世界公认十大顶级思维 1. 第一性原理：回归本质，拆解到最基础原理 2. 复利思维：做时间的朋友，抵御短期诱惑 3. 奥卡姆剃刀：如无必要，勿增实体，极简是最高效的 4. 贝叶斯思维：根据新信息不断修正你的根概率 5. 遍历性：只要有一次出局，基础概率就毫无意义 6. 幸存者偏差：不要只看成功者，死人不会说话 7. 熵增定律：系统不主动输入能量，必将走向混乱，自律的物理本质 8. 二八法则：死磕那20%的核心高价值区 9. 逆向思维：反过来想，查理芒格的最爱 10. 反脆弱：从不确定性和波动中获益

Entropy shows up in engines, molecules, and your phone’s data. This graphic lays out three perspectives. Mechanistic: dS = δQ_rev / T for reversible processes. Statistical: S = k_B ln Ω linking to microstate count. Informational: H = −∑_i p_i log p_i for probability-based uncertainty. These concepts power real-world tech like high-efficiency turbines in power plants, explain why cream mixes into coffee irreversibly, enable JPEG and MP3 compression to shrink files, and help train AI by measuring how much new info each prediction adds.

Mathematical bridge between the Time Domain and the Frequency Domain ✍️ The Fourier Transform answers a simple question: "what's inside this signal?".... Think about a musical chord. When you press three keys on a piano at the same time, you hear one combined sound. A trained musician can listen closely and identify each individual note hidden within that chord. That's what the Fourier Transform does, mathematically. It takes any complicated signal and identifies the simple, pure waves inside it, along with their amounts. The diagram illustrates this well. The red, messy wave on the left represents your real-world signal it could be a voice, a heartbeat, or a musical note. The clean blue waves spreading out are the simple components hidden within that signal. The spikes on the right act as a scoreboard a tall spike means that component is a significant part of the signal, while no spike indicates it's absent. The most amazing part is this: "any signal, no matter how rough or complex, can be perfectly reconstructed by adding enough simple smooth waves." A human voice, a stock market chart, or even a picture they are all made of simple ripples layered on top of one another. You can think of it like a prism splitting white light into a rainbow. White light appears as one simple thing, but the prism shows it is made up of many colors mixed together. The Fourier Transform serves as that prism for sound, images, radio signals, or anything else. It uncovers the hidden components that have always been there.

William Zinsser taught writing at Yale, then wrote the book that has fixed more bad writing than every English class combined. Here are 10 cuts from "On Writing Well" that instantly make your writing twice as strong. 1) Delete every word doing no work

Someone asked me to recommend a good book on parenting. I would guess that most books about parenting are tedious. So my recommendation is to read autobiographies, the early parts of which are usually implicitly about parenting.

🛠️🧭 Principles for AI Coding → How to Build Production AI Agents with Claude. 𝗠𝘆 𝟳-𝗦𝘁𝗲𝗽 𝗿𝗼𝗮𝗱𝗺𝗮𝗽 𝗳𝗼𝗿 𝗯𝘂𝗶𝗹𝗱𝗶𝗻𝗴 𝘁𝗲𝘀𝘁𝗮𝗯𝗹𝗲, 𝗺𝗼𝗱𝘂𝗹𝗮𝗿, 𝗮𝗻𝗱 𝘀𝗮𝗳𝗲-𝘁𝗼-𝗰𝗵𝗮𝗻𝗴𝗲 𝗔𝗜-𝗴𝗲𝗻𝗲𝗿𝗮𝘁𝗲𝗱 𝗰𝗼𝗱𝗲. Most AI engineers are hearing: “Code is cheap.” I think this is dangerously wrong. Bad code is now more expensive because AI can multiply it faster. My 7-Step roadmap for using AI coding agents like a pro: 》𝗦𝘁𝗲𝗽 𝟭: Turn the Prompt into a Design Mission ✸ Define the goal, constraints, risk, and acceptance criteria. ✸ Tell the agent what “done” means before it starts. ✸ Do not start from a vague request. → Example: “Interview me first. Find the failure paths.” 》𝗦𝘁𝗲𝗽 𝟮: Build a Shared Design Concept ✸ Make the agent ask questions before planning. ✸ Resolve unclear decisions early. ✸ Do not confuse a generated plan with understanding. → Example: “What can break before we write the PRD?” 》𝗦𝘁𝗲𝗽 𝟯: Create a Shared Language ✸ Define domain terms, modules, actions, and data names. ✸ Use the same words in prompts, code, tests, and docs. ✸ Do not let the agent rename concepts every sprint. → Example: “PatientRiskScore” means one thing everywhere. 》𝗦𝘁𝗲𝗽 𝟰: Give the Agent a Module Map ✸ Show where the change belongs. ✸ Name the module, interface, dependencies, and boundaries. ✸ Do not let the agent wander through the repo. → Example: Retry logic belongs behind PaymentGateway. 》𝗦𝘁𝗲𝗽 𝟱: Force Small Feedback Loops ✸ Use tests, types, linting, logs, and browser checks. ✸ Make the agent check after small changes. ✸ Do not let it write 800 lines before testing. → Example: Red test, code, type check, green test, refactor. 》𝗦𝘁𝗲𝗽 𝟲: Design Deep Modules ✸ Hide complexity behind simple interfaces. ✸ Build fewer, stronger boundaries. ✸ Do not create 40 tiny helper files. → Example: One ingestion service hides parsing, chunking, storage, and errors. 》𝗦𝘁𝗲𝗽 𝟳: Own the Interface, Verify the Code ✸ You design contracts, schemas, methods, and review points. ✸ Let the agent implement low-risk internals. ✸ Check tests, logs, affected modules, and regression risk. → Example: Approve the tool schema before Claude writes the handler. 𝗣𝗹𝗲𝗮𝘀𝗲 𝗥𝗲𝗺𝗲𝗺𝗯𝗲𝗿: Let Claude write the code, but do not let it design the system alone. Before you ship, check the interface, the tests, the failure paths, and the modules it touched. That is where AI engineers stand out now. -- 📥 Skip the trial-and-error of building production AI agents. Watch my free 30-min training get 88 pages of production guides. Join 46,000 AI engineers, architects, and directors. 👉 maryammiradi.com/free-ai-age…

Next time you think of giving up, remember this photo of Elon in 2008.

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