与「Database」相关的搜索结果

AI 写代码最容易埋的坑之一，就是数据库表设计错了，等上线后才发现改不动。 发现一个 Claude Code 专用 Agent：Database Architect。 它不只是帮你生成 SQL，而是提前参与数据库架构设计。 主要能做这些事： 数据建模：表结构、主外键、索引、约束 技术选型：PostgreSQL、MySQL、MongoDB、向量数据库 多租户设计：RLS、Schema 隔离、独立数据库 性能规划：读写模式、分库分片、扩展方案 数据迁移：生成迁移脚本和回滚方案 安装命令： npx claude-code-templates@latest --agent database/database-architect 我觉得最实用的是让 Agent 先分析业务访问模式，再设计表结构，而不是代码写完才补数据库。 不过生成的 DDL 和迁移脚本仍需要人工审核，尤其是生产环境的数据变更。 你现在用 AI 开发项目，会让它直接设计数据库，还是自己先把表结构定好？ 项目： #ClaudeCode# #AI# #Agent# #Database#

Anthropic just published an engineering guide on how to build always-on agents. The biggest misconception: an always-on agent does NOT run 24/7. It sleeps almost all day, wakes up for short runs, saves state, and goes back to sleep. The 5 core pieces I distilled: 1. Event Inbox Put a database table between webhooks and your agent. Acknowledge webhooks fast, run only one active worker per user so runs never collide, and batch rapid messages together. 2. Timers Keep timers in a separate table from the inbox. Split them into one-off follow-ups and recurring schedules. A background poller drops due timers into the inbox. 3. Lean Memory + Dreaming Only store what cannot be looked up again: user instructions, user notes, past messages, and task progress. Load a small index at wakeup, not every file. Run periodic dreaming passes to clean out stale facts. 4. Unattended Permissions Never put raw API keys inside the agent sandbox — inject them through an outside proxy. Use Deferred Tool Calls to pause risky actions for approval without blocking the worker. 5. Outbox Queue outgoing replies so your agent paces notifications instead of spamming users at 3 AM.

for the first time ever, I'm sharing everything I do across all my SaaS to rank on Google and get mentioned by AI every SEO and AEO lesson I've learned is here: 1. we show up in Reddit threads. AI pulls a lot of its answers from Reddit, so Bazzly finds the threads that are fresh, and even the ones already ranking on Google, and gets our products into the top reply 2. we get our products listed #1# in listicles that already rank on Google, with Outrank. when someone asks ChatGPT for the best tool, those are the pages it reads 3. we turn Reddit questions into blog articles. Bazzly shows us what people actually ask, and we feed those questions to Outrank 4. I regularly sit down and find new niches where our products could be useful. I research each one and feed it to Outrank, and Outrank finds new ones on its own too, so our keyword plans keep expanding into new markets 5. we publish an SEO article on our own blogs every day, also with Outrank 6. Outrank helps us refresh old articles regularly. AI and Google both prefer recent pages, so every page has to stay current 7. we build new backlinks every week with Outrank, and every placement gets verified. we also pitch guest posts using Outrank's free guest post database of 278 checked sites, all DR 20+ 8. we regularly ship extremely useful free tools for potential users, and each one ranks on Google and brings people into our products 9. every month we check which AI answers name our products and which don't Outrank's AI Visibility Checker asks ChatGPT, Claude, Gemini and Perplexity real buyer questions, then shows whether we got named, which competitors did, and which sources got cited we feed those sources back into Bazzly, Outrank and our internal social content all of this brings in new users, and then we run loops that turn them into champions who spread the word 10. we incentivize our users to share honest reviews of our products on Instagram, TikTok, X and LinkedIn, and on public review sites 11. we let our users become affiliates, and then we help them make better content about our products. in the last 30 days, ~$8K has been generated by Revid affiliates alone basically every SEO and AEO lesson I learn building Outrank and Bazzly goes straight into every product I run and the idea behind all of it is simple AI recommends the most mentioned product so we make sure every one of our products gets mentioned everywhere Google and AI look: all sorts of blogs, listicles, Reddit, review sites and social and this setup does the job extremely well, for every product we run

The thing I learned this year is that when engineers say they like working on “hard problems”, they actually don't. What they like is puzzle solving, and having a repertoire of patterns that they can pattern match onto traditionally hard problems, usually involving databases or something with scale, etc. And they know that the industry used to pay you a lot for how deep and broad this repertoire was. Unfortunately, in 2026, well, you have a cheat code machine that has infinite breadth and depth for pattern matching. So the question is, do you actually like hard problems? Because there are now hard problems that are a complection of engineering, product, society, culture, politics, and they are fairly intractable, probably unsolvable! Because if you do want to work on those actually hard problems, there is so much money that people will pay you for it. But you’ll have to face that truth about yourself first.

50 legendary internet rabbit holes you can spend hours exploring👇 1. — Watch the world via live satellite imagery 2. — See every plane currently in the sky 3. — Track all ships at sea in real time 4. — Live map of winds and storms 5. — Watch lightning strikes hitting Earth in real time 6. — Live list of recent earthquakes 7. — Ocean cables carrying the internet 8. — Watch forests disappear from space 9. — The world's statistics, second by second 10. — Current number of tweets and searches being posted 11. — Compare the true sizes of countries 12. — Maps from centuries ago 13. — Archive of 150,000 historical maps 14. — World map drawn by volunteers 15. — Look out the window of a random person around the world 16. — Virtual walks through cities 17. — Teleport to a random spot on Earth 18. — Catalog of the world's strangest places 19. — Interactive knowledge experiences 20. — Scale journey from atom to universe (Updated HTML5 link) 21. — Explore the solar system in 3D 22. — Real sky map in your browser 23. — NASA's astronomy picture of the day 24. — NASA's entire visual archive, free 25. — Visual articles told through data 26. — The state of the world with real data 27. — What we mistakenly think we know about the world 28. — Visualizing complex data 29. — World Bank's open data 30. — Turkey's official statistics database 31. — Archive of millions of books, films, and software 32. — 70,000 free books whose copyrights have expired 33. — Record of every book in the world 34. — U.S. Library of Congress digital archive 35. — Europe's cultural heritage archive 36. — America's digital library collection 37. — Tour museums from home 38. — Download artworks in high resolution 39. — Archive of 250,000 artworks 40. — Forgotten visual treasures of history 41. — Free archive of culture and education 42. — Met Museum's open collection 43. — Family tree of music genres 44. — Pick a country and decade to listen to that era 45. — Turn Wikipedia edits into audio 46. — Random knowledge well 47. — Time, sunrises, and sky events 48. — Live stream of science news 49. — Free preprints of scientific papers 50. — Visualize data with live code Save this. You’ll definitely need some of these later. 🔖 Follow @justinbrave21 for more useful websites, AI tools & tech resources.

这是纯宝藏：20 个 AI agent 技能，覆盖从构建到上线的完整闭环。 规格、架构、agent、后端、数据、UI、测试、安全、分析、交付。 定义任务 1. bmad-spec 把想法变成结构化的规格。 2. domain-modeling 为系统里的核心概念和关系建模。 3. codebase-design 规划代码库该怎么组织。 4. google-agents-cli-workflow 用 Google Agents CLI 搭建 agent 工作流。 5. prompt-optimizer 改进和打磨 prompt。 构建系统 6. google-agents-cli-adk-code 用 Google 的工具构建 agent 代码。 7. mastra 构建 AI agent 系统的技能。 8. typesafe-ai 用更强的类型安全构建 AI 工作流。 9. fastify-best-practices Fastify 后端的更好模式。 10. neon-postgres 使用 Neon + Postgres。 让它可用 11. prisma-database-setup 用 Prisma 搭建数据库层。 12. clerk-orgs 加入组织与认证工作流。 13. emil-design-eng 改进产品设计和前端执行。 14. review-animations 审查并改进 UI 动画。 15. remotion-best-practices 构建更好的程序化视频工作流。 验证真实效果 16. playwright-cli 测试真实浏览器路径。 17. semgrep 扫描代码库的安全问题。 18. sentry-fix-issues 发现并修复生产环境问题。 19. instrument-product-analytics 追踪用户在产品里实际做了什么。 20. email-best-practices 改进产品邮件流程。 这个闭环： 想法 → 规格 → 架构 → 构建 → 浏览器测试 → 捕获失败 → 衡量用户 → 改进下一个版本。 找出你项目里最大的瓶颈，先装上能攻克它的那个技能。 收藏这条，为你的产品建一个第二大脑。

据我的职场观察，日本人普遍对软件、数据和系统本身缺乏客观的认识，很难认可软件应有的价值。 我倒不认为日本公司是舍不得为软件花钱，很可能是大多数企业根本都不知道自己要买什么服务。 因此从结果上看，个人感觉日本企业普遍没有SaaS文化，尤其是在传统制造业里，这点和美国非常不同。 而且我当初作为外国人入职时明显感到，同样作为是工程师，只要不是IT专业，很多人对现代软件开发的基本概念都不懂。 身边的日本同事可能非常了解机械、电气、材料、生产工艺或者设备设计，但可能甚至连python是什么都不知道，更谈不上数据结构化。 这就导致大部分人可能甚至听都没听说过class、instance、library、API、database、backend，更加不知道软件系统能够把多少重复性的工程工作自动化。 结果就是公司可能并不是不愿意花钱开发软件，而是自己连软件需求都没有能力准确描述。 比如一个电气工程师如果不知道python可以批量处理Excel，不知道数据库可以管理设备参数，不知道API可以让不同系统交换数据，不知道后台程序可以自动检查设计条件、追踪revision，甚至不知道很多规则性的判断本身就可以抽象成程序，那在面对低效率重复工作时，这个电气工程师想到的只可能是继续增加Excel、增加人工确认步骤，或者要求别人做一个看起来像软件的表单。（所以大手企业里有很多xlsm的macro，这就又增加了维护成本） 做工程的不懂软件，无法准确提出需求，于是把开发外包给SIer或者外部软件公司。 外面的软件工程师虽然懂程序，又不懂发电厂、电力系统、机械设计这些专业知识以及实际的workflow。 公司员工说不清楚自己真正需要什么，外边的软件公司也不知道为什么这个工程流程必须要这样设计。最后做出来的系统四不像，根本没有符合实际的workflow，这就导致软件不好用。 这又反过来强化传统行业对软件无用的刻板印象，于是企业更加不愿意为软件和内部开发能力投资。这是个恶性循环。 日本肯定有很优秀的软件公司和工程师，但在我看来，在日本既懂工程，又能把工程数据抽象成数据结构、算法、数据库和软件系统的人太少了。 这也是我到日本之后感受很强烈的地方。 而且我的本科并不是计算机专业，而是机械工程。 我不知道其他的大学怎么样，但至少在华科，即便是机械工程，python也是作为必修课计入学分的。 当年我也不懂为什么要学这个，我以为只是为了让我们学习Numpy这些数学工具当研究室工具人。 但我现在知道了，其实从这一点上来看，中国的本科教育可能是非常成功的。python课程的设计目的可能只是至少让工程师理解计算机可以做什么，能够自己写一些程序解决工程问题。 我认为这种基础教育非常重要。所以我之前有质疑过日本的本科基础教育。。 工程师并不需要会开发大型软件，但至少应该知道什么事情可以自动化、什么问题适合用数据库解决、什么工作可以写脚本处理、什么时候应该调用API，以及一个软件系统大致是怎样组成的。 只有具备这些基本认知，才有可能提出好的软件需求。否则就跟现在日本一样，天天喊DX，结果就只是买几个极其难用的系统、增加几个网页界面、把Excel上传到云端，然后继续用和二十年前几乎相同的方法。 这并不是真正的软件化，纯粹浪费时间罢了。 日本更需要解决的问题可能是把python等纳入本科基础教育。（但我没在日本读本科，不是很懂日本本科教育到底学不学这些）

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