@berryxia

You can now create more with Claude Design. We've doubled token limits across every plan.

Qwen3.7 Preview By @Alibaba_Qwen lands on Arena for Text and Vision. In Text Arena, Qwen3.7 Max Preview ranks #13 overall. Alibaba is now the #6 lab in this arena. - #7 Math - #9 Expert - #9 Software & IT - #10 Coding In Vision Arena: Qwen3.7 Plus Preview ranks #16 overall, making Alibaba the #5 lab. Congrats to the @Alibaba_Qwen team on the latest progress!

“Discovery may become the job of AI. Understanding remains ours.” Sam Altman says biology may be too complex for humans to discover alone. A smarter system could solve what we can’t, then explain it in a way we can understand. Video

today, Krea 2 goes live to everyone. to celebrate, we're offering unlimited Krea 2 generations to all our subscribers for the entire week. try it for free 👇 Video

Anthropic CEO: "Software is going to become cheap, maybe essentially free." this 2-minute clip is the most important thing I watched this month. ↓ Save this before everyone copies the playbook Not only the people who refuse AI, but also those who think signing up for Claude counts as using it. the part nobody wanted to hear: > you don't need a co-founder who codes > you don't need $50K for an MVP > you don't need 6 months of development > you need Claude and one weekend most people will watch this and worry about their job. A few will open Claude this weekend and vibe code their first product. I used Claude to build my first product. No code. No cost. No CS degree. I did exactly this and documented the whole process step by step. Full guide in the article below. Video

好像连读本地微信数据库也会警告，发就更别想了。 wx-cli 已GG，卡比的 cli 宇宙阵亡一个。

请推荐一款能上飞机的好用充电宝

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xAI 算法开源后，解读内容铺天盖地。 我敢说一句颠覆多数人认知的实话： 市面上 95% 的分析，是 AI 批量生产的同质化废话， 连源码文件名都没翻过一次。 「多互动」「多发帖」「账号要垂直」 这种谁都会说的话，说了等于没说。 真正藏在 xAI 算法深处的机制， 大多数人连名字都没听过， 却在教别人怎么运营 X。 你见过哪篇解读，是真的牛逼有价值，欢迎推荐！ 如果没有我就来个收费的文章😁 绝对牛逼！ 开玩笑，我也是每天在一点点的啃~~ 但是真的惊喜不断！

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写了一个微信读书可视化报告skill：yao-weread-skill，已开源到GitHub 它可以把微信读书数据，生成一份本地可视化读书报告，包括： 1、近2年阅读时长与节律 2、书架书籍分析 3、阅读分类、作者、出版社偏好 4、笔记、划线、想法语义分析 5、词云、热力图、雷达图等 26 个图表 GitHub地址： github.com/yaojingang/yao-op…

New Google paper: A forecast needs context, not just history. Some patterns are caused by events, not time. Nexus reframes forecasting as a reasoning problem, where events and numbers have to explain each other. Nexus argues that forecasting improves when models read the world around the numbers, not just the numbers themselves. In the Zillow tests, one Claude-based version cut average MAPE by 86.6% versus direct chain-of-thought prompting. That matters because most time series models are fluent in pattern, but mute about cause. A housing inventory curve can reflect seasonality, mortgage pressure, migration, layoffs, and local supply, while a stock price can be bent by earnings, regulation, hype, and fear. Nexus separates those jobs instead of asking one prompt to do everything. One agent turns messy historical text into a clean event timeline, one reads the broad regime, another tracks local shocks, and a synthesizer reconciles them with calibration from past errors. The interesting result is not merely that context helps, but that structure helps the language model use context without losing the time series. The evidence is still narrow: Zillow counts, seven equities, post-cutoff data, and single-run evaluations, so this is not a universal law of forecasting. But the direction is clear: future forecasters will not only extrapolate curves; they will argue about what made the curve move. ---- Paper Link – arxiv. org/abs/2605.14389 Paper Title: "Nexus : An Agentic Framework for Time Series Forecasting"

🚨 New Gemini App UI Update This looks so cool 🤩 like I love it , btw use Gemini flash it's routing to Gemini 3.2 Everything is updated in the app , here is your first experience Video

Yann LeCun says that within a year to 18 months, we'll have a general method for training hierarchical world models These models would learn from video and real-world data, then help plan actions in robotics, healthcare, and other areas "then scale them toward a universal world model" Video

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I spent nearly 30 years in school to earn my PhD in agronomy. Classrooms, exams, field trials, thesis writing — the whole formal grind. Then AI taught me the one lesson no professor ever fully delivered: Your future depends less on what you were taught… and way more on how well you can teach yourself in a world that changes every single month. School gave me structure, patience, and deep knowledge of soil, roots, crops, and ecosystems. But it never handed me a playbook for turning real field data into web apps. It never showed me how to use open-source projects, prompts, and code to solve actual problems for farmers, researchers, and environmental teams. So I had to learn all over again. No grades. No syllabus. No one telling me what chapter comes next. Messy prompts, broken code, late nights, failed prototypes… and slowly, real clarity. That’s why I’m here as Mai Xiaomai — bridging two worlds. The patience I learned from the soil and the speed I’m getting from AI. I share practical tools, GitHub finds, small experiments, and honest notes from someone living between agronomy and artificial intelligence. The soil is rich. If you work in agriculture, environmental science, climate, food systems, or you’re simply curious about real-world AI, come grow with me. What’s one skill you had to teach yourself that school never prepared you for? #MyPiece

微信读书Cli安装和配置教程。 1. 官方指令，复制发给Codex或Claude Code 下载 cdn.weread.qq.com/skills/wer… 安装 skill 2. 或者用 X 上网友 @eviljer 做的优化版Skill npx skills add jerlinn/jerlin-weread 3. 如果需要API key，访问微信官方这个页面获取 weread.qq.com/r/weread-skill… 4. 使用案例 直接跟AI说：“调用微信读书skill 查看被讨厌勇气的高亮划线”

Chinese students are buying GPT-5.4/5.5 and Claude API access from Xianyu/Taobao proxy sellers for almost 96-97% cheaper People are apparently burning 100M+ tokens a day for like $1 and vibecoding nonstop.

Terence Tao says the math behind today’s LLMs is actually simple. Training and running them mostly uses linear algebra, matrix multiplication, and a bit of calculus, material an undergraduate can handle. We understand how to build and operate these models. The real mystery is why they work so well on some tasks and fail on others, and why we cannot predict that in advance. We lack good rules for forecasting performance across tasks, so progress is largely empirical. A key reason is the nature of real-world data. Pure noise is well understood, perfectly structured data is well understood, but natural text sits in between, partly structured and partly random. Mathematics for that middle regime is thin, similar to how physics struggles at meso-scales between atoms and continua. Because of this gap, we can describe the mechanisms but cannot yet explain capability jumps or give reliable task-level predictions. That mismatch, simple machinery versus hard-to-predict behavior, is the core puzzle. ---- Video from 'Dr Brian Keating' YT Channel (Link in comment) Video

Here’s the list: linus-sellout-tweets.com

You can now use X Premium subscriptions in Hermes Agent, and Hermes Agent can now search X posts. x.ai/news/grok-hermes

You can now use X Premium subscriptions in Hermes Agent, and Hermes Agent can now search X posts. x.ai/news/grok-hermes

We're now on Day 4 of nonstop autonomous operations with F.03 humanoid robots running 24/7 until failure nitter.net/i/broadcasts/1OxwblMvX…

Introducing Zero The programming language for agents. I wanted a systems language that was faster, smaller, and easier for agents to use and repair. Explicit capabilities. JSON diagnostics. Typed safe fixes. Made for agents on day zero.

OPENAI 🔥: In the future, Codex will be able to control other desktop devices with the Codex installation. All your Mac Minis, your desktop station at work, or even your grandparents' old computers can form your own "Codex network". Along with the upcoming "Locked Use" setting, this feature will allow Codex to invoke Computer Use capabilities on other machines from your main device.

ANTHROPIC 🔥: Claude Mythos model has been spotted on Google Cloud Console. -claude-mythos 👀 It is hard to imagine that Anthropic would change its mind and release it publicly but they could act as a model provider for those companies who have access to the model and run their stuff on GCP.

You can now run MagicPath as a native canvas inside Codex to design and build functional apps. It's pretty incredible. Here's how to do it 👇 Video