ChatOps如何變革企業業務 - 壹讀 【編者按】本文作者為日誌分析軟體公司Logz.io的聯合創始人 Tomer Levy，主要介紹ChatOps的特點與發展歷程，以及將來可能帶來的業務變革。文章系國內ITOM管理平台OneAPM編譯呈現。 ChatOps通過自動化和透明的工作流，連接了人、機器人和工具，使人們看到工作和系統的完整狀態。這一透明度加強了反饋迴路，增強了協作。因此，有人稱它為「即時通訊devops。」 這些解決方案讓用戶可以直接通過聊天窗口訪問重要信息，大大減少了上下文切換的繁瑣操作。 亞馬遜Web服務(AWS)首席宣傳官Jeff Barr在這篇博文中寫道： 因為你能讓機器人訪問任意的AWS APIs，你可以通過任何自己想要的方式與AWS資源互動。 ChatOps功能提供的服務中已經包含機器人，可以連接多個應用，支持諸如 AWS 之類的基礎設施平台。 如今，先進的研發和IT運營團隊使用全面的在線聊天室和機器人，推動企業活動，而無需尋求他人協助，甚至不需要開會協商。 這一新的溝通方法使實時共享和協作更加簡便，devops也更為完善。 工具 最初，藉助 Hubot，Lita，和Err 之類的ChatOps機器人，開發人員可以直接從他們的聊天窗口運行代碼。 Slack提供了內置的Slack機器人，同時還提供了多個第三方解決方案，比如StackStorm， Deploybot 和Blockspring，可以與其他聊天產品，比如Atlassian的HipChat和IRC相配合。 人們常用的另一個選擇是HipChat，據說是企業用戶首要的ChatOps解決方案，與Slack直接競爭。 在Logz.io，我們使用Slack和Hubot，事實證明這一工具非常有用。 ChatOps是關鍵任務型服務 隨著ChatOps項目的發展，它逐漸成為我們團隊使用的主要工具。 ChatOps系統從一個很酷的個人項目轉變為一項關鍵任務型服務，在任何devops環境中都應該被視為一項重要發展。 參考，它需要遵守嚴格的合規規定，因此需要穩固安全的ChatOps系統。 另一項管理方面的考量是可用性。 ChatOps不是一個臨時的小項目。 人人可用的機器人 去年11月，Slack做了一些改進，推出了一個更加先進的功能：用戶可以用它實現Lyft打車(類似於國內的滴滴打車)。
hubot LibreS3: Von der Amazon-Cloud auf die eigenen Server LibreS3: Erste Beta-Version der freien S3-Implementation veröffentlicht Amazons Simple Storage Service (S3) ist ein beliebter Cloud-Speicherdienst, der unter anderem auch von unzähligen Web-Apps genutzt wird. Mit LibreS3 Version 0.1 existiert jetzt auch eine API-kompatible Open-Source-Implementation des Dienstes. Die Software stammt von Skylable und wurde unter der freien GPL-2.0-Lizenz veröffentlicht. Da es sich um eine erste Beta-Version handelt, werden derzeit nur etwa 90 Prozent der Rest-APIs von Amazon unterstützt. Dennoch soll die Software bereits stabil laufen, so das Team Skylable. LibreS3: Quellcode und Binärpakete stehen zum Download bereit Den Quellcode zu LibreS3 findet ihr auf dem Git-Server von Skylable oder auf GitHub . Auch wenn noch nicht alle APIs unterstützt werden, dürfte LibreS3 schnell seine Fans finden. via www.golem.de Drücke die Tasten ◄ ► für weitere Artikel◄►
Bot Platforms — easy and quick way to build advanced bots In an earlier post, I described the bot lifecycle. Bot development involves multiple stages in building a good bot: requirements, spec, script, architect, dev, test, deploy, publish, monitor, promote, analyze, repeat. Bot platforms and other tools are emerging that make bot building easier. Some tools make it easy to create template-based bots. These may be adequate for simple use cases, but they don’t scale to more advanced bots. Other tools are more full featured and give developers the ability to create advanced bots quickly and easily. My company, Gupshup, has been building bots long before it became trendy. We channeled that experience into building a bot platform. Let’s take a deeper look at what a good bot platform does. End to end support A bot platform supports the end-to-end process of bot building. 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. Seamlessly integrated While there are tools that support individual stages, they may not work well together. Hide the plumbing Best practices
Tutorial: Creating a Basic Weather Chatbot – Chatbot’s Life Do you want to develop a ChatBot but don’t know where to start? Sometimes the best way it’s just trying out, You can start with something simple but functional and at the same time learn about the topic and also get ideas for your future projects. So let’s get started! On this tutorial we are going to use Telegram as the chatbot platform and Python for a fast, powerful and easy development. Ok so first we need to get a Token for our bot, to do that go to telegram and search for the “BotFather” and send the command /newbot It will ask you for a name and an Username for the Bot (You can’t repeat a username so you have to create a unique one, usually with Bot at the end), in the last question it will answer with the Token and a Url that takes you to the chat with your bot. The token looks something like 123456:ABC-DEF1234ghIkl-zyx57W2v1u123ew11, but we’ll simply use <token> to refer to it. Now, copy the Token on a safe place, we will use it later. First we have to install it with pip. Great!
The Structure of Scientific Revolutions 1962 book by Thomas S. Kuhn The Structure of Scientific Revolutions is a book about the history of science by philosopher Thomas S. Kuhn. For example, Kuhn's analysis of the Copernican Revolution emphasized that, in its beginning, it did not offer more accurate predictions of celestial events, such as planetary positions, than the Ptolemaic system, but instead appealed to some practitioners based on a promise of better, simpler solutions that might be developed at some point in the future. History[edit] The Structure of Scientific Revolutions was first published as a monograph in the International Encyclopedia of Unified Science, then as a book by University of Chicago Press in 1962. Central ideas regarding the process of scientific investigation and discovery had been anticipated by Ludwik Fleck in Fleck (1935). Kuhn was not confident about how his book would be received. Synopsis[edit] Basic approach[edit] Historical examples of chemistry[edit] Copernican Revolution[edit] Coherence[edit]
Webhook: Kinderleichtes CMS erobert Kickstarter Webhook: Das CMS soll vor allem einfach sein Content-Management-Systeme (CMS) gibt es wie Sand am Meer. Aber nicht jeder benötigt den Funktionsumfang von WordPress, um eine kleine Seite hochzuziehen. Das Projekt mit dem eher verwirrenden Namen stammt von dem amerikanischen Webdesigner Dave Snider, der unter anderem Seiten wie Tested.com, Comicvine.com oder TV.com gestaltet hat. Webhook setzt auf Grunt, NodeJS und Firebase Alle Daten werden mittels Firebase im JSON-Format abgelegt. Auf Kickstarter konnte das Webhook-Team schon jetzt das angestrebte Ziel von 20.000 US-Dollar übertreffen. Drücke die Tasten ◄ ► für weitere Artikel◄►
Ultimate Guide to Leveraging NLP & Machine Learning for your Chatbot The Code and data for this tutorial is on Github. The vast majority of production systems today are retrieval-based, or a combination of retrieval-based and generative. Google’s Smart Reply is a good example. If you want me to write more articles like this, please let me know here. In this post we’ll work with the Ubuntu Dialog Corpus (paper, github). The training data consists of 1,000,000 examples, 50% positive (label 1) and 50% negative (label 0). Note that the dataset generation script has already done a bunch of preprocessing for us — it hastokenized, stemmed, and lemmatized the output using the NLTK tool. The data set comes with test and validations sets. The are various ways to evaluate how well our model does. At this point you may be wondering how the 9 distractors were chosen. Before starting with fancy Neural Network models let’s build some simple baseline models to help us understand what kind of performance we can expect. def evaluate_recall(y, y_test, k=1): num_correct = 0
Artificial intelligence AI research is highly technical and specialized, and is deeply divided into subfields that often fail to communicate with each other.[5] Some of the division is due to social and cultural factors: subfields have grown up around particular institutions and the work of individual researchers. AI research is also divided by several technical issues. Some subfields focus on the solution of specific problems. Others focus on one of several possible approaches or on the use of a particular tool or towards the accomplishment of particular applications. The central problems (or goals) of AI research include reasoning, knowledge, planning, learning, natural language processing (communication), perception and the ability to move and manipulate objects.[6] General intelligence is still among the field's long-term goals.[7] Currently popular approaches include statistical methods, computational intelligence and traditional symbolic AI. History[edit] Research[edit] Goals[edit] Planning[edit] Logic-based