江湖傳聞,在數碼山巔有一座名為「Claude Code」的宗門。此宗門的大師 Claude 武功高強,但真正令他稱霸江湖的,是他麾下的三大法寶:MCP 神器、子代理門徒、以及技能秘笈。
許多初入門的弟子都搞不清這三者的差別,今日便由老衲娓娓道來。
江湖傳聞,在數碼山巔有一座名為「Claude Code」的宗門。此宗門的大師 Claude 武功高強,但真正令他稱霸江湖的,是他麾下的三大法寶:MCP 神器、子代理門徒、以及技能秘笈。
許多初入門的弟子都搞不清這三者的差別,今日便由老衲娓娓道來。
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I was talking to a coworker recently about general techniques that almost always form the core of any effort to write very fast, down-to-the-metal hot path code on the JVM, and they pointed out that there really isn't a particularly good place to go for this information. It occurred to me that, really, I had more or less picked up all of it by word of mouth and experience, and there just aren't any good reference sources on the topic. So… here's my word of mouth.
This is by no means a comprehensive gist. It's also important to understand that the techniques that I outline in here are not 100% absolute either. Performance on the JVM is an incredibly complicated subject, and while there are rules that almost always hold true, the "almost" remains very salient. Also, for many or even most applications, there will be other techniques that I'm not mentioning which will have a greater impact. JMH, Java Flight Recorder, and a good profiler are your very best friend! Mea
I came across HikariCP and I was amazed by the benchmarks and I wanted to try it instead of my default choice C3P0 and to my surprise I struggled to get the configurations right probably because the configurations differ based on what combination of tech stack you are using.
I have setup Spring Boot project with JPA, Web, Security starters (Using [Spring Initializer][1]) to use PostgreSQL as a database with HikariCP as connection pooling.
I have used Gradle as build tool and I would like to share what worked for me for the following assumptions:
This gist is related to SO post https://stackoverflow.com/questions/26490967/how-do-i-configure-hikaricp-in-my-spring-boot-app-in-my-application-properties-f
Whichever route you take to implementing containers, you’ll want to steer clear of common pitfalls that can undermine the efficiency of your Docker stack.
The beauty of containers—and an advantage of containers over virtual machines—is that it is easy to make multiple containers interact with one another in order to compose a complete application. There is no need to run a full application inside a single container. Instead, break your application down as much as possible into discrete services, and distribute services across multiple containers. This maximizes flexibility and reliability.
It is possible to install a complete Linux operating system inside a container. In most cases, however, this is not necessary. If your goal is to host just a single application or part of an application in the container, you need to install only the essential
| 本文約2014年前後在網路上流傳的,收集了百來篇關於機器學習和深度學習的資料,含各種文檔,視頻,源碼等。而且原文也會不定期的更新,望看到文章的朋友能夠學到更多。 | |
| 88d45181jw1f07djl14e0j20av064dgg.jpg | |
| Brief History of Machine Learning | |
| 介紹:這是一篇介紹機器學習歷史的文章,介紹很全面,從感知機、神經網路、決策樹、SVM、Adaboost 到隨機森林、DeepLearning. | |
| http://www.datascienceassn.org/sites/default/files/users/user1/DataScienceMozer2014.pdf | |
| Deep Learning in Neural Networks: AnOverview | |
| 介紹:這是瑞士人工智慧實驗室 JurgenSchmidhuber 寫的最新版本《神經網路與深度學習綜述》本綜述的特點是以時間排序,從 1940 年開始講起,到 60-80 年代,80-90 年代,一直講到 2000 年後及最近幾年的進展。涵蓋了 deep learning 裡各種 tricks,引用非常全面. |
It's now here, in The Programmer's Compendium. The content is the same as before, but being part of the compendium means that it's actively maintained.
| [ | |
| { | |
| "Name": "Ship Flink runtime to cluster", | |
| "Path": "s3://<your_bucket>/flink/ship_flink_runtime.sh" | |
| }, | |
| { | |
| "Name": "Ship application to cluster", | |
| "Path": "s3://<your_bucket>/flink/ship_app.sh" | |
| } | |
| ] |
Make sure everything is up to date in the App Store
/bin/bash -c "$(curl -fsSL https://raw.githubusercontent.com/Homebrew/install/HEAD/install.sh)"