flyer

Time flies.

[flyer @flyer103]

对于个人项目，越来越习惯用 hermes 开发。公司项目，还是习惯用 claude code。

能想到这样做的差异，可能主要在于交互界面上。个人项目和任务通过 discord 交互，公司项目在 terminal 或 IDE 中交互更安心些

[Akshay 🚀 @akshay_pachaar]

https://t.co/VoQByJUrBU

[flyer @flyer103]

K8s cost optimization tip:

Many pods have requests 2-3x their actual usage

Example:
- Request: 4CPU/8GB
- Actual: Avg 0.5CPU/1.2GB

Fix:
Request 0.5CPU/1.5GB, Limit 1CPU/3GB
= Save 87.5% CPU cost

I'll share a tool that does this automatically

#Kubernetes #FinOps

[flyer @flyer103]

Working on an open-source K8s tool: Resource Right-sizer

Helps optimize resource requests and reduce cloud costs by 20-40%

Features:
- Auto-analyze actual usage
- Generate optimization recommendations
- One-click apply

GitHub: https://t.co/HvJ0TmMSUw

#Kubernetes #CloudCost

[Math Files @Math_files]

The physicist Richard Feynman could explain the universe more clearly than almost anyone.

And it's because he read books he couldn’t understand.

As a young man, Feynman tried to read Principles of Quantum Mechanics by Paul Dirac, a book so dense that even expert physicists struggle with it.

And of course, he understood none of it. But he kept going, page after page, revisiting it over a span of years, wrestling with ideas far above his level.

Most people avoid this kind of frustration. But that’s what most people don’t understand about learning.

Learning requires that kind of frustration. It requires that feeling of immense challenge. And that’s why reading above your level, not just at your level, slowly rewires your brain.

Later in life, Feynmann distilled everything he learned into Six Easy Pieces, a masterclass in clarity.

But that clarity was earned by years of struggling with ideas that felt almost impossible.

[Math Files @Math_files]

Bayes’ theorem is probably the single most important thing any rational person can learn.

So many of our debates and disagreements that we shout about are because we don’t understand Bayes’ theorem or how human rationality often works.

Bayes’ theorem is named after the 18th-century Thomas Bayes, and essentially it’s a formula that asks: when you are presented with all of the evidence for something, how much should you believe it?

Bayes’ theorem teaches us that our beliefs are not fixed; they are probabilities. Our beliefs change as we weigh new evidence against our assumptions, or our priors. In other words, we all carry certain ideas about how the world works, and new evidence can challenge them.

For example, somebody might believe that smoking is safe, that stress causes mouth ulcers, or that human activity is unrelated to climate change. These are their priors, their starting points. They can be formed by our culture, our biases, or even incomplete information.

Now imagine a new study comes along that challenges one of your priors. A single study might not carry enough weight to overturn your existing beliefs. But as studies accumulate, eventually the scales may tip. At some point, your prior will become less and less plausible.

Bayes’ theorem argues that being rational is not about black and white. It’s not even about true or false. It’s about what is most reasonable based on the best available evidence. But for this to work, we need to be presented with as much high-quality data as possible. Without evidence—without belief-forming data—we are left only with our priors and biases. And those aren’t all that rational.

[flyer @flyer103]

两天时间一直在分析现有的实现，反复通过 claude code + (人工 review + 代码阅读) 提问，最终还是没能清晰理解 😂 项目中的关键功能从设计的时候，还是需要维护好文档，尤其是交互的时序图和关键设计点

[ℏεsam @Hesamation]

https://t.co/LsZLoCMqTN

[Nathan Lambert @natolambert]

My raw thoughts on the job market -- both for those hiring and those searching -- at the cutting edge of AI.

https://t.co/pP9MbIrZqG

[flyer @flyer103]

The Cursor calling tool is not robust enough, and its MCP does not feel very usable

[flyer @flyer103]

Doubao has a great feature of dividing videos into chapters and paragraphs while watching and summarizing key points. Enriching this "companion viewing" would be like having someone explain the video in - depth, which is very useful for in - depth video understanding.

[Andrew Ng @AndrewYNg]

Some people today are discouraging others from learning programming on the grounds AI will automate it. This advice will be seen as some of the worst career advice ever given. I disagree with the Turing Award and Nobel prize winner who wrote, “It is far more likely that the programming occupation will become extinct [...] than that it will become all-powerful. More and more, computers will program themselves.”​ Statements discouraging people from learning to code are harmful!

In the 1960s, when programming moved from punchcards (where a programmer had to laboriously make holes in physical cards to write code character by character) to keyboards with terminals, programming became easier. And that made it a better time than before to begin programming. Yet it was in this era that Nobel laureate Herb Simon wrote the words quoted in the first paragraph. Today’s arguments not to learn to code continue to echo his comment.

As coding becomes easier, more people should code, not fewer!

Over the past few decades, as programming has moved from assembly language to higher-level languages like C, from desktop to cloud, from raw text editors to IDEs to AI assisted coding where sometimes one barely even looks at the generated code (which some coders recently started to call vibe coding), it is getting easier with each step.

I wrote previously that I see tech-savvy people coordinating AI tools to move toward being 10x professionals — individuals who have 10 times the impact of the average person in their field. I am increasingly convinced that the best way for many people to accomplish this is not to be just consumers of AI applications, but to learn enough coding to use AI-assisted coding tools effectively.

One question I’m asked most often is what someone should do who is worried about job displacement by AI. My answer is: Learn about AI and take control of it, because one of the most important skills in the future will be the ability to tell a computer exactly what you want, so it can do that for you. Coding (or getting AI to code for you) is a great way to do that.

When I was working on the course Generative AI for Everyone and needed to generate AI artwork for the background images, I worked with a collaborator who had studied art history and knew the language of art. He prompted Midjourney with terminology based on the historical style, palette, artist inspiration and so on — using the language of art — to get the result he wanted. I didn’t know this language, and my paltry attempts at prompting could not deliver as effective a result.

Similarly, scientists, analysts, marketers, recruiters, and people of a wide range of professions who understand the language of software through their knowledge of coding can tell an LLM or an AI-enabled IDE what they want much more precisely, and get much better results. As these tools are continuing to make coding easier, this is the best time yet to learn to code, to learn the language of software, and learn to make computers do exactly what you want them to do.

[Original text: https://t.co/HdI3Jb9HmF ]

[月姫 @ReiwaLuna06013]

当你精力最充沛时，先做最困难的任务💪👏👍

[Min Choi @minchoi]

DeepSeek R1 broke the Internet just 5 days ago.

People can't believe how powerful and "intelligent" it is. Unlocking new possibilities.

10 wild examples:

[flyer @flyer103]

In the past, I used python to process data, but now I use large language models to process data 😂

[flyer @flyer103]

As AI tools become more and more useful, the ability to set goals and build workflows becomes more and more important.

[flyer @flyer103]

Cursor is awesome

[flyer @flyer103]

连续短期出差还是有些疲惫

[flyer @flyer103]

View my verified achievement from @linuxfoundation. https://t.co/4tPhZ5Cgbn via @credly

[flyer @flyer103]

现在选择浏览器，会把 tab 页放在做侧边栏作为必选项，其次是便于分类管理 tab 页。Edge 正好满足，但试用了 Arc，发现它在侧边栏增加了类似 Firefox 的 pin 页面的能力，页面阅读体验也更好些