rayworks

[rayworks @rayworks007]

"When you don't create things, you become defined by your tastes rather than ability. Your tastes only narrow and exclude people. So create."

[rayworks @rayworks007]

While working on a marketing project from 2016 to 2019, I finally had enough spare time to build some library projects and explore open‑source solutions. In retrospect, it was one of my most productive stretches.

[AImaster @CeoSpaceY]

how to speak?
教授提到，你的好想法就像你的孩子，你绝不会想让他们衣衫褴褛地走向世界。通过这些技巧，可以确保你的想法得到应有的尊重和高价值呈现 [

[Alex Prompter @alex_prompter]

Harvard, Andrew Ng, and Karpathy will teach you AI engineering for free. Most people just do it in the wrong order:

Almost all of it is free, and the order matters as much as the resources.

1. Start with Python. It's the language the AI field runs on, and Harvard's CS50P teaches it better than most paid bootcamps.

2. Once the basics click, learn how Python is used in AI. Andrew Ng's "AI Python for Beginners" is a free four-part course that bridges writing code and building with models.

3. From there, get a feel for how LLMs work under the hood. 3Blue1Brown's visual explainers make transformers and attention click.

4. When you want to go deeper, build a small model yourself. Andrej Karpathy's "Zero to Hero" series takes you from one neuron to a working model, line by line.

5. Next, learn how AI agents actually work. Anthropic's "Building Effective Agents" is the most grounded guide, and its lesson is to use composable patterns, not heavy frameworks.

6. For hands-on practice, take the CrewAI short course. It teaches you to treat agents like a team of people working together.

7. After that, connect your agents to the real world. That's what MCP does, wiring models to tools, APIs, and databases, and the official docs are the cleanest place to start.

8. Now build real projects. The open-source ai-engineering-hub repo has dozens of working examples across LLMs, RAG, and agents you can adapt into your own work.

9. Finally, read one book instead of ten. Chip Huyen's "AI Engineering" covers what you need to ship real applications.

The throughline is simple. Frameworks come and go, so don't build your skills around them. Master the fundamentals once, and everything on top gets easier, and you'll stay ahead of the people chasing the framework of the week.

[Kai @real_kai42]

🤠
Kimi Code也在招人，感兴趣直接发我邮箱 [email protected]

感谢大佬们帮忙扩散 捧场

[宝玉 @dotey]

开源项目推荐：Claude Code From Scratch

这是一本学习 Claude Code 的开源电子书，严格来说不仅仅是电子书，还有代码，不需要你去看 Claude Code 的 50 万行代码。

用 ~4300 行代码（TypeScript 和 Python 两个版本分别实现）复现了 Claude Code 的核心架构——Agent Loop、13 个工具（含并行执行 + 流式早期启动）、4 层上下文压缩、语义记忆召回、技能系统、多 Agent、MCP 集成……每一步都对照真实源码讲解它怎么做的 → 我们怎么简化的。

有 13 章内容，每一章都是一份分步教程，跟着动手写几千行代码，快速理解 Claude Code 这样最好用的 coding agent 的精髓。

读完你就能大致理解了 coding agent 的工作原理，我跟着快速浏览了下都有了些新的收获，推荐有兴趣的可以看看。

有中英文版：
https://t.co/x2F11US2g0

[rayworks @rayworks007]

A nice Chrome extension keeps tabs on your tabs. Turn your "New tabs" page into a mission control, so you can close them easily.
https://t.co/foI0SAsaCW

[rayworks @rayworks007]

'Lecture 7 : Agentic Coding' in The Missing Semester of Your CS Education 2026
https://t.co/YUZwpzceAh

[yibie @yibie]

推荐这篇，Anthropic 官方出品的 Claude Code 最佳实践。不是产品文档，是工程团队自己的使用方式——怎么组织文件结构让 Agent 理解、怎么写清晰的任务描述、什么时候该介入。

Claude Code Best Practices：Anthropic 团队自己的用法

Anthropic 工程博客。团队分享他们在 Claude Code 中的真实使用方式：

• 任务越具体越好，少用模糊指令
• 文件结构影响 Agent 的理解能力
• 复杂任务拆成多轮比一轮更可靠
• 知道自己什么时候该介入——不是所有事都适合 AI

原文：https://t.co/7MK7jxNyAI

#ClaudeCode #最佳实践 #工程

[Alisa Liu @alisawuffles]

I'm joining OpenAI next week!🥹 The job search turned out to be really challenging but also super rewarding, so I wrote a small blog to share what I learned along the way and hopefully make the process a little less mysterious for the next person. https://t.co/6FigSBdenD

[rayworks @rayworks007]

"The great pleasure in life is doing what people say you cannot do."

[悟空知识分享 @wym668]

《人生》🔮

99.99%的人好像开车在高速公路上，怕开的比别人慢，但居然不知道自己要去哪里。

[Vivek Galatage @vivekgalatage]

What Every Programmer Should Know about How CPUs Work by @mattgodbolt

https://t.co/V1usGa2akA

[心灵书舫 | SoulSloop @SoulSloop99]

世界顶尖哲学家的30句名言，精辟之极。值得收藏！

一、苏格拉底

1.活着不是目的，好好活着才是。

2.人生在世，知道自己需要什么，只不过是一个人的本能；而懂得自己不需要什么，这却是一个人的智慧。

3.一个人能否有成就，只看他是否具备自尊心与自信心这两个条件。

二、柏拉图

1.孩子害怕黑暗，情有可原；人生真正的悲剧，是成人害怕光明。

2.人生最遗憾的，莫过于轻易地放弃了不该放弃的，固执地坚持了不该坚持的。

3.一切皆流，无一静止。

三、亚里士多德

1.幸福是把灵魂安放在最适当的位置。

2.贤 哲追求的不是享乐，而是源于痛苦的自由。

3.每天反复做的事情造就了我们，然后你会发现，卓越不是一种行为，而是一种习惯。

四、笛卡尔

1.我思故我在。

2.一个人为情感所支配，行为便没有自主之权，而受命运的宰割。

3.无法做出决策的人，或欲望过大，或觉悟不足。

五、卢梭

1.人生而自由，却无往不在枷锁中。

2.忍耐是苦涩的，但它的果实却是甘甜的。

3.我们手里的金钱是保持自由的一种工具。

六、康德

1.自由不是让你想做什么就做什么，自由是教你不想做什么，就可以不做什么。

https://t.co/IXvwep2yce上有两件东西能震撼人们的心灵：一件是我们心中崇高的道德标准；另一件是我们头顶上灿烂的星空。

3.三样东西有助于缓解生命的辛劳：希望、睡眠和微笑。

七、黑格尔

1.存在即合理。

2.人是靠思想站立起来的。

3.在纯粹的光明中就像在纯黑暗中一样，看不清什么东西。

八、叔本华

1.人能够做他所想做的，但不能要他所想要的。

2.没有相当程度的孤独，是不可能有内心的平和。

3.没有人生活在过去，也没有人生活在未来，现在是生命确实占有的唯一形态。

九、费尔巴哈

1.理论所不能解决的疑难问题，实践将为你解决。

2.爱就是成就一个人。

3.人只有靠眼睛才能上升到天上，因此理论是从注视天空开始的。

十、尼采

1.每一个不曾起舞的日子，都是对生命的辜负。

2.其实人跟树是一样的，越是向往高处的阳光，它的根就越要伸向黑暗的地底。

3.当你注视着深渊的时候,深渊也在注视着你。

[Viking @vikingmute]

分享一篇文章：《How LLMs Actually Work》
https://t.co/apHhTvjdiB

好像是前几天 HackerNews 排名第一来着，类似的文章很多，但是这篇深入浅出和直观的例子非常适合有一定编程但没深入学Transformer的人阅读，里面的比喻也恰当，一看就是活人写的，没什么 AI 味道。

最近重新爱上了写东西，写了两篇技术文章，之后还会继续写，而且我的一个原则，活人写，绝对不用 AI，写作是一种乐趣，梳理逻辑，表达观点，不要让这种乐趣被 AI 剥夺。

[Curious Minds @CuriousMindsHub]

How to write clearly:

[宝玉 @dotey]

书的配套资源（包括样章）：https://t.co/L0CS14mua8

[slash1s @slash1sol]

A DEVELOPER TAUGHT GIT WITH A BOX OF CHILDREN'S TOYS AND ENGINEERS WITH TEN YEARS IN SAY IT'S THE FIRST TIME THE THING EVER ACTUALLY MADE SENSE

90 minutes, one table, a pile of Tinkertoys. No wall of jargon -- he builds a real Git repo out of plastic rods right in front of you.

-> The moment he snaps the first pieces together, Git stops being scary command-line magic and becomes what it really is: a chain of tiny objects pointing at each other.

Branches, merges, rebase, the staging area -- every concept that's ever burned you at 2am -- he rebuilds with toys until a four year old could follow. He calls Git a two-trick pony. After this you'll see exactly why.

Memorizing commands was never the skill -> holding the graph in your head is. And with an AI agent now committing and rebasing on your machine all day, that mental model is the only thing between you and a history you can't read.

Scroll the comments and you'll see the same thing over and over: this is the talk that finally made Git click and made people the one their whole team comes to when it breaks.

Bookmark & watch it today. It's the 1.5 hours that pays you back for the rest of your career ↓

[Vivek Galatage @vivekgalatage]

The Algorithm Visualizer is my favorite tool for explaining algorithms while teaching.

The accompanying quick explainer is also quite useful.

https://t.co/1Z1Pztb94Y

[rayworks @rayworks007]

🕵️‍♂️Code smell : "App.get().getDomainProvider().getXxxService().doSth()"
Demeter's Law: Don't talk to strangers!

[rayworks @rayworks007]

https://t.co/H5bjye2Do8