YF

[自己装逼的才谷 @realCaigu]

她 22 岁，但她的模型已经运行在了 100 强企业内部，且 a16z 刚刚领头了她 2100 万美元。

“尽可能推出一个最简陋的假版本，然后去卖掉它。”

17 分钟时间内，麻省理工的毕业生 Jessica Wu 完整阐述了让她在过去 24 个月取得成功的 YC 策略。

价值可能超过你读一个两年的 MBA.

[YF @YF3508]

@HiTw93 九溪吗？

[YF @YF3508]

@wolfyxbt 狼兄，现在如果有低电价，是否值得再去挖矿

[SkalskiP @skalskip92]

there's no catch; SAM3 is open source and really good

one of the things it does really well is object tracking, even in crazy complex scenes like basketball

probably my favorite computer vision model ever

[Thariq @trq212]

a prompt I've been using a lot recently:

implement <SPEC> and while you do, keep a running implementation-notes.html file (or markdown) with decisions you had to make weren't in the spec, things you had to change, tradeoffs you had to make or anything else I should know

[YF @YF3508]

@dotey （“If you can't beat them, join them.”）

[宝玉 @dotey]

https://t.co/ppWKvUk98n

[Tw93 @HiTw93]

https://t.co/NqTIdZ6hCY

[YF @YF3508]

@HiTw93 codex好像没有进度条
😀

[🌞Sunny哥 @BTC__Sunny]

马云10年前关起门来的一课，一旦流出，打工人直接醒悟

这不是公开演讲，不是作秀采访
是湖畔大学里拉上窗帘，清场闭馆
对着几十个核心创业者，连讲三天三夜的真心话

无录像，无笔记，严禁外传
网上搜不到，花钱买不着
所有商业课，培训课全都不敢教，不能教，不会教

为什么？
因为这里讲的是真正的财富逻辑，权力规则，底层翻身路径
一旦公开，没人再愿意乖乖帮别人打工。

[YF @YF3508]

@op7418 藏师傅，为什么Claude客户端那么容易耗token,pro 用户，两个文件处理就100%，干等5小时，求给个思路

[GitHubDaily @GitHub_Daily]

很多不搞技术的朋友，装了 Claude Code，跑了几个提示词，然后就不知道怎么深入了。

虽然官方文档讲了一堆功能，但怎么组合使用、从哪学起，完全没头绪。

偶然在 GitHub 上找到 claude-howto 这份结构化的 Claude Code 实战教程。

提供了 10 个教程模块和可直接复制使用的配置模板，还用流程图展示每个功能的内部运作方式。

GitHub：https://t.co/aOUlOJs76D

从斜杠命令、记忆系统、技能配置，到子代理、钩子脚本、插件开发，按从入门到进阶的顺序编排。

每个模块学完还能跑内置测验，帮你找出知识盲区。整套学完大概 11 到 13 小时，但 15 分钟就能上手第一个模板。

如果你用 Claude Code 只停留在对话阶段，想把它真正用出效率来，这份指南值得跟着走一遍。

[YF @YF3508]

@op7418 马上下载

[YF @YF3508]

@passluo 搞定了codex，cc先用中转了

[YF @YF3508]

@qingnianxiaozhe @passluo 这个gpt的ID需要和apple一样嘛？

[YF @YF3508]

@caijingshujuku @grok 帮我总结视频内容，并发给我

[郭宇 guoyu.eth @turingou]

开年最值得阅读的一篇文章。

[Andrew Ng @AndrewYNg]

Even though I’m a much better Python than JavaScript developer, with AI assistance, I’ve been writing a lot of JavaScript code recently. AI-assisted coding, including vibe coding, is making specific programming languages less important, even though learning one is still helpful to make sure you understand the key concepts. This is helping many developers write code in languages we’re not familiar with, which lets us get code working in many more contexts!

My background is in machine learning engineering and back-end development, but AI-assisted coding is making it easy for me to build front-end systems (the part of a website or app that users interact with) using JavaScript (JS) or TypeScript (TS), languages that I am weak in. Generative AI is making syntax less important, so we can all simultaneously be Python, JS, TS, C++, Java, and even Cobol developers. Perhaps one day, instead of being “Python developers" or “C++ developers,” many more of us will just be “developers”!

But understanding the concepts behind different languages is still important. That’s why learning at least one language like Python still offers a great foundation for prompting LLMs to generate code in Python and other languages. If you move from one programming language to another that carries out similar tasks but with different syntax — say, from JS to TS, or C++ to Java, or Rust to Go — once you’ve learned the first set of concepts, you’ll know a lot of the concepts needed to prompt an LLM to code in the second language. (Although TensorFlow and PyTorch are not programming languages, learning the concepts of deep learning behind TensorFlow will also make it much easier to get an LLM to write PyTorch code for you, and vice versa!) In addition, you’ll be able to understand much of the generated code (perhaps with a little LLM assistance).

Different programming languages reflect different views of how to organize computation, and understanding the concepts is still important. For example, someone who does not understand arrays, dictionaries, caches, and memory will be less effective at getting an LLM to write code in most languages.

Similarly, a Python developer who moves toward doing more front-end programming with JS would benefit from learning the concepts behind front-end systems. For example, if you want an LLM to build a front end using the React framework, it will benefit you to understand how React breaks front ends into reusable UI components, and how it updates the DOM data structure that determines what web pages look like. This lets you prompt the LLM much more precisely, and helps you understand how to fix issues if something goes wrong. Similarly, if you want an LLM to help you write code in CUDA or ROCm, it helps to understand how GPUs organize compute and memory.

Just as people who are fluent in multiple human languages can communicate more easily with other people, LLMs are making it easier for developers to build systems in multiple contexts. If you haven’t already done so, I encourage you to try having an LLM write some code in a language you’d like to learn but perhaps haven’t yet gotten around to, and see if it helps you get some new applications to work.

[Original text: https://t.co/NdjaPgwwuk ]