科技纪年/Tech Craft Chronicles

现在最香的玩法，就是把 Cloudflare 当“免费高速公路”，GitHub 当“仓库管理员”，再让 edgetunnel 当“交通指挥”。不用买服务器、不用折腾运维，大多数时候都能稳定工作。唯一的小脾气是免费服务偶尔会“请个假”，所以多备一个方案，就像带把备用雨伞一样安心。 Think of Cloudflare as a free global highway, GitHub as the warehouse manager, and edgetunnel as the traffic controller. No VPS, no heavy maintenance, and surprisingly smooth for daily use. The only catch? Free services sometimes take an unexpected coffee break. Keeping a backup setup is like carrying an extra umbrella—rarely needed, but very comforting. 超详细保姆级步骤/Super Detailed Step-by-Step： 1.注册账号 / Register Accounts 准备好 GitHub 账号 和 Cloudflare 账号（邮箱就能注册） Get a GitHub account and a Cloudflare account (email only) 2.Fork 项目 / Fork edgetunnel 打开github 爱菊痛，点右上角 Fork 到自己仓库 3.连接 Cloudflare Pages / Connect to Cloudflare Pages 登录 Cloudflare → Pages → Connect to GitHub → 选中你刚 Fork 的仓库 Cloudflare Dashboard → Pages → Connect to GitHub → Select your forked repo. 4.部署项目 / Deploy 框架选 Pages，直接点 Deploy（不用改任何设置） Choose Pages framework and click Deploy (no changes ...

搭建一个免费、本地、可控的“网络 + Mock API + Python 小实验室”，用 v2rayN/sing-box 管理网络环境，用本地 Fake/Mock API 做开发测试。 以前折腾网络和接口，像同时修路、修水管、还要猜服务器心情。现在 v2rayN + 本地 Mock API + Python 像自带迷你实验室：网络自己管，接口自己造，数据自己演。免费、可控、离线友好。小缺点？本地服务偶尔也会“罢工”，但至少这次老板就是你 😂 Networking and APIs used to feel like fixing roads, plumbing, and server moods at once. Now v2rayN + local Mock API + Python feels like owning a mini lab: control the network, fake the backend, test freely, and stay local. Mostly smooth—though local servers sometimes take coffee breaks too 😂 本地天气 API 实验室（Fake Weather API Lab） Local Fake Weather API Lab - 免费本地 Mock API 开发小实验这是一个免费、本地、可控的网络 + Mock API + Python 小实验室。通过 v2rayN/sing-box 管理网络环境，用本地 Fake API 进行开发测试。 Step 1. 安装基础工具 / Install Basic Tools做什么？ / What to do? 安装实验所需的核心工具。 / Install local tools.Mac 操作： / Mac Commands: python3 --version git --versionWindows 用户请前往官网下载安装 Python 和 Git。 / Windows users please download Python and Git from official websites. Step 2. 安装 v2rayN / sing-box / Install v2rayN or sing-box做什么...

AI + Terminal + Claude Code/OpenCode = 自动整理 YouTube 素材 + 自动生成博客草稿 + 自动管理代码和文件 姐妹们！用小内存Mac低配本深耕本地 AI 一个月，我彻底改观了✨ 原来最香的根本不是付费云端大模型，而是把终端用成专属生产力搭子！ 不用花钱、上手简单，每天自动帮我规整 YouTube 素材、生成博客草稿，文件也整理得清清楚楚。 谁说小内存(8G)只能追剧摸鱼？我直接用它流畅跑 Qwen3:4B + 智能 Agent，全程离线运行、隐私完全可控，这波反常识玩法真的太丝滑了！ My real experience with an Tiny RAM Mac after using local AI for a month. Cloud AI is overrated. My favorite workflow now is Ollama + Qwen3:4B + Terminal Agent. It’s free, user-friendly, works offline and keeps all your data private. This handy tool sorts my YouTube materials, writes blog drafts and organizes files automatically. Who says Tiny RAM(8G) is only for streaming videos? This offline AI setup runs surprisingly smoothly! 1. 准备素材文件夹 / Prepare Material Folder  mkdir ~/ytm 把你的视频、截图、字幕、笔记全扔进去，AI 才能干活！/ Dump all your crap in. AI works. Magic 2. 一键安装全家桶 / Install Everything  bash brew install eza bat zoxide glow lazygit yazi chafa tlrc ollama git python 3. 下载 Qwen3:4B（8GB Mac 最佳） / Download Model ollama pull qw...

OpenCode像“开源版AI程序员宿舍”，模型随便换；free-claude-code则像偷偷帮你省话费的中间商，让Claude Code假装自己还在连大厂API。最爽的是：本地能干活、免费额度能续命、老电脑也能抢救一下。缺点嘛，本地模型偶尔会像熬夜实习生，认真，但方向不一定对。 OpenCode feels like an open-source AI hacker apartment where you can swap models freely, while free-claude-code acts like a sneaky middleman saving your API bill behind the scenes. The best part: local privacy, free cloud credits, and even old laptops get a second life. Downsides? Local models sometimes behave like sleep-deprived interns — hardworking, but occasionally gloriously confused. 照着抄就行/Just copy it as is. 1. 安装 Ollama / Install Ollama https://ollama.com (下载安装/Download N Install) 2. 安装 OpenCode / Install OpenCode Mac/Linux： curl -fsSL https://opencode.ai/install | bash 3. 拉取本地最强免费模型 / Pull Best Free Model ollama pull qwen3-coder 4. 安装 free-claude-code 代理 / Install free-claude-code uv tool install git+https://github.com/Alishahryar1/free-claude-code.git 5. 启动代理服务 / Start Proxy fcc-server (新开终端/Open New Terminal) fcc-claude 6. 直接让AI给你干活 / ...

Claude Code + Ollama，就像把“硅谷AI程序员”塞进你电脑：不用API账单、不怕代码外泄、断网也能加班。Mac用户爽得像开外挂，Windows用户偶尔会被WSL2教育做人。本地模型虽然偶尔像实习生发呆，但写网站、改脚本、做自动化已经很能打，钱包终于不用天天流血了。 Claude Code + Ollama feels like hiring a tiny AI engineer who lives inside your laptop: no API bills, no cloud paranoia, and it still works offline. Mac users get near-magic performance, while Windows users occasionally fight WSL2 like a boss battle. Local models may zone out sometimes, but for coding, automation, and side projects, it’s insanely practical. 都说2026年AI编程非得烧显卡？错了！今天手把手教你白嫖Claude Code + Ollama + Qwen3-Coder，连核显都能跑。Mac M2亲测，比找对象还轻松，别再说你不会。 They said AI coding in 2026 needs a 4090? Wrong! Today I'm handing you Claude Code + Ollama + Qwen3-Coder for free — runs even on integrated graphics. Tested on Mac M2, easier than finding a date. Stop saying you can't. 1. 装Ollama / Install Ollama 官网下载/Download Link：https://ollama.com 2. 装Claude Code / Install Claude Code Mac/Linux: curl -fsSL https://claude.ai/install.sh | bash Windows: irm...

每天晚上，把你那些乱七八糟的微信待办、消费Excel、健身步数、会议纪要、课程笔记……一股脑全扔给AI！别急着疯狂清洗！根据《From Garbage to Gold》研究，在高维度数据里，AI反而能从“脏乱差”中交叉验证出真规律，比你手洗半天还准。正确姿势：先丢进 AI Leak Checker 检查，别把手机号、客户名、身份证傻傻传上云。 再甩给ChatGPT/Claude，用固定提示词： “基于这些原始乱数据，给我生成：明天Top3要事、时间冲突预警、最近消费/效率趋势、今天值得表扬的地方。” AI会自动发现：你周三开会后像被吸了魂、外卖吃多就睡不好、总拖延报税…… 进阶玩家用 Zapier 或 n8n 自动生成日报，发邮件或手机推送。更狠的直接接WiFi小票打印机，凌晨吐出一张纸质“人生简报”，仪式感拉满，刷手机时间直线下降。最后提醒：AI是你的实习生，不是你爸。别什么都问它，保留自己动脑的权利，不然迟早变AI的“数据奶牛” Every night, dump all your messy stuff — WeChat todos, expense Excel, fitness steps, meeting notes, browser tabs — straight into AI. No cleaning!According to the “From Garbage to Gold” research, AI sometimes finds real patterns better in chaotic but rich data than in perfectly scrubbed datasets. All those overlapping “noisy” signals help it triangulate truth.Smart Routine:Paste into AI Leak Checker first — don’t accidentally leak your phone number or client secrets. Then feed to ChatGPT/Claude with this prompt: “From this raw messy data, generate: Top 3 priorities for to...