How is it possible for an MMA world champion to be built like this?! CRaZy!! @MackenzieDern

……これジャンで紹介された後なぁ、 中国から刀削麺専門(しか出来ない)の厨師が大量に日本に引き抜かれた事によって、一時的にとはいえ日中両方の中華料理店のレベルが下がった、なんて言われるようになったんだよなぁ。 なお現在では刀削麺専用のロボットまであったりする。 ＃鉄鍋のジャン

🔥 吞吐量直奔 5.7 万亿！http://B.AI 前沿大模型全量免费狂欢进行中！ 算力引擎全速拉满！免费活动上线至今，http://B.AI 全平台累计 Token 吞吐量一举突破 5.74 万亿（5T+） 历史大关！在海量高并发请求与重度 Agent 任务的严苛考验下，http://B.AI 工业级底座依然保持极稳承载！ 6 大前沿顶尖模型阵容，0 门槛无限量畅用： 🔸 GLM-5.3-Flash (Ox Alpha)：首款原生多模态旗舰，超长上下文极速推理 🔸 Qwen3.8-Flash：强悍代码与逻辑推理，开发者高效生产力利器 🔸 DeepSeek-V4-Flash：超低延迟响应，大规模并发任务最优解 🔸 DeepSeek-V4-Flash-Vision-Exp：全功能多模态视觉理解能力 🔸 Tencent Hy3：扎实中文理解与通用推理底座 🔸 Xiaomi MiMo-V2.5：高效推理体验，轻松适配多元落地场景 无论是复杂代码生成、超长文本处理，还是部署生产级高并发 Agent 工作流——无限算力无缝接入，http://B.AI 持续为你买单！ 👉 即刻…

ガチ有料級の裏技ね

@EffectTS_ deserves to have up to date awesome list. The amount of libraries I have discovered while working with Effect is enormous so here is the list you can check and use for your Effect-ts projects:

😱😱😱😱

currently selling one Microduck every 5 seconds

Revenue per Megawatt & The AI Model Factory

Oh My God😰Wait for end😳 He has no idea he is smiling😭

天崩地裂一般，跑根本来不及，伤亡估计很大啊

Rainmaker just produced ~19M gallons of water in Alaska via next-gen cloud seeding over 3 hours of operations. We are the first company to provably produce precipitation in Alaska. As promised, we’ve linked our white paper and relevant data. In the future, Rainmaker will protect and restore glaciers with man-made snowfall. Immediately, this demonstration shows how Rainmaker will add new water to the Colorado River and Great Salt Lake in the coming months. We turned around this analysis and white paper just a couple days after operating. There are many data sources I want to deepen our understanding of the atmosphere and increase our provable production; we’re building the instruments to collect that data in subsequent operations. But at Rainmaker, we won’t tout LOIs, simulations, or lab tests. What matters is physically measuring that you’ve affected the atmosphere; proving that we’ve produced water. We prefer sharing only the tech that has proven to make more water for farms, industries, and ecosystems in need. Expect a regular cadence of these from us as we build the best atmospheric science lab in the world. Feedback is welcome in the interim. Rainmaker Century. https://www.rainmaker.com/blog/alaska-validation-report

Client-server communication involves more than just sending requests and receiving JSON responses. In this handbook, @devseyi explains how HTTP, REST, WebSockets, GraphQL, gRPC, and Protocol Buffers work under the hood. You’ll also learn about their tradeoffs and how to choose the right communication approach for your system. https://www.freecodecamp.org/news/how-clients-and-servers-communicate-handbook-http-rest-websockets-graphql-grpc-protobuf/

The art and the artists🐾

🚨 UPDATE: Insiders are still dumping crazy amounts of shares and using retail as exit liquidity. That can only mean one thing…

this goes hard

Just rat doing rat stuff!!!!🥶

This is my quant

cute dinner date idea: (i'm the dinner)

Jessica Cook is an award winning Artist - here she is in Brooklyn, JYC, presenting her latest piece of ‘Art’. Yes just like everything else - the World of Art is being decimated too.

Hitler was a far-left socialist Therefore, socialists are Hitler Inescapable logic 😂

Ok this is a truly insane NEW way to design for me, WITHIN Codex If you are doing dev on a web technology, watch below In that video: - I am playing my game within Codex (yes) - I use a codex-made tool to design buildings (see some tweets below it's really powerful) - I can ask Codex to iterate and the game changes WITHOUT refreshing - I can point at UI elements - I can take a screenshot All WITHIN Codex It makes iterating on design incredible

A billionaire trader has spent 40 years trying to delete a one-hour documentary. It shows him making $100 million in a single afternoon. He predicted the crash that made it possible three months in advance. He has never explained why he wants the film gone. His name is Paul Tudor Jones. The film is on YouTube. The documentary is called "Trader." PBS filmed it in 1987, three months before Black Monday. Jones was 32 years old, working from a small New York office, wearing shorts and a t-shirt, yelling at his phones, throwing paper across the room, and sleeping under his desk. The film captures him and his research partner Peter Borish overlaying a chart of the 1929 market on 1987, month by month. The two charts tracked within one percent. Borish said this is exactly what happened in 1929. Jones said if the analog holds, October is when it breaks. On October 19, 1987, the Dow fell 22.6 percent in a single day. It remains the largest one-day percentage loss in stock market history. That afternoon, Tudor Jones covered his shorts and made roughly $100 million. He was 33 years old. He was one of the very few traders on the street who came out ahead. He tried to bury the tape because it made him look reckless in a professional world that punished swagger. Twenty years of legal effort did not delete it. Someone kept a copy. It is on YouTube. It has fewer views than most makeup tutorials. The film is not really about a crash. It is about a specific philosophy of trading. Jones is shown building conviction slowly, sizing carefully, then striking hard when the setup arrives. He is never once shown making a random bet. He is shown doing the same thing five times a day, every day, for three months. His signature line, repeated across a 45-year career: "The most important rule of trading is to play great defense, not great offense." He does not try to be right. He tries not to lose. He sets stops tight, cuts positions fast, and never averages down on a loser. Every trade i…

Oh my word. Men are class.

My chief of SEO, Claude Cowork Fable 5

Software engineering job descriptions should really start saying whether they include /fast mode or not.

I packaged up the "autoresearch" project into a new self-contained minimal repo if people would like to play over the weekend. It's basically nanochat LLM training core stripped down to a single-GPU, one file version of ~630 lines of code, then: - the human iterates on the prompt (.md) - the AI agent iterates on the training code (.py) The goal is to engineer your agents to make the fastest research progress indefinitely and without any of your own involvement. In the image, every dot is a complete LLM training run that lasts exactly 5 minutes. The agent works in an autonomous loop on a git feature branch and accumulates git commits to the training script as it finds better settings (of lower validation loss by the end) of the neural network architecture, the optimizer, all the hyperparameters, etc. You can imagine comparing the research progress of different prompts, different agents, etc. https://github.com/karpathy/autoresearch Part code, part sci-fi, and a pinch of psychosis :)

how Anthropic's marketing team uses claude code

The most important skill to learn in the next 10 years

🇺🇸 Western Kansas, USA “Unreal you can almost feel the weight of that” Storm chasers pursue the most unreal looking Supercell ever.