朋友 C 帮忙 vibe coding 重写了一下博客，抄了一下 Shu 的设计 😂 先发一篇短文试试水。
https://linghao.io/posts/ai-assisted-system-design-interview-prep

最近放了个长假，积压了很多东西没看，有些 fomo，尤其是 AI 领域这一天一个新进展…… 不过换个角度看，这样的好处在于时间能够帮你筛掉一些过了当下并不太值得看的东西 😄

之前我就称赞过 HuggingFace 团队发布的小模型训练分享既接轨最新进展又提供足够多细节，没想到这次他们更是直接发布了一篇预计阅读时间 2-4 天的模型训练全攻略。
虽说从开源模型百花齐放的结果来看我们已经知道模型训练没有太多 rocket science，但是这么完整和细节的分享还是令人惊叹👍
https://huggingface.co/spaces/HuggingFaceTB/smol-training-playbook

https://store.steampowered.com/app/3255860/Hymer_2000/
两三个小时就能走完全流程；喜欢这个娓娓道来的叙事。

https://openrouter.ai/state-of-ai
很有意思的数据。比如说：50%的开源模型流量都用来 roleplay 了 😂

https://store.steampowered.com/app/3275880/Chronoquartz/
这个游戏有点像 Minit + Outer Wilds，完成度很高，作为小团队小体量游戏很令人惊喜。

New blog post: https://linghao.io/posts/why-you-should-work-on-ai-engineering

一位朋友写过类似的话题：
https://yuangu002.github.io/software-engineer-thoughts/

https://laike9m.com/blog/avoid-mini-frameworks,171/

laike9m 这篇文章特别准确地指出了大公司 promo driven 的工程文化经常导致的一种病态。我深有共鸣，而且想补充一点：很多工程师在造这些 mini framework 轮子的时候同时也违反了不要 premature optimization 的准则。我在 Google 7 年，好几次看到有人试图做一个非常 generic 的系统，但他们最初却只能 onboard 个位数的用户。这种常识基本上每次都以失败告终，因为你很难在没有太多有代表性的用户的参与的前提下设计和搭建有着足够好抽象的系统。而那些过早的抽象和优化只会妨碍最初那批用户的 onboarding。

https://linghao.io/posts/2025-games-of-the-year
🏆🎮 水一份个人向年度游戏榜单。

新年快乐！感谢关注🙏

Veritasium 终于出了介绍 ASML 光刻机的视频！
https://youtu.be/MiUHjLxm3V0?si=fj5ZanNKT-eUM_R9

A few days late but I'm so proud to share what I've been working on for the past 3 months with an amazing team: Gemini Personal Intelligence 🚀
https://blog.google/innovation-and-ai/products/gemini-app/personal-intelligence/

You can now connect Gemini with other beloved Google products (starting with Search, YouTube, Photos, Gmail and other Workspace tools) in one click. Combined with retrieval of past conversations, Gemini is able to intelligently fetch relevant context and provide deeply personalized responses. And all of this is built with privacy and user control in mind.

This is one of the first steps toward a universal assistant that is personal, proactive and powerful. No doubt there will be some issues, but even in the early days of developing the MVP we've had so many users experience a wow moment when Gemini really "gets" them with a great personalized response.

Personal context is messy and mistakes are unavoidable. My focus has been about memory and correctability -- making sure that users can course correct when that happens. It's been a very challenging but interesting problem space with many open questions. I feel very lucky to have the opportunity to work on frontier applications at a company that is truly battle ready at the every layer of the AI stack.

Any feedback is highly appreciated as we continue to perfect the feature and envision the future!