kuany

investor in Monolith,prev @Zai_org

[Andrew M. Dai @AndrewDai]

Tired of seeing benchmarks saying we are at AGI but models still can't understand a 3D protein view or a floor plan? When a visual model gets something wrong, is it failing to see or failing to reason about what it sees?

We've built two benchmarks that answers these questions. Explore DecoyCount and ScienceVision here: https://t.co/sFbUIbWAbN and https://t.co/hWX0lVMVot

And thank you @mercor and @UniPat_AI for your partnership in building these!

[Anthropic @AnthropicAI]

We’re sharing how our biology research team worked with Claude to map the first 3D structure of a vault particle from bacterial life.

Vault particles are a longstanding mystery. They are barrel-shaped objects, among the largest protein structures found in our cells. They were first discovered by Leonard Rome and Nancy Kedersha in 1986, but their function remains unknown.

With Claude, we’ve found results that might bring us closer to solving that mystery.

Until now, vaults were only known in the cells of complex things like animals and plants. We have shown that similar proteins from bacteria (and the viruses that infect them) can also assemble into vaults. Finding the structures in these simpler forms of life may make it easier to investigate their function in our own cells.

For our life sciences team, the project was also a demonstration of the potential (and the limitations) of using AI to accelerate science.

In our full piece, we discuss how Claude was able to dramatically speed up some stages—manually determining the vault particle’s structure would have taken weeks to months of painstaking work, but here it took days. We also discuss how Claude made some simple mistakes along the way, and how we collaborated with our machine learning researchers to address them.

Read more: https://t.co/KowVVCSPp8

[Mercor @mercor]

We're proud to have collaborated on a new benchmark built by @GoogleAI: Physical-Engineering's Last Exam (PLE).

Physical engineers design the skyscrapers, power grids, and chip fabs we rely on, yet no existing benchmark tested whether AI agents could do that work end to end.

Together with Google and practicing engineers, we built 85 long-horizon tasks across mechanical, electrical, civil, and structural engineering. On the private test set, all frontier models score below 50%:

1. Claude Opus 5.5: 48.3%
2. GPT 6 Astra: 45.8%
3. Claude Sonnet 5.5: 43.5%
4. Gemini 4 Argon: 40.6%
5. Claude Opus 5: 40.0%

On the tasks we co-authored, the top agent satisfied 91% of individual constraints, but only 41.3% of its deliverable items passed every hard safety check.

Learning how AI fails at physical engineering can help us improve future models and build a safer world.

This benchmark is also an open competition. Bring your own agent setup and submit it for evaluation on our private test set at https://t.co/nOTWOUaV5v

[Rikiya Takehi @rikiyatakehi]

We asked GPT-6 Astra to edit Qwen3.5-0.8B weights to improve math.
It directly changed only 0.016% of weights, fixing 0.8B's reasoning loops & improving math up to 6x and even instruction following 3x, without gradients or training.

We call this Weight-Space Programming: frontier LLMs can now natively read/write neural network weights! 🧵
Blog: https://t.co/bOWCcFhCVq

[真白大小姐 @Mashiro_0619]

Claude风控天机泄露😱

1. Claude 的风控是积分制：注册时会先给账号打分，之后根据行为加分或减分，低于一定分值就会直接封号（很少加分）。

2. 注册：邮箱和 IP 的权重最高。IP 不一定要家宽，但一定要干净，例如自建 IP。Gmail 已经不是最优选，他们现在使用的是一些其他高权重邮箱。注册时直接弹 KYC 的账号可以直接放弃。注册环节很重要，否则后续稍有高危行为就可能直接封号。使用手机 App 注册会加分。

3. 支付：刚注册就订阅会减分，直接订阅 Max 会大幅减分，最好间隔一两天再订阅 Pro。订阅时弹出 KYC 意味着账号处于考察期，但不一定会封；简而言之，A 社想先看 KYC 结果再决定。KYC 使用中国护照没有问题，最大的问题是不能一人多扫；闲鱼上很多账号都属于这种情况，要小心。

4. 注册后的 IP：最重要的是稳定，千万不要随便切换，尽可能一直使用同一个 IP；同样要求干净即可，不要求一定家宽，最好与注册 IP 一致。代理一定要开启全局模式并启用 TUN，这是重点。

5. 老号：老号权重非常高，也就是积分很多，容错率更高。账号积分会随着账号寿命增加，不过 2023 年的老号才是显著更高。

6. 使用：不要一上来就把周限额用完，尽量控制并平均使用；用量大的宁可开两个号轮换使用，也不要一次性用完。使用 CLI 和 Desktop 都可以，Desktop 似乎会少量加分。尽量少用网页端，最好只保留手机端和 Desktop 登录。

7. 时区和语言基本不减分，或仅极少量减分，没必要特意调整。

8. 高并发、反复提问同一个问题、频繁登录、频繁切换 IP 都会大幅减分。

9. 手机端使用会少量加分。开启记忆功能会加分，甚至可能记忆文件越多分数越高。

10. 支付方式为国外实体正规银行信用卡会加分，其次是 Google Play。Apple Pay 不了解；官方礼品卡不减分，但要注意来源是否为黑卡；虚拟卡直接绑定会减分。

11. 到达斩杀线后可能进入观察期。例如及格线是 60 分，账号 55 分就进入观察；如果继续扣分直接掉到 40 分，就会直接封号。观察期的典型特征是掉登录。有的高权重号可能经历两三个观察期才被封。

12. 多设备登录会大幅扣分，最好只在一台电脑上使用一个客户端。

13. Windows 和 macOS 没什么区别，Linux 使用会减分。

14. 跟 Claude 说自己是中国人也没事，不会因为记忆文件就封号。

15. 充值 Credits 会加分，不过成本太高，尚未进行大量实验。

16. 大量使用 Fable 会减分。

17. 升级 Max 大概率会直接进入观察期，除非是积分特别多的高权重号。观察期内有减分行为，大概率会封号。

18. 在工作时间使用不会扣分，不会出现因为使用北京时间就封号的情况。

[OpenAI @OpenAI]

We’ve acquired Orchestra Bio, a platform that helps biopharma teams coordinate the complex process of drug development.

Together, we’ll build on Rosalind, our AI models and tools for life sciences research, to streamline those operations and help new treatments reach patients sooner.

Welcome @ashokaraj_ and team!

https://t.co/vi51OhznQn

[Sac @Saccc_c]

花费16天，我用 Opus 5.5 将小王子的故事制作成了3D剧情游戏

结合Tripo+Blender，Opus 5.5 将小王子故事中的每一个星球变成了可探索的3D场景

希望大家能支持一手，后续会开源整个游戏，以及详细制作流程

Welcome to The Little Prince: Across the Stars✨：https://t.co/qr4wR0jLu3

[kuany @Thiscouldb79]

Next to @CathieDWood Wood is Golden State Warriors legend and investor Andre Iguodala🤝 @warriors

[kuany @Thiscouldb79]

Where is AI drug discovery making real progress—and who actually gets paid? My take on the science, the hype, and the businesses behind it.

#AI #Biotech #DrugDiscovery #sf #sanfrancisco #techweek #AI4S https://t.co/jAV53HJPk1

[kuany @Thiscouldb79]

AI4bio，大厂正在投入更多资金

1. Biohub联合18亿美元资源建设生物训练数据，代表大模型公司的投入正在延伸到实验数据生产。其中GoogleDeepMind、IsomorphicLabs 和Meta 投入了3亿美元。DOE承诺五年内超过5亿美元。Biohub 的5亿美元于四月宣布。NIH 贡献的数据集基于之前超过5亿美元的联邦资金。

2.Anthropic更加激进的药物研发目标和湿实验室建造改造计划。
9月官方确认在湾区建立分子生物学实验室，把 Claude 的生物训练、假设生成和人工实验验证放在同一团队中。

3.OpenAI早在GPT-5 接入 Ginkgo 的自动化云实验室，设计实验、接收结果并迭代，完成超过3.6万组蛋白合成反应。

4.礼来 × NVIDIA News： 双方宣布五年内共同投入最高10亿美元，在湾区建立联合AI实验室，开发生物与化学基础模型并连接湿实验

5.几乎所有的LLM大厂都开始更快地节奏接触生物初创公司或如同Isomorphic一样寻求多种形式的湿实验室合作，希望他们成为新的进程中的infra角色。

新的scale需要什么？

data, measurement systems, compute and experiments
数据、测量系统、计算资源和实验

垂直整合之下，每一层的参与者都具有新的机会。
数据公司、实验公司、模型公司。

今天的biohub发布：Folding proteins by hand、esmGFP（一种由他们的ESM3模型设计的荧光蛋白）、68亿蛋白质序列的地图，以及一条活体斑马鱼在发育过程中逐细胞成像。图1

围绕着下一个LLM scaling方向的豪赌或许已经开始了。

[Healthcare AI Guy @HealthcareAIGuy]

BREAKING: The US government, Google, Meta and Biohub are launching a $1.8B push to build the data layer for AI biology.

The goal: create the largest open, AI-ready biological dataset ever assembled, giving models better data to predict, understand and engineer biology.

[kuany @Thiscouldb79]

The discussion above is absolutely good and little too focused on biology. If we step back and ask how LLMs can ultimately support the entire pipeline, I see five main entry points:

1.LLMs are particularly well suited to summarizing data and uncovering insights.
2.LLMs are well suited to maintaining verifiable experimental records and feeding results back into the workflow, enabling end-to-end observability.
3. As a “brain,” an LLM can use tools to orchestrate different protein-folding models in dry-lab experiments, control instruments in wet labs, and even coordinate human researchers.
4.From a systems engineering perspective, LLM-as-a-judge is a better design choice.
5.LLMs can break down silos between different stages of a workflow and between departments.

[Artem Gazizov @artemg3141]

nobody sees the true bottleneck to AI drug discovery's growth (capital allocation view)

here is the chain of thought:
-> ~90% of drugs fail in trials, mostly because we fail to understand how disease works (efficacy).
-> drugs with human genetic support are ~2x more likely to get approved.
-> LLMs excel at reasoning across a massive corpus of data, so they can improve our understanding of disease.
-> LLMs enable hypothesis generation at scale and move the bottleneck to wet labs and data.

for capital allocation, this means tracking what shapes wet-lab demand:
-> target and disease biology
-> target types (which point to modality, and so to manufacturing needs)
-> trial design
-> why trials were discontinued
-> etc.

to track that, we need large-scale standardization of existing biological and financial data.

interestingly, this is not a trivial task to accomplish at scale ($/tokens spent to achieve X% data extraction accuracy).

the edge goes to whoever has properly structured data that an LLM can reason from.

to accomplish this, i built self-improving agents that extract intelligence at scale on:
-> target and disease biology
-> clinical trials and human readouts
-> chemistry data

if you invest in biotech institutionally or run a biotech, i would love to chat!!

[kuany @Thiscouldb79]

Ghost 发布了 Core，一台放在家里、24 小时在线的“个人 AI 电脑”。主打本地模型推理、隐私、持续积累个人上下文，以及主动提供帮助；可以通过电脑、手机或 iMessage 与它交互。

配置是 RTX PRO 4000 Blackwell SFF、64GB 内存、1TB SSD 和 Ryzen 5 7600，官网售价 3,499 美元，无订阅费。

简单算一下：这张卡有 24GB 显存，比较适合跑 4-bit 量化的 27–32B 模型，官网也列出了 Qwen3.8-27B、Gemma 4-31B 等。值得关注的是，它尝试把本地模型、长期记忆和主动 Agent 整合成一台个人设备——软件能否持续理解用户、完成任务，会决定这台硬件的价值。

[Zain Javaid @zainmfj]

Introducing Core, The Personal Al Computer

The most powerful technology humanity has built should belong to you

Order today: https://t.co/tlNxSu5B5C

[kuany @Thiscouldb79]

cool！Have a try for your PA

[Shengkun Ye @shengkunye]

We just raised $7.7M to kill every subscription for agents.

Introducing https://t.co/uBBT6t7hyZ, the openrouter for agent tools.

Your agents can now:

> discover, run, and pay for tools at runtime
> use 2,500 APIs through one connection: leads, SEO, marketing, search, ecommerce, stocks, video/image/music/3D gen, agent email & phone
> pay per call, 0 subscriptions

[kuany @Thiscouldb79]

最近在tech week听了一些小型的分享和二三手的消息。但始终，让人觉得insightful的前沿工作者只给人一种启发：
面对LLM driven的科技浪潮
应该更积极，应该更实践
Be more proactive. Be more hands-on.

#sf #techweek #AI #LLM #frontier #drug #SanFrancisco https://t.co/Nunb2ifDKZ

[Andrew Curran @AndrewCurran_]

Project Prometheus has $6.2 billion in funding and will focus on AI design in aerospace, computers and cars. The co-CEO is Vik Bajaj. Project Prometheus already has nearly 100 employees, some of them hired away from OpenAI, DeepMind and Meta. No word yet where they will be based.

[Peter Zhou @peterzhou8964]

知名分析师透露OpenAI将研发手机

天风国际证券分析师郭明錤发布报告称，OpenAI正联合联发科、高通开发手机处理器，并由立讯精密独家承担系统协同设计与整机制造，计划2028年量产一款颠覆传统交互逻辑的AI手机。

[kuany @Thiscouldb79]

cool！I’ll be there~

[Div Garg @divgarg]

We're taking over a coffee shop on Union st. for 3 days of SF @Techweek_ by @a16z .

The goal is to make it the busiest pop-up in the city this week.

Mon to Wed, 9am to 4pm, franko & friends in the marina (next to the equinox): https://t.co/c9oSxrzpxV

Coffee and matcha on us.

What's there: our agent running live on android, the smart glasses checkout steve and i did on stage at snapdragon summit (look at a thing on the shelf, the agent buys it, confirmation on your watch), and our merch in the new branding.

two parties.

monday 4 to 6pm is the private opening: dj, drinks, friends, family, partners, VCs. invite only, dm me.

wednesday 4 to 6pm we close it out with a happy hour.

and some more serious stuff on thursday: devices x ai pitch night, 5 to 8pm. speakers from @Qualcomm, @deutschetelekom and @docomo, then founders building for phones, glasses, cars and robots pitch live.
rsvp here: https://t.co/Neil7GpiTt

we're hiring engineers and researchers, and we want to meet device makers.

come say hi, bring a +1.

[Mgoes (bio/acc 🤖💉) @m_goes_distance]

it is genuinely insane how much progress biology has made in 2026 alone

we've got:

- obesity drugs pushing 20–30%+ weight loss
- gene therapy that can restore hearing
- genetically engineered viruses approved to attack cancer
- targeted drugs that can nearly double survival in late-stage pancreatic cancer
- personalized gene-editing therapies built for a single patient
- immune cells genetically reprogrammed to hunt tumors
- AI designing proteins and biological systems humans didn't know how to make
- robots running thousands of biological experiments autonomously

the frontier is moving faster than most people realize.

bio/acc

[kuany @Thiscouldb79]

https://t.co/pmfR4b4YvC
如果你的tech week活动信息也在Agent里，可以直接把link发给AI看我们哪场活动可能偶遇！

我在Monolith投资LLM相关，关注LLM/上游Capex下游应用/LLM for Science&robotics.公众号和小红书同名，期待建联！