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He Qu

@chen_rena20812

Engineer @ Oracle I sell the red pills 💊 in AI 💻 x Bio 🧬 https://t.co/g2ZUDfa3kz

San Francisco Katılım Nisan 2026
94 Takip Edilen14 Takipçiler
Alpha哥AI投资日记 🌴 📈
这份高盛报告,基本印证了我最近对 Coding 模型的判断:商品化可能比市场预期来得更快。 OpenRouter 上,中国模型已经吃掉 89% 的 Coding Token,但按金额算,占比只有 5%—16%。这两个数字放在一起,已经把模型层未来的生意讲得很清楚了:量越来越大,单价越来越低。 当 GLM 5.2、Qwen 这类开源模型把价格压到美国高端同档模型的 1/4 甚至 1/8,性能领先带来的溢价很难长期维持。中国厂商再靠 MoE 架构和亏损抢量,整个剧本确实很像新能源车。 未来用户、会用Agent 的公司和应用层会吃到最大的红利。模型公司的 Token 用量和 ARR 即使继续暴涨,股东最后能拿到多少利润,可能是另一回事。
J.D.@Jadzo1_

Goldman Sachs: LLM primer A week or so ago there was a lot of questions about the model layer economics when LLMs are without a doubt viewed more and more as a commodity+ reaching a level where being on the frontier of intelligence is no longer the swaying factor. Economics 101, in a market with many substitutes like restaurants, price undercutting becomes a crucial factor and just like EV's is where China wins. Now these talks have been pushed to the background as the Lag 7 has revived on the back of Zucks considerations of an AI cloud business+producing an AI chip (positive read through to SUMCO/ I sold to early 😞). Back to the initial topic, Goldman provides a few insights into the landscape: → China's top coding models (GLM5.2, Qwen3.7 Max) sit at ~$1 per 1M blended tokens while US SOTA runs $4-8 for the same rung of output → And they are selling it below cost. GS pegs the value for money agentic model at a -30% EBIT margin today and the coding model at -39%, cash rich balance sheets eating the loss until it flips to +14% and +22% by 2030 on their numbers → The reason they can serve that cheap is architecture, sub-8% of params activated per token across the board, DeepSeek V4 Pro firing 49B of 1.6T and GLM5.2 40B of 744B, fewer FLOPs and a structural floor under the price → The adoption already shows up on OpenRouter where China models are 5-16% of spend by task but 85% of agent tokens and 89% of code tokens, winning wherever duration and volume make cost per task the number that matters → And the blended token price rolled over with it, SDLLMTK peaked around 2.07 in early June and sits at 1.67 now This seems somewhat similar to the EV playbook to me, we the consumers should win/benefit from a price war but the return to equity shareholders is more ify.

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He Qu@chen_rena20812·
The arteries of Silicon Valley
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He Qu@chen_rena20812·
@grapeot AI有什么迁移成本?
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鸭哥
鸭哥@grapeot·
AI benchmark 是 AI 圈的零百加速:最容易刷高、最容易吵赢,也最容易把产品拖进比价货架。 电车和油车之争里,零百是最适合赛博斗蛐蛐的指标。一个数字,谁高谁低,粉丝立刻能宣布胜利。 但真正贵的车,从来不是靠零百卖贵的。 Dodge Demon 170 在合适赛道上能跑出约 1.7 秒零百,Tesla Plaid 也能把零百刷到 2 秒左右。可它们没有 Ferrari 的定价权。 更极端的例子是赛道。 Porsche 911 GT3 约 500 马力,纽北 6 分 59 秒。仰望 U9 Xtreme 超过 3000 马力,也是 6 分 59 秒。 功率差了 6 倍,圈速一样。 这说明单点指标可以很强,但系统效率不一定强。马力、轮胎、刹车、空气动力、热管理、重量、底盘几何共同工作,才决定一台车到底好不好开、能不能持续快。 AI 产品也一样。 MMLU、SWE-bench、HumanEval 这些分数当然有用。它们是入场券。用户和投资人需要一个锚点,先判断你是不是玩具。 但刷上去的那一刻,你也把自己放进了比价货架。 你说自己 91 分,采购马上会问:另一个模型 89 分但便宜 40%,我为什么买你? 这就是 benchmark 的悖论:它能帮你获得注意力,但很难帮你保住定价权。 MIT 关于 OpenRouter 用量的研究里有个很狠的数字:开源模型发布后大约 13 周就能追平闭源模型的 benchmark 分数,成本只有六分之一左右。但闭源模型仍然占绝大多数收入。 分数差距缩小了,钱并没有自动流向分数最高或者成本最低的一边。 原因和车一样:定价权不来自单一指标最强,来自不可复制。 Ferrari 卖的不是某个圈速数字,而是几十年的赛事叙事、限量分配、客户资格和手动 V12 这种别人复制不了的物件。 AI 产品真正能卖贵的地方,也不在裸模型分数,而在 context 基础设施、工作流嵌入、权限系统、eval 沉淀、反馈闭环、用户信任和团队迁移成本。 这些东西不上榜,也很难被一张表比较。但它们决定用户在 13 周后还会不会留下。 所以 benchmark 优势的正确用法,不是继续刷下一张榜。 它更像一个 13 周施工窗口:趁分数领先,把用户数据接进来,把权限和流程调好,把产品嵌进真实工作流,把高分变成默认位置。 如果 13 周后开源追上来,你留下的只有一张过期截图,定价会被摧毁。 如果 13 周后用户已经把工作流迁进来了,benchmark 追平改变的只是你的底层模型采购价,不是你的客户关系。 这篇文章从电车油车之争写到 AI 模型刷榜,核心就一句话:可量化是入场券,不是定价权。定价权来自不可复制。 yage.ai/share/ev-oil-a…
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Universe🌌is big, life is bigger!
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He Qu@chen_rena20812·
@VoidAsuka Embrace Open source, democratize AI for everyone!
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Ruiqi Gao@RuiqiGao·
Monday last week was my final day at Google Brain/DeepMind. Seven years since my first internship, all spent alongside researchers I deeply admire, on problems that never stopped fascinating me. I'm grateful for every part of it. Now on to @AnthropicAI, to help build an even better Claude!
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He Qu@chen_rena20812·
Your AI is thirsty for HBM; Your brain is thirsty for GLP-1
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He Qu@chen_rena20812·
@Charles77xixi Codex 压缩更好,记忆用户输入的每一句话符合第一性原理。实践中256K的窗口经过多轮压缩都能用
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Charles在路上
Charles在路上@Charles77xixi·
虽然claude code和codex的上下文自动压缩都叫 compact,但二者的实现和思想其实完全不一样,这两个产品对上下文的处理几乎是相反的。 Claude Code 把上下文压缩的力度拆成了从轻到重好几档,能轻度压缩解决就尽量轻度压缩,非必要不丢弃上下文,因为轻度压缩本质上是不会损失任何上下文的,只是把上下文用占位符的形式在内存中留住,主体内容放在磁盘里。 最轻级别的压缩:把特别大的 tool_result 挪到磁盘上,内存上下文里留个2 KB 大小的预览,Agent 想要的时候还能用 Read 把原文捞回来。 再重一点的压缩:把过时的旧内容原地删成占位符,同时尽量保住缓存的热度别让旧内容完全失效。 全量压缩:只有到最后实在塞不下了,它才会进行全量压缩,将上下文精简成摘要,只有全量压缩才会真的丢弃一部分上下文。 Codex 的压缩相对来说更直接也更粗糙,基本就一层处理。到达上下文阈值之后,它把 AI 自己的回复和 tool_result 都删掉、并生成一份摘要。 但 Codex 的压缩有个原则,用户输入的每一句话都保留原文 ,砍的只是 AI 生成的内容,还有那些原始的文件和堆栈 dump。它的设计思想是,只要把人的意图完整留住,AI 生成的那部分上下文哪怕丢了也可以重新生成。 在使用层面上还是有挺大差别的,要连续记住一堆架构决策、跨很多文件、来回改动的重任务,Claude 的记忆保持明显更好,Codex 在工具密集的长会话里更容易把架构记忆丢掉。
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He Qu@chen_rena20812·
To C App的底层规则(PMF, 推荐算法, SEO)没有因为AI而改变, 但AI帮助普通人更好的认识了这些规则
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@hcwww_ Congrats! Hanchen!
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Hanchen Wang
Hanchen Wang@hcwww_·
I still remember those early days, when we sat together and started building it. Back in 2024, we had to explain what an agent was. Coding tools were dumb, and base LLMs had not yet started eating the harness. What a journey. Now, back to build!
Kexin Huang@KexinHuang5

Today, we're excited to share that Biomni is published in @ScienceMagazine. Biomedical research is still fragmented, manual, and difficult to scale. In this work, we introduce Biomni - the first general-purpose biomedical AI agent with an integrated biology environment that can reason, plan, and execute end-to-end scientific workflows. We show that, with the right environment and harness, AI can automate large-scale omics analyses, orchestrate laboratory robotics, optimize molecular properties, and even train new AI models for biology. We also introduce a reinforcement learning recipe for continually improving biomedical AI agents, enabling open-source models to achieve frontier-level performance. It's surreal to look back. We started the Biomni project in early 2024, when agentic AI was still nascent. It is exciting to see tens of thousands of biologists collaborating with agents every day to accelerate science. Try Biomni: biomni.phylo.bio Read more: science.org/doi/10.1126/sc… This work is not possible without this truly inter-disciplinary team: @serena2z @hcwww_ @YuanhaoQ Minta Lu, Ryan Li, @yusufroohani Lin Qiu @shiyi_c98 Gavin Junze Di @rickwierenga @kavi_deniz Sherry @TianweiShe Shruti Jennefer Xin Zhou @MWheelerMD Jon Bernstein @MengdiWang10 @PengHeAtlas @zhou_jingtian @SnyderShot @lecong Aviv Regev @jure @StanfordAILab @genentech @phylo_bio @arcinstitute @UW @berkeley_ai @RetroBio_ @tamarindbio @Princeton @UCSF

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He Qu@chen_rena20812·
@thsottiaux Why don't try tirzepatide💉, they work wonders
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Tibo
Tibo@thsottiaux·
You know OpenAI is cooking when the sushi and tacos orders pile at the entrance of the office at 11pm. Bizarrely we need to eat to cook.
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He Qu@chen_rena20812·
@zechengzh Mirage🌲 virtual filesystem help GPT 5.6 sol agent perform better than fable 5!
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Zecheng Zhang
Zecheng Zhang@zechengzh·
Tomorrow I’ll show how to unify your data, services, and systems into a single virtual filesystem for AI agents using Mirage github.com/strukto-ai/mir… Instead of building custom integrations for every tool, expose everything through a familiar filesystem interface. Join if you’re curious about the future of agent system. #AIAgents #AgentSystem #VirtualFilesystem #Mirage
cyber•Fund@cyberfund

Less preaching, more practice. This Thursday three founders show what their in-production agents can do. An overnight software factory, graph memory, a whole company in one terminal. Live Q&A after every demo.

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