing could become the next bottleneck. Monkey prices are already near prior highs and CRO capacity is tight. AIDD pushing more candidates into preclinical development would only add demand. ?? But clinical trials are still the bottleneck? This is the biggest pushback I keep coming back to. No matter how fast discovery becomes, drugs still need to go through preclinical → Phase I → Phase II → Phase III → approval. You still need patients, time and capital. But that doesn’t mean the bottleneck trade won’t work. More viable candidates — especially with higher success rates — still means more demand throughout the development process. And who knows: clinical trials themselves may eventually be optimized by AI. ?? What breaks the trade? Near term, the picks-and-shovels trade breaks if experimental budgets stop growing, AI-generated designs don’t translate into useful wet-lab hits, or capacity catches up too quickly. Longer term, the thesis breaks if AI drugs look great in discovery / Phase I but fail at normal rates in Phase II/III. That is why Phase II matters so much. ?? Milestones Late 2026–2027: first Isomorphic-designed drugs enter human trials; more AI-native programs move into IND-enabling work / tox. 2028–2030: clinical trial results start telling us whether AI-designed drugs actually perform better than conventional drugs. Calling all the "bottleneck bros". :) @jukan05 @zephyr_z9 @aleabitoreddit @ParadisLabs +++ More comprehensive analysis:

我觉得目前 macOS 的多媒体播放器都太难用了。不仅性能差，功能迭代也很慢。 所以我最近两个月一直在开发多媒体播放器 Khua Player，专为现代 macOS 和 Apple 芯片打造。 为了追求极限性能，用 C/C++ 等原生代码优化播放核心，尽可能压低体积、内存和 CPU/GPU 开销，打开快、操作跟手、播放丝滑。 也用上了 macOS 的原生 AI 能力：本地生成字幕、翻译字幕，再加上实时补帧、4K/HDR、Finder 预览 MKV 格式等。损坏或没下载完的视频，也能尽量救回来播放。 已完全开源，网址：

About $30 *BILLION* of proceeds 💵... and >1,000x returns for the earliest investors 📈. Let's break down the winners and losers of the Cursor Acquisition by SpaceX 👇: *1) We have to start with the only loser:* Alameda, or rather the nutcases who sold its stake for 1.0x. They handed over a >1,000x return to the buyers of that stake, less than 3.5 years later. A gloriously stupid move. I would love to know if anyone managing that estate still has a job? *2) The largest winners in $ terms: a16z and Thrive:* The three consecutive rounds which they led / participated in (Series A / B / C) created ~$14bn of proceeds. A job well done. The lesson here: when venture growth works, nothing else truly compare in the sheer $ returns in generates. Put a lot of $'s in, get a mega amount of $ out. *3) Early investors: hitting a total home run:* NEO (Ali Partovi), OpenAI Startup Fund, and many angels (Nat Friedman, Patrick Collison, Jeff Dean, BoxGroup, Arash Ferdowsi and many more...) are sitting on hundreds of times their money. While I'm sure it was not on Elon's bingo cards to wire a few billions to OpenAI, this is an angel's wet dream come true for the individual investors involved. NEO & Ali Partovi at >1,000x notably on a decent check is likely pocketing ~$1.5bn in the transaction. *4) Nearly ~$30bn of total proceeds to investors, a blended 8x+ return on ~$3.3bn invested* The magnitude of this exit dwarfs many large tech IPOs. For Funds involved, LPs, and early angels, this is a transformational outcome - and it will have positive repercussions on the whole ecosystem. Congrats to @martin_casado, @JoshuaKushner, @apartovi and all involved. This is what venture is all about!

I have been following this guy's content for 4 years. He's not an EV guy at all. Just listen to the great things he has to say about the @cybertruck and Self-Driving: @ Brandon_c_clark on Instagram