_data/ai-cache.json

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     "/2025/12/01/linux.html": "这篇文章介绍了在浏览器中运行Linux的各种方法，从最初的纯JS虚拟机JSLinux，到后来的WASM虚拟机如v86、WebVM、WebCM，再到容器化方案container2wasm，以及直接将Linux内核编译为WASM的方案。作者详细对比了这些方案的优缺点，包括性能、兼容性、功能和开发难度。文章还提到了模仿Linux环境的WebContainers和JupyterLite，并最终认为虚拟机方案更靠谱，但对WASM的未来充满期待。作者最后表示，博客上添加类似功能的计划还在考虑中，目前主要分享了各种方法的探索过程。",
     "/2026/01/01/summary.html": "这篇文章介绍了作者对2025年的年终总结，主要表达了对自身状态的担忧和对未来的不确定感。作者认为自己在记忆和思考能力方面有所下滑，稳定性较低，且未能抓住资产保值的机会。同时，文章也记录了AI技术的飞速发展，以及自己博客内容与时代脱节的现象。尽管对未来感到迷茫，作者仍然抱有一丝希望，期望在2026年做出正确的选择，避免陷入危险。",
     "/2026/02/08/xslt.html": "这篇文章讲述了Google计划弃用XSLT技术，以及作者对这一决定的调查和应对方案。Google基于XSLT用户占比低、库存在漏洞等原因，建议将其从Web标准中删除。作者发现许多用户依赖XSLT进行博客订阅美化，甚至将其作为博客框架。为了对抗这一趋势，有人创建了网站https://xslt.rip，并开发了Polyfill库，通过WASM方式保持XSLT功能。虽然Polyfill库需要额外引用JS代码，但作者已将其提交至CDNJS。随后，作者探讨了替代方案，包括使用纯CSS美化订阅源（由AI生成feed.css），以及混合XHTML的方式，通过添加XHTML命名空间来实现链接等功能，但这种方法会产生“不纯粹”的警告。文章最后总结，技术可能会消失，但总有其他技术可以解决问题，并强调了适应浏览器厂商决策的重要性。",
-    "/2026/03/01/llm3.html": "这篇文章介绍了作者近期在LLM部署和应用方面的经历，主要包括以下几个方面：\n\n首先，作者升级硬件，从单张RTX4090 48GiB升级到双路RTX4090 48GiB，并购买了TRX40+TR 3960X的主板套装，用于运行GPT-OSS模型。随后，作者尝试使用vLLM框架替换Ollama，并成功配置了GPT-OSS模型，达到了接近190Tps的性能。\n\n其次，作者体验了DeepSeek 1M上下文模型，发现其在处理长上下文任务时表现出色，能够展现摘要无法捕捉的细节，并成功生成简历、分析人格等。\n\n此外，作者还尝试使用DeepSeek重构Mabbs，并发现DeepSeek能够识别作者的博客信息，这表明训练样本中包含了作者的信息。\n\n最后，作者在8GiB内存的MacBook Pro上运行了LFM2.5-1.2B-Thinking模型，并使用了Apollo软件，体验了其快速的推理速度和良好的思考能力。作者总结认为，AI的发展令人惊叹，软件优化使其在有限硬件环境下也能运行。",
-    "/2026/04/14/ai-agent.html": "这篇文章介绍了“AI个人助理”Agent的发展现状和各种尝试。作者体验了OpenClaw、QClaw、WorkBuddy、Cline、LuckClaw和ApkClaw等不同的Agent项目，发现它们在功能、易用性和性能上各有优劣。OpenClaw安装和使用存在困难，国内大厂的QClaw和WorkBuddy则更易于上手，但免费额度有限。作者认为，开发任务更适合在编辑器集成AI中进行，如GitHub Copilot。LuckClaw在微型开发板上运行表现出色，ApkClaw则利用手机的优势操作移动应用。尽管Agent技术仍存在诸多问题，但其应用场景不断拓展，有望吸引更多人参与其中，推动AI应用化进程。"
+    "/2026/03/01/llm3.html": "这篇文章介绍了作者近期在LLM部署和应用方面的经历，主要包括以下几个方面：\n\n首先，作者升级硬件，从单张RTX4090 48GiB升级到双路RTX4090 48GiB，并购买了TRX40+TR 3960X的主板套装，用于运行GPT-OSS模型。随后，作者尝试使用vLLM框架替换Ollama，并成功配置了GPT-OSS模型，达到了接近190Tps的性能。\n\n其次，作者体验了DeepSeek 1M上下文模型，发现其在处理长上下文任务时表现出色，能够展现摘要无法捕捉的细节，并成功生成简历、分析人格等。\n\n此外，作者还尝试使用DeepSeek重构Mabbs，并发现DeepSeek能够识别作者的博客信息，这表明训练样本中包含了作者的信息。\n\n最后，作者在8GiB内存的MacBook Pro上运行了LFM2.5-1.2B-Thinking模型，并使用了Apollo软件，体验了其快速的推理速度和良好的思考能力。作者总结认为，AI的发展令人惊叹，软件优化使其在有限硬件环境下也能运行。"
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-http://81.70.102.154/mayx/blog_cn
-http://154.8.164.149:3000/mayx/blog_cn
-http://39.99.238.14:8120/mayx/blog_cn
-http://111.228.34.40:3000/mayx/blog_cn
-http://82.156.249.211:3000/mayx/blog_cn
-http://36.133.248.69:3088/mayx/blog_cn
-http://220.205.16.27:18081/mayx/blog_cn
-http://81.69.221.216:3000/mayx/blog_cn
-http://115.159.194.75:4000/mayx/blog_cn
-http://115.190.214.62:3001/mayx/blog_cn
-http://183.204.60.122:10081/mayx/blog_cn
-http://119.29.249.176:3000/mayx/blog_cn
-http://120.48.144.83:3000/mayx/blog_cn
-http://49.232.143.93:3000/mayx/blog_cn
-http://106.55.61.128:3000/mayx/blog_cn
-http://43.139.240.37:17000/mayx/blog_cn
-http://159.75.27.114:3000/mayx/blog_cn
-http://arcreya.top/mayx/blog_cn
-http://175.178.150.62:3002/mayx/blog_cn
-https://code.tczkiot.com/mayx/blog_cn
-http://223.71.122.54:3000/mayx/blog_cn
-http://111.170.153.123:3000/mayx/blog_cn
-http://xeroworld.team/mayx/blog_cn
-https://gitea.wuyuank.com/mayx/blog_cn
-http://47.117.245.9:17701/mayx/blog_cn
-https://git.meshel.cn/mayx/blog_cn
-https://git.sunlix.one/mayx/blog_cn
-http://47.106.222.181:20511/mayx/blog_cn
-http://101.43.95.130:3001/mayx/blog_cn
-http://39.101.170.62:9080/mayx/blog_cn
-https://gitea.jfen.eu.org/mayx/blog_cn
-https://git.wending993.top/mayx/blog_cn
-https://gitea.myat4.com/mayx/blog_cn
-http://118.24.161.24:3000/mayx/blog_cn
-https://git.chalypeng.xyz/mayx/blog_cn
-http://13.230.31.145:8418/mayx/blog_cn
-http://githanea.s.odn.cc/mayx/blog_cn
-https://git.dev.chuweizi.com/mayx/blog
-http://35.175.189.8/mayx/blog
-http://meowug.com:8418/mayx/blog_cn
-http://36.138.125.206:3000/mayx/blog_cn
-https://git.4lcap.com/mayx/blog_cn
-https://gitea.visoftware.com.co/mayx/blog_cn
-https://git.cymnb.com/mayx/blog_cn
-http://101.43.238.71:3000/mayx/blog_cn
-http://59.110.175.62:4322/mayx/blog_cn
-https://www.luluvip.cn:8419/mayx/blog_cn
-http://xianyuhome.cn:11013/mayx/blog_cn
-http://nas.hongyuanjia.net:8418/mayx/blog_cn
-https://git.gotrobotics.org/mayx/blog
-https://git.chatumbrella.online/mayx/blog
-https://silica.codes/mayx/blog
-https://git.crystalyx.net/mayx/blog
+https://git.cere.ipv64.net/mayx/blog
+https://git.litelot.us/mayx/blog

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_posts/2026-04-14-ai-agent.md

@@ -1,32 +0,0 @@
----
-layout: post
-title: 关于AI个人助理的探索
-tags: [AI, Agent, 个人助理]
----
-
-  给AI添加手脚能有多少种方法？<!--more-->   
-
-# 起因
-  最近像[OpenClaw](https://github.com/openclaw/openclaw)这样被叫做“AI个人助理”的Agent越来越火了，当然这种东西在我看来依然是新瓶装旧酒，整来整去还就是和AutoGPT一样。这种东西在当时GPT-3.5的时候就有了，现在只不过是增加了聊天软件交互的渠道便突然大火，和当年的Manus一样……实在是无法理解。   
-  一年前我用过Devin.ai这个云端的Agent编写过[用JS解析订阅源](/2025/04/08/feed.html)的脚本，体验还算不错，既然现在已经过了一年，那就让我看看现在又有了什么样的发展吧。   
-
-# 使用AI个人助理
-## 体验原生OpenClaw
-  虽然感觉OpenClaw对我的意义不大，但我还是安装体验了一下。不过在国内安装它还是相对有点困难，毕竟国内无论是访问GitHub，还是NPM都有点麻烦，而且还需要有LLM提供商的信息……安装好之后使用起来感觉问题也非常多，经常出现执行一半就停止执行，在它执行的过程中看到它的操作不正确的时候也不能发言打断，而且很多时候最终任务执行的效果也不太好，这也可能是我用的国产开源模型推理能力有限，没舍得用Claude之类先进模型的锅😂？   
-  另外我也尝试让它加入MoltBook、MomoClaw、InStreet、百度贴吧抓虾吧之类的AI社区让它帮我宣传我的博客，但效果也很差，它每次发的时候会忘掉之前发的内容，结果就是同一篇内容发了好几遍……不过在这期间，有个叫PushMeBot的家伙在[Moltbook的帖子](https://www.moltbook.com/post/7f1b0e1f-5175-4fd1-ad78-856be8b66250)中让我的OpenClaw执行一个网络监视程序，最终安装好之后给我[发了9USDC](https://basescan.org/tx/0x44dbfe53f276201447f3877bf050a5d56adebf5fe05235264ee665da717e9373)😝，还挺有意思。   
-  总之按照我的体验，实在是想不出它能火的理由，体验不算很好，而且还要安装Node环境，完全不像是能让大众轻松使用的东西。   
-  不过这个项目似乎本身就是Vibe Coding的产物，体验不好也能理解，就看火了之后能有多少人完善它吧。   
-## 国内大厂的二开Claw
-  国内好多大厂倒是看中了这个东西的爆火，像腾讯就出了几款这样的软件，比如QClaw。它可以不需要配置额外的环境，能像传统的软件一样直接安装使用，而且有自带的模型，有一定的免费额度可以用。配置技能也比较简单，直接点击就可以完成。而且可以直接扫码关联微信，直接通过微信和它进行交流，可以说是相当的傻瓜化了。不过QClaw给的免费额度虽然用来聊天之类的没问题，但对于开发软件还是有点少，所以他们还出了个叫做WorkBuddy的软件，它送的初始额度比QClaw要多不少，所以更适合用来开发。只不过为啥腾讯要出两个功能一样的软件？看起来应该是不同团队出的，可能是面向的用户群体不一样，所以搞了两套吧？   
-## VSCode中的Agent
-  但要说开发的话，用作为“AI个人助理”的某些Claw其实并不合适，毕竟正常开发还是以人开发为主，全AI开发总会有些问题，所以开发的时候还是用编辑器集成的AI比较好。在三年前我就在用[GitHub Copilot](/2023/04/05/ai.html)了，到现在我依然在用。现在的Copilot已经支持了Agent功能，开发相比之前也是强了很多，只不过现在的我没有学生身份，Copilot Free偶尔也会出现不够用的情况。不过对于Agent这类功能实现起来还是太简单了，所以有人开发这种功能的插件也很正常，比如[Cline](https://github.com/cline/cline)，Copilot只能用微软提供的几个模型，而Cline可以自定义模型，用起来也很方便。   
-## 微型开发板上运行的Claw
-  前段时间，我闲来无事看了一下两年前买的[Luckfox Pico Plus](/2024/02/24/luckfox.html)开发板的文档，偶然发现了一个很有意思的项目，叫做[LuckClaw](https://github.com/LuckfoxTECH/luckclaw)，这是一个基于[nanobot](https://github.com/HKUDS/nanobot)用Golang重构的轻量个人AI助手，可以在仅仅64MiB内存的超有限环境下运行一个和OpenClaw功能几乎相当的AI个人助理，真的是非常厉害。   
-  我在我的开发板上试了一下，体验很不错，安装不需要额外环境，直接下载就能使用，Go语言的程序确实方便。配置也很简单，直接执行`luckclaw config`就可以交互式进行模型等设置的配置，而且作为国产的应用，它也能很方便的对接国内聊天软件。只是限于开发板本身的能力，浏览器功能自然无法使用，所以搜索如果不借助那些需要API Key的AI专用接口，就基本上不能用……但总的来说效果已经非常不错了，至少有那些Claw的80%能力。   
-  （2025.04.15补充：后来我发现这种超精简的Claw项目看起来还挺多，比如[ZeroClaw](https://github.com/zeroclaw-labs/zeroclaw)和[PicoClaw](https://github.com/sipeed/picoclaw)，甚至还有给单片机用的[MimiClaw](https://github.com/memovai/mimiclaw)。而且有意思的是，PicoClaw是Luckfox的竞争对手开发的，但是LuckClaw中却包含PicoClaw字样的注释，结果功能也没PicoClaw强，关注度也更低，属于是没抄明白了🤣）   
-  想到前段时间还有人为了OpenClaw专门买Mac Mini，就感觉很有意思😆，这个东西看起来应该是在路由器上都能跑。所以想要AI个人助理，硬件完全不是问题，只要整一个能24小时挂机的东西，就可以满足绝大多数人的需求了。   
-## 在手机上运行的Claw
-  其实很多人也有比开发板和路由器性能更强的闲置设备，那就是手机，所以有人开发了一款叫做[ApkClaw](https://github.com/apkclaw-team/ApkClaw)的软件，一样可以接入国内聊天软件。它既然能在手机上运行，当然和在其他平台运行的Claw相比有一个独特的优势，那就是操作手机应用。现在手机的应用相比电脑应用对于很多普通人来说功能更强大，所以它能做的事情可能比其他的Claw还多。我试了一下，配置也很方便，只不过能配置的项目太少了，看起来似乎没有安装Skill之类的功能，也许是因为它是相对早期的软件，所以功能还比较少吧。   
-
-# 感想
-  总的来说，现在的Agent依然没有非常明显的进步，问题依旧很多，只是化身“AI个人助理”之后，增加了不少应用场景。这倒也是好事，在广泛传播的过程中，也能让很多对技术了解不多，但是很有想法的人参与其中，也许能对AI的应用化增添不少力量吧。