AI 与未来
Letting AI Speed Up the Search, Not the Judgment
A practical reflection on using AI to broaden investigation while keeping source checking, trade-off decisions, and accountability with people.
文章
AI 与未来
理解 AI——从上下文工程到构建可靠的 AI 产品。
面向开发者的实用 AI 写作：如何从 AI 工具获得优质结果、理解语言模型的实际工作原理、构建可靠的 AI 驱动产品，以及驾驭 LLM、智能体和上下文工程快速变化的格局。为希望更智慧地使用 AI 的工程师而写。
AI 与未来
Asking AI to Name Its Assumptions
A practical reflection on making AI-assisted work safer by asking the model to expose assumptions before accepting its answer.
AI 与未来
Treating AI Suggestions as Draft Evidence
A practical reflection on using AI output as a starting point for investigation, not as a finished answer that replaces verification.
AI 与未来
Keeping AI Changes Small Enough to Review
A practical reflection on using AI safely by keeping generated changes narrow, inspectable, and tied to evidence.
AI 与未来
Letting AI Draft the Boring First Pass
A practical reflection on using AI for the plain first draft, while keeping human judgment focused on evidence, shape, and responsibility.
AI 与未来
Asking AI to Show Its Working Context
A practical reflection on asking AI systems to expose their working context so engineers can verify evidence, assumptions, and gaps.
AI 与未来
Letting AI Draft While Humans Keep the Map
A practical reflection on using AI to draft faster while humans keep the system map: constraints, sources, ownership, and verification paths.
AI 与未来
What AI Cannot Know From the Prompt Alone
A practical reflection on why prompts are not enough: AI needs source context, runtime evidence, domain constraints, and human judgment to become useful engineering help.
AI 与未来
Using AI Without Outsourcing Judgment
A practical reflection on using AI as a thinking partner while keeping human judgment anchored in source code, tests, logs, and product context.
AI 与未来
AI Tools for Reading a Codebase
A practical guide to using AI tools while reading a codebase: where summaries help, where they mislead, and how engineers can combine AI assistance with source-level verification.
AI 与未来
From Prototype Demo to Production Behavior
A grounded AI product note on the gap between an impressive prototype demo and the production behavior users can safely depend on.
AI 与未来
Human Feedback Loops for AI Work
A grounded look at AI work habits: why useful AI workflows need human feedback loops, evidence checks, test results, and team memory instead of one-off prompt outputs.
AI 与未来
The Cost of Trusting a Fluent AI Answer
A grounded look at fluent AI answers: why confident language can feel like evidence, where that creates risk, and how teams can keep AI useful by checking claims against sources, tests, and real context.
AI 与未来
Vector Databases Explained
A beginner-friendly explanation of vector databases: how embeddings represent meaning, why similarity search helps AI products, and what teams need to handle around chunking, metadata, freshness, evaluation, and cost.
AI 与未来
AI in Cybersecurity
A practical reflection on AI in cybersecurity: how AI helps detection, triage, secure coding, and incident response while creating new risks around automation, privacy, prompt injection, and false confidence.
AI 与未来
Reducing Hallucinations in LLMs
A practical guide to reducing LLM hallucinations with clearer tasks, bounded context, retrieval, citations, verification, refusals, evaluation sets, and product design that makes uncertainty visible.
AI 与未来
Fine-Tuning vs. Prompting vs. RAG
A practical comparison of prompting, RAG, and fine-tuning for AI products: what each approach changes, when it helps, where it fails, and how teams can choose the smallest reliable intervention.
AI 与未来
Ethical Considerations in AI Products
A practical article on product ethics for AI teams: consent, data boundaries, bias and fairness, human review, explanations, risk controls, and incentives that shape whether AI features remain useful and responsible.
AI 与未来
Retrieval-Augmented Generation (RAG) Basics
A beginner-friendly explanation of Retrieval-Augmented Generation: how documents are split into chunks, turned into embeddings, found with vector search, and used to ground AI answers with citations, evaluation, and clear failure checks.
AI 与未来
The Context Window Bottleneck
A practical look at context window limits as one of the quiet bottlenecks in AI workflows. The article explains why long chats, big codebases, stale summaries, and missing working memory can make AI assistance drift, and how teams can work around the limit without turning every task into a document dump.
AI 与未来
When an AI Answer Needs a Test
A calm explainer on treating AI answers as claims that need proportionate verification. The article shows how engineers can keep responsibility by testing AI output against evidence, context, and real system behavior before acting on it.
AI 与未来
Khi AI chạm vào việc hằng ngày, ý tưởng bắt đầu nở ra
Khi AI đủ thực tế để bước vào những việc nhỏ trong văn phòng, ý tưởng không còn nằm ở các bản demo xa xôi. Chúng xuất hiện ngay trong onboarding, hỗ trợ khách hàng, đối chiếu tài chính, chuẩn bị họp, đọc tin tức, kiểm tra tài liệu và rất nhiều workflow lặp lại khác. Bài viết là một góc nhìn bình tĩnh về cách nhận ra các điểm nghẽn ấy, cải tiến từng chút và giữ con người ở phần phán đoán quan trọng.
AI 与未来
Cognitive Debt in AI-Assisted Development: When Code Grows Faster Than Understanding
AI coding tools can make a team produce code faster than it can understand, review, debug, and safely operate that code. This article explains cognitive debt, how it differs from technical debt and intent debt, why agentic coding makes it more visible, and the practical controls teams can use: smaller batches, better PR rationale, human-owned invariants, disposable prototypes, and AI used to repay understanding instead of only generating more work.
AI 与未来
Beyond the Hype: What LLMs Actually Understand
LLMs can feel like they understand us because they handle patterns, context, and language-shaped reasoning well. This explainer separates useful machine understanding from human understanding so teams can verify, constrain, and use AI with better judgment.
AI 与未来
AI Literacy: From Prompting to Orchestrating Agents
Using AI more often does not automatically mean working at a higher level. This guide maps three levels of AI literacy with a visual ladder, concrete tool examples, workflow keys, and a path toward deeper practical guides for each level.
AI 与未来
Your Fear of AI Is the Size of Your Ambition: Why It’s Time to Aim Dramatically Bigger
Two people can look at the same AI breakthrough and feel opposite things — terror or fuel — and the difference reveals something uncomfortable: our fear of the future is roughly the size of our ambition. If your plan is to keep doing exactly what you do, a machine that does it cheaper is frightening. If your plan is to build something dramatically bigger, it’s the best news you’ve ever gotten. This is a clear, energising case for retiring “don’t boil the ocean,” trading the 1.05x present for the 10x future, and why — through ephemeralization and the Jevons paradox — raising your ambitions tends to create more, not less.
AI 与未来
From SDLC to ADLC: How AI Agents Are Rewriting the Software Development Lifecycle
Software development is going through its biggest shift since the graphical interface. The phases engineers once ran by hand — planning, coding, testing, deployment — are increasingly executed by AI agents working in parallel. This is a clear, visual guide to the move from the classic SDLC to the emerging ADLC (Agentic Development Life Cycle): the two lifecycles drawn side by side, the six concrete shifts happening right now, what the early numbers suggest, what it actually means for an engineer’s day-to-day, and five low-risk best practices to start moving faster — without handing over the judgment that still has to be yours.
AI 与未来
AI Agent 全解析：它是什么、如何运作、能为你做什么
所有人突然都在谈论“AI agent”——一种不只回答问题、而是真正去完成工作的软件。但 agent 究竟是什么？它和你已经在用的 chatbot 有何不同？它真正有帮助的地方在哪里，又在哪里会悄悄制造麻烦？这是一份面向所有人的友好、轻术语指南——无论你是否懂技术：每个 agent 运行的简单循环、构成它的五大要素、从 co-pilot 到 auto-pilot 的自主程度层级、工作与生活中接地气的示例、需要注意的失败模式，以及本周就能让第一个实用 agent 运转起来的具体方法。
AI 与未来
从 Prompt 到 Workflow：设计每周为你省下数小时的 AI Workflow
大多数人每次只用一个问题来使用 AI——也因此把它的大部分价值留在了桌面上。真正的杠杆不是一句巧妙的 prompt，而是一个可重复的 AI workflow：你设计一次、便永远复用的一小串步骤，把每周两小时的重复杂活变成两分钟的审阅。这是一份面向所有人（无论是否懂技术）的实用、案例丰富的指南：prompt 与 workflow 的区别、每个 workflow 都共有的简单结构、可直接照搬到办公室与日常生活的 workflow、从手动到全自动的三个层级、自己动手设计的六个步骤，以及一个用来证明你真正省下多少时间的简易估算法。
AI 与未来
2026 年真正值得你关注的 AI 趋势——以及如何把它们变成实实在在的价值
每周都冒出又一个“这将改变一切”的 AI 头条，想从噪音里分辨出信号，实在令人疲惫。这是一趟冷静、有据的导览，带你看清 2026 年真正重要的 AI 趋势——agentic AI、多模态（multimodal）、嵌进你已在用的 app 里的 AI、设备端 AI，以及判断力与核查作为人类优势的崛起——再配上几乎所有文章都略过的部分：一套把任何趋势变成工作与生活中真实价值的实用框架。你会得到一个“炒作还是价值”的过滤器、价值阶梯、一个简单的价值方程、一个具体例子，以及一份 30 天计划——好让你不再追逐新闻，而开始累积复利般的收益。
AI 与未来
AI Is Rewiring Every Software Role: How Thinking Changes for BA, PO, PM, Dev, QA and Beyond
AI isn't just a faster tool bolted onto how we build software — it's quietly moving the center of gravity of every role. The work that used to define a BA, PO, PM, developer, or QA engineer is being automated from the bottom up, and what remains is a different job built on judgment, taste, and verification. This is a clear-eyed, role-by-role look at what's fading, what's rising, and what to watch out for as teams move from AI-assisted to AI-first — so you can see what may be lost, what's added, and how to stay genuinely valuable in a changed era.
AI 与未来
上下文工程：区分优秀 AI 用户与普通用户的关键技能
大多数 AI 结果令人失望，不是因为模型不好，而是因为上下文太薄。上下文工程实用指南——模型窗口中有什么、优质上下文的五大支柱、常见反模式，以及能持续产出更好结果的可复用模板。
AI 与未来
Generative AI in Daily Life and Work: A Practical Catalog of What You Can Use Today
Generative AI has quietly moved from headline to household tool — yet most people still use it for little more than the occasional question. This is a hands-on catalog of the most common, genuinely useful applications across work, learning, creativity, and everyday life, organized so you can find at least one thing to try today. No hype, no jargon — just real use cases you can apply immediately, plus the one skill that makes all of them work and the limits you should never ignore.
AI 与未来
Prompt Engineering 本质上只是清晰沟通
好的 prompt 不是魔法句子，而是清楚地提供 context、意图、constraints、examples 和 feedback。
AI 与未来
AI in QA: Automated Test Generation
A practical look at AI-assisted QA and automated test generation: where AI helps with breadth, edge cases, fixtures, and test skeletons, and why human judgment still owns risk, truth, and trust.
AI 与未来
Running Local LLMs for Privacy
A practical explanation of local LLMs for privacy-sensitive work: what improves when prompts and documents stay on owned machines, what quality and operations costs remain, and how teams can adopt local models responsibly.
AI 与未来
AI Agents: The Next Frontier
A practical look at AI agents beyond the hype: what changes when models can use tools, follow workflows, remember context, ask for approval, and produce auditable work, plus where human judgment still matters.
AI 与未来
Data Privacy in the Era of AI
A practical article on data privacy in AI work: how prompts, documents, logs, retrieval, training, and evaluation create new data paths, and how teams can use AI while keeping consent, minimization, access, and retention clear.
AI 与未来
Multimodal AI: Text, Vision, and Audio
A practical introduction to multimodal AI: how text, images, audio, and video change product workflows, what becomes possible, and why evaluation, privacy, accessibility, and human review matter more as inputs become richer.
AI 与未来
AI for Code Refactoring
A practical reflection on using AI for code refactoring: where it helps with mapping, options, tests, and mechanical changes, and why engineers still need boundaries, evidence, and verification before trusting the result.
AI 与未来
Evaluating LLM Performance
A practical guide to evaluating LLM performance with task-specific datasets, rubrics, human review, regression checks, latency, cost, safety, and production feedback instead of relying on impressive demos.