OLD TOKYO 1960s-1970s 【1960-70 年代の東京】 ♪ 上を向いて歩こう ♪ ♪ Sukiyaki ♪ youtu.be/WuIupcQXtdg?si=NLzX… via @YouTube
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An MIT professor taught the same math course for 62 years, and the day he retired, students from every country on earth showed up online to watch him give his final lecture.
I opened the playlist at 2am and ended up watching three of them back to back.
His name is Gilbert Strang. The course is MIT 18.06 Linear Algebra.
Every machine learning engineer, every data scientist, every quant, every self-taught programmer who actually understands how AI works learned the math from this one man. Most of them never set foot on MIT's campus. They just opened a free playlist on YouTube and let him teach.
Here's the story almost nobody tells you.
Strang joined the MIT math faculty in 1962. He retired in 2023. That is 61 years of standing at the same chalkboard teaching the same subject to 18-year-olds.
The interesting part is what he did when MIT launched OpenCourseWare in 2002. Most professors were skeptical. They worried that putting their lectures online would make their classrooms irrelevant. Strang did not hesitate. He said his life's mission was to open mathematics to students everywhere. He filmed every lecture and gave it away.
The decision quietly changed how the world learns math.
For decades linear algebra was taught the wrong way. Professors started with abstract vector spaces and proofs about field axioms. Students drowned in the abstraction. Most never recovered. They walked out believing they were bad at math when they had simply been taught in an order that nobody's brain is built to absorb.
Strang inverted the entire curriculum.
He started with matrix multiplication. Something you can write down on paper. Something you can compute by hand. Something you can see. Then he showed his students that everything else in linear algebra eigenvectors, singular value decomposition, orthogonality, the four fundamental subspaces was just a different lens for understanding what the matrix was actually doing under the hood.
His rule was strict. If a student could not explain a concept using a concrete 3 by 3 example, that student did not actually understand the concept yet. The abstraction was supposed to come last, not first. The intuition was the foundation. The proofs were just confirmation that the intuition was correct.
The second thing Strang changed was the classroom itself. He said please and thank you to his students. Every single lecture. He paused mid-derivation to ask "am I OK?" to check if anyone was lost. He never used the word "obviously" or "trivially" because he knew exactly what those words do to a student who is one step behind. He treated 19-year-olds learning math for the first time the way he treated his own colleagues. With patience. With respect. With the assumption that they belonged in the room.
For 62 years.
The result is something that has never happened in the history of education. A single math professor became the default teacher of his subject for the entire planet.
Universities in India, China, Brazil, Nigeria, every country with a computer science department, started telling their own students to just watch Strang's lectures. The University of Illinois revised its linear algebra course to do almost no in-person lecturing. The reason was honest. The professor said they could not compete with the videos.
His final lecture was in May 2023.
The auditorium was packed with students who had never met him before. He walked to the chalkboard, taught for an hour, and at the end the entire room stood and applauded. He looked confused for a moment, like he genuinely did not understand why they were cheering. Then he smiled and waved them off and walked out.
His written comment under the YouTube video of that final lecture was four sentences long. He said teaching had been a wonderful life. He said he was grateful to everyone who saw the importance of linear algebra. He said the movement of teaching it well would continue because it was right.
That was it. No book promotion. No farewell speech. No legacy management.
The man whose teaching is the foundation of modern AI just thanked the audience and went home.
20 million views. Zero ego. The entire engine of the AI revolution sits on top of math that millions of people learned for free from one quiet professor in Cambridge.
The course is still on MIT OpenCourseWare. Every lecture, every problem set, every exam, every solution. Free.
The most important math course of the 21st century is sitting one click away from you. Most people will never open it.
Qianlong's personal secretary, no one can match the running script for 3... youtube.com/shorts/ULiegbHpX… via @YouTube
驾驭工程:从 Claude Code 源码到 AI 编码最佳实践
中文别名:《马书》
这是一本围绕 Harness Engineering(驾驭工程)的中文技术书。它以 Claude Code v2.1.88 的公开发布包与 source map 还原结果为分析材料,不试图复刻官方产品文档,而是从真实工程实现中提炼 AI 编码 Agent 的架构模式、上下文策略、权限体系和生产实践。
github.com/ZhangHanDong/harn…
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Supervised Machine Learning: Regression and Classification
clcoding.com/2026/04/supervi…
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Spin Up Linux Virtual Machines Quickly with KVM Using Cloud Images
When most people start with virtualization, they reach for tools like VirtualBox or VMware.
You install the software, open the interface, create a new virtual machine, attach an ISO, and go through the operating system installation step by step.
It works.
Linux also provides a native virtualization stack built around KVM and QEMU.
With it, you can work directly with virtual machine disk images and build systems in a faster and more flexible way.
Instead of performing a full operating system installation each time, you can start from an existing image, prepare it for your environment, and run it as a virtual machine.
This approach is common in real-world environments where systems need to be created quickly, tested, replaced, or rebuilt without repeating the same installation process each time.
In this guide, you will use KVM to build a simple lab environment by importing and working with disk images.
You will go through the process step by step, from preparing the image to creating the virtual machine and accessing it.
Continue reading:
blog.sysxplore.com/p/spin-up…
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詹姆斯律师手记:民主党在攒家底,共和党在败家当。
美国这三十年,其实就两句话:民主党在攒家底,共和党在败家当。
克林顿接手老布什留下的烂摊子,八年干出财政盈余、信息革命、中产繁荣。他交班的时候,美国国库是满的,世界是服的,老百姓兜里是有钱的。然后小布什来了,减税、打仗、减税、再打仗。八年下来,国库空了,国债飙了,金融危机爆了。克林顿攒的家底,被一把火烧干净。
奥巴马接手的是1929年以来最大的坑。他花八年把经济从棺材里拉出来,医保改革、汽车业救活、股市翻倍、失业率腰斩。他交班的时候,美国经济在轨道上。然后特朗普一期来了。减税给富人,关税砸自己人,疫情来了连口罩都管不住。四年下来,国债又爆了,社会裂了,盟友跑了。奥巴马留下的轨道,被一脚踹歪。
拜登接手的是疫情、通胀、国会山暴乱的烂摊子。他花四年把经济从悬崖边拽回来——就业创纪录,芯片法案落地,基建开修,通胀压到3%以下。他交班的时候,经济是热的,供应链是稳的,盟友是信的。然后特朗普二期来了。加关税、炸伊朗、辱盟友、撕协议。半年不到,油价涨了,通胀翘了,就业掉了,盟友跑了。
克林顿攒的,小布什烧了。奥巴马建的,特朗普拆了。拜登修的,特朗普砸了。三十年轮转,每一次民主党把美国从坑里拉出来,共和党就把它再踹回去。
这一次,特朗普二期来得更猛——不是烧,是炸。霍尔木兹海峡关着,油价锁在100美元以上,全球供应链断了,盟友一个接一个翻脸。他上台时美国经济是“软着陆”,半年后硬生生被拽回“滞胀”边缘。
民主党在攒家底,共和党在败家当。这话不是立场,是账本。克林顿的盈余、奥巴马的复苏、拜登的软着陆——每一笔都是真金白银攒下来的。小布什的战争、特朗普的关税、特朗普二期的中东泥潭——每一笔都是真金白银烧掉的。
家底再厚,也经不起这么败。这三十年,美国不是在伟大,是在轮回。问题是,下一次民主党再攒起来的时候,还够共和党再败一次吗?
LangChain & LangGraph Interview Q&A
drive.google.com/file/d/1Mdc…
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You can play with now low-level dev skills for c, c++, rust, zig etc and more systems and low-level programming ones
you can check here: skills.sh/mohitmishra786/low…
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Read all about eBPF at ebpf.io/
An Applied Introduction to eBPF with Go
sazak.io/articles/an-applied…
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Introduction to Probability, Statistics, and Random Processes: amzn.to/3UkLHXZ
—————
#DataScience #Mathematics #DataScientist
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You can know the algorithm and still miss the idea behind it.
That's where intuition comes from.
Gary Sutton is going live to break down the thinking behind seminal algorithms — the original problems, the ideas that shaped them, and why they still matter.
April 2nd, 3pm ET: hubs.la/Q048x3Ld0
Check out his book, Timeless Algorithms, The Seminal Papers: hubs.la/Q048xb6x0
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Learn Probability Through Problems (exercises, hints, and solutions provided): amzn.to/3vZed8W
—————
#Statistics #DataScience #Mathematics
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From Introductory to Advanced C++ - Learning Guidelines - Slobodan Dmitrovic - Meeting C++ 2025
youtube.com/watch?v=n8P11hDY…
#cpp
#cplusplus
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An exciting read on caches, LRUs, and FIFO. Save this for a detailed reading.
brooker.co.za/blog/2023/12/1…
LRU isn't always a Linked List: Lessons from a 20GB ccache limit.
Hit the ceiling mid chromium build (multiple build configs).
* ~114k compilation units.
* 21% hit rate.
* 326 evictions.
* cache thrashing itself trying to stay under the limit.
The fix is obvious - bump max_size. But the more interesting part is how ccache actually implements LRU.
Most people assume LRU means a linked list - head points to the most recently used entry, tail to the least, O(1) eviction on both ends. Textbook stuff. ccache does none of that.
Instead, it uses mtime. Every cache entry is a file. When the cache hits the defined size limit, ccache scans the entire cache directory, sorts every file by its last-modified timestamp, and deletes from the oldest end until it's back under the limit.
That's it. The filesystem is the data structure. No in-memory bookkeeping, no separate eviction index, no pointer shuffling. Just stat() calls and a sort.
The tradeoff is real - eviction is O(n log n) where n is the number of cached files. Mine had ~149k of them. Not free. But for a build cache that evicts occasionally rather than constantly, it's a perfectly reasonable approximation of LRU without the complexity of maintaining a proper eviction structure across processes and restarts.
The cold cache problem is what actually hurts. My chromium build has ~114k translation units - across all build configs I am trying locally. First build is always a total miss - every compilation writes a new entry, and if your cache is already full, every write triggers another eviction pass. Paying the O(n log n) sort cost repeatedly while simultaneously trying to build.
Hit rate climbs after a complete build of the same codebase with the same flags. ccache rewards consistency - same compiler binary, same flags, same include paths. Drift any of those, and you're back to misses.
For now, bumped the max size to 50GB and moving on.
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有人刚刚开源了整个常春藤盟校的数学和科学课程。
斯坦福、麻省理工、哈佛……全部课程。集中在一个地方。完全免费。
Github:github.com/Developer-Y/math-…
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An Applied Introduction to eBPF with Go
sazak.io/articles/an-applied…





























