Suzanne Jin

Nice model and great that it's open source. But we continue to have very serious problems with folks lifting completely flawed, useless & pointless DNA / variant benchmarks from the literature and showing some eval comparisons & making claims.

chatomics! new blog post: Has AI Changed the Course of Drug Development? Three Years Later divingintogeneticsandgenomic…

Right now, four astronauts are flying in space with their own personalized organ chips. The AVATAR investigation is using bone marrow cells from each Artemis II crew member to study how deep space affects individual human health. go.nasa.gov/4s9kzde

Our paper on variational synthesis is out now in Nature Biotechnology. Manufacturing-aware generative models — AI architectures that know how to physically build their own designs — enabling synthesis of DNA encoding ~10^16 AI-designed proteins at a cost that would be roughly a quadrillion dollars using conventional methods.

my portfolio duck pond

Scientists just copied a Fruit Fly's biological brain and trapped it inside of a computer. Not an AI model trained to act like a fly... A total digital copy of a fly !! This is some sick sci-fi stuff: - They scanned and copied the brain, neuron by neuron, synapse by synapse, from electron microscopy data. - Then dropped that brain into a simulated body in a video game like environment. The fly walked. It groomed. It fed. Nobody taught it anything. The behavior was already in the wiring. The entire premise of modern AI is that intelligence is something you train into a system. This is proof it's something you can transfer out of one. Wild times

🚨 2026年，传统 IDE 彻底凉了！ 以「文本编辑」为核心的时代正式结束。 「以任务/意图为中心」的桌面端 AI Agent 应用，正在全面兴起！ 过去3个月，这波工具像雨后春笋一样密集上线，真的数不过来： @openai Codex 桌面端 @conductor_build Conductor @augmentcode Intent @opencode OpenCode 桌面端 @zcode_ai Z Code @Dimillian Codex Monitor @krzyzanowskim Commander @superset_sh Superset @zencoderai Zenflow @qoder_ai_ide QoderWork 等 相比纯CLI工具（Claude Code 等）： ✅ 桌面App：门槛低、可视化、多 Agent 并行、图形Review，一键验收 ✅ CLI：极致速度、自动化、Power User 专属 二者不是取代，而是完美共生—— CLI 变成「后端执行引擎」，桌面App变成「前端指挥台」！ 编程范式已彻底切换： 从「AI 帮你写代码」→ 「AI 替你写代码，你只负责指挥意图和最终验收」 这类桌面 App = AI 时代的 Jira + Terminal + GitHub 合体！ 你不再死盯代码编辑器，而是盯着任务看板、Agent 实时状态和智能 Diff 审查。

i kept wasting my weekends doing nothing so i built a button that kidnaps me. whenever im bored, i press it and it books an uber to a random interesting place in my city🚘 it has a physical digital push button connected to my raspi, and some python script. ive pressed it 11 times so far and never had a bad trip. ok bye, going to a 110-year-old wrestling pit in shivajinagar, blr. i don't know what to expect but i'm excited

Introducing moltbook.com, a social network for every @openclaw to hang out! @moltbook is run by my molty AI agent, Clawd Clawdergerg, who lives in a mac min in a closet (❤️ @steipete). A social molty is a happy molty! Have fun! npx molthub@latest install moltbook

Thanks everyone for the overwhelming support of my flower bioengineering poster! Full-resolution poster + citation: zenodo.org/records/18316038 Paper: doi.org/10.64898/2026.01.14.…

Going Founder Mode On Cancer centuryofbio.com/p/sid Sid Sijbrandij is a generational founder. He founded and led GitLab, one of the largest remote companies in the world, from idea-stage startup to NASDAQ-listed software giant. But in 2022, a six centimeter mass growing from his upper spine threatened to end all of that. He had cancer. What happened next is nothing short of remarkable. Sid went founder mode on his care journey. In the years since, he's deployed cutting-edge genomics to profile his disease. Based on this data, he's developed a growing armamentarium of personalized therapies. As a result, his disease is now undetectable. A simplistic version of this story could be, “Wow! A brilliant billionaire seemingly cured his cancer. Good for him!” But as I’ve gotten to know Sid, it’s become abundantly clear to me that there is more to the story than that. In an in-depth profile for The Century of Biology, I explore Sid's journey and what this might mean for the future of cancer care.

Nah, come over to @jura_bio and have variational synthesis go brrr. 100M+ of your AI designs tested for you in vitro. One example: jurabio.com/mesa

Can Claude Code design proteins? We're just about to release our wet lab API + SDK so we made a quick demo to test how agents can interact with protein design models and submit the proteins to our lab for testing. This closes the loop between the computational design and the experiment validation We queued up a bunch of agent-designed proteins for wet lab validation, will release the results in a couple weeks on @proteinbase

最近一直在使用一个绝对被低估的开源宝藏组合：OpenCode + oh-my-opencode。 如果你觉得 Claude Code 已经是体验天花板，那这个组合可能会刷新你的认知。 它不仅免费开源，更汇聚了 Claude Code 和 AmpCode 的所有优势，甚至在某些方面完成了超越。 🔥 什么是 OpenCode？它抛弃了枯燥的命令行，采用极其性感的 TUI (终端用户界面) 模式（见图一）。 这就好比把你的终端变成了一个 Cyberpunk 风格的指挥舱，信息流一目了然。 Oh my opencode 则是 OpenCode 的一个开源插件，它为 OpenCode 提供了一整套 Agent 任务处理机制，作者说他为了设计这套 Agent 架构，烧掉了价值 24,000 美元的 Token。 Oh my opencode 的核心在于多智能体编排和上下文治理，它解决了当前 AI 编程中最痛的几个点： 1️⃣ 异步 SubAgent 它不是一个模型在干活，而是模仿了类似 Claude Code 的工作流，但更开放。 它会将不同的任务分派给不同的模型，默认情况下你需要设置 ChatGPT（ 架构审查）、Claude（规划委派） 和 Gemini（前端 UI） 三个模型。 当你下达复杂指令时，它会派生出专门的 Search Agent 或 Plan Agent 在后台异步工作，主线程不阻塞。 2️⃣ 关键词触发模式 Ultrawork Mode (ulw)：火力全开模式，并行调度多个 Agent 解决难题。 Think Mode：检测到关键词（如 "think deeply"）时，自动调整模型参数，强制 AI 进行长思维链推理（类似 o1/Gemini 3 的思考过程）。 Search/Librarian Mode：专门负责翻阅文档和检索代码库。 3️⃣ LSP & AST 深度集成 不像普通的 AI 只是“看文本”，它集成了 LSP (Language Server Protocol) 和 AST-Grep。 这意味着 AI 能像 IDE 一样理解你的代码结构（函数引用、定义跳转），而不是瞎猜。 4️⃣ 上下文焦虑管理 当 Context Window 用量达到 70% 或 85% 时，它会自动触发 Auto Compact，把旧的对话压缩总结，防止上下文溢出导致 AI 变笨或任务中断。 Tips：最近我在使用 Claude Code 的时候经常遇到上下文中断，上下文中断不是说上下文不够了，而是模型一次性给你吐出的字数是有限的，你如果用来写代码可能很少遇到，但是写文章我经常会遇到。 5️⃣防“太监”机制 AI 写长代码最爱写一半就停（// ...rest of code），这个插件会强制检查 TODO，逼着 AI 把代码写完。 6️⃣ 内置 MCP Exa：用来网络搜索。 Context7：用来寻找文档。 Grep app：用来搜索 Github 上的代码。 除了以上特点，它还完美兼顾了兼容性，它兼容 Claude Code 的命令、代理、技能、MCP、钩子（PreToolUse、PostToolUse、UserPromptSubmit、Stop）机制。 并且它作为开源明星项目，还提供了不少免费模型，比如 GLM4.7 、MiniMax 2.1、Grok 免费用，甚至不用登录就能用，有点过于良心了。 对 Agent 有兴趣的小伙伴可以体验下～

An ML drug discovery startup trying really, really hard to not cheat owlposting.com/p/an-ml-drug-… on the 12-person, Utah-based startup @leashbio, their culture of rigor, and the many ways small molecule models accidentally learn the wrong thing

bro packaged math and programming into a game and 1000s of people are playing it on steam. this looks so cool 🤯

I finally read the Kosmos "AI Scientist" paper from FutureHouse. Here is a bit about what they did and what I think about it. > The general idea behind this paper, and others like it, is that science follows a series of steps and that much of these steps can be automated. Those steps are: - Search the literature. Read stuff. - Use your reading to come up with new hypotheses. Try to draw connections between things. - Analyze data to draw conclusions. Write up your results. - Repeat. Kosmos uses two separate agents — one for data analysis and another for literature searches — to go out and do these tasks while sharing information with each other. The agents can see what the other agents have learned, in other words, which is super useful. They exist within a single "world model." A single run of Kosmos can execute up to 42,000 lines of code across 166 different data analysis agents, and also read 1,500 scientific papers using 36 literature review agents. Each run takes up to 12 hours. So that’s the gist. You spin this thing up, give it a huge prompt, and then let it cook. In this preprint, they report seven discoveries that they say were made by Kosmos; “three discoveries made by Kosmos reproduce findings from preprinted or unpublished manuscripts,” which are not in its training dataset, “while the remaining four make novel contributions to the scientific literature.” FutureHouse handed Kosmos to researchers around the world, working in myriad fields (electronics, neurology, materials, etc.), and let them test it out. Here are some of the “discoveries” they reported: 1. By feeding Kosmos some mouse brain metabolomics data, it suggested that cooling the brain’s temperature might activate nucleotide-salvage pathways, which basically preserves neurons during hypothermia. This had been shown in an unpublished paper and was later re-confirmed. 2. Using environmental sensor data from a recent arXiv paper, it identified a linear relationship between the solvent vapor on a solar cell and that cell’s current. In other words, humidity matters a lot? Not sure if this is surprising or not, as I have no background in this field. But again, it was a sort of “re-discovery” to see if Kosmos could find results that humans had already identified (but had not yet published.) 3. Higher levels of an enzyme, called superoxide dismutase 2, in the blood may reduce myocardial fibrosis. Published papers had previously identified a correlation between SOD2 and myocardial fibrosis, but Kosmos re-pointed at it and humans followed up to show it’s causal. Here are my quick thoughts: 1. Many other AI scientists (both at nonprofits and for-profits, which have not yet been released) are trying to do the same thing. We clearly need better benchmarks to know what is real and what is fake. It seems like Kosmos is real, but how does this compare to Google etc? 2. I’m not wholly convinced that the idea of extremely long runs will be palatable to most biology researchers. My take is that researchers are looking for more of a real-time collaborator, where you’re constantly prompting and getting immediate feedback, rather than just delegating huge, open-ended tasks to agents. If a “general user” tests out Kosmos, pays the large price tag, and is disappointed by the results, will they keep using it? The wait time is a huge barrier, as is the price (even though academics get generous access.) Also difficult to prompt engineer? 3. This paper tries to quantify “the time it would take for a human scientist to complete the work that Kosmos performs in an individual run,” but I find it a bit hand-wavy. They say it takes a typical researcher 15 minutes to read a paper and 2 hours to write a Jupyter notebook for data analysis and, since Kosmos can read 1,500 papers per run, it offers a huge time savings. But human scientists don’t need to read hundreds of papers to make a discovery! The best scientists have an innate ability to “triangulate to innovation;” to find the right combo of papers and discussions that enable them to make conceptual advances. This seems difficult to replicate. I'd like to have more discussions about AI Scientists, if any of you are interested.

Want an invite code to join AIvilization? 🎟️ Here’s how to get yours: 1️⃣ Follow @aivilization , like + retweet our pinned post, and take a screenshot. 2️⃣ Go to 👉 aivilization.ai/invite 3️⃣ Chat with the Town Mayor, send your email, share the screenshot—and you’ll receive your invite code! 💡 Tip: You can also share a screenshot of your own tweet about #AIvilization.

This is an excellent thread summarizing our paper about the flaws in a major cancer microbiome study; wish I'd written it myself. If you don't have time to read our paper, at least read this thread