Mio 的网页端原来只是一个单页聊天框。v0.0.4 推倒重来，照着微信的样子重建了整个 web 体验——四个 tab、聊天列表、通讯录、发现页、个人中心、聊天式引导、打字感知的消息合并。三十多个组件全部从零写，过了两轮 audit，没装一个新依赖，界面全中文。

Mio's web app was a single-page chat. v0.0.4 tears it down and rebuilds it as a full WeChat clone — 4-tab layout, chat list, contacts, discover marketplace, profile page, chat-style onboarding, and typing-aware message batching. 30+ components, 2 audit rounds, zero new dependencies. All in Chinese.

Telegram 从 v0.0.2 就有多模态了，Web 端一直只能打字。v0.0.5 补齐了这块短板——两阶段媒体上传、带编解码器协商的语音录制、表情选择器、两轮完整安全审计，还有一个默默吞掉所有语音消息的 bug。38 个文件改动，17 个安全修复，28 分钟。

Telegram had multimodal since v0.0.2. The web chat was still text-only. v0.0.5 closes the gap — two-phase media upload, voice recording with codec negotiation, an emoji picker, two full security audit rounds, and a silent bug that was dropping every voice message. 38 files changed, 17 security fixes, 28 minutes.

Gemini 3 Pro 又贵又慢——第一个 token 要等 8-10 秒。v0.0.6 把聊天切到 Gemini 3 Flash（minimal thinking），首 token 1-2 秒，成本直降 4 倍；同时把人格提取等高价值任务升级到 3.1 Pro。还有：能叫出游戏名字的视觉提示词、不再把中文听成英文的语音转写、以及一次消灭 80 行重复代码的 DRY 重构。

Gemini 3 Pro was burning money and making users wait 8-10 seconds for a first token. v0.0.6 switches chat to Gemini 3 Flash with minimal thinking — 1-2 second TTFT, 4x cost reduction — while keeping 3.1 Pro for premium tasks like personality extraction. Plus: vision prompts that actually name what they see, Chinese transcription that stops hallucinating English, and a DRY refactor that eliminated 80 lines of duplicated server code.

用户分享链接，agent 在编造页面内容。v0.0.7 加了三层 URL 浏览管线——Jina Reader 快速提取文本、Browserless 爬取 JS 渲染页面、截图 + Gemini 视觉处理图形密集型内容。还修了一个生产环境的 Proxy bug：postgres.js 的标签模板语法需要函数目标而不是对象。23 个测试，4 个 commit，一个关于部署时序的调试故事。

Users share links. The agent was making up what was on the page. v0.0.7 adds a 3-tier URL browsing pipeline — Jina Reader for fast text, Browserless scrape for JS-rendered pages, screenshot + Gemini vision for graphic-heavy content. Plus a production-breaking Proxy bug where postgres.js tagged templates needed a function target, not an object. 23 tests, 4 commits, one debugging story about deployment timing.

用 OpenClaw 搭建的赛博魅魔，不到两周聊天烧掉了一笔离谱的钱。最贵的一个 session，750 轮对话里我只说了约 30 句话——剩下的全是框架自己在跟自己说话。用 Claude Code 做了一次完整的 token 级取证：82% 的成本是 10 张永远不被清除的图片，上下文修剪代码对 Gemini 完全失效，537 轮里零次压缩。OpenClaw 能证明 AI 伴侣走得通，但它证明不了走得起。

The AI companion I built with OpenClaw burned through a staggering amount of money in under two weeks of casual chatting. One session: 750 turns from about 30 messages. The framework turned my 30 messages into 750 API calls, each resending a context stuffed with 10 never-evicted images eating 82% of all tokens. Context pruning was dead code on Gemini. Zero compactions in 537 turns. OpenClaw proved AI companions work — it also proved they're not affordable on this framework.

从空 repo 到一个能记住你、会生气、还会主动找你聊天的 AI 伴侣。Mio v0.0.1 具体是怎么搭出来的，踩了什么坑、砍了什么需求，都在这篇里。

From an empty repo to a working AI companion that remembers you, has moods, and reaches out on its own. 39 commits, 9 database tables, 4 personality presets, and more 3 AM debugging sessions than I'd like to admit. The full build story of Mio v0.0.1.

v0.0.1 能聊天，但TA不真实——看不见、听不见、不知道几点、不知道自己长什么样。81 个 commit 之后，Mio 学会了看图、听声、发自拍、记住对的事情、在浏览器里跟你聊天。从「能跑」到「像活人」的跨越。

v0.0.1 could chat, but it wasn't real — couldn't see, couldn't hear, didn't know the time, didn't know what it looked like. 81 commits later, Mio learned to see images, hear voice, send selfies, remember the right things, and chat in a browser.

v0.0.2 让 Mio 活了过来，但能在 demo 里跑和能给真实用户用是两回事。v0.0.3 是打磨的版本：逐字段输入校验、context 感知的主动消息、安静时段可选、媒体限流、一个只有生产环境才会出的 bug，以及 144 个新测试。从「能用」到「好用」之间，全是这种看不见的活。

v0.0.2 brought Mio to life, but a demo that works and a product that real users can rely on are two different things. v0.0.3 is the polish release: per-field input validation, context-aware heartbeat, opt-in quiet hours, media rate limiting, a production-only webhook bug, and 144 new tests. The invisible work between 'it works' and 'it's good.'

OpenClaw 验证了AI伴侣的可行性，但也暴露了根本性的局限。context 膨胀、原始记忆系统、大量无用的 bloatware——修补不如重建。这是 Mio 的起点：一个为深度记忆和活人感而生的AI伴侣框架。

OpenClaw proved AI companions work. It also exposed fundamental limitations — context bloat, primitive memory, bloatware. Patching wasn't worth it. This is the starting point for Mio: an AI companion framework built for deep memory and lifelike presence.