Halfrost-Field
✍🏻 这里是写博客的地方 —— Halfrost-Field 冰霜之地
A Chinese-language collection of deep-dive technical articles by one developer, covering Go internals, spatial search algorithms, machine learning notes, and data structures, written by reading open-source framework source code.
Halfrost-Field, which the author calls "the land of frost," is a personal repository where a developer named halfrost collects the technical articles he writes for his blog. The badges at the top show a large running total of words written and times read, so this is a long-running writing project rather than a piece of software you install and run.
The stated reason for building the repository is simple. The author believes that reading the source code of open source frameworks is one of the best ways to improve as a programmer, so he reads that code, analyzes it in detail, and writes up what he learned. The repository is the place where those write-ups live, and he says he keeps adding to it over time.
The articles are organized into sections by topic. The Go section is the largest and goes deep into how the Go language works underneath: how slices and maps are built, how interfaces and reflection work, and how channels handle concurrency. There is also a group of articles on spatial search algorithms such as Geohash and Google's S2 library, which deal with indexing points on a map. Other sections cover machine learning notes that follow Andrew Ng's well known Stanford course, plus JavaScript, iOS development, and general algorithm and data structure topics.
Most of the writing is in Chinese, and nearly every entry in the tables is a link out to a separate article file inside the repository. So the repository itself is mostly an index, a table of contents that points to the full pieces. If you can read Chinese and want to understand how these systems work from the inside, it is a study resource. The author invites readers to star the repository if they want to follow along as new articles are added.
Where it fits
- Study how Go slices, maps, interfaces, channels, and reflection work under the hood through detailed written explanations
- Learn spatial indexing algorithms like Geohash and Google S2 through the author's source-code walkthroughs
- Follow Andrew Ng's machine learning course with supplementary notes that connect theory to code