Last night I started a journey to build Electron completely from the ground up, for investigating a subtle issue in VSCode.

前段时间在 V2EX 的一个帖子 /t/943948 中看到了一个有趣的问题：

在中文的亲属称谓体系中，我们会有“爸爸的爷爷”与“爷爷的爸爸”是同一个人，即“爸爸”和“爷爷”这两个称谓是可交换的。那么是否可以找到一个准则，以归纳所有这样的可交换称谓对？

欲解决这个问题，我们可以先使用群论对亲属关系进行建模，在此基础上分析其代数结构，进而得出亲属关系可交换的条件。本系列期用两篇博文阐述这一理论。此为其第一篇，将介绍亲属半群的建立以及该代数结构的相关性质。

This post is written to dictate some opinionated explanation that dispels my confusion to Kotlin coroutines during learning.

Recently I was planning to make a small contribution to project DefinitelyTyped/DefinitelyTyped. It is a huge repository that hosts and provides the type declaration files for thousands of packages on npm, with a structure like the following

types/
├── 11ty__eleventy-img
├── 1line-aa
├── 3box
├── ...and more of them...

Each sub-folder under the types/ directory corresponds to a specific npm package.

The package I am interested in is marked, whose type declaration files reside in types/marked/ directory. Towards this end, cloning the whole repository and doing a full checkout is considered worthless, since my disk space and network traffic would be eaten up by a bunch of irrelevant files.

“封建”一词很有意思。作为一个土生土长的中国词，它于千年前便有了固定而确切的含义。然而在近代西方思潮的冲击下，这个词的意义先是遭到了替换，而后又在近百年的传播中演化成了与本义毫不相干的概念。如今的人们谈及封建一词时，心中不免会有困惑，因为其现代含义与汉字构成相去甚远。更糟糕的是，在一些语境下，这个词会回归至其近代甚至是古代的含义，从而对相关的交流造成混乱。同一词的释义在多种语境下不一致，无谓的争吵便在所难免。

粗略地说，“封建”至少有四种释义，即古代中国的封建制度、古代欧洲的 Feudalism、近现代中国与 Feudalism 的对译，以及当代民间语境中的封建。

Several users reported to encounter "Error 804: forward compatibility was attempted on non supported HW" during the usage of some customized PyTorch docker images on our GPU cluster.

At first glance I recognized the culprit to be a version mismatch between installed driver on the host and required driver in the image. The corrupted images as they described were built targeting CUDA == 11.3 with a corresponding driver version == 465 , while some of our hosts are shipped with driver version 460. As a solution I told them to downgrade the targeting CUDA version by choosing a base image such as nvidia/cuda:11.2.0-devel-ubuntu18.04, which indeed well solved the problem.

But later on I suspected the above hypothesis being the real cause. An observed counterexample was that another line of docker images targeting even higher CUDA version would run normally on those hosts, for example, the latest ghcr.io/pytorch/pytorch:2.0.0-devel built for CUDA == 11.7. This won’t be the case if CUDA version mismatch truly matters.

Afterwards I did a bit of research concerning the problem and learnt some interesting stuff which this post is going to share. In short, the recently released minor version compatibility allows applications built for newer CUDA to run on machines with some older drivers, but libnvidia-container doesn’t correcly handle it due to a bug and eventually leads to such an error.

Towards thorough comprehension, this post will first introduce the constitution of CUDA components, following with the compatibility policy of different components, and finally unravel the bug and devise a workaround for it. But before diving deep, I’ll give two Dockerfile samples to illustrate the problem.

GPG (the GNU Privacy Guard) is a complete and free implementation of the OpenPGP standard. Based on various mature algorithms to select from, GPG acts as a convenient tool for daily cryptographic communication.

GPG has two primary functionalities: (1) it encrypts and signs your data for secure transfering and verifiable information integrity, and (2) it features a versatile key management system to construct and promote web of trust. GPG also has a well-designed command line interface for easy integration with other applications such as git.

This article is going to briefly elaborate some key concepts and usage of GPG, and then present demonstration to cryptographically sign git commits with the help of GPG.

Some days ago, I made the decision to shrink the footprint of Windows system on my laptop and reallocate the disk space to the Ubuntu system that resides next to it. Ubuntu is competent for my daily use of programming and web browsing so I hardly launched the OEM-shipped Windows since the laptop was bought. The Windows takes up a not-so-small portion of my SSD space, which can be better utilized instead of wasted in vain.

#gk:immersive
(before)
| --- Windows C: (256 GB) --- | --- Ubuntu / (256 GB) --- |
(after)
| --- Windows C: (120 GB) --- | --- Ubuntu / (392 GB) --- |

The time I composed my first program can be back to my junior high school age. It was the first day of PC lesson, and everybody crowded to the computer classroom. We were told to learn “programming” there. The kids who were talented would be selected and trained for OI . Others instead would go to an ordinary class and learn something more general.

I was anxious. Before the time I had no concept of what “programming” is, nor had I ever gone through a real PC lesson. The PC lesson in my primary school barely taught anything. Over the time the teachers let us play games instead. I could type merely a dozen of characters per minute, since I’d never received a thorough typing training. I was ignorant of inside the metal box. I was a complete computer idiot.

Both Dart and Go support decentralized package distribution. One is able to directly adopt an existing git repository as dependency, easing the effort of distributing packages.

Sometimes we might expect more fine-grained control on what to pull from a git repository. For example, to lock a package’s version, we would specify a particular tag, commit or branch name to pull from. Or if it’s a mono-repo, we would choose a sub-directory from the repository root. This post summarizes how to achieve these purposes in both languages.